# Deep Lake

## v3.9.16

- [Deep Lake Docs](https://docs-v3.activeloop.ai/readme.md): We hope you enjoy Docs for Deep Lake.
- [Installation](https://docs-v3.activeloop.ai/setup/installation.md): Installing the Deep Lake Python package
- [User Authentication](https://docs-v3.activeloop.ai/setup/authentication.md): Registration and authentication in Deep Lake.
- [Workload Identities (Azure Only)](https://docs-v3.activeloop.ai/setup/authentication/workload-identities.md): How to authenticate using workload identities instead of user credentials.
- [Storage and Credentials](https://docs-v3.activeloop.ai/setup/storage-and-creds.md): How to access datasets in other clouds and manage their credentials.
- [Storage Options](https://docs-v3.activeloop.ai/setup/storage-and-creds/storage-options.md): How to authenticate using Activeloop storage, AWS S3, and Google Cloud Storage.
- [Setting up Deep Lake in Your Cloud](https://docs-v3.activeloop.ai/setup/storage-and-creds/managed-credentials.md): How to store Deep Lake data in your own cloud and manage credentials with Deep Lake
- [Microsoft Azure](https://docs-v3.activeloop.ai/setup/storage-and-creds/managed-credentials/microsoft-azure.md): Azure-specific information for connecting data to Deep Lake
- [Configure Azure SSO on Activeloop](https://docs-v3.activeloop.ai/setup/storage-and-creds/managed-credentials/microsoft-azure/configure-azure-sso-on-activeloop.md): Enabling Azure SSO on Activeloop
- [Provisioning Federated Credentials](https://docs-v3.activeloop.ai/setup/storage-and-creds/managed-credentials/microsoft-azure/provisioning-federated-credentials.md): How to setup Federated Credentials in Azure
- [Enabling CORS](https://docs-v3.activeloop.ai/setup/storage-and-creds/managed-credentials/microsoft-azure/enabling-cors.md): How to enable Cross-Origin Resource Sharing in your Azure account.
- [Google Cloud](https://docs-v3.activeloop.ai/setup/storage-and-creds/managed-credentials/google-cloud.md): Azure-specific information for connecting data to Deep Lake
- [Provisioning Federated Credentials](https://docs-v3.activeloop.ai/setup/storage-and-creds/managed-credentials/google-cloud/provisioning-federated-credentials.md): How to setup Federated Credentials in Google Cloud
- [Enabling CORS](https://docs-v3.activeloop.ai/setup/storage-and-creds/managed-credentials/google-cloud/enabling-cors.md): How to enable Cross-Origin Resource Sharing in your GCS account.
- [Amazon Web Services](https://docs-v3.activeloop.ai/setup/storage-and-creds/managed-credentials/amazon-web-services.md): AWS-specific information for connecting data to Deep Lake
- [Provisioning Role-Based Access](https://docs-v3.activeloop.ai/setup/storage-and-creds/managed-credentials/amazon-web-services/provisioning-rbac.md): How to provision Role-Based Access in S3
- [Enabling CORS](https://docs-v3.activeloop.ai/setup/storage-and-creds/managed-credentials/amazon-web-services/enabling-cors.md): How to enable Cross-Origin Resource Sharing in your AWS S3 buckets.
- [Deep Learning](https://docs-v3.activeloop.ai/examples/dl.md): Using Deep Lake for managing data in Deep Learning applications.
- [Deep Learning Quickstart](https://docs-v3.activeloop.ai/examples/dl/quickstart.md): A jump-start guide to using Deep Lake for Deep Learning.
- [Deep Learning Guide](https://docs-v3.activeloop.ai/examples/dl/guide.md): The comprehensive guide for Deep Lake in Deep Learning applications.
- [Step 1: Hello World](https://docs-v3.activeloop.ai/examples/dl/guide/hello-world.md): Installing Deep Lake and accessing your first Deep Lake Dataset.
- [Step 2: Creating Deep Lake Datasets](https://docs-v3.activeloop.ai/examples/dl/guide/creating-datasets.md): Creating and storing Deep Lake Datasets.
- [Step 3: Understanding Compression](https://docs-v3.activeloop.ai/examples/dl/guide/understanding-compression.md): Using compression to achieve optimal performance in Deep Lake.
- [Step 4: Accessing and Updating Data](https://docs-v3.activeloop.ai/examples/dl/guide/accessing-datasets.md): Learn how Deep Lake Datasets can be accessed or loaded from a variety of storage locations.
- [Step 5: Visualizing Datasets](https://docs-v3.activeloop.ai/examples/dl/guide/visualizing-datasets.md): Visualizing and inspecting your datasets.
- [Step 6: Using Activeloop Storage](https://docs-v3.activeloop.ai/examples/dl/guide/using-activeloop-storage.md): Storing and loading datasets from Deep Lake Storage.
- [Step 7: Connecting Deep Lake Datasets to ML Frameworks](https://docs-v3.activeloop.ai/examples/dl/guide/connecting-to-ml-frameworks.md): Connecting Deep Lake Datasets to machine learning frameworks such as PyTorch and TensorFlow.
- [Step 8: Parallel Computing](https://docs-v3.activeloop.ai/examples/dl/guide/parallel-computing.md): Running computations and processing data in parallel.
- [Step 9: Dataset Version Control](https://docs-v3.activeloop.ai/examples/dl/guide/dataset-version-control.md): Managing changes to your datasets using Version Control.
- [Step 10: Dataset Filtering](https://docs-v3.activeloop.ai/examples/dl/guide/dataset-filtering.md): Filtering datasets using user-defined-functions or SQL-style queries.
- [Deep Learning Tutorials](https://docs-v3.activeloop.ai/examples/dl/tutorials.md): Tutorials for using Deep Lake in deep-learning applications.
- [Creating Datasets](https://docs-v3.activeloop.ai/examples/dl/tutorials/creating-datasets.md): Workflows for creating Deep Lake datasets
- [Creating Complex Datasets](https://docs-v3.activeloop.ai/examples/dl/tutorials/creating-datasets/creating-complex-datasets.md): Converting a multi-annotation dataset to Deep Lake format is helpful for understanding how to use Deep Lake with rich data.
- [Creating Object Detection Datasets](https://docs-v3.activeloop.ai/examples/dl/tutorials/creating-datasets/creating-object-detection-datasets.md): Converting an object detection dataset to Deep Lake format is a great way to get started with datasets of increasing complexity.
- [Creating Time-Series Datasets](https://docs-v3.activeloop.ai/examples/dl/tutorials/creating-datasets/creating-time-series-datasets.md): Deep Lake is a powerful tool for easily storing and sharing time-series datasets with your team.
- [Creating Datasets with Sequences](https://docs-v3.activeloop.ai/examples/dl/tutorials/creating-datasets/creating-datasets-with-sequences.md): Deep Lake sequences are a powerful tool for storing temporal annotations such as bounding boxes in each frame of a video.
- [Creating Video Datasets](https://docs-v3.activeloop.ai/examples/dl/tutorials/creating-datasets/creating-video-datasets.md): Get started with video datasets using Deep Lake.
- [Training Models](https://docs-v3.activeloop.ai/examples/dl/tutorials/training-models.md): Workflows for training models using Deep Lake datasets
- [Splitting Datasets for Training](https://docs-v3.activeloop.ai/examples/dl/tutorials/training-models/splitting-datasets-training.md): How to Split Datasets for Training in Deep Lake
- [Training an Image Classification Model in PyTorch](https://docs-v3.activeloop.ai/examples/dl/tutorials/training-models/training-classification-pytorch.md): Training an image classification model is a great way to get started with model training using Deep Lake datasets.
- [Training Models Using MMDetection](https://docs-v3.activeloop.ai/examples/dl/tutorials/training-models/training-mmdet.md): How to Train Deep Learning models using Deep Lake's integration with MMDetection
- [Training Models Using PyTorch Lightning](https://docs-v3.activeloop.ai/examples/dl/tutorials/training-models/training-lightning.md): How to Train models using Deep Lake and PyTorch Lightning
- [Training on AWS SageMaker](https://docs-v3.activeloop.ai/examples/dl/tutorials/training-models/training-sagemaker.md): How to Train models on AWS SageMaker using Deep Lake datasets
- [Training an Object Detection and Segmentation Model in PyTorch](https://docs-v3.activeloop.ai/examples/dl/tutorials/training-models/training-od-and-seg-pytorch.md): Training an object detection and segmentation model is a great way to learn about complex data preprocessing for training models.
- [Updating Datasets](https://docs-v3.activeloop.ai/examples/dl/tutorials/updating-datasets.md): Updating Deep Lake datasets
- [Data Processing Using Parallel Computing](https://docs-v3.activeloop.ai/examples/dl/tutorials/data-processing-using-parallel-computing.md): Deeplake offers built-in methods for parallelizing dataset computations in order to achieve faster data processing.
- [Deep Learning Playbooks](https://docs-v3.activeloop.ai/examples/dl/playbooks.md): How to perform complex workflows using Deep Lake.
- [Querying, Training and Editing Datasets with Data Lineage](https://docs-v3.activeloop.ai/examples/dl/playbooks/training-with-lineage.md): How to use queries and version control while training models.
- [Evaluating Model Performance](https://docs-v3.activeloop.ai/examples/dl/playbooks/evaluating-model-performance.md): How to compare ground-truth annotations with model predictions
- [Training Reproducibility Using Deep Lake and Weights & Biases](https://docs-v3.activeloop.ai/examples/dl/playbooks/training-reproducibility-wandb.md): How to achieve full reproducibility of model training using Deep Lake and W\&B
- [Working with Videos](https://docs-v3.activeloop.ai/examples/dl/playbooks/working-with-videos.md): How manage video datasets and train models using Deep Lake.
- [Deep Lake Dataloaders](https://docs-v3.activeloop.ai/examples/dl/dataloaders.md): Overview of Deep Lake's dataloader built and optimized in C++
- [API Summary](https://docs-v3.activeloop.ai/examples/dl/api.md): Summary of the most important low-level Deep Lake commands.
- [RAG](https://docs-v3.activeloop.ai/examples/rag.md): Using Deep Lake for Vector Store in RAG applications.
- [RAG Quickstart](https://docs-v3.activeloop.ai/examples/rag/quickstart.md): A jump-start guide to using Deep Lake for Vector Search.
- [RAG Tutorials](https://docs-v3.activeloop.ai/examples/rag/tutorials.md): Tutorials for using Deep Lake in Vector Store applications
- [Vector Store Basics](https://docs-v3.activeloop.ai/examples/rag/tutorials/vector-store-basics.md): Creating the Deep Lake Vector Store
- [Vector Search Options](https://docs-v3.activeloop.ai/examples/rag/tutorials/vector-search-options.md): Overview of Vector Search Options in Deep Lake
- [LangChain API](https://docs-v3.activeloop.ai/examples/rag/tutorials/vector-search-options/langchain-api.md): Vector Search using Deep Lake in LangChain
- [Deep Lake Vector Store API](https://docs-v3.activeloop.ai/examples/rag/tutorials/vector-search-options/vector-store-api.md): Running Vector Search in the Deep Lake Vector Store module.
- [Managed Database REST API](https://docs-v3.activeloop.ai/examples/rag/tutorials/vector-search-options/rest-api.md): Running Vector Search in the Deep Lake Tensor Database using the REST API
- [Customizing Your Vector Store](https://docs-v3.activeloop.ai/examples/rag/tutorials/step-4-customizing-vector-stores.md): Customizing the Deep Lake Vector Store
- [Image Similarity Search](https://docs-v3.activeloop.ai/examples/rag/tutorials/image-similarity-search.md): Using Deep Lake for image similarity search
- [Improving Search Accuracy using Deep Memory](https://docs-v3.activeloop.ai/examples/rag/tutorials/deepmemory.md): Using Deep Memory to improve the accuracy of your Vector Search
- [LangChain Integration](https://docs-v3.activeloop.ai/examples/rag/langchain-integration.md): Using Deep Lake as a Vector Store in LangChain
- [LlamaIndex Integration](https://docs-v3.activeloop.ai/examples/rag/llamaindex-integration.md): Using Deep Lake as a Vector Store in LlamaIndex
- [Managed Tensor Database](https://docs-v3.activeloop.ai/examples/rag/managed-database.md): Deep Lake Managed Database
- [REST API](https://docs-v3.activeloop.ai/examples/rag/managed-database/rest-api.md): How to Use the Deep Lake REST API
- [Migrating Datasets to the Tensor Database](https://docs-v3.activeloop.ai/examples/rag/managed-database/migrating-datasets-to-the-tensor-database.md): Migrating datasets to the Tensor Database
- [Deep Memory](https://docs-v3.activeloop.ai/examples/rag/deep-memory.md): Overview of Deep Lake tools for increasing retrieval accuracy
- [How it Works](https://docs-v3.activeloop.ai/examples/rag/deep-memory/how-it-works.md): Understanding Deep Memory
- [Tensor Query Language (TQL)](https://docs-v3.activeloop.ai/examples/tql.md): Deep Lake offers a performant SQL-style query engine for data analysis.
- [TQL Syntax](https://docs-v3.activeloop.ai/examples/tql/syntax.md): How to properly format TQL queries
- [Index for ANN Search](https://docs-v3.activeloop.ai/examples/tql/ann-index.md): Overview of Deep Lake's Index implementation for ANN search.
- [Caching and Optimization](https://docs-v3.activeloop.ai/examples/tql/ann-index/caching-and-optimization.md): Understanding Caching to Increase Query Performance in Deep Lake
- [Sampling Datasets](https://docs-v3.activeloop.ai/examples/tql/sampling.md): Implementation of samplers in TQL
- [Best Practices](https://docs-v3.activeloop.ai/technical-details/best-practices.md): How to use Deep Lake at scale with best practices.
- [Creating Datasets at Scale](https://docs-v3.activeloop.ai/technical-details/best-practices/creating-datasets-at-scale.md): Creating large Deep Lake datasets with high performance and reliability
- [Training Models at Scale](https://docs-v3.activeloop.ai/technical-details/best-practices/training-models-at-scale.md): Train models at scale using Deep Lake
- [Storage Synchronization and "with" Context](https://docs-v3.activeloop.ai/technical-details/best-practices/storage-synchronization.md): Synchronizing data with long-term storage and achieving optimal performance using Deep Lake.
- [Restoring Corrupted Datasets](https://docs-v3.activeloop.ai/technical-details/best-practices/restoring-corrupted-datasets.md): Restoring Deep Lake datasets that may be corrupted.
- [Concurrent Writes](https://docs-v3.activeloop.ai/technical-details/best-practices/concurrent-writes.md): Concurrent writes in Deep Lake
- [Concurrency Using Zookeeper Locks](https://docs-v3.activeloop.ai/technical-details/best-practices/concurrent-writes/concurrency-using-zookeeper-locks.md): Using Zookeeper for locking Deep Lake datasets.
- [Deep Lake Data Format](https://docs-v3.activeloop.ai/technical-details/data-format.md): Understanding the data layout in Deep Lake
- [Tensor Relationships](https://docs-v3.activeloop.ai/technical-details/data-format/tensor-relationships.md): Understanding the correct data layout for successful visualization.
- [Version Control and Querying](https://docs-v3.activeloop.ai/technical-details/data-format/version-control-and-querying.md): Understanding Deep Lake's Version control and Querying Layout
- [Dataset Visualization](https://docs-v3.activeloop.ai/technical-details/visualization.md): How to visualize Deep Lake datasets
- [Visualizer Integration](https://docs-v3.activeloop.ai/technical-details/visualization/visualizer-integration.md): How to embed our visualizer in your application.
- [Shuffling in Dataloaders](https://docs-v3.activeloop.ai/technical-details/shuffling.md): Understanding data shuffling in Deep Lake's pytorch dataloader
- [How to Contribute](https://docs-v3.activeloop.ai/technical-details/how-to-contribute.md): Guidelines for open source enthusiasts to contribute to our open-source data format.

## v3.9.0

- [Deep Lake Docs](https://docs-v3.activeloop.ai/v3.9.0/readme.md): We hope you enjoy Docs for Deep Lake.
- [Installation](https://docs-v3.activeloop.ai/v3.9.0/setup/installation.md): Installing the Deep Lake Python package
- [User Authentication](https://docs-v3.activeloop.ai/v3.9.0/setup/authentication.md): Registration and authentication in Deep Lake.
- [Workload Identities (Azure Only)](https://docs-v3.activeloop.ai/v3.9.0/setup/authentication/workload-identities.md): How to authenticate using workload identities instead of user credentials.
- [Storage and Credentials](https://docs-v3.activeloop.ai/v3.9.0/setup/storage-and-creds.md): How to access datasets in other clouds and manage their credentials.
- [Storage Options](https://docs-v3.activeloop.ai/v3.9.0/setup/storage-and-creds/storage-options.md): How to authenticate using Activeloop storage, AWS S3, and Google Cloud Storage.
- [Setting up Deep Lake in Your Cloud](https://docs-v3.activeloop.ai/v3.9.0/setup/storage-and-creds/managed-credentials.md): How to store Deep Lake data in your own cloud and manage credentials with Deep Lake
- [Microsoft Azure](https://docs-v3.activeloop.ai/v3.9.0/setup/storage-and-creds/managed-credentials/microsoft-azure.md): Azure-specific information for connecting data to Deep Lake
- [Provisioning Federated Credentials](https://docs-v3.activeloop.ai/v3.9.0/setup/storage-and-creds/managed-credentials/microsoft-azure/provisioning-federated-credentials.md): How to setup Federated Credentials in Azure
- [Enabling CORS](https://docs-v3.activeloop.ai/v3.9.0/setup/storage-and-creds/managed-credentials/microsoft-azure/enabling-cors.md): How to enable Cross-Origin Resource Sharing in your Azure account.
- [Amazon Web Services](https://docs-v3.activeloop.ai/v3.9.0/setup/storage-and-creds/managed-credentials/amazon-web-services.md): AWS-specific information for connecting data to Deep Lake
- [Provisioning Role-Based Access](https://docs-v3.activeloop.ai/v3.9.0/setup/storage-and-creds/managed-credentials/amazon-web-services/provisioning-rbac.md): How to provision Role-Based Access in S3
- [Enabling CORS](https://docs-v3.activeloop.ai/v3.9.0/setup/storage-and-creds/managed-credentials/amazon-web-services/enabling-cors.md): How to enable Cross-Origin Resource Sharing in your AWS S3 buckets.
- [Deep Learning](https://docs-v3.activeloop.ai/v3.9.0/examples/dl.md): Using Deep Lake for managing data in Deep Learning applications.
- [Deep Learning Quickstart](https://docs-v3.activeloop.ai/v3.9.0/examples/dl/quickstart.md): A jump-start guide to using Deep Lake for Deep Learning.
- [Deep Learning Guide](https://docs-v3.activeloop.ai/v3.9.0/examples/dl/guide.md): The comprehensive guide for Deep Lake in Deep Learning applications.
- [Step 1: Hello World](https://docs-v3.activeloop.ai/v3.9.0/examples/dl/guide/hello-world.md): Installing Deep Lake and accessing your first Deep Lake Dataset.
- [Step 2: Creating Deep Lake Datasets](https://docs-v3.activeloop.ai/v3.9.0/examples/dl/guide/creating-datasets.md): Creating and storing Deep Lake Datasets.
- [Step 3: Understanding Compression](https://docs-v3.activeloop.ai/v3.9.0/examples/dl/guide/understanding-compression.md): Using compression to achieve optimal performance in Deep Lake.
- [Step 4: Accessing and Updating Data](https://docs-v3.activeloop.ai/v3.9.0/examples/dl/guide/accessing-datasets.md): Learn how Deep Lake Datasets can be accessed or loaded from a variety of storage locations.
- [Step 5: Visualizing Datasets](https://docs-v3.activeloop.ai/v3.9.0/examples/dl/guide/visualizing-datasets.md): Visualizing and inspecting your datasets.
- [Step 6: Using Activeloop Storage](https://docs-v3.activeloop.ai/v3.9.0/examples/dl/guide/using-activeloop-storage.md): Storing and loading datasets from Deep Lake Storage.
- [Step 7: Connecting Deep Lake Datasets to ML Frameworks](https://docs-v3.activeloop.ai/v3.9.0/examples/dl/guide/connecting-to-ml-frameworks.md): Connecting Deep Lake Datasets to machine learning frameworks such as PyTorch and TensorFlow.
- [Step 8: Parallel Computing](https://docs-v3.activeloop.ai/v3.9.0/examples/dl/guide/parallel-computing.md): Running computations and processing data in parallel.
- [Step 9: Dataset Version Control](https://docs-v3.activeloop.ai/v3.9.0/examples/dl/guide/dataset-version-control.md): Managing changes to your datasets using Version Control.
- [Step 10: Dataset Filtering](https://docs-v3.activeloop.ai/v3.9.0/examples/dl/guide/dataset-filtering.md): Filtering datasets using user-defined-functions or SQL-style queries.
- [Deep Learning Tutorials](https://docs-v3.activeloop.ai/v3.9.0/examples/dl/tutorials.md): Tutorials for using Deep Lake in deep-learning applications.
- [Creating Datasets](https://docs-v3.activeloop.ai/v3.9.0/examples/dl/tutorials/creating-datasets.md): Workflows for creating Deep Lake datasets
- [Creating Complex Datasets](https://docs-v3.activeloop.ai/v3.9.0/examples/dl/tutorials/creating-datasets/creating-complex-datasets.md): Converting a multi-annotation dataset to Deep Lake format is helpful for understanding how to use Deep Lake with rich data.
- [Creating Object Detection Datasets](https://docs-v3.activeloop.ai/v3.9.0/examples/dl/tutorials/creating-datasets/creating-object-detection-datasets.md): Converting an object detection dataset to Deep Lake format is a great way to get started with datasets of increasing complexity.
- [Creating Time-Series Datasets](https://docs-v3.activeloop.ai/v3.9.0/examples/dl/tutorials/creating-datasets/creating-time-series-datasets.md): Deep Lake is a powerful tool for easily storing and sharing time-series datasets with your team.
- [Creating Datasets with Sequences](https://docs-v3.activeloop.ai/v3.9.0/examples/dl/tutorials/creating-datasets/creating-datasets-with-sequences.md): Deep Lake sequences are a powerful tool for storing temporal annotations such as bounding boxes in each frame of a video.
- [Creating Video Datasets](https://docs-v3.activeloop.ai/v3.9.0/examples/dl/tutorials/creating-datasets/creating-video-datasets.md): Get started with video datasets using Deep Lake.
- [Training Models](https://docs-v3.activeloop.ai/v3.9.0/examples/dl/tutorials/training-models.md): Workflows for training models using Deep Lake datasets
- [Splitting Datasets for Training](https://docs-v3.activeloop.ai/v3.9.0/examples/dl/tutorials/training-models/splitting-datasets-training.md): How to Split Datasets for Training in Deep Lake
- [Training an Image Classification Model in PyTorch](https://docs-v3.activeloop.ai/v3.9.0/examples/dl/tutorials/training-models/training-classification-pytorch.md): Training an image classification model is a great way to get started with model training using Deep Lake datasets.
- [Training Models Using MMDetection](https://docs-v3.activeloop.ai/v3.9.0/examples/dl/tutorials/training-models/training-mmdet.md): How to Train Deep Learning models using Deep Lake's integration with MMDetection
- [Training Models Using PyTorch Lightning](https://docs-v3.activeloop.ai/v3.9.0/examples/dl/tutorials/training-models/training-lightning.md): How to Train models using Deep Lake and PyTorch Lightning
- [Training on AWS SageMaker](https://docs-v3.activeloop.ai/v3.9.0/examples/dl/tutorials/training-models/training-sagemaker.md): How to Train models on AWS SageMaker using Deep Lake datasets
- [Training an Object Detection and Segmentation Model in PyTorch](https://docs-v3.activeloop.ai/v3.9.0/examples/dl/tutorials/training-models/training-od-and-seg-pytorch.md): Training an object detection and segmentation model is a great way to learn about complex data preprocessing for training models.
- [Updating Datasets](https://docs-v3.activeloop.ai/v3.9.0/examples/dl/tutorials/updating-datasets.md): Updating Deep Lake datasets
- [Data Processing Using Parallel Computing](https://docs-v3.activeloop.ai/v3.9.0/examples/dl/tutorials/data-processing-using-parallel-computing.md): Deeplake offers built-in methods for parallelizing dataset computations in order to achieve faster data processing.
- [Deep Learning Playbooks](https://docs-v3.activeloop.ai/v3.9.0/examples/dl/playbooks.md): How to perform complex workflows using Deep Lake.
- [Querying, Training and Editing Datasets with Data Lineage](https://docs-v3.activeloop.ai/v3.9.0/examples/dl/playbooks/training-with-lineage.md): How to use queries and version control while training models.
- [Evaluating Model Performance](https://docs-v3.activeloop.ai/v3.9.0/examples/dl/playbooks/evaluating-model-performance.md): How to compare ground-truth annotations with model predictions
- [Training Reproducibility Using Deep Lake and Weights & Biases](https://docs-v3.activeloop.ai/v3.9.0/examples/dl/playbooks/training-reproducibility-wandb.md): How to achieve full reproducibility of model training using Deep Lake and W\&B
- [Working with Videos](https://docs-v3.activeloop.ai/v3.9.0/examples/dl/playbooks/working-with-videos.md): How manage video datasets and train models using Deep Lake.
- [Deep Lake Dataloaders](https://docs-v3.activeloop.ai/v3.9.0/examples/dl/dataloaders.md): Overview of Deep Lake's dataloader built and optimized in C++
- [API Summary](https://docs-v3.activeloop.ai/v3.9.0/examples/dl/api.md): Summary of the most important low-level Deep Lake commands.
- [RAG](https://docs-v3.activeloop.ai/v3.9.0/examples/rag.md): Using Deep Lake for Vector Store in RAG applications.
- [RAG Quickstart](https://docs-v3.activeloop.ai/v3.9.0/examples/rag/quickstart.md): A jump-start guide to using Deep Lake for Vector Search.
- [RAG Tutorials](https://docs-v3.activeloop.ai/v3.9.0/examples/rag/tutorials.md): Tutorials for using Deep Lake in Vector Store applications
- [Vector Store Basics](https://docs-v3.activeloop.ai/v3.9.0/examples/rag/tutorials/vector-store-basics.md): Creating the Deep Lake Vector Store
- [Vector Search Options](https://docs-v3.activeloop.ai/v3.9.0/examples/rag/tutorials/vector-search-options.md): Overview of Vector Search Options in Deep Lake
- [LangChain API](https://docs-v3.activeloop.ai/v3.9.0/examples/rag/tutorials/vector-search-options/langchain-api.md): Vector Search using Deep Lake in LangChain
- [Deep Lake Vector Store API](https://docs-v3.activeloop.ai/v3.9.0/examples/rag/tutorials/vector-search-options/vector-store-api.md): Running Vector Search in the Deep Lake Vector Store module.
- [Managed Database REST API](https://docs-v3.activeloop.ai/v3.9.0/examples/rag/tutorials/vector-search-options/rest-api.md): Running Vector Search in the Deep Lake Tensor Database using the REST API
- [Customizing Your Vector Store](https://docs-v3.activeloop.ai/v3.9.0/examples/rag/tutorials/step-4-customizing-vector-stores.md): Customizing the Deep Lake Vector Store
- [Image Similarity Search](https://docs-v3.activeloop.ai/v3.9.0/examples/rag/tutorials/image-similarity-search.md): Using Deep Lake for image similarity search
- [Improving Search Accuracy using Deep Memory](https://docs-v3.activeloop.ai/v3.9.0/examples/rag/tutorials/deepmemory.md): Using Deep Memory to improve the accuracy of your Vector Search
- [LangChain Integration](https://docs-v3.activeloop.ai/v3.9.0/examples/rag/langchain-integration.md): Using Deep Lake as a Vector Store in LangChain
- [LlamaIndex Integration](https://docs-v3.activeloop.ai/v3.9.0/examples/rag/llamaindex-integration.md): Using Deep Lake as a Vector Store in LlamaIndex
- [Managed Tensor Database](https://docs-v3.activeloop.ai/v3.9.0/examples/rag/managed-database.md): Deep Lake Managed Database
- [REST API](https://docs-v3.activeloop.ai/v3.9.0/examples/rag/managed-database/rest-api.md): How to Use the Deep Lake REST API
- [Migrating Datasets to the Tensor Database](https://docs-v3.activeloop.ai/v3.9.0/examples/rag/managed-database/migrating-datasets-to-the-tensor-database.md): Migrating datasets to the Tensor Database
- [Deep Memory](https://docs-v3.activeloop.ai/v3.9.0/examples/rag/deep-memory.md): Overview of Deep Lake tools for increasing retrieval accuracy
- [How it Works](https://docs-v3.activeloop.ai/v3.9.0/examples/rag/deep-memory/how-it-works.md): Understanding Deep Memory
- [Tensor Query Language (TQL)](https://docs-v3.activeloop.ai/v3.9.0/examples/tql.md): Deep Lake offers a performant SQL-style query engine for data analysis.
- [TQL Syntax](https://docs-v3.activeloop.ai/v3.9.0/examples/tql/syntax.md): How to properly format TQL queries
- [Index for ANN Search](https://docs-v3.activeloop.ai/v3.9.0/examples/tql/ann-index.md): Overview of Deep Lake's Index implementation for ANN search.
- [Caching and Optimization](https://docs-v3.activeloop.ai/v3.9.0/examples/tql/ann-index/caching-and-optimization.md): Understanding Caching to Increase Query Performance in Deep Lake
- [Sampling Datasets](https://docs-v3.activeloop.ai/v3.9.0/examples/tql/sampling.md): Implementation of samplers in TQL
- [Best Practices](https://docs-v3.activeloop.ai/v3.9.0/technical-details/best-practices.md): How to use Deep Lake at scale with best practices.
- [Creating Datasets at Scale](https://docs-v3.activeloop.ai/v3.9.0/technical-details/best-practices/creating-datasets-at-scale.md): Creating large Deep Lake datasets with high performance and reliability
- [Training Models at Scale](https://docs-v3.activeloop.ai/v3.9.0/technical-details/best-practices/training-models-at-scale.md): Train models at scale using Deep Lake
- [Storage Synchronization and "with" Context](https://docs-v3.activeloop.ai/v3.9.0/technical-details/best-practices/storage-synchronization.md): Synchronizing data with long-term storage and achieving optimal performance using Deep Lake.
- [Restoring Corrupted Datasets](https://docs-v3.activeloop.ai/v3.9.0/technical-details/best-practices/restoring-corrupted-datasets.md): Restoring Deep Lake datasets that may be corrupted.
- [Concurrent Writes](https://docs-v3.activeloop.ai/v3.9.0/technical-details/best-practices/concurrent-writes.md): Concurrent writes in Deep Lake
- [Concurrency Using Zookeeper Locks](https://docs-v3.activeloop.ai/v3.9.0/technical-details/best-practices/concurrent-writes/concurrency-using-zookeeper-locks.md): Using Zookeeper for locking Deep Lake datasets.
- [Deep Lake Data Format](https://docs-v3.activeloop.ai/v3.9.0/technical-details/data-format.md): Understanding the data layout in Deep Lake
- [Tensor Relationships](https://docs-v3.activeloop.ai/v3.9.0/technical-details/data-format/tensor-relationships.md): Understanding the correct data layout for successful visualization.
- [Version Control and Querying](https://docs-v3.activeloop.ai/v3.9.0/technical-details/data-format/version-control-and-querying.md): Understanding Deep Lake's Version control and Querying Layout
- [Dataset Visualization](https://docs-v3.activeloop.ai/v3.9.0/technical-details/visualization.md): How to visualize Deep Lake datasets
- [Visualizer Integration](https://docs-v3.activeloop.ai/v3.9.0/technical-details/visualization/visualizer-integration.md): How to embed our visualizer in your application.
- [Shuffling in Dataloaders](https://docs-v3.activeloop.ai/v3.9.0/technical-details/shuffling.md): Understanding data shuffling in Deep Lake's pytorch dataloader
- [How to Contribute](https://docs-v3.activeloop.ai/v3.9.0/technical-details/how-to-contribute.md): Guidelines for open source enthusiasts to contribute to our open-source data format.

## v3.8.27

- [Deep Lake Docs](https://docs-v3.activeloop.ai/v3.8.27/readme.md): We hope you enjoy Docs for Deep Lake.
- [Vector Store Quickstart](https://docs-v3.activeloop.ai/v3.8.27/quickstart.md): A jump-start guide to using Deep Lake for Vector Search.
- [Deep Learning Quickstart](https://docs-v3.activeloop.ai/v3.8.27/quickstart-dl.md): A jump-start guide to using Deep Lake for Deep Learning.
- [Storage & Credentials](https://docs-v3.activeloop.ai/v3.8.27/storage-and-credentials.md): How to access datasets in other clouds and manage their credentials.
- [Storage Options](https://docs-v3.activeloop.ai/v3.8.27/storage-and-credentials/storage-options.md): How to authenticate using Activeloop storage, AWS S3, and Google Cloud Storage.
- [User Authentication](https://docs-v3.activeloop.ai/v3.8.27/storage-and-credentials/user-authentication.md): Registration and authentication in Deep Lake.
- [Workload Identities (Azure Only)](https://docs-v3.activeloop.ai/v3.8.27/storage-and-credentials/user-authentication/workload-identities.md): How to authenticate using workload identities instead of user credentials.
- [Storing Deep Lake Data in Your Own Cloud](https://docs-v3.activeloop.ai/v3.8.27/storage-and-credentials/managed-credentials.md): How to store Deep Lake data in your own cloud and manage credentials with Deep Lake
- [Microsoft Azure](https://docs-v3.activeloop.ai/v3.8.27/storage-and-credentials/managed-credentials/microsoft-azure.md): Azure-specific information for connecting data to Deep Lake
- [Provisioning Federated Credentials](https://docs-v3.activeloop.ai/v3.8.27/storage-and-credentials/managed-credentials/microsoft-azure/provisioning-federated-credentials.md): How to setup Federated Credentials in Azure
- [Enabling CORS](https://docs-v3.activeloop.ai/v3.8.27/storage-and-credentials/managed-credentials/microsoft-azure/enabling-cors.md): How to enable Cross-Origin Resource Sharing in your Azure account.
- [Amazon Web Services](https://docs-v3.activeloop.ai/v3.8.27/storage-and-credentials/managed-credentials/amazon-web-services.md): AWS-specific information for connecting data to Deep Lake
- [Provisioning Role-Based Access](https://docs-v3.activeloop.ai/v3.8.27/storage-and-credentials/managed-credentials/amazon-web-services/provisioning-role-based-access.md): How to provision Role-Based Access in S3
- [Enabling CORS](https://docs-v3.activeloop.ai/v3.8.27/storage-and-credentials/managed-credentials/amazon-web-services/enabling-cors.md): How to enable Cross-Origin Resource Sharing in your AWS S3 buckets.
- [Introduction](https://docs-v3.activeloop.ai/v3.8.27/performance-features/introduction.md): C++ implementations of Deep Lake optimized for faster data fetching and computations
- [Performant Dataloader](https://docs-v3.activeloop.ai/v3.8.27/performance-features/performant-dataloader.md): Overview of Deep Lake's dataloader built and optimized in C++
- [Tensor Query Language (TQL)](https://docs-v3.activeloop.ai/v3.8.27/performance-features/querying-datasets.md): Deep Lake offers a highly-performant SQL-style query engine for filtering your data.
- [TQL Syntax](https://docs-v3.activeloop.ai/v3.8.27/performance-features/querying-datasets/query-syntax.md): How to properly format TQL queries
- [Sampling Datasets](https://docs-v3.activeloop.ai/v3.8.27/performance-features/querying-datasets/sampling-datasets.md): Implementation of samplers in TQL
- [Deep Memory](https://docs-v3.activeloop.ai/v3.8.27/performance-features/deep-memory.md): Overview of Deep Lake tools for increasing retrieval accuracy
- [How it Works](https://docs-v3.activeloop.ai/v3.8.27/performance-features/deep-memory/how-it-works.md): Understanding Deep Memory
- [Index for ANN Search](https://docs-v3.activeloop.ai/v3.8.27/performance-features/index-for-ann-search.md): Overview of Deep Lake's Index implementation for ANN search.
- [Caching and Optimization](https://docs-v3.activeloop.ai/v3.8.27/performance-features/index-for-ann-search/caching-and-optimization.md): Understanding Caching to Increase Query Performance in Deep Lake
- [Managed Tensor Database](https://docs-v3.activeloop.ai/v3.8.27/performance-features/managed-database.md): Deep Lake Managed Database
- [REST API](https://docs-v3.activeloop.ai/v3.8.27/performance-features/managed-database/rest-api.md): How to Use the Deep Lake REST API
- [Migrating Datasets to the Tensor Database](https://docs-v3.activeloop.ai/v3.8.27/performance-features/managed-database/migrating-datasets-to-the-tensor-database.md): Migrating datasets to the Tensor Database
- [Getting Started](https://docs-v3.activeloop.ai/v3.8.27/example-code/getting-started.md): Comprehensive guides for getting started with Deep Lake
- [Vector Store](https://docs-v3.activeloop.ai/v3.8.27/example-code/getting-started/vector-store.md): The comprehensive guide for Deep Lake in Vector Storage and Search applications.
- [Step 1: Hello World](https://docs-v3.activeloop.ai/v3.8.27/example-code/getting-started/vector-store/step-1-hello-world.md): Installing Deep Lake
- [Step 2: Creating Deep Lake Vector Stores](https://docs-v3.activeloop.ai/v3.8.27/example-code/getting-started/vector-store/step-2-creating-deep-lake-vector-stores.md): Creating the Deep Lake Vector Store
- [Step 3: Performing Search in Vector Stores](https://docs-v3.activeloop.ai/v3.8.27/example-code/getting-started/vector-store/step-3-performing-search-in-the-vector-store.md): Running search in the Deep Lake Vector Store
- [Step 4: Customizing Vector Stores](https://docs-v3.activeloop.ai/v3.8.27/example-code/getting-started/vector-store/step-4-customizing-vector-stores.md): Customizing the Deep Lake Vector Store
- [Deep Learning](https://docs-v3.activeloop.ai/v3.8.27/example-code/getting-started/deep-learning.md): The comprehensive guide for Deep Lake in Deep Learning applications.
- [Step 1: Hello World](https://docs-v3.activeloop.ai/v3.8.27/example-code/getting-started/deep-learning/hello-world.md): Installing Deep Lake and accessing your first Deep Lake Dataset.
- [Step 2: Creating Deep Lake Datasets](https://docs-v3.activeloop.ai/v3.8.27/example-code/getting-started/deep-learning/creating-datasets-manually.md): Creating and storing Deep Lake Datasets.
- [Step 3: Understanding Compression](https://docs-v3.activeloop.ai/v3.8.27/example-code/getting-started/deep-learning/understanding-compression.md): Using compression to achieve optimal performance in Deep Lake.
- [Step 4: Accessing and Updating Data](https://docs-v3.activeloop.ai/v3.8.27/example-code/getting-started/deep-learning/accessing-datasets.md): Learn how Deep Lake Datasets can be accessed or loaded from a variety of storage locations.
- [Step 5: Visualizing Datasets](https://docs-v3.activeloop.ai/v3.8.27/example-code/getting-started/deep-learning/visualizing-datasets.md): Visualizing and inspecting your datasets.
- [Step 6: Using Activeloop Storage](https://docs-v3.activeloop.ai/v3.8.27/example-code/getting-started/deep-learning/using-activeloop-storage.md): Storing and loading datasets from Deep Lake Storage.
- [Step 7: Connecting Deep Lake Datasets to ML Frameworks](https://docs-v3.activeloop.ai/v3.8.27/example-code/getting-started/deep-learning/connecting-to-ml-frameworks.md): Connecting Deep Lake Datasets to machine learning frameworks such as PyTorch and TensorFlow.
- [Step 8: Parallel Computing](https://docs-v3.activeloop.ai/v3.8.27/example-code/getting-started/deep-learning/parallel-computing.md): Running computations and processing data in parallel.
- [Step 9: Dataset Version Control](https://docs-v3.activeloop.ai/v3.8.27/example-code/getting-started/deep-learning/dataset-version-control.md): Managing changes to your datasets using Version Control.
- [Step 10: Dataset Filtering](https://docs-v3.activeloop.ai/v3.8.27/example-code/getting-started/deep-learning/dataset-filtering.md): Filtering datasets using user-defined-functions or SQL-style queries.
- [Tutorials (w Colab)](https://docs-v3.activeloop.ai/v3.8.27/example-code/tutorials.md): Common workflows with Deep Lake (includes Colab notebooks)
- [Vector Store Tutorials](https://docs-v3.activeloop.ai/v3.8.27/example-code/tutorials/vector-store.md): Tutorials for using Deep Lake in Vector Store applications
- [Vector Search Options](https://docs-v3.activeloop.ai/v3.8.27/example-code/tutorials/vector-store/vector-search-options.md): Overview of Vector Search Options in Deep Lake
- [Deep Lake Vector Store API](https://docs-v3.activeloop.ai/v3.8.27/example-code/tutorials/vector-store/vector-search-options/deep-lake-vector-store-api.md): Running Vector Search in the Deep Lake Vector Store module.
- [REST API](https://docs-v3.activeloop.ai/v3.8.27/example-code/tutorials/vector-store/vector-search-options/rest-api.md): Running Vector Search in the Deep Lake Tensor Database using the REST API
- [LangChain API](https://docs-v3.activeloop.ai/v3.8.27/example-code/tutorials/vector-store/vector-search-options/langchain-api.md): Vector Search using Deep Lake in LangChain
- [Image Similarity Search](https://docs-v3.activeloop.ai/v3.8.27/example-code/tutorials/vector-store/image-similarity-search.md): Using Deep Lake for image similarity search
- [Deep Lake Vector Store in LangChain](https://docs-v3.activeloop.ai/v3.8.27/example-code/tutorials/vector-store/deep-lake-vector-store-in-langchain.md): Using Deep Lake as a Vector Store in LangChain
- [Deep Lake Vector Store in LlamaIndex](https://docs-v3.activeloop.ai/v3.8.27/example-code/tutorials/vector-store/deep-lake-vector-store-in-llamaindex.md): Using Deep Lake as a Vector Store in LlamaIndex
- [Improving Search Accuracy using Deep Memory](https://docs-v3.activeloop.ai/v3.8.27/example-code/tutorials/vector-store/improving-search-accuracy-using-deep-memory.md): Using Deep Memory to improve the accuracy of your Vector Search
- [Deep Learning Tutorials](https://docs-v3.activeloop.ai/v3.8.27/example-code/tutorials/deep-learning.md): Tutorials for using Deep Lake in deep-learning applications.
- [Creating Datasets](https://docs-v3.activeloop.ai/v3.8.27/example-code/tutorials/deep-learning/creating-datasets.md): Workflows for creating Deep Lake datasets
- [Creating Complex Datasets](https://docs-v3.activeloop.ai/v3.8.27/example-code/tutorials/deep-learning/creating-datasets/creating-complex-datasets.md): Converting a multi-annotation dataset to Deep Lake format is helpful for understanding how to use Deep Lake with rich data.
- [Creating Object Detection Datasets](https://docs-v3.activeloop.ai/v3.8.27/example-code/tutorials/deep-learning/creating-datasets/creating-object-detection-datasets.md): Converting an object detection dataset to Deep Lake format is a great way to get started with datasets of increasing complexity.
- [Creating Time-Series Datasets](https://docs-v3.activeloop.ai/v3.8.27/example-code/tutorials/deep-learning/creating-datasets/creating-time-series-datasets.md): Deep Lake is a powerful tool for easily storing and sharing time-series datasets with your team.
- [Creating Datasets with Sequences](https://docs-v3.activeloop.ai/v3.8.27/example-code/tutorials/deep-learning/creating-datasets/creating-datasets-with-sequences.md): Deep Lake sequences are a powerful tool for storing temporal annotations such as bounding boxes in each frame of a video.
- [Creating Video Datasets](https://docs-v3.activeloop.ai/v3.8.27/example-code/tutorials/deep-learning/creating-datasets/creating-video-datasets.md): Get started with video datasets using Deep Lake.
- [Training Models](https://docs-v3.activeloop.ai/v3.8.27/example-code/tutorials/deep-learning/training-models.md): Workflows for training models using Deep Lake datasets
- [Splitting Datasets for Training](https://docs-v3.activeloop.ai/v3.8.27/example-code/tutorials/deep-learning/training-models/splitting-datasets-for-training.md): How to Split Datasets for Training in Deep Lake
- [Training an Image Classification Model in PyTorch](https://docs-v3.activeloop.ai/v3.8.27/example-code/tutorials/deep-learning/training-models/training-an-image-classification-model-in-pytorch.md): Training an image classification model is a great way to get started with model training using Deep Lake datasets.
- [Training Models Using MMDetection](https://docs-v3.activeloop.ai/v3.8.27/example-code/tutorials/deep-learning/training-models/training-models-using-mmdetection.md): How to Train Deep Learning models using Deep Lake's integration with MMDetection
- [Training Models Using PyTorch Lightning](https://docs-v3.activeloop.ai/v3.8.27/example-code/tutorials/deep-learning/training-models/training-models-using-pytorch-lightning.md): How to Train models using Deep Lake and PyTorch Lightning
- [Training on AWS SageMaker](https://docs-v3.activeloop.ai/v3.8.27/example-code/tutorials/deep-learning/training-models/training-on-aws-sagemaker.md): How to Train models on AWS SageMaker using Deep Lake datasets
- [Training an Object Detection and Segmentation Model in PyTorch](https://docs-v3.activeloop.ai/v3.8.27/example-code/tutorials/deep-learning/training-models/training-an-object-detection-and-segmentation-model-in-pytorch.md): Training an object detection and segmentation model is a great way to learn about complex data preprocessing for training models.
- [Updating Datasets](https://docs-v3.activeloop.ai/v3.8.27/example-code/tutorials/deep-learning/updating-datasets.md): Updating Deep Lake datasets
- [Data Processing Using Parallel Computing](https://docs-v3.activeloop.ai/v3.8.27/example-code/tutorials/deep-learning/data-processing-using-parallel-computing.md): Deeplake offers built-in methods for parallelizing dataset computations in order to achieve faster data processing.
- [Concurrent Writes](https://docs-v3.activeloop.ai/v3.8.27/example-code/tutorials/concurrent-writes.md): Concurrent writes in Deep Lake
- [Concurrency Using Zookeeper Locks](https://docs-v3.activeloop.ai/v3.8.27/example-code/tutorials/concurrent-writes/concurrency-using-zookeeper-locks.md): Using Zookeeper for locking Deep Lake datasets.
- [Playbooks](https://docs-v3.activeloop.ai/v3.8.27/example-code/playbooks.md): How to perform complex workflows using Deep Lake.
- [Querying, Training and Editing Datasets with Data Lineage](https://docs-v3.activeloop.ai/v3.8.27/example-code/playbooks/training-with-lineage.md): How to use queries and version control while training models.
- [Evaluating Model Performance](https://docs-v3.activeloop.ai/v3.8.27/example-code/playbooks/evaluating-model-performance.md): How to compare ground-truth annotations with model predictions
- [Training Reproducibility Using Deep Lake and Weights & Biases](https://docs-v3.activeloop.ai/v3.8.27/example-code/playbooks/training-reproducibility-with-wandb.md): How to achieve full reproducibility of model training using Deep Lake and W\&B
- [Working with Videos](https://docs-v3.activeloop.ai/v3.8.27/example-code/playbooks/working-with-videos.md): How manage video datasets and train models using Deep Lake.
- [Low-Level API Summary](https://docs-v3.activeloop.ai/v3.8.27/example-code/api-basics.md): Summary of the most important low-level Deep Lake commands.
- [Best Practices](https://docs-v3.activeloop.ai/v3.8.27/technical-details/best-practices.md): How to use Deep Lake at scale with best practices.
- [Creating Datasets at Scale](https://docs-v3.activeloop.ai/v3.8.27/technical-details/best-practices/creating-datasets-at-scale.md): Creating large Deep Lake datasets with high performance and reliability
- [Training Models at Scale](https://docs-v3.activeloop.ai/v3.8.27/technical-details/best-practices/training-models-at-scale.md): Train models at scale using Deep Lake
- [Storage Synchronization and "with" Context](https://docs-v3.activeloop.ai/v3.8.27/technical-details/best-practices/storage-synchronization.md): Synchronizing data with long-term storage and achieving optimal performance using Deep Lake.
- [Restoring Corrupted Datasets](https://docs-v3.activeloop.ai/v3.8.27/technical-details/best-practices/restoring-corrupted-datasets.md): Restoring Deep Lake datasets that may be corrupted.
- [Data Layout](https://docs-v3.activeloop.ai/v3.8.27/technical-details/data-layout.md): Understanding the data layout in Deep Lake
- [Version Control and Querying](https://docs-v3.activeloop.ai/v3.8.27/technical-details/version-control-and-querying.md): Understanding Deep Lake's Version control and Querying Layout
- [Dataset Visualization](https://docs-v3.activeloop.ai/v3.8.27/technical-details/dataset-visualization.md): How to visualize Deep Lake datasets
- [Tensor Relationships](https://docs-v3.activeloop.ai/v3.8.27/technical-details/tensor-relationships.md): Understanding the correct data layout for successful visualization.
- [Visualizer Integration](https://docs-v3.activeloop.ai/v3.8.27/technical-details/visualizer-integration.md): How to embed our visualizer in your application.
- [Shuffling in dataloaders](https://docs-v3.activeloop.ai/v3.8.27/technical-details/shuffling-in-dataloaders.md): Understanding data shuffling in Deep Lake's pytorch dataloader
- [How to Contribute](https://docs-v3.activeloop.ai/v3.8.27/technical-details/how-to-contribute.md): Guidelines for open source enthusiasts to contribute to our open-source data format.

## v3.8.19

- [Deep Lake Docs](https://docs-v3.activeloop.ai/v3.8.19/readme.md): We hope you enjoy Docs for Deep Lake.
- [Vector Store Quickstart](https://docs-v3.activeloop.ai/v3.8.19/quickstart.md): A jump-start guide to using Deep Lake for Vector Search.
- [Deep Learning Quickstart](https://docs-v3.activeloop.ai/v3.8.19/quickstart-dl.md): A jump-start guide to using Deep Lake for Deep Learning.
- [Storage & Credentials](https://docs-v3.activeloop.ai/v3.8.19/storage-and-credentials.md): How to access datasets in other clouds and manage their credentials.
- [Storage Options](https://docs-v3.activeloop.ai/v3.8.19/storage-and-credentials/storage-options.md): How to authenticate using Activeloop storage, AWS S3, and Google Cloud Storage.
- [User Authentication](https://docs-v3.activeloop.ai/v3.8.19/storage-and-credentials/user-authentication.md): Registration and authentication in Deep Lake.
- [Storing Deep Lake Data in Your Own Cloud](https://docs-v3.activeloop.ai/v3.8.19/storage-and-credentials/managed-credentials.md): How to store Deep Lake data in your own cloud and manage credentials with Deep Lake
- [Microsoft Azure](https://docs-v3.activeloop.ai/v3.8.19/storage-and-credentials/managed-credentials/microsoft-azure.md): Azure-specific information for connecting data to Deep Lake
- [Provisioning Federated Credentials](https://docs-v3.activeloop.ai/v3.8.19/storage-and-credentials/managed-credentials/microsoft-azure/provisioning-federated-credentials.md): How to setup Federated Credentials in Azure
- [Enabling CORS](https://docs-v3.activeloop.ai/v3.8.19/storage-and-credentials/managed-credentials/microsoft-azure/enabling-cors.md): How to enable Cross-Origin Resource Sharing in your Azure account.
- [Amazon Web Services](https://docs-v3.activeloop.ai/v3.8.19/storage-and-credentials/managed-credentials/amazon-web-services.md): AWS-specific information for connecting data to Deep Lake
- [Provisioning Role-Based Access](https://docs-v3.activeloop.ai/v3.8.19/storage-and-credentials/managed-credentials/amazon-web-services/provisioning-role-based-access.md): How to provision Role-Based Access in S3
- [Enabling CORS](https://docs-v3.activeloop.ai/v3.8.19/storage-and-credentials/managed-credentials/amazon-web-services/enabling-cors.md): How to enable Cross-Origin Resource Sharing in your AWS S3 buckets.
- [Introduction](https://docs-v3.activeloop.ai/v3.8.19/performance-features/introduction.md): C++ implementations of Deep Lake optimized for faster data fetching and computations
- [Performant Dataloader](https://docs-v3.activeloop.ai/v3.8.19/performance-features/performant-dataloader.md): Overview of Deep Lake's dataloader built and optimized in C++
- [Tensor Query Language (TQL)](https://docs-v3.activeloop.ai/v3.8.19/performance-features/querying-datasets.md): Deep Lake offers a highly-performant SQL-style query engine for filtering your data.
- [TQL Syntax](https://docs-v3.activeloop.ai/v3.8.19/performance-features/querying-datasets/query-syntax.md): How to properly format TQL queries
- [Sampling Datasets](https://docs-v3.activeloop.ai/v3.8.19/performance-features/querying-datasets/sampling-datasets.md): Implementation of samplers in TQL
- [Deep Memory](https://docs-v3.activeloop.ai/v3.8.19/performance-features/deep-memory.md): Overview of Deep Lake tools for increasing retrieval accuracy
- [How it Works](https://docs-v3.activeloop.ai/v3.8.19/performance-features/deep-memory/how-it-works.md): Understanding Deep Memory
- [Index for ANN Search](https://docs-v3.activeloop.ai/v3.8.19/performance-features/index-for-ann-search.md): Overview of Deep Lake's Index implementation for ANN search.
- [Caching and Optimization](https://docs-v3.activeloop.ai/v3.8.19/performance-features/index-for-ann-search/caching-and-optimization.md): Understanding Caching to Increase Query Performance in Deep Lake
- [Managed Tensor Database](https://docs-v3.activeloop.ai/v3.8.19/performance-features/managed-database.md): Deep Lake Managed Database
- [REST API](https://docs-v3.activeloop.ai/v3.8.19/performance-features/managed-database/rest-api.md): How to Use the Deep Lake REST API
- [Migrating Datasets to the Tensor Database](https://docs-v3.activeloop.ai/v3.8.19/performance-features/managed-database/migrating-datasets-to-the-tensor-database.md): Migrating datasets to the Tensor Database
- [Getting Started](https://docs-v3.activeloop.ai/v3.8.19/example-code/getting-started.md): Comprehensive guides for getting started with Deep Lake
- [Vector Store](https://docs-v3.activeloop.ai/v3.8.19/example-code/getting-started/vector-store.md): The comprehensive guide for Deep Lake in Vector Storage and Search applications.
- [Step 1: Hello World](https://docs-v3.activeloop.ai/v3.8.19/example-code/getting-started/vector-store/step-1-hello-world.md): Installing Deep Lake
- [Step 2: Creating Deep Lake Vector Stores](https://docs-v3.activeloop.ai/v3.8.19/example-code/getting-started/vector-store/step-2-creating-deep-lake-vector-stores.md): Creating the Deep Lake Vector Store
- [Step 3: Performing Search in Vector Stores](https://docs-v3.activeloop.ai/v3.8.19/example-code/getting-started/vector-store/step-3-performing-search-in-the-vector-store.md): Running search in the Deep Lake Vector Store
- [Step 4: Customizing Vector Stores](https://docs-v3.activeloop.ai/v3.8.19/example-code/getting-started/vector-store/step-4-customizing-vector-stores.md): Customizing the Deep Lake Vector Store
- [Deep Learning](https://docs-v3.activeloop.ai/v3.8.19/example-code/getting-started/deep-learning.md): The comprehensive guide for Deep Lake in Deep Learning applications.
- [Step 1: Hello World](https://docs-v3.activeloop.ai/v3.8.19/example-code/getting-started/deep-learning/hello-world.md): Installing Deep Lake and accessing your first Deep Lake Dataset.
- [Step 2: Creating Deep Lake Datasets](https://docs-v3.activeloop.ai/v3.8.19/example-code/getting-started/deep-learning/creating-datasets-manually.md): Creating and storing Deep Lake Datasets.
- [Step 3: Understanding Compression](https://docs-v3.activeloop.ai/v3.8.19/example-code/getting-started/deep-learning/understanding-compression.md): Using compression to achieve optimal performance in Deep Lake.
- [Step 4: Accessing and Updating Data](https://docs-v3.activeloop.ai/v3.8.19/example-code/getting-started/deep-learning/accessing-datasets.md): Learn how Deep Lake Datasets can be accessed or loaded from a variety of storage locations.
- [Step 5: Visualizing Datasets](https://docs-v3.activeloop.ai/v3.8.19/example-code/getting-started/deep-learning/visualizing-datasets.md): Visualizing and inspecting your datasets.
- [Step 6: Using Activeloop Storage](https://docs-v3.activeloop.ai/v3.8.19/example-code/getting-started/deep-learning/using-activeloop-storage.md): Storing and loading datasets from Deep Lake Storage.
- [Step 7: Connecting Deep Lake Datasets to ML Frameworks](https://docs-v3.activeloop.ai/v3.8.19/example-code/getting-started/deep-learning/connecting-to-ml-frameworks.md): Connecting Deep Lake Datasets to machine learning frameworks such as PyTorch and TensorFlow.
- [Step 8: Parallel Computing](https://docs-v3.activeloop.ai/v3.8.19/example-code/getting-started/deep-learning/parallel-computing.md): Running computations and processing data in parallel.
- [Step 9: Dataset Version Control](https://docs-v3.activeloop.ai/v3.8.19/example-code/getting-started/deep-learning/dataset-version-control.md): Managing changes to your datasets using Version Control.
- [Step 10: Dataset Filtering](https://docs-v3.activeloop.ai/v3.8.19/example-code/getting-started/deep-learning/dataset-filtering.md): Filtering datasets using user-defined-functions or SQL-style queries.
- [Tutorials (w Colab)](https://docs-v3.activeloop.ai/v3.8.19/example-code/tutorials.md): Common workflows with Deep Lake (includes Colab notebooks)
- [Vector Store Tutorials](https://docs-v3.activeloop.ai/v3.8.19/example-code/tutorials/vector-store.md): Tutorials for using Deep Lake in Vector Store applications
- [Vector Search Options](https://docs-v3.activeloop.ai/v3.8.19/example-code/tutorials/vector-store/vector-search-options.md): Overview of Vector Search Options in Deep Lake
- [Deep Lake Vector Store API](https://docs-v3.activeloop.ai/v3.8.19/example-code/tutorials/vector-store/vector-search-options/deep-lake-vector-store-api.md): Running Vector Search in the Deep Lake Vector Store module.
- [REST API](https://docs-v3.activeloop.ai/v3.8.19/example-code/tutorials/vector-store/vector-search-options/rest-api.md): Running Vector Search in the Deep Lake Tensor Database using the REST API
- [LangChain API](https://docs-v3.activeloop.ai/v3.8.19/example-code/tutorials/vector-store/vector-search-options/langchain-api.md): Vector Search using Deep Lake in LangChain
- [Image Similarity Search](https://docs-v3.activeloop.ai/v3.8.19/example-code/tutorials/vector-store/image-similarity-search.md): Using Deep Lake for image similarity search
- [Deep Lake Vector Store in LangChain](https://docs-v3.activeloop.ai/v3.8.19/example-code/tutorials/vector-store/deep-lake-vector-store-in-langchain.md): Using Deep Lake as a Vector Store in LangChain
- [Deep Lake Vector Store in LlamaIndex](https://docs-v3.activeloop.ai/v3.8.19/example-code/tutorials/vector-store/deep-lake-vector-store-in-llamaindex.md): Using Deep Lake as a Vector Store in LlamaIndex
- [Improving Search Accuracy using Deep Memory](https://docs-v3.activeloop.ai/v3.8.19/example-code/tutorials/vector-store/improving-search-accuracy-using-deep-memory.md): Using Deep Memory to improve the accuracy of your Vector Search
- [Deep Learning Tutorials](https://docs-v3.activeloop.ai/v3.8.19/example-code/tutorials/deep-learning.md): Tutorials for using Deep Lake in deep-learning applications.
- [Creating Datasets](https://docs-v3.activeloop.ai/v3.8.19/example-code/tutorials/deep-learning/creating-datasets.md): Workflows for creating Deep Lake datasets
- [Creating Complex Datasets](https://docs-v3.activeloop.ai/v3.8.19/example-code/tutorials/deep-learning/creating-datasets/creating-complex-datasets.md): Converting a multi-annotation dataset to Deep Lake format is helpful for understanding how to use Deep Lake with rich data.
- [Creating Object Detection Datasets](https://docs-v3.activeloop.ai/v3.8.19/example-code/tutorials/deep-learning/creating-datasets/creating-object-detection-datasets.md): Converting an object detection dataset to Deep Lake format is a great way to get started with datasets of increasing complexity.
- [Creating Time-Series Datasets](https://docs-v3.activeloop.ai/v3.8.19/example-code/tutorials/deep-learning/creating-datasets/creating-time-series-datasets.md): Deep Lake is a powerful tool for easily storing and sharing time-series datasets with your team.
- [Creating Datasets with Sequences](https://docs-v3.activeloop.ai/v3.8.19/example-code/tutorials/deep-learning/creating-datasets/creating-datasets-with-sequences.md): Deep Lake sequences are a powerful tool for storing temporal annotations such as bounding boxes in each frame of a video.
- [Creating Video Datasets](https://docs-v3.activeloop.ai/v3.8.19/example-code/tutorials/deep-learning/creating-datasets/creating-video-datasets.md): Get started with video datasets using Deep Lake.
- [Training Models](https://docs-v3.activeloop.ai/v3.8.19/example-code/tutorials/deep-learning/training-models.md): Workflows for training models using Deep Lake datasets
- [Splitting Datasets for Training](https://docs-v3.activeloop.ai/v3.8.19/example-code/tutorials/deep-learning/training-models/splitting-datasets-for-training.md): How to Split Datasets for Training in Deep Lake
- [Training an Image Classification Model in PyTorch](https://docs-v3.activeloop.ai/v3.8.19/example-code/tutorials/deep-learning/training-models/training-an-image-classification-model-in-pytorch.md): Training an image classification model is a great way to get started with model training using Deep Lake datasets.
- [Training Models Using MMDetection](https://docs-v3.activeloop.ai/v3.8.19/example-code/tutorials/deep-learning/training-models/training-models-using-mmdetection.md): How to Train Deep Learning models using Deep Lake's integration with MMDetection
- [Training Models Using PyTorch Lightning](https://docs-v3.activeloop.ai/v3.8.19/example-code/tutorials/deep-learning/training-models/training-models-using-pytorch-lightning.md): How to Train models using Deep Lake and PyTorch Lightning
- [Training on AWS SageMaker](https://docs-v3.activeloop.ai/v3.8.19/example-code/tutorials/deep-learning/training-models/training-on-aws-sagemaker.md): How to Train models on AWS SageMaker using Deep Lake datasets
- [Training an Object Detection and Segmentation Model in PyTorch](https://docs-v3.activeloop.ai/v3.8.19/example-code/tutorials/deep-learning/training-models/training-an-object-detection-and-segmentation-model-in-pytorch.md): Training an object detection and segmentation model is a great way to learn about complex data preprocessing for training models.
- [Updating Datasets](https://docs-v3.activeloop.ai/v3.8.19/example-code/tutorials/deep-learning/updating-datasets.md): Updating Deep Lake datasets
- [Data Processing Using Parallel Computing](https://docs-v3.activeloop.ai/v3.8.19/example-code/tutorials/deep-learning/data-processing-using-parallel-computing.md): Deeplake offers built-in methods for parallelizing dataset computations in order to achieve faster data processing.
- [Concurrent Writes](https://docs-v3.activeloop.ai/v3.8.19/example-code/tutorials/concurrent-writes.md): Concurrent writes in Deep Lake
- [Concurrency Using Zookeeper Locks](https://docs-v3.activeloop.ai/v3.8.19/example-code/tutorials/concurrent-writes/concurrency-using-zookeeper-locks.md): Using Zookeeper for locking Deep Lake datasets.
- [Playbooks](https://docs-v3.activeloop.ai/v3.8.19/example-code/playbooks.md): How to perform complex workflows using Deep Lake.
- [Querying, Training and Editing Datasets with Data Lineage](https://docs-v3.activeloop.ai/v3.8.19/example-code/playbooks/training-with-lineage.md): How to use queries and version control while training models.
- [Evaluating Model Performance](https://docs-v3.activeloop.ai/v3.8.19/example-code/playbooks/evaluating-model-performance.md): How to compare ground-truth annotations with model predictions
- [Training Reproducibility Using Deep Lake and Weights & Biases](https://docs-v3.activeloop.ai/v3.8.19/example-code/playbooks/training-reproducibility-with-wandb.md): How to achieve full reproducibility of model training using Deep Lake and W\&B
- [Working with Videos](https://docs-v3.activeloop.ai/v3.8.19/example-code/playbooks/working-with-videos.md): How manage video datasets and train models using Deep Lake.
- [Low-Level API Summary](https://docs-v3.activeloop.ai/v3.8.19/example-code/api-basics.md): Summary of the most important low-level Deep Lake commands.
- [Best Practices](https://docs-v3.activeloop.ai/v3.8.19/technical-details/best-practices.md): How to use Deep Lake at scale with best practices.
- [Creating Datasets at Scale](https://docs-v3.activeloop.ai/v3.8.19/technical-details/best-practices/creating-datasets-at-scale.md): Creating large Deep Lake datasets with high performance and reliability
- [Training Models at Scale](https://docs-v3.activeloop.ai/v3.8.19/technical-details/best-practices/training-models-at-scale.md): Train models at scale using Deep Lake
- [Storage Synchronization and "with" Context](https://docs-v3.activeloop.ai/v3.8.19/technical-details/best-practices/storage-synchronization.md): Synchronizing data with long-term storage and achieving optimal performance using Deep Lake.
- [Restoring Corrupted Datasets](https://docs-v3.activeloop.ai/v3.8.19/technical-details/best-practices/restoring-corrupted-datasets.md): Restoring Deep Lake datasets that may be corrupted.
- [Data Layout](https://docs-v3.activeloop.ai/v3.8.19/technical-details/data-layout.md): Understanding the data layout in Deep Lake
- [Version Control and Querying](https://docs-v3.activeloop.ai/v3.8.19/technical-details/version-control-and-querying.md): Understanding Deep Lake's Version control and Querying Layout
- [Dataset Visualization](https://docs-v3.activeloop.ai/v3.8.19/technical-details/dataset-visualization.md): How to visualize Deep Lake datasets
- [Tensor Relationships](https://docs-v3.activeloop.ai/v3.8.19/technical-details/tensor-relationships.md): Understanding the correct data layout for successful visualization.
- [Visualizer Integration](https://docs-v3.activeloop.ai/v3.8.19/technical-details/visualizer-integration.md): How to embed our visualizer in your application.
- [Shuffling in dataloaders](https://docs-v3.activeloop.ai/v3.8.19/technical-details/shuffling-in-dataloaders.md): Understanding data shuffling in Deep Lake's pytorch dataloader
- [How to Contribute](https://docs-v3.activeloop.ai/v3.8.19/technical-details/how-to-contribute.md): Guidelines for open source enthusiasts to contribute to our open-source data format.

## v3.8.16

- [Deep Lake Docs](https://docs-v3.activeloop.ai/v3.8.16/readme.md): We hope you enjoy Docs for Deep Lake.
- [Vector Store Quickstart](https://docs-v3.activeloop.ai/v3.8.16/quickstart.md): A jump-start guide to using Deep Lake for Vector Search.
- [Deep Learning Quickstart](https://docs-v3.activeloop.ai/v3.8.16/quickstart-dl.md): A jump-start guide to using Deep Lake for Deep Learning.
- [Storage & Credentials](https://docs-v3.activeloop.ai/v3.8.16/storage-and-credentials.md): How to access datasets in other clouds and manage their credentials.
- [Storage Options](https://docs-v3.activeloop.ai/v3.8.16/storage-and-credentials/storage-options.md): How to authenticate using Activeloop storage, AWS S3, and Google Cloud Storage.
- [User Authentication](https://docs-v3.activeloop.ai/v3.8.16/storage-and-credentials/user-authentication.md): Registration and authentication in Deep Lake.
- [Storing Deep Lake Data in Your Own Cloud](https://docs-v3.activeloop.ai/v3.8.16/storage-and-credentials/managed-credentials.md): How to store Deep Lake data in your own cloud and manage credentials with Deep Lake
- [Microsoft Azure](https://docs-v3.activeloop.ai/v3.8.16/storage-and-credentials/managed-credentials/microsoft-azure.md): Azure-specific information for connecting data to Deep Lake
- [Provisioning Federated Credentials](https://docs-v3.activeloop.ai/v3.8.16/storage-and-credentials/managed-credentials/microsoft-azure/provisioning-federated-credentials.md): How to setup Federated Credentials in Azure
- [Enabling CORS](https://docs-v3.activeloop.ai/v3.8.16/storage-and-credentials/managed-credentials/microsoft-azure/enabling-cors.md): How to enable Cross-Origin Resource Sharing in your Azure account.
- [Amazon Web Services](https://docs-v3.activeloop.ai/v3.8.16/storage-and-credentials/managed-credentials/amazon-web-services.md): AWS-specific information for connecting data to Deep Lake
- [Provisioning Role-Based Access](https://docs-v3.activeloop.ai/v3.8.16/storage-and-credentials/managed-credentials/amazon-web-services/provisioning-role-based-access.md): How to provision Role-Based Access in S3
- [Enabling CORS](https://docs-v3.activeloop.ai/v3.8.16/storage-and-credentials/managed-credentials/amazon-web-services/enabling-cors.md): How to enable Cross-Origin Resource Sharing in your AWS S3 buckets.
- [Introduction](https://docs-v3.activeloop.ai/v3.8.16/performance-features/introduction.md): C++ implementations of Deep Lake optimized for faster data fetching and computations
- [Performant Dataloader](https://docs-v3.activeloop.ai/v3.8.16/performance-features/performant-dataloader.md): Overview of Deep Lake's dataloader built and optimized in C++
- [Tensor Query Language (TQL)](https://docs-v3.activeloop.ai/v3.8.16/performance-features/querying-datasets.md): Deep Lake offers a highly-performant SQL-style query engine for filtering your data.
- [TQL Syntax](https://docs-v3.activeloop.ai/v3.8.16/performance-features/querying-datasets/query-syntax.md): How to properly format TQL queries
- [Sampling Datasets](https://docs-v3.activeloop.ai/v3.8.16/performance-features/querying-datasets/sampling-datasets.md): Implementation of samplers in TQL
- [Deep Memory](https://docs-v3.activeloop.ai/v3.8.16/performance-features/deep-memory.md): Overview of Deep Lake tools for increasing retrieval accuracy
- [How it Works](https://docs-v3.activeloop.ai/v3.8.16/performance-features/deep-memory/how-it-works.md): Understanding Deep Memory
- [Index for ANN Search](https://docs-v3.activeloop.ai/v3.8.16/performance-features/index-for-ann-search.md): Overview of Deep Lake's Index implementation for ANN search.
- [Caching and Optimization](https://docs-v3.activeloop.ai/v3.8.16/performance-features/index-for-ann-search/caching-and-optimization.md): Understanding Caching to Increase Query Performance in Deep Lake
- [Managed Tensor Database](https://docs-v3.activeloop.ai/v3.8.16/performance-features/managed-database.md): Deep Lake Managed Database
- [REST API](https://docs-v3.activeloop.ai/v3.8.16/performance-features/managed-database/rest-api.md): How to Use the Deep Lake REST API
- [Migrating Datasets to the Tensor Database](https://docs-v3.activeloop.ai/v3.8.16/performance-features/managed-database/migrating-datasets-to-the-tensor-database.md): Migrating datasets to the Tensor Database
- [Getting Started](https://docs-v3.activeloop.ai/v3.8.16/example-code/getting-started.md): Comprehensive guides for getting started with Deep Lake
- [Vector Store](https://docs-v3.activeloop.ai/v3.8.16/example-code/getting-started/vector-store.md): The comprehensive guide for Deep Lake in Vector Storage and Search applications.
- [Step 1: Hello World](https://docs-v3.activeloop.ai/v3.8.16/example-code/getting-started/vector-store/step-1-hello-world.md): Installing Deep Lake
- [Step 2: Creating Deep Lake Vector Stores](https://docs-v3.activeloop.ai/v3.8.16/example-code/getting-started/vector-store/step-2-creating-deep-lake-vector-stores.md): Creating the Deep Lake Vector Store
- [Step 3: Performing Search in Vector Stores](https://docs-v3.activeloop.ai/v3.8.16/example-code/getting-started/vector-store/step-3-performing-search-in-the-vector-store.md): Running search in the Deep Lake Vector Store
- [Step 4: Customizing Vector Stores](https://docs-v3.activeloop.ai/v3.8.16/example-code/getting-started/vector-store/step-4-customizing-vector-stores.md): Customizing the Deep Lake Vector Store
- [Deep Learning](https://docs-v3.activeloop.ai/v3.8.16/example-code/getting-started/deep-learning.md): The comprehensive guide for Deep Lake in Deep Learning applications.
- [Step 1: Hello World](https://docs-v3.activeloop.ai/v3.8.16/example-code/getting-started/deep-learning/hello-world.md): Installing Deep Lake and accessing your first Deep Lake Dataset.
- [Step 2: Creating Deep Lake Datasets](https://docs-v3.activeloop.ai/v3.8.16/example-code/getting-started/deep-learning/creating-datasets-manually.md): Creating and storing Deep Lake Datasets.
- [Step 3: Understanding Compression](https://docs-v3.activeloop.ai/v3.8.16/example-code/getting-started/deep-learning/understanding-compression.md): Using compression to achieve optimal performance in Deep Lake.
- [Step 4: Accessing and Updating Data](https://docs-v3.activeloop.ai/v3.8.16/example-code/getting-started/deep-learning/accessing-datasets.md): Learn how Deep Lake Datasets can be accessed or loaded from a variety of storage locations.
- [Step 5: Visualizing Datasets](https://docs-v3.activeloop.ai/v3.8.16/example-code/getting-started/deep-learning/visualizing-datasets.md): Visualizing and inspecting your datasets.
- [Step 6: Using Activeloop Storage](https://docs-v3.activeloop.ai/v3.8.16/example-code/getting-started/deep-learning/using-activeloop-storage.md): Storing and loading datasets from Deep Lake Storage.
- [Step 7: Connecting Deep Lake Datasets to ML Frameworks](https://docs-v3.activeloop.ai/v3.8.16/example-code/getting-started/deep-learning/connecting-to-ml-frameworks.md): Connecting Deep Lake Datasets to machine learning frameworks such as PyTorch and TensorFlow.
- [Step 8: Parallel Computing](https://docs-v3.activeloop.ai/v3.8.16/example-code/getting-started/deep-learning/parallel-computing.md): Running computations and processing data in parallel.
- [Step 9: Dataset Version Control](https://docs-v3.activeloop.ai/v3.8.16/example-code/getting-started/deep-learning/dataset-version-control.md): Managing changes to your datasets using Version Control.
- [Step 10: Dataset Filtering](https://docs-v3.activeloop.ai/v3.8.16/example-code/getting-started/deep-learning/dataset-filtering.md): Filtering datasets using user-defined-functions or SQL-style queries.
- [Tutorials (w Colab)](https://docs-v3.activeloop.ai/v3.8.16/example-code/tutorials.md): Common workflows with Deep Lake (includes Colab notebooks)
- [Vector Store Tutorials](https://docs-v3.activeloop.ai/v3.8.16/example-code/tutorials/vector-store.md): Tutorials for using Deep Lake in Vector Store applications
- [Vector Search Options](https://docs-v3.activeloop.ai/v3.8.16/example-code/tutorials/vector-store/vector-search-options.md): Overview of Vector Search Options in Deep Lake
- [Deep Lake Vector Store API](https://docs-v3.activeloop.ai/v3.8.16/example-code/tutorials/vector-store/vector-search-options/deep-lake-vector-store-api.md): Running Vector Search in the Deep Lake Vector Store module.
- [REST API](https://docs-v3.activeloop.ai/v3.8.16/example-code/tutorials/vector-store/vector-search-options/rest-api.md): Running Vector Search in the Deep Lake Tensor Database using the REST API
- [LangChain API](https://docs-v3.activeloop.ai/v3.8.16/example-code/tutorials/vector-store/vector-search-options/langchain-api.md): Vector Search using Deep Lake in LangChain
- [Image Similarity Search](https://docs-v3.activeloop.ai/v3.8.16/example-code/tutorials/vector-store/image-similarity-search.md): Using Deep Lake for image similarity search
- [Deep Lake Vector Store in LangChain](https://docs-v3.activeloop.ai/v3.8.16/example-code/tutorials/vector-store/deep-lake-vector-store-in-langchain.md): Using Deep Lake as a Vector Store in LangChain
- [Deep Lake Vector Store in LlamaIndex](https://docs-v3.activeloop.ai/v3.8.16/example-code/tutorials/vector-store/deep-lake-vector-store-in-llamaindex.md): Using Deep Lake as a Vector Store in LlamaIndex
- [Improving Search Accuracy using Deep Memory](https://docs-v3.activeloop.ai/v3.8.16/example-code/tutorials/vector-store/improving-search-accuracy-using-deep-memory.md): Using Deep Memory to improve the accuracy of your Vector Search
- [Deep Learning Tutorials](https://docs-v3.activeloop.ai/v3.8.16/example-code/tutorials/deep-learning.md): Tutorials for using Deep Lake in deep-learning applications.
- [Creating Datasets](https://docs-v3.activeloop.ai/v3.8.16/example-code/tutorials/deep-learning/creating-datasets.md): Workflows for creating Deep Lake datasets
- [Creating Complex Datasets](https://docs-v3.activeloop.ai/v3.8.16/example-code/tutorials/deep-learning/creating-datasets/creating-complex-datasets.md): Converting a multi-annotation dataset to Deep Lake format is helpful for understanding how to use Deep Lake with rich data.
- [Creating Object Detection Datasets](https://docs-v3.activeloop.ai/v3.8.16/example-code/tutorials/deep-learning/creating-datasets/creating-object-detection-datasets.md): Converting an object detection dataset to Deep Lake format is a great way to get started with datasets of increasing complexity.
- [Creating Time-Series Datasets](https://docs-v3.activeloop.ai/v3.8.16/example-code/tutorials/deep-learning/creating-datasets/creating-time-series-datasets.md): Deep Lake is a powerful tool for easily storing and sharing time-series datasets with your team.
- [Creating Datasets with Sequences](https://docs-v3.activeloop.ai/v3.8.16/example-code/tutorials/deep-learning/creating-datasets/creating-datasets-with-sequences.md): Deep Lake sequences are a powerful tool for storing temporal annotations such as bounding boxes in each frame of a video.
- [Creating Video Datasets](https://docs-v3.activeloop.ai/v3.8.16/example-code/tutorials/deep-learning/creating-datasets/creating-video-datasets.md): Get started with video datasets using Deep Lake.
- [Training Models](https://docs-v3.activeloop.ai/v3.8.16/example-code/tutorials/deep-learning/training-models.md): Workflows for training models using Deep Lake datasets
- [Splitting Datasets for Training](https://docs-v3.activeloop.ai/v3.8.16/example-code/tutorials/deep-learning/training-models/splitting-datasets-for-training.md)
- [Training an Image Classification Model in PyTorch](https://docs-v3.activeloop.ai/v3.8.16/example-code/tutorials/deep-learning/training-models/training-an-image-classification-model-in-pytorch.md): Training an image classification model is a great way to get started with model training using Deep Lake datasets.
- [Training Models Using MMDetection](https://docs-v3.activeloop.ai/v3.8.16/example-code/tutorials/deep-learning/training-models/training-models-using-mmdetection.md): How to Train Deep Learning models using Deep Lake's integration with MMDetection
- [Training Models Using PyTorch Lightning](https://docs-v3.activeloop.ai/v3.8.16/example-code/tutorials/deep-learning/training-models/training-models-using-pytorch-lightning.md): How to Train models using Deep Lake and PyTorch Lightning
- [Training on AWS SageMaker](https://docs-v3.activeloop.ai/v3.8.16/example-code/tutorials/deep-learning/training-models/training-on-aws-sagemaker.md): How to Train models on AWS SageMaker using Deep Lake datasets
- [Training an Object Detection and Segmentation Model in PyTorch](https://docs-v3.activeloop.ai/v3.8.16/example-code/tutorials/deep-learning/training-models/training-an-object-detection-and-segmentation-model-in-pytorch.md): Training an object detection and segmentation model is a great way to learn about complex data preprocessing for training models.
- [Updating Datasets](https://docs-v3.activeloop.ai/v3.8.16/example-code/tutorials/deep-learning/updating-datasets.md): Updating Deep Lake datasets
- [Data Processing Using Parallel Computing](https://docs-v3.activeloop.ai/v3.8.16/example-code/tutorials/deep-learning/data-processing-using-parallel-computing.md): Deeplake offers built-in methods for parallelizing dataset computations in order to achieve faster data processing.
- [Concurrent Writes](https://docs-v3.activeloop.ai/v3.8.16/example-code/tutorials/concurrent-writes.md): Concurrent writes in Deep Lake
- [Concurrency Using Zookeeper Locks](https://docs-v3.activeloop.ai/v3.8.16/example-code/tutorials/concurrent-writes/concurrency-using-zookeeper-locks.md): Using Zookeeper for locking Deep Lake datasets.
- [Playbooks](https://docs-v3.activeloop.ai/v3.8.16/example-code/playbooks.md): How to perform complex workflows using Deep Lake.
- [Querying, Training and Editing Datasets with Data Lineage](https://docs-v3.activeloop.ai/v3.8.16/example-code/playbooks/training-with-lineage.md): How to use queries and version control while training models.
- [Evaluating Model Performance](https://docs-v3.activeloop.ai/v3.8.16/example-code/playbooks/evaluating-model-performance.md): How to compare ground-truth annotations with model predictions
- [Training Reproducibility Using Deep Lake and Weights & Biases](https://docs-v3.activeloop.ai/v3.8.16/example-code/playbooks/training-reproducibility-with-wandb.md): How to achieve full reproducibility of model training using Deep Lake and W\&B
- [Working with Videos](https://docs-v3.activeloop.ai/v3.8.16/example-code/playbooks/working-with-videos.md): How manage video datasets and train models using Deep Lake.
- [Low-Level API Summary](https://docs-v3.activeloop.ai/v3.8.16/example-code/api-basics.md): Summary of the most important low-level Deep Lake commands.
- [Best Practices](https://docs-v3.activeloop.ai/v3.8.16/technical-details/best-practices.md): How to use Deep Lake at scale with best practices.
- [Creating Datasets at Scale](https://docs-v3.activeloop.ai/v3.8.16/technical-details/best-practices/creating-datasets-at-scale.md): Creating large Deep Lake datasets with high performance and reliability
- [Training Models at Scale](https://docs-v3.activeloop.ai/v3.8.16/technical-details/best-practices/training-models-at-scale.md): Train models at scale using Deep Lake
- [Storage Synchronization and "with" Context](https://docs-v3.activeloop.ai/v3.8.16/technical-details/best-practices/storage-synchronization.md): Synchronizing data with long-term storage and achieving optimal performance using Deep Lake.
- [Restoring Corrupted Datasets](https://docs-v3.activeloop.ai/v3.8.16/technical-details/best-practices/restoring-corrupted-datasets.md): Restoring Deep Lake datasets that may be corrupted.
- [Data Layout](https://docs-v3.activeloop.ai/v3.8.16/technical-details/data-layout.md): Understanding the data layout in Deep Lake
- [Version Control and Querying](https://docs-v3.activeloop.ai/v3.8.16/technical-details/version-control-and-querying.md): Understanding Deep Lake's Version control and Querying Layout
- [Dataset Visualization](https://docs-v3.activeloop.ai/v3.8.16/technical-details/dataset-visualization.md): How to visualize Deep Lake datasets
- [Tensor Relationships](https://docs-v3.activeloop.ai/v3.8.16/technical-details/tensor-relationships.md): Understanding the correct data layout for successful visualization.
- [Visualizer Integration](https://docs-v3.activeloop.ai/v3.8.16/technical-details/visualizer-integration.md): How to embed our visualizer in your application.
- [Shuffling in dataloaders](https://docs-v3.activeloop.ai/v3.8.16/technical-details/shuffling-in-dataloaders.md): Understanding data shuffling in Deep Lake's pytorch dataloader
- [How to Contribute](https://docs-v3.activeloop.ai/v3.8.16/technical-details/how-to-contribute.md): Guidelines for open source enthusiasts to contribute to our open-source data format.

## v3.8.2

- [Deep Lake Docs](https://docs-v3.activeloop.ai/v3.8.2/readme.md): We hope you enjoy Docs for Deep Lake.
- [Vector Store Quickstart](https://docs-v3.activeloop.ai/v3.8.2/quickstart.md): A jump-start guide to using Deep Lake for Vector Search.
- [Deep Learning Quickstart](https://docs-v3.activeloop.ai/v3.8.2/quickstart-dl.md): A jump-start guide to using Deep Lake for Deep Learning.
- [Storage & Credentials](https://docs-v3.activeloop.ai/v3.8.2/storage-and-credentials.md): How to access datasets in other clouds and manage their credentials.
- [Storage Options](https://docs-v3.activeloop.ai/v3.8.2/storage-and-credentials/storage-options.md): How to authenticate using Activeloop storage, AWS S3, and Google Cloud Storage.
- [User Authentication](https://docs-v3.activeloop.ai/v3.8.2/storage-and-credentials/user-authentication.md): Registration and authentication in Deep Lake.
- [Storing Deep Lake Data in Your Own Cloud](https://docs-v3.activeloop.ai/v3.8.2/storage-and-credentials/managed-credentials.md): How to store Deep Lake data in your own cloud and manage credentials with Deep Lake
- [Microsoft Azure](https://docs-v3.activeloop.ai/v3.8.2/storage-and-credentials/managed-credentials/microsoft-azure.md): Azure-specific information for connecting data to Deep Lake
- [Provisioning Federated Credentials](https://docs-v3.activeloop.ai/v3.8.2/storage-and-credentials/managed-credentials/microsoft-azure/provisioning-federated-credentials.md): How to setup Federated Credentials in Azure
- [Enabling CORS](https://docs-v3.activeloop.ai/v3.8.2/storage-and-credentials/managed-credentials/microsoft-azure/enabling-cors.md): How to enable Cross-Origin Resource Sharing in your Azure account.
- [Amazon Web Services](https://docs-v3.activeloop.ai/v3.8.2/storage-and-credentials/managed-credentials/amazon-web-services.md): AWS-specific information for connecting data to Deep Lake
- [Provisioning Role-Based Access](https://docs-v3.activeloop.ai/v3.8.2/storage-and-credentials/managed-credentials/amazon-web-services/provisioning-role-based-access.md): How to provision Role-Based Access in S3
- [Enabling CORS](https://docs-v3.activeloop.ai/v3.8.2/storage-and-credentials/managed-credentials/amazon-web-services/enabling-cors.md): How to enable Cross-Origin Resource Sharing in your AWS S3 buckets.
- [Introduction](https://docs-v3.activeloop.ai/v3.8.2/performance-features/introduction.md): C++ implementations of Deep Lake optimized for faster data fetching and computations
- [Performant Dataloader](https://docs-v3.activeloop.ai/v3.8.2/performance-features/performant-dataloader.md): Overview of Deep Lake's dataloader built and optimized in C++
- [Tensor Query Language (TQL)](https://docs-v3.activeloop.ai/v3.8.2/performance-features/querying-datasets.md): Deep Lake offers a highly-performant SQL-style query engine for filtering your data.
- [TQL Syntax](https://docs-v3.activeloop.ai/v3.8.2/performance-features/querying-datasets/query-syntax.md): How to properly format TQL queries
- [Sampling Datasets](https://docs-v3.activeloop.ai/v3.8.2/performance-features/querying-datasets/sampling-datasets.md): Implementation of samplers in TQL
- [Deep Memory](https://docs-v3.activeloop.ai/v3.8.2/performance-features/deep-memory.md): Overview of Deep Lake tools for increasing retrieval accuracy
- [How it Works](https://docs-v3.activeloop.ai/v3.8.2/performance-features/deep-memory/how-it-works.md): Understanding Deep Memory
- [Index for ANN Search](https://docs-v3.activeloop.ai/v3.8.2/performance-features/index-for-ann-search.md): Overview of Deep Lake's Index implementation for ANN search.
- [Caching and Optimization](https://docs-v3.activeloop.ai/v3.8.2/performance-features/index-for-ann-search/caching-and-optimization.md): Understanding Caching to Increase Query Performance in Deep Lake
- [Managed Tensor Database](https://docs-v3.activeloop.ai/v3.8.2/performance-features/managed-database.md): Deep Lake Managed Database
- [REST API](https://docs-v3.activeloop.ai/v3.8.2/performance-features/managed-database/rest-api.md): How to Use the Deep Lake REST API
- [Migrating Datasets to the Tensor Database](https://docs-v3.activeloop.ai/v3.8.2/performance-features/managed-database/migrating-datasets-to-the-tensor-database.md): Migrating datasets to the Tensor Database
- [Getting Started](https://docs-v3.activeloop.ai/v3.8.2/example-code/getting-started.md): Comprehensive guides for getting started with Deep Lake
- [Vector Store](https://docs-v3.activeloop.ai/v3.8.2/example-code/getting-started/vector-store.md): The comprehensive guide for Deep Lake in Vector Storage and Search applications.
- [Step 1: Hello World](https://docs-v3.activeloop.ai/v3.8.2/example-code/getting-started/vector-store/step-1-hello-world.md): Installing Deep Lake
- [Step 2: Creating Deep Lake Vector Stores](https://docs-v3.activeloop.ai/v3.8.2/example-code/getting-started/vector-store/step-2-creating-deep-lake-vector-stores.md): Creating the Deep Lake Vector Store
- [Step 3: Performing Search in Vector Stores](https://docs-v3.activeloop.ai/v3.8.2/example-code/getting-started/vector-store/step-3-performing-search-in-the-vector-store.md): Running search in the Deep Lake Vector Store
- [Step 4: Customizing Vector Stores](https://docs-v3.activeloop.ai/v3.8.2/example-code/getting-started/vector-store/step-4-customizing-vector-stores.md): Customizing the Deep Lake Vector Store
- [Deep Learning](https://docs-v3.activeloop.ai/v3.8.2/example-code/getting-started/deep-learning.md): The comprehensive guide for Deep Lake in Deep Learning applications.
- [Step 1: Hello World](https://docs-v3.activeloop.ai/v3.8.2/example-code/getting-started/deep-learning/hello-world.md): Installing Deep Lake and accessing your first Deep Lake Dataset.
- [Step 2: Creating Deep Lake Datasets](https://docs-v3.activeloop.ai/v3.8.2/example-code/getting-started/deep-learning/creating-datasets-manually.md): Creating and storing Deep Lake Datasets.
- [Step 3: Understanding Compression](https://docs-v3.activeloop.ai/v3.8.2/example-code/getting-started/deep-learning/understanding-compression.md): Using compression to achieve optimal performance in Deep Lake.
- [Step 4: Accessing and Updating Data](https://docs-v3.activeloop.ai/v3.8.2/example-code/getting-started/deep-learning/accessing-datasets.md): Learn how Deep Lake Datasets can be accessed or loaded from a variety of storage locations.
- [Step 5: Visualizing Datasets](https://docs-v3.activeloop.ai/v3.8.2/example-code/getting-started/deep-learning/visualizing-datasets.md): Visualizing and inspecting your datasets.
- [Step 6: Using Activeloop Storage](https://docs-v3.activeloop.ai/v3.8.2/example-code/getting-started/deep-learning/using-activeloop-storage.md): Storing and loading datasets from Deep Lake Storage.
- [Step 7: Connecting Deep Lake Datasets to ML Frameworks](https://docs-v3.activeloop.ai/v3.8.2/example-code/getting-started/deep-learning/connecting-to-ml-frameworks.md): Connecting Deep Lake Datasets to machine learning frameworks such as PyTorch and TensorFlow.
- [Step 8: Parallel Computing](https://docs-v3.activeloop.ai/v3.8.2/example-code/getting-started/deep-learning/parallel-computing.md): Running computations and processing data in parallel.
- [Step 9: Dataset Version Control](https://docs-v3.activeloop.ai/v3.8.2/example-code/getting-started/deep-learning/dataset-version-control.md): Managing changes to your datasets using Version Control.
- [Step 10: Dataset Filtering](https://docs-v3.activeloop.ai/v3.8.2/example-code/getting-started/deep-learning/dataset-filtering.md): Filtering datasets using user-defined-functions or SQL-style queries.
- [Tutorials (w Colab)](https://docs-v3.activeloop.ai/v3.8.2/example-code/tutorials.md): Common workflows with Deep Lake (includes Colab notebooks)
- [Vector Store Tutorials](https://docs-v3.activeloop.ai/v3.8.2/example-code/tutorials/vector-store.md): Tutorials for using Deep Lake in Vector Store applications
- [Vector Search Options](https://docs-v3.activeloop.ai/v3.8.2/example-code/tutorials/vector-store/vector-search-options.md): Overview of Vector Search Options in Deep Lake
- [Deep Lake Vector Store API](https://docs-v3.activeloop.ai/v3.8.2/example-code/tutorials/vector-store/vector-search-options/deep-lake-vector-store-api.md): Running Vector Search in the Deep Lake Vector Store module.
- [REST API](https://docs-v3.activeloop.ai/v3.8.2/example-code/tutorials/vector-store/vector-search-options/rest-api.md): Running Vector Search in the Deep Lake Tensor Database using the REST API
- [LangChain API](https://docs-v3.activeloop.ai/v3.8.2/example-code/tutorials/vector-store/vector-search-options/langchain-api.md): Vector Search using Deep Lake in LangChain
- [Image Similarity Search](https://docs-v3.activeloop.ai/v3.8.2/example-code/tutorials/vector-store/image-similarity-search.md): Using Deep Lake for image similarity search
- [Deep Lake Vector Store in LangChain](https://docs-v3.activeloop.ai/v3.8.2/example-code/tutorials/vector-store/deep-lake-vector-store-in-langchain.md): Using Deep Lake as a Vector Store in LangChain
- [Improving Search Accuracy using Deep Memory](https://docs-v3.activeloop.ai/v3.8.2/example-code/tutorials/vector-store/improving-search-accuracy-using-deep-memory.md): Using Deep Memory to improve the accuracy of your Vector Search
- [Deep Learning Tutorials](https://docs-v3.activeloop.ai/v3.8.2/example-code/tutorials/deep-learning.md): Tutorials for using Deep Lake in deep-learning applications.
- [Creating Datasets](https://docs-v3.activeloop.ai/v3.8.2/example-code/tutorials/deep-learning/creating-datasets.md): Workflows for creating Deep Lake datasets
- [Creating Complex Datasets](https://docs-v3.activeloop.ai/v3.8.2/example-code/tutorials/deep-learning/creating-datasets/creating-complex-datasets.md): Converting a multi-annotation dataset to Deep Lake format is helpful for understanding how to use Deep Lake with rich data.
- [Creating Object Detection Datasets](https://docs-v3.activeloop.ai/v3.8.2/example-code/tutorials/deep-learning/creating-datasets/creating-object-detection-datasets.md): Converting an object detection dataset to Deep Lake format is a great way to get started with datasets of increasing complexity.
- [Creating Time-Series Datasets](https://docs-v3.activeloop.ai/v3.8.2/example-code/tutorials/deep-learning/creating-datasets/creating-time-series-datasets.md): Deep Lake is a powerful tool for easily storing and sharing time-series datasets with your team.
- [Creating Datasets with Sequences](https://docs-v3.activeloop.ai/v3.8.2/example-code/tutorials/deep-learning/creating-datasets/creating-datasets-with-sequences.md): Deep Lake sequences are a powerful tool for storing temporal annotations such as bounding boxes in each frame of a video.
- [Creating Video Datasets](https://docs-v3.activeloop.ai/v3.8.2/example-code/tutorials/deep-learning/creating-datasets/creating-video-datasets.md): Get started with video datasets using Deep Lake.
- [Training Models](https://docs-v3.activeloop.ai/v3.8.2/example-code/tutorials/deep-learning/training-models.md): Workflows for training models using Deep Lake datasets
- [Training an Image Classification Model in PyTorch](https://docs-v3.activeloop.ai/v3.8.2/example-code/tutorials/deep-learning/training-models/training-an-image-classification-model-in-pytorch.md): Training an image classification model is a great way to get started with model training using Deep Lake datasets.
- [Training Models Using MMDetection](https://docs-v3.activeloop.ai/v3.8.2/example-code/tutorials/deep-learning/training-models/training-models-using-mmdetection.md): How to Train Deep Learning models using Deep Lake's integration with MMDetection
- [Training Models Using PyTorch Lightning](https://docs-v3.activeloop.ai/v3.8.2/example-code/tutorials/deep-learning/training-models/training-models-using-pytorch-lightning.md): How to Train models using Deep Lake and PyTorch Lightning
- [Training on AWS SageMaker](https://docs-v3.activeloop.ai/v3.8.2/example-code/tutorials/deep-learning/training-models/training-on-aws-sagemaker.md): How to Train models on AWS SageMaker using Deep Lake datasets
- [Training an Object Detection and Segmentation Model in PyTorch](https://docs-v3.activeloop.ai/v3.8.2/example-code/tutorials/deep-learning/training-models/training-an-object-detection-and-segmentation-model-in-pytorch.md): Training an object detection and segmentation model is a great way to learn about complex data preprocessing for training models.
- [Updating Datasets](https://docs-v3.activeloop.ai/v3.8.2/example-code/tutorials/deep-learning/updating-datasets.md): Updating Deep Lake datasets
- [Data Processing Using Parallel Computing](https://docs-v3.activeloop.ai/v3.8.2/example-code/tutorials/deep-learning/data-processing-using-parallel-computing.md): Deeplake offers built-in methods for parallelizing dataset computations in order to achieve faster data processing.
- [Concurrent Writes](https://docs-v3.activeloop.ai/v3.8.2/example-code/tutorials/concurrent-writes.md): Concurrent writes in Deep Lake
- [Concurrency Using Zookeeper Locks](https://docs-v3.activeloop.ai/v3.8.2/example-code/tutorials/concurrent-writes/concurrency-using-zookeeper-locks.md): Using Zookeeper for locking Deep Lake datasets.
- [Playbooks](https://docs-v3.activeloop.ai/v3.8.2/example-code/playbooks.md): How to perform complex workflows using Deep Lake.
- [Querying, Training and Editing Datasets with Data Lineage](https://docs-v3.activeloop.ai/v3.8.2/example-code/playbooks/training-with-lineage.md): How to use queries and version control while training models.
- [Evaluating Model Performance](https://docs-v3.activeloop.ai/v3.8.2/example-code/playbooks/evaluating-model-performance.md): How to compare ground-truth annotations with model predictions
- [Training Reproducibility Using Deep Lake and Weights & Biases](https://docs-v3.activeloop.ai/v3.8.2/example-code/playbooks/training-reproducibility-with-wandb.md): How to achieve full reproducibility of model training using Deep Lake and W\&B
- [Working with Videos](https://docs-v3.activeloop.ai/v3.8.2/example-code/playbooks/working-with-videos.md): How manage video datasets and train models using Deep Lake.
- [Low-Level API Summary](https://docs-v3.activeloop.ai/v3.8.2/example-code/api-basics.md): Summary of the most important low-level Deep Lake commands.
- [Best Practices](https://docs-v3.activeloop.ai/v3.8.2/technical-details/best-practices.md): How to use Deep Lake at scale with best practices.
- [Creating Datasets at Scale](https://docs-v3.activeloop.ai/v3.8.2/technical-details/best-practices/creating-datasets-at-scale.md): Creating large Deep Lake datasets with high performance and reliability
- [Training Models at Scale](https://docs-v3.activeloop.ai/v3.8.2/technical-details/best-practices/training-models-at-scale.md): Train models at scale using Deep Lake
- [Storage Synchronization and "with" Context](https://docs-v3.activeloop.ai/v3.8.2/technical-details/best-practices/storage-synchronization.md): Synchronizing data with long-term storage and achieving optimal performance using Deep Lake.
- [Restoring Corrupted Datasets](https://docs-v3.activeloop.ai/v3.8.2/technical-details/best-practices/restoring-corrupted-datasets.md): Restoring Deep Lake datasets that may be corrupted.
- [Data Layout](https://docs-v3.activeloop.ai/v3.8.2/technical-details/data-layout.md): Understanding the data layout in Deep Lake
- [Version Control and Querying](https://docs-v3.activeloop.ai/v3.8.2/technical-details/version-control-and-querying.md): Understanding Deep Lake's Version control and Querying Layout
- [Dataset Visualization](https://docs-v3.activeloop.ai/v3.8.2/technical-details/dataset-visualization.md): How to visualize Deep Lake datasets
- [Tensor Relationships](https://docs-v3.activeloop.ai/v3.8.2/technical-details/tensor-relationships.md): Understanding the correct data layout for successful visualization.
- [Visualizer Integration](https://docs-v3.activeloop.ai/v3.8.2/technical-details/visualizer-integration.md): How to embed our visualizer in your application.
- [Shuffling in dataloaders](https://docs-v3.activeloop.ai/v3.8.2/technical-details/shuffling-in-dataloaders.md): Understanding data shuffling in Deep Lake's pytorch dataloader
- [How to Contribute](https://docs-v3.activeloop.ai/v3.8.2/technical-details/how-to-contribute.md): Guidelines for open source enthusiasts to contribute to our open-source data format.

## v3.8.0

- [Deep Lake Docs](https://docs-v3.activeloop.ai/v3.7.3/master.md): We hope you enjoy Docs for Deep Lake.
- [Vector Store Quickstart](https://docs-v3.activeloop.ai/v3.7.3/quickstart.md): A jump-start guide to using Deep Lake for Vector Search.
- [Deep Learning Quickstart](https://docs-v3.activeloop.ai/v3.7.3/quickstart-dl.md): A jump-start guide to using Deep Lake for Deep Learning.
- [Storage & Credentials](https://docs-v3.activeloop.ai/v3.7.3/storage-and-credentials.md): How to access datasets in other clouds and manage their credentials.
- [Storage Options](https://docs-v3.activeloop.ai/v3.7.3/storage-and-credentials/storage-options.md): How to authenticate using Activeloop storage, AWS S3, and Google Cloud Storage.
- [User Authentication](https://docs-v3.activeloop.ai/v3.7.3/storage-and-credentials/user-authentication.md): Registration and authentication in Deep Lake.
- [Storing Deep Lake Data in Your Own Cloud](https://docs-v3.activeloop.ai/v3.7.3/storage-and-credentials/managed-credentials.md): How to store Deep Lake data in your own cloud and manage credentials with Deep Lake
- [Microsoft Azure](https://docs-v3.activeloop.ai/v3.7.3/storage-and-credentials/managed-credentials/microsoft-azure.md): Azure-specific information for connecting data to Deep Lake
- [Provisioning Federated Credentials](https://docs-v3.activeloop.ai/v3.7.3/storage-and-credentials/managed-credentials/microsoft-azure/provisioning-federated-credentials.md): How to setup Federated Credentials in Azure
- [Enabling CORS](https://docs-v3.activeloop.ai/v3.7.3/storage-and-credentials/managed-credentials/microsoft-azure/enabling-cors.md): How to enable Cross-Origin Resource Sharing in your Azure account.
- [Amazon Web Services](https://docs-v3.activeloop.ai/v3.7.3/storage-and-credentials/managed-credentials/amazon-web-services.md): AWS-specific information for connecting data to Deep Lake
- [Provisioning Role-Based Access](https://docs-v3.activeloop.ai/v3.7.3/storage-and-credentials/managed-credentials/amazon-web-services/provisioning-role-based-access.md): How to provision Role-Based Access in S3
- [Enabling CORS](https://docs-v3.activeloop.ai/v3.7.3/storage-and-credentials/managed-credentials/amazon-web-services/enabling-cors.md): How to enable Cross-Origin Resource Sharing in your AWS S3 buckets.
- [Introduction](https://docs-v3.activeloop.ai/v3.7.3/performance-features/introduction.md): C++ implementations of Deep Lake optimized for faster data fetching and computations
- [Performant Dataloader](https://docs-v3.activeloop.ai/v3.7.3/performance-features/performant-dataloader.md): Overview of Deep Lake's dataloader built and optimized in C++
- [Tensor Query Language (TQL)](https://docs-v3.activeloop.ai/v3.7.3/performance-features/querying-datasets.md): Deep Lake offers a highly-performant SQL-style query engine for filtering your data.
- [TQL Syntax](https://docs-v3.activeloop.ai/v3.7.3/performance-features/querying-datasets/query-syntax.md): How to properly format TQL queries
- [Sampling Datasets](https://docs-v3.activeloop.ai/v3.7.3/performance-features/querying-datasets/sampling-datasets.md): Implementation of samplers in TQL
- [Deep Memory](https://docs-v3.activeloop.ai/v3.7.3/performance-features/deep-memory.md): Overview of Deep Lake tools for increasing retrieval accuracy
- [How it Works](https://docs-v3.activeloop.ai/v3.7.3/performance-features/deep-memory/how-it-works.md): Understanding Deep Memory
- [Index for ANN Search](https://docs-v3.activeloop.ai/v3.7.3/performance-features/index-for-ann-search.md): Overview of Deep Lake's Index implementation for ANN search.
- [Caching and Optimization](https://docs-v3.activeloop.ai/v3.7.3/performance-features/index-for-ann-search/caching-and-optimization.md): Understanding Caching to Increase Query Performance in Deep Lake
- [Managed Tensor Database](https://docs-v3.activeloop.ai/v3.7.3/performance-features/managed-database.md): Deep Lake Managed Database
- [REST API](https://docs-v3.activeloop.ai/v3.7.3/performance-features/managed-database/rest-api.md): How to Use the Deep Lake REST API
- [Migrating Datasets to the Tensor Database](https://docs-v3.activeloop.ai/v3.7.3/performance-features/managed-database/migrating-datasets-to-the-tensor-database.md): Migrating datasets to the Tensor Database
- [Getting Started](https://docs-v3.activeloop.ai/v3.7.3/getting-started.md): Comprehensive guides for getting started with Deep Lake
- [Vector Store](https://docs-v3.activeloop.ai/v3.7.3/getting-started/vector-store.md): The comprehensive guide for Deep Lake in Vector Storage and Search applications.
- [Step 1: Hello World](https://docs-v3.activeloop.ai/v3.7.3/getting-started/vector-store/step-1-hello-world.md): Installing Deep Lake
- [Step 2: Creating Deep Lake Vector Stores](https://docs-v3.activeloop.ai/v3.7.3/getting-started/vector-store/step-2-creating-deep-lake-vector-stores.md): Creating the Deep Lake Vector Store
- [Step 3: Performing Search in Vector Stores](https://docs-v3.activeloop.ai/v3.7.3/getting-started/vector-store/step-3-performing-search-in-the-vector-store.md): Running search in the Deep Lake Vector Store
- [Step 4: Customizing Vector Stores](https://docs-v3.activeloop.ai/v3.7.3/getting-started/vector-store/step-4-customizing-vector-stores.md): Customizing the Deep Lake Vector Store
- [Deep Learning](https://docs-v3.activeloop.ai/v3.7.3/getting-started/deep-learning.md): The comprehensive guide for Deep Lake in Deep Learning applications.
- [Step 1: Hello World](https://docs-v3.activeloop.ai/v3.7.3/getting-started/deep-learning/hello-world.md): Installing Deep Lake and accessing your first Deep Lake Dataset.
- [Step 2: Creating Deep Lake Datasets](https://docs-v3.activeloop.ai/v3.7.3/getting-started/deep-learning/creating-datasets-manually.md): Creating and storing Deep Lake Datasets.
- [Step 3: Understanding Compression](https://docs-v3.activeloop.ai/v3.7.3/getting-started/deep-learning/understanding-compression.md): Using compression to achieve optimal performance in Deep Lake.
- [Step 4: Accessing and Updating Data](https://docs-v3.activeloop.ai/v3.7.3/getting-started/deep-learning/accessing-datasets.md): Learn how Deep Lake Datasets can be accessed or loaded from a variety of storage locations.
- [Step 5: Visualizing Datasets](https://docs-v3.activeloop.ai/v3.7.3/getting-started/deep-learning/visualizing-datasets.md): Visualizing and inspecting your datasets.
- [Step 6: Using Activeloop Storage](https://docs-v3.activeloop.ai/v3.7.3/getting-started/deep-learning/using-activeloop-storage.md): Storing and loading datasets from Deep Lake Storage.
- [Step 7: Connecting Deep Lake Datasets to ML Frameworks](https://docs-v3.activeloop.ai/v3.7.3/getting-started/deep-learning/connecting-to-ml-frameworks.md): Connecting Deep Lake Datasets to machine learning frameworks such as PyTorch and TensorFlow.
- [Step 8: Parallel Computing](https://docs-v3.activeloop.ai/v3.7.3/getting-started/deep-learning/parallel-computing.md): Running computations and processing data in parallel.
- [Step 9: Dataset Version Control](https://docs-v3.activeloop.ai/v3.7.3/getting-started/deep-learning/dataset-version-control.md): Managing changes to your datasets using Version Control.
- [Step 10: Dataset Filtering](https://docs-v3.activeloop.ai/v3.7.3/getting-started/deep-learning/dataset-filtering.md): Filtering datasets using user-defined-functions or SQL-style queries.
- [Tutorials (w Colab)](https://docs-v3.activeloop.ai/v3.7.3/tutorials.md): Common workflows with Deep Lake (includes Colab notebooks)
- [Vector Store Tutorials](https://docs-v3.activeloop.ai/v3.7.3/tutorials/vector-store.md): Tutorials for using Deep Lake in Vector Store applications
- [Vector Search Options](https://docs-v3.activeloop.ai/v3.7.3/tutorials/vector-store/vector-search-options.md): Overview of Vector Search Options in Deep Lake
- [Deep Lake Vector Store API](https://docs-v3.activeloop.ai/v3.7.3/tutorials/vector-store/vector-search-options/deep-lake-vector-store-api.md): Running Vector Search in the Deep Lake Vector Store module.
- [REST API](https://docs-v3.activeloop.ai/v3.7.3/tutorials/vector-store/vector-search-options/rest-api.md): Running Vector Search in the Deep Lake Tensor Database using the REST API
- [LangChain API](https://docs-v3.activeloop.ai/v3.7.3/tutorials/vector-store/vector-search-options/langchain-api.md): Vector Search using Deep Lake in LangChain
- [Image Similarity Search](https://docs-v3.activeloop.ai/v3.7.3/tutorials/vector-store/image-similarity-search.md): Using Deep Lake for image similarity search
- [Deep Lake Vector Store in LangChain](https://docs-v3.activeloop.ai/v3.7.3/tutorials/vector-store/deep-lake-vector-store-in-langchain.md): Using Deep Lake as a Vector Store in LangChain
- [Deep Learning Tutorials](https://docs-v3.activeloop.ai/v3.7.3/tutorials/deep-learning.md): Tutorials for using Deep Lake in deep-learning applications.
- [Creating Datasets](https://docs-v3.activeloop.ai/v3.7.3/tutorials/deep-learning/creating-datasets.md): Workflows for creating Deep Lake datasets
- [Creating Complex Datasets](https://docs-v3.activeloop.ai/v3.7.3/tutorials/deep-learning/creating-datasets/creating-complex-datasets.md): Converting a multi-annotation dataset to Deep Lake format is helpful for understanding how to use Deep Lake with rich data.
- [Creating Object Detection Datasets](https://docs-v3.activeloop.ai/v3.7.3/tutorials/deep-learning/creating-datasets/creating-object-detection-datasets.md): Converting an object detection dataset to Deep Lake format is a great way to get started with datasets of increasing complexity.
- [Creating Time-Series Datasets](https://docs-v3.activeloop.ai/v3.7.3/tutorials/deep-learning/creating-datasets/creating-time-series-datasets.md): Deep Lake is a powerful tool for easily storing and sharing time-series datasets with your team.
- [Creating Datasets with Sequences](https://docs-v3.activeloop.ai/v3.7.3/tutorials/deep-learning/creating-datasets/creating-datasets-with-sequences.md): Deep Lake sequences are a powerful tool for storing temporal annotations such as bounding boxes in each frame of a video.
- [Creating Video Datasets](https://docs-v3.activeloop.ai/v3.7.3/tutorials/deep-learning/creating-datasets/creating-video-datasets.md): Get started with video datasets using Deep Lake.
- [Training Models](https://docs-v3.activeloop.ai/v3.7.3/tutorials/deep-learning/training-models.md): Workflows for training models using Deep Lake datasets
- [Training an Image Classification Model in PyTorch](https://docs-v3.activeloop.ai/v3.7.3/tutorials/deep-learning/training-models/training-an-image-classification-model-in-pytorch.md): Training an image classification model is a great way to get started with model training using Deep Lake datasets.
- [Training Models Using MMDetection](https://docs-v3.activeloop.ai/v3.7.3/tutorials/deep-learning/training-models/training-models-using-mmdetection.md): How to Train Deep Learning models using Deep Lake's integration with MMDetection
- [Training Models Using PyTorch Lightning](https://docs-v3.activeloop.ai/v3.7.3/tutorials/deep-learning/training-models/training-models-using-pytorch-lightning.md): How to Train models using Deep Lake and PyTorch Lightning
- [Training on AWS SageMaker](https://docs-v3.activeloop.ai/v3.7.3/tutorials/deep-learning/training-models/training-on-aws-sagemaker.md): How to Train models on AWS SageMaker using Deep Lake datasets
- [Training an Object Detection and Segmentation Model in PyTorch](https://docs-v3.activeloop.ai/v3.7.3/tutorials/deep-learning/training-models/training-an-object-detection-and-segmentation-model-in-pytorch.md): Training an object detection and segmentation model is a great way to learn about complex data preprocessing for training models.
- [Updating Datasets](https://docs-v3.activeloop.ai/v3.7.3/tutorials/deep-learning/updating-datasets.md): Updating Deep Lake datasets
- [Data Processing Using Parallel Computing](https://docs-v3.activeloop.ai/v3.7.3/tutorials/deep-learning/data-processing-using-parallel-computing.md): Deeplake offers built-in methods for parallelizing dataset computations in order to achieve faster data processing.
- [Concurrent Writes](https://docs-v3.activeloop.ai/v3.7.3/tutorials/concurrent-writes.md): Concurrent writes in Deep Lake
- [Concurrency Using Zookeeper Locks](https://docs-v3.activeloop.ai/v3.7.3/tutorials/concurrent-writes/concurrency-using-zookeeper-locks.md): Using Zookeeper for locking Deep Lake datasets.
- [Playbooks](https://docs-v3.activeloop.ai/v3.7.3/playbooks.md): How to perform complex workflows using Deep Lake.
- [Querying, Training and Editing Datasets with Data Lineage](https://docs-v3.activeloop.ai/v3.7.3/playbooks/training-with-lineage.md): How to use queries and version control while training models.
- [Evaluating Model Performance](https://docs-v3.activeloop.ai/v3.7.3/playbooks/evaluating-model-performance.md): How to compare ground-truth annotations with model predictions
- [Training Reproducibility Using Deep Lake and Weights & Biases](https://docs-v3.activeloop.ai/v3.7.3/playbooks/training-reproducibility-with-wandb.md): How to achieve full reproducibility of model training using Deep Lake and W\&B
- [Working with Videos](https://docs-v3.activeloop.ai/v3.7.3/playbooks/working-with-videos.md): How manage video datasets and train models using Deep Lake.
- [Low-Level API Summary](https://docs-v3.activeloop.ai/v3.7.3/api-basics.md): Summary of the most important low-level Deep Lake commands.
- [Best Practices](https://docs-v3.activeloop.ai/v3.7.3/technical-details/best-practices.md): How to use Deep Lake at scale with best practices.
- [Creating Datasets at Scale](https://docs-v3.activeloop.ai/v3.7.3/technical-details/best-practices/creating-datasets-at-scale.md): Creating large Deep Lake datasets with high performance and reliability
- [Training Models at Scale](https://docs-v3.activeloop.ai/v3.7.3/technical-details/best-practices/training-models-at-scale.md): Train models at scale using Deep Lake
- [Storage Synchronization and "with" Context](https://docs-v3.activeloop.ai/v3.7.3/technical-details/best-practices/storage-synchronization.md): Synchronizing data with long-term storage and achieving optimal performance using Deep Lake.
- [Restoring Corrupted Datasets](https://docs-v3.activeloop.ai/v3.7.3/technical-details/best-practices/restoring-corrupted-datasets.md): Restoring Deep Lake datasets that may be corrupted.
- [Data Layout](https://docs-v3.activeloop.ai/v3.7.3/technical-details/data-layout.md): Understanding the data layout in Deep Lake
- [Version Control and Querying](https://docs-v3.activeloop.ai/v3.7.3/technical-details/version-control-and-querying.md): Understanding Deep Lake's Version control and Querying Layout
- [Dataset Visualization](https://docs-v3.activeloop.ai/v3.7.3/technical-details/dataset-visualization.md): How to visualize Deep Lake datasets
- [Tensor Relationships](https://docs-v3.activeloop.ai/v3.7.3/technical-details/tensor-relationships.md): Understanding the correct data layout for successful visualization.
- [Visualizer Integration](https://docs-v3.activeloop.ai/v3.7.3/technical-details/visualizer-integration.md): How to embed our visualizer in your application.
- [Shuffling in dataloaders](https://docs-v3.activeloop.ai/v3.7.3/technical-details/shuffling-in-dataloaders.md): Understanding data shuffling in Deep Lake's pytorch dataloader
- [How to Contribute](https://docs-v3.activeloop.ai/v3.7.3/technical-details/how-to-contribute.md): Guidelines for open source enthusiasts to contribute to our open-source data format.

## v3.7.2

- [Deep Lake Docs](https://docs-v3.activeloop.ai/v3.7.2/master.md): We hope you enjoy Docs for Deep Lake.
- [Vector Store Quickstart](https://docs-v3.activeloop.ai/v3.7.2/quickstart.md): A jump-start guide to using Deep Lake for Vector Search.
- [Deep Learning Quickstart](https://docs-v3.activeloop.ai/v3.7.2/quickstart-dl.md): A jump-start guide to using Deep Lake for Deep Learning.
- [Storage & Credentials](https://docs-v3.activeloop.ai/v3.7.2/storage-and-credentials.md): How to access datasets in other clouds and manage their credentials.
- [Storage Options](https://docs-v3.activeloop.ai/v3.7.2/storage-and-credentials/storage-options.md): How to authenticate using Activeloop storage, AWS S3, and Google Cloud Storage.
- [User Authentication](https://docs-v3.activeloop.ai/v3.7.2/storage-and-credentials/user-authentication.md): Registration and authentication in Deep Lake.
- [Storing Deep Lake Data in Your Own Cloud](https://docs-v3.activeloop.ai/v3.7.2/storage-and-credentials/managed-credentials.md): How to store Deep Lake data in your own cloud and manage credentials with Deep Lake
- [Microsoft Azure](https://docs-v3.activeloop.ai/v3.7.2/storage-and-credentials/managed-credentials/microsoft-azure.md): Azure-specific information for connecting data to Deep Lake
- [Provisioning Federated Credentials](https://docs-v3.activeloop.ai/v3.7.2/storage-and-credentials/managed-credentials/microsoft-azure/provisioning-federated-credentials.md): How to setup Federated Credentials in Azure
- [Enabling CORS](https://docs-v3.activeloop.ai/v3.7.2/storage-and-credentials/managed-credentials/microsoft-azure/enabling-cors.md): How to enable Cross-Origin Resource Sharing in your Azure account.
- [Amazon Web Services](https://docs-v3.activeloop.ai/v3.7.2/storage-and-credentials/managed-credentials/amazon-web-services.md): AWS-specific information for connecting data to Deep Lake
- [Provisioning Role-Based Access](https://docs-v3.activeloop.ai/v3.7.2/storage-and-credentials/managed-credentials/amazon-web-services/provisioning-role-based-access.md): How to provision Role-Based Access in S3
- [Enabling CORS](https://docs-v3.activeloop.ai/v3.7.2/storage-and-credentials/managed-credentials/amazon-web-services/enabling-cors.md): How to enable Cross-Origin Resource Sharing in your AWS S3 buckets.
- [Introduction](https://docs-v3.activeloop.ai/v3.7.2/performance-features/introduction.md): C++ implementations of Deep Lake optimized for faster data fetching and computations
- [Performant Dataloader](https://docs-v3.activeloop.ai/v3.7.2/performance-features/performant-dataloader.md): Overview of Deep Lake's dataloader built and optimized in C++
- [Tensor Query Language (TQL)](https://docs-v3.activeloop.ai/v3.7.2/performance-features/querying-datasets.md): Deep Lake offers a highly-performant SQL-style query engine for filtering your data.
- [TQL Syntax](https://docs-v3.activeloop.ai/v3.7.2/performance-features/querying-datasets/query-syntax.md): How to properly format TQL queries
- [Sampling Datasets](https://docs-v3.activeloop.ai/v3.7.2/performance-features/querying-datasets/sampling-datasets.md): Implementation of samplers in TQL
- [Index for ANN Search](https://docs-v3.activeloop.ai/v3.7.2/performance-features/index-for-ann-search.md): Overview of Deep Lake's Index implementation for ANN search.
- [Caching and Optimization](https://docs-v3.activeloop.ai/v3.7.2/performance-features/index-for-ann-search/caching-and-optimization.md): Understanding Caching to Increase Query Performance in Deep Lake
- [Managed Tensor Database](https://docs-v3.activeloop.ai/v3.7.2/performance-features/managed-database.md): Deep Lake Managed Database
- [REST API](https://docs-v3.activeloop.ai/v3.7.2/performance-features/managed-database/rest-api.md): How to Use the Deep Lake REST API
- [Migrating Datasets to the Tensor Database](https://docs-v3.activeloop.ai/v3.7.2/performance-features/managed-database/migrating-datasets-to-the-tensor-database.md): Migrating datasets to the Tensor Database
- [Getting Started](https://docs-v3.activeloop.ai/v3.7.2/getting-started.md): Comprehensive guides for getting started with Deep Lake
- [Vector Store](https://docs-v3.activeloop.ai/v3.7.2/getting-started/vector-store.md): The comprehensive guide for Deep Lake in Vector Storage and Search applications.
- [Step 1: Hello World](https://docs-v3.activeloop.ai/v3.7.2/getting-started/vector-store/step-1-hello-world.md): Installing Deep Lake
- [Step 2: Creating Deep Lake Vector Stores](https://docs-v3.activeloop.ai/v3.7.2/getting-started/vector-store/step-2-creating-deep-lake-vector-stores.md): Creating the Deep Lake Vector Store
- [Step 3: Performing Search in Vector Stores](https://docs-v3.activeloop.ai/v3.7.2/getting-started/vector-store/step-3-performing-search-in-the-vector-store.md): Running search in the Deep Lake Vector Store
- [Step 4: Customizing Vector Stores](https://docs-v3.activeloop.ai/v3.7.2/getting-started/vector-store/step-4-customizing-vector-stores.md): Customizing the Deep Lake Vector Store
- [Deep Learning](https://docs-v3.activeloop.ai/v3.7.2/getting-started/deep-learning.md): The comprehensive guide for Deep Lake in Deep Learning applications.
- [Step 1: Hello World](https://docs-v3.activeloop.ai/v3.7.2/getting-started/deep-learning/hello-world.md): Installing Deep Lake and accessing your first Deep Lake Dataset.
- [Step 2: Creating Deep Lake Datasets](https://docs-v3.activeloop.ai/v3.7.2/getting-started/deep-learning/creating-datasets-manually.md): Creating and storing Deep Lake Datasets.
- [Step 3: Understanding Compression](https://docs-v3.activeloop.ai/v3.7.2/getting-started/deep-learning/understanding-compression.md): Using compression to achieve optimal performance in Deep Lake.
- [Step 4: Accessing and Updating Data](https://docs-v3.activeloop.ai/v3.7.2/getting-started/deep-learning/accessing-datasets.md): Learn how Deep Lake Datasets can be accessed or loaded from a variety of storage locations.
- [Step 5: Visualizing Datasets](https://docs-v3.activeloop.ai/v3.7.2/getting-started/deep-learning/visualizing-datasets.md): Visualizing and inspecting your datasets.
- [Step 6: Using Activeloop Storage](https://docs-v3.activeloop.ai/v3.7.2/getting-started/deep-learning/using-activeloop-storage.md): Storing and loading datasets from Deep Lake Storage.
- [Step 7: Connecting Deep Lake Datasets to ML Frameworks](https://docs-v3.activeloop.ai/v3.7.2/getting-started/deep-learning/connecting-to-ml-frameworks.md): Connecting Deep Lake Datasets to machine learning frameworks such as PyTorch and TensorFlow.
- [Step 8: Parallel Computing](https://docs-v3.activeloop.ai/v3.7.2/getting-started/deep-learning/parallel-computing.md): Running computations and processing data in parallel.
- [Step 9: Dataset Version Control](https://docs-v3.activeloop.ai/v3.7.2/getting-started/deep-learning/dataset-version-control.md): Managing changes to your datasets using Version Control.
- [Step 10: Dataset Filtering](https://docs-v3.activeloop.ai/v3.7.2/getting-started/deep-learning/dataset-filtering.md): Filtering datasets using user-defined-functions or SQL-style queries.
- [Tutorials (w Colab)](https://docs-v3.activeloop.ai/v3.7.2/tutorials.md): Common workflows with Deep Lake (includes Colab notebooks)
- [Vector Store Tutorials](https://docs-v3.activeloop.ai/v3.7.2/tutorials/vector-store.md): Tutorials for using Deep Lake in Vector Store applications
- [Vector Search Options](https://docs-v3.activeloop.ai/v3.7.2/tutorials/vector-store/vector-search-options.md): Overview of Vector Search Options in Deep Lake
- [Deep Lake Vector Store API](https://docs-v3.activeloop.ai/v3.7.2/tutorials/vector-store/vector-search-options/deep-lake-vector-store-api.md): Running Vector Search in the Deep Lake Vector Store module.
- [REST API](https://docs-v3.activeloop.ai/v3.7.2/tutorials/vector-store/vector-search-options/rest-api.md): Running Vector Search in the Deep Lake Tensor Database using the REST API
- [LangChain API](https://docs-v3.activeloop.ai/v3.7.2/tutorials/vector-store/vector-search-options/langchain-api.md): Vector Search using Deep Lake in LangChain
- [Image Similarity Search](https://docs-v3.activeloop.ai/v3.7.2/tutorials/vector-store/image-similarity-search.md): Using Deep Lake for image similarity search
- [Deep Lake Vector Store in LangChain](https://docs-v3.activeloop.ai/v3.7.2/tutorials/vector-store/deep-lake-vector-store-in-langchain.md): Using Deep Lake as a Vector Store in LangChain
- [Deep Learning Tutorials](https://docs-v3.activeloop.ai/v3.7.2/tutorials/deep-learning.md): Tutorials for using Deep Lake in deep-learning applications.
- [Creating Datasets](https://docs-v3.activeloop.ai/v3.7.2/tutorials/deep-learning/creating-datasets.md): Workflows for creating Deep Lake datasets
- [Creating Complex Datasets](https://docs-v3.activeloop.ai/v3.7.2/tutorials/deep-learning/creating-datasets/creating-complex-datasets.md): Converting a multi-annotation dataset to Deep Lake format is helpful for understanding how to use Deep Lake with rich data.
- [Creating Object Detection Datasets](https://docs-v3.activeloop.ai/v3.7.2/tutorials/deep-learning/creating-datasets/creating-object-detection-datasets.md): Converting an object detection dataset to Deep Lake format is a great way to get started with datasets of increasing complexity.
- [Creating Time-Series Datasets](https://docs-v3.activeloop.ai/v3.7.2/tutorials/deep-learning/creating-datasets/creating-time-series-datasets.md): Deep Lake is a powerful tool for easily storing and sharing time-series datasets with your team.
- [Creating Datasets with Sequences](https://docs-v3.activeloop.ai/v3.7.2/tutorials/deep-learning/creating-datasets/creating-datasets-with-sequences.md): Deep Lake sequences are a powerful tool for storing temporal annotations such as bounding boxes in each frame of a video.
- [Creating Video Datasets](https://docs-v3.activeloop.ai/v3.7.2/tutorials/deep-learning/creating-datasets/creating-video-datasets.md): Get started with video datasets using Deep Lake.
- [Training Models](https://docs-v3.activeloop.ai/v3.7.2/tutorials/deep-learning/training-models.md): Workflows for training models using Deep Lake datasets
- [Training an Image Classification Model in PyTorch](https://docs-v3.activeloop.ai/v3.7.2/tutorials/deep-learning/training-models/training-an-image-classification-model-in-pytorch.md): Training an image classification model is a great way to get started with model training using Deep Lake datasets.
- [Training Models Using MMDetection](https://docs-v3.activeloop.ai/v3.7.2/tutorials/deep-learning/training-models/training-models-using-mmdetection.md): How to Train Deep Learning models using Deep Lake's integration with MMDetection
- [Training Models Using PyTorch Lightning](https://docs-v3.activeloop.ai/v3.7.2/tutorials/deep-learning/training-models/training-models-using-pytorch-lightning.md): How to Train models using Deep Lake and PyTorch Lightning
- [Training on AWS SageMaker](https://docs-v3.activeloop.ai/v3.7.2/tutorials/deep-learning/training-models/training-on-aws-sagemaker.md): How to Train models on AWS SageMaker using Deep Lake datasets
- [Training an Object Detection and Segmentation Model in PyTorch](https://docs-v3.activeloop.ai/v3.7.2/tutorials/deep-learning/training-models/training-an-object-detection-and-segmentation-model-in-pytorch.md): Training an object detection and segmentation model is a great way to learn about complex data preprocessing for training models.
- [Updating Datasets](https://docs-v3.activeloop.ai/v3.7.2/tutorials/deep-learning/updating-datasets.md): Updating Deep Lake datasets
- [Data Processing Using Parallel Computing](https://docs-v3.activeloop.ai/v3.7.2/tutorials/deep-learning/data-processing-using-parallel-computing.md): Deeplake offers built-in methods for parallelizing dataset computations in order to achieve faster data processing.
- [Concurrent Writes](https://docs-v3.activeloop.ai/v3.7.2/tutorials/concurrent-writes.md): Concurrent writes in Deep Lake
- [Concurrency Using Zookeeper Locks](https://docs-v3.activeloop.ai/v3.7.2/tutorials/concurrent-writes/concurrency-using-zookeeper-locks.md): Using Zookeeper for locking Deep Lake datasets.
- [Playbooks](https://docs-v3.activeloop.ai/v3.7.2/playbooks.md): How to perform complex workflows using Deep Lake.
- [Querying, Training and Editing Datasets with Data Lineage](https://docs-v3.activeloop.ai/v3.7.2/playbooks/training-with-lineage.md): How to use queries and version control while training models.
- [Evaluating Model Performance](https://docs-v3.activeloop.ai/v3.7.2/playbooks/evaluating-model-performance.md): How to compare ground-truth annotations with model predictions
- [Training Reproducibility Using Deep Lake and Weights & Biases](https://docs-v3.activeloop.ai/v3.7.2/playbooks/training-reproducibility-with-wandb.md): How to achieve full reproducibility of model training using Deep Lake and W\&B
- [Working with Videos](https://docs-v3.activeloop.ai/v3.7.2/playbooks/working-with-videos.md): How manage video datasets and train models using Deep Lake.
- [Low-Level API Summary](https://docs-v3.activeloop.ai/v3.7.2/api-basics.md): Summary of the most important low-level Deep Lake commands.
- [Best Practices](https://docs-v3.activeloop.ai/v3.7.2/technical-details/best-practices.md): How to use Deep Lake at scale with best practices.
- [Creating Datasets at Scale](https://docs-v3.activeloop.ai/v3.7.2/technical-details/best-practices/creating-datasets-at-scale.md): Creating large Deep Lake datasets with high performance and reliability
- [Training Models at Scale](https://docs-v3.activeloop.ai/v3.7.2/technical-details/best-practices/training-models-at-scale.md): Train models at scale using Deep Lake
- [Storage Synchronization and "with" Context](https://docs-v3.activeloop.ai/v3.7.2/technical-details/best-practices/storage-synchronization.md): Synchronizing data with long-term storage and achieving optimal performance using Deep Lake.
- [Restoring Corrupted Datasets](https://docs-v3.activeloop.ai/v3.7.2/technical-details/best-practices/restoring-corrupted-datasets.md): Restoring Deep Lake datasets that may be corrupted.
- [Data Layout](https://docs-v3.activeloop.ai/v3.7.2/technical-details/data-layout.md): Understanding the data layout in Deep Lake
- [Version Control and Querying](https://docs-v3.activeloop.ai/v3.7.2/technical-details/version-control-and-querying.md): Understanding Deep Lake's Version control and Querying Layout
- [Dataset Visualization](https://docs-v3.activeloop.ai/v3.7.2/technical-details/dataset-visualization.md): How to visualize Deep Lake datasets
- [Tensor Relationships](https://docs-v3.activeloop.ai/v3.7.2/technical-details/tensor-relationships.md): Understanding the correct data layout for successful visualization.
- [Visualizer Integration](https://docs-v3.activeloop.ai/v3.7.2/technical-details/visualizer-integration.md): How to embed our visualizer in your application.
- [Shuffling in dataloaders](https://docs-v3.activeloop.ai/v3.7.2/technical-details/shuffling-in-dataloaders.md): Understanding data shuffling in Deep Lake's pytorch dataloader
- [How to Contribute](https://docs-v3.activeloop.ai/v3.7.2/technical-details/how-to-contribute.md): Guidelines for open source enthusiasts to contribute to our open-source data format.

## v3.7.1

- [Deep Lake Docs](https://docs-v3.activeloop.ai/v3.7.1/master.md): We hope you enjoy Docs for Deep Lake.
- [Vector Store Quickstart](https://docs-v3.activeloop.ai/v3.7.1/quickstart.md): A jump-start guide to using Deep Lake for Vector Search.
- [Deep Learning Quickstart](https://docs-v3.activeloop.ai/v3.7.1/quickstart-dl.md): A jump-start guide to using Deep Lake for Deep Learning.
- [Storage & Credentials](https://docs-v3.activeloop.ai/v3.7.1/storage-and-credentials.md): How to access datasets in other clouds and manage their credentials.
- [Storage Options](https://docs-v3.activeloop.ai/v3.7.1/storage-and-credentials/storage-options.md): How to authenticate using Activeloop storage, AWS S3, and Google Cloud Storage.
- [User Authentication](https://docs-v3.activeloop.ai/v3.7.1/storage-and-credentials/user-authentication.md): Registration and authentication in Deep Lake.
- [Storing Deep Lake Data in Your Own Cloud](https://docs-v3.activeloop.ai/v3.7.1/storage-and-credentials/managed-credentials.md): How to store Deep Lake data in your own cloud and manage credentials with Deep Lake
- [Microsoft Azure](https://docs-v3.activeloop.ai/v3.7.1/storage-and-credentials/managed-credentials/microsoft-azure.md): Azure-specific information for connecting data to Deep Lake
- [Provisioning Federated Credentials](https://docs-v3.activeloop.ai/v3.7.1/storage-and-credentials/managed-credentials/microsoft-azure/provisioning-federated-credentials.md): How to setup Federated Credentials in Azure
- [Enabling CORS](https://docs-v3.activeloop.ai/v3.7.1/storage-and-credentials/managed-credentials/microsoft-azure/enabling-cors.md): How to enable Cross-Origin Resource Sharing in your Azure account.
- [Amazon Web Services](https://docs-v3.activeloop.ai/v3.7.1/storage-and-credentials/managed-credentials/amazon-web-services.md): AWS-specific information for connecting data to Deep Lake
- [Provisioning Role-Based Access](https://docs-v3.activeloop.ai/v3.7.1/storage-and-credentials/managed-credentials/amazon-web-services/provisioning-role-based-access.md): How to provision Role-Based Access in S3
- [Enabling CORS](https://docs-v3.activeloop.ai/v3.7.1/storage-and-credentials/managed-credentials/amazon-web-services/enabling-cors.md): How to enable Cross-Origin Resource Sharing in your AWS S3 buckets.
- [Introduction](https://docs-v3.activeloop.ai/v3.7.1/performance-features/introduction.md): C++ implementations of Deep Lake optimized for faster data fetching and computations
- [Performant Dataloader](https://docs-v3.activeloop.ai/v3.7.1/performance-features/performant-dataloader.md): Overview of Deep Lake's dataloader built and optimized in C++
- [Tensor Query Language (TQL)](https://docs-v3.activeloop.ai/v3.7.1/performance-features/querying-datasets.md): Deep Lake offers a highly-performant SQL-style query engine for filtering your data.
- [TQL Syntax](https://docs-v3.activeloop.ai/v3.7.1/performance-features/querying-datasets/query-syntax.md): How to properly format TQL queries
- [Sampling Datasets](https://docs-v3.activeloop.ai/v3.7.1/performance-features/querying-datasets/sampling-datasets.md): Implementation of samplers in TQL
- [Index for ANN Search](https://docs-v3.activeloop.ai/v3.7.1/performance-features/index-for-ann-search.md): Overview of Deep Lake's Index implementation for ANN search.
- [Caching and Optimization](https://docs-v3.activeloop.ai/v3.7.1/performance-features/index-for-ann-search/caching-and-optimization.md): Understanding Caching to Increase Query Performance in Deep Lake
- [Managed Tensor Database](https://docs-v3.activeloop.ai/v3.7.1/performance-features/managed-database.md): Deep Lake Managed Database
- [REST API](https://docs-v3.activeloop.ai/v3.7.1/performance-features/managed-database/rest-api.md): How to Use the Deep Lake REST API
- [Migrating Datasets to the Tensor Database](https://docs-v3.activeloop.ai/v3.7.1/performance-features/managed-database/migrating-datasets-to-the-tensor-database.md): Migrating datasets to the Tensor Database
- [Getting Started](https://docs-v3.activeloop.ai/v3.7.1/getting-started.md): Comprehensive guides for getting started with Deep Lake
- [Vector Store](https://docs-v3.activeloop.ai/v3.7.1/getting-started/vector-store.md): The comprehensive guide for Deep Lake in Vector Storage and Search applications.
- [Step 1: Hello World](https://docs-v3.activeloop.ai/v3.7.1/getting-started/vector-store/step-1-hello-world.md): Installing Deep Lake
- [Step 2: Creating Deep Lake Vector Stores](https://docs-v3.activeloop.ai/v3.7.1/getting-started/vector-store/step-2-creating-deep-lake-vector-stores.md): Creating the Deep Lake Vector Store
- [Step 3: Performing Search in Vector Stores](https://docs-v3.activeloop.ai/v3.7.1/getting-started/vector-store/step-3-performing-search-in-the-vector-store.md): Running search in the Deep Lake Vector Store
- [Step 4: Customizing Vector Stores](https://docs-v3.activeloop.ai/v3.7.1/getting-started/vector-store/step-4-customizing-vector-stores.md): Customizing the Deep Lake Vector Store
- [Deep Learning](https://docs-v3.activeloop.ai/v3.7.1/getting-started/deep-learning.md): The comprehensive guide for Deep Lake in Deep Learning applications.
- [Step 1: Hello World](https://docs-v3.activeloop.ai/v3.7.1/getting-started/deep-learning/hello-world.md): Installing Deep Lake and accessing your first Deep Lake Dataset.
- [Step 2: Creating Deep Lake Datasets](https://docs-v3.activeloop.ai/v3.7.1/getting-started/deep-learning/creating-datasets-manually.md): Creating and storing Deep Lake Datasets.
- [Step 3: Understanding Compression](https://docs-v3.activeloop.ai/v3.7.1/getting-started/deep-learning/understanding-compression.md): Using compression to achieve optimal performance in Deep Lake.
- [Step 4: Accessing and Updating Data](https://docs-v3.activeloop.ai/v3.7.1/getting-started/deep-learning/accessing-datasets.md): Learn how Deep Lake Datasets can be accessed or loaded from a variety of storage locations.
- [Step 5: Visualizing Datasets](https://docs-v3.activeloop.ai/v3.7.1/getting-started/deep-learning/visualizing-datasets.md): Visualizing and inspecting your datasets.
- [Step 6: Using Activeloop Storage](https://docs-v3.activeloop.ai/v3.7.1/getting-started/deep-learning/using-activeloop-storage.md): Storing and loading datasets from Deep Lake Storage.
- [Step 7: Connecting Deep Lake Datasets to ML Frameworks](https://docs-v3.activeloop.ai/v3.7.1/getting-started/deep-learning/connecting-to-ml-frameworks.md): Connecting Deep Lake Datasets to machine learning frameworks such as PyTorch and TensorFlow.
- [Step 8: Parallel Computing](https://docs-v3.activeloop.ai/v3.7.1/getting-started/deep-learning/parallel-computing.md): Running computations and processing data in parallel.
- [Step 9: Dataset Version Control](https://docs-v3.activeloop.ai/v3.7.1/getting-started/deep-learning/dataset-version-control.md): Managing changes to your datasets using Version Control.
- [Step 10: Dataset Filtering](https://docs-v3.activeloop.ai/v3.7.1/getting-started/deep-learning/dataset-filtering.md): Filtering datasets using user-defined-functions or SQL-style queries.
- [Tutorials (w Colab)](https://docs-v3.activeloop.ai/v3.7.1/tutorials.md): Common workflows with Deep Lake (includes Colab notebooks)
- [Vector Store Tutorials](https://docs-v3.activeloop.ai/v3.7.1/tutorials/vector-store.md): Tutorials for using Deep Lake in Vector Store applications
- [Vector Search Options](https://docs-v3.activeloop.ai/v3.7.1/tutorials/vector-store/vector-search-options.md): Overview of Vector Search Options in Deep Lake
- [Deep Lake Vector Store API](https://docs-v3.activeloop.ai/v3.7.1/tutorials/vector-store/vector-search-options/deep-lake-vector-store-api.md): Running Vector Search in the Deep Lake Vector Store module.
- [REST API](https://docs-v3.activeloop.ai/v3.7.1/tutorials/vector-store/vector-search-options/rest-api.md): Running Vector Search in the Deep Lake Tensor Database using the REST API
- [LangChain API](https://docs-v3.activeloop.ai/v3.7.1/tutorials/vector-store/vector-search-options/langchain-api.md): Vector Search using Deep Lake in LangChain
- [Image Similarity Search](https://docs-v3.activeloop.ai/v3.7.1/tutorials/vector-store/image-similarity-search.md): Using Deep Lake for image similarity search
- [Deep Lake Vector Store in LangChain](https://docs-v3.activeloop.ai/v3.7.1/tutorials/vector-store/deep-lake-vector-store-in-langchain.md): Using Deep Lake as a Vector Store in LangChain
- [Deep Learning Tutorials](https://docs-v3.activeloop.ai/v3.7.1/tutorials/deep-learning.md): Tutorials for using Deep Lake in deep-learning applications.
- [Creating Datasets](https://docs-v3.activeloop.ai/v3.7.1/tutorials/deep-learning/creating-datasets.md): Workflows for creating Deep Lake datasets
- [Creating Complex Datasets](https://docs-v3.activeloop.ai/v3.7.1/tutorials/deep-learning/creating-datasets/creating-complex-datasets.md): Converting a multi-annotation dataset to Deep Lake format is helpful for understanding how to use Deep Lake with rich data.
- [Creating Object Detection Datasets](https://docs-v3.activeloop.ai/v3.7.1/tutorials/deep-learning/creating-datasets/creating-object-detection-datasets.md): Converting an object detection dataset to Deep Lake format is a great way to get started with datasets of increasing complexity.
- [Creating Time-Series Datasets](https://docs-v3.activeloop.ai/v3.7.1/tutorials/deep-learning/creating-datasets/creating-time-series-datasets.md): Deep Lake is a powerful tool for easily storing and sharing time-series datasets with your team.
- [Creating Datasets with Sequences](https://docs-v3.activeloop.ai/v3.7.1/tutorials/deep-learning/creating-datasets/creating-datasets-with-sequences.md): Deep Lake sequences are a powerful tool for storing temporal annotations such as bounding boxes in each frame of a video.
- [Creating Video Datasets](https://docs-v3.activeloop.ai/v3.7.1/tutorials/deep-learning/creating-datasets/creating-video-datasets.md): Get started with video datasets using Deep Lake.
- [Training Models](https://docs-v3.activeloop.ai/v3.7.1/tutorials/deep-learning/training-models.md): Workflows for training models using Deep Lake datasets
- [Training an Image Classification Model in PyTorch](https://docs-v3.activeloop.ai/v3.7.1/tutorials/deep-learning/training-models/training-an-image-classification-model-in-pytorch.md): Training an image classification model is a great way to get started with model training using Deep Lake datasets.
- [Training Models Using MMDetection](https://docs-v3.activeloop.ai/v3.7.1/tutorials/deep-learning/training-models/training-models-using-mmdetection.md): How to Train Deep Learning models using Deep Lake's integration with MMDetection
- [Training Models Using PyTorch Lightning](https://docs-v3.activeloop.ai/v3.7.1/tutorials/deep-learning/training-models/training-models-using-pytorch-lightning.md): How to Train models using Deep Lake and PyTorch Lightning
- [Training on AWS SageMaker](https://docs-v3.activeloop.ai/v3.7.1/tutorials/deep-learning/training-models/training-on-aws-sagemaker.md): How to Train models on AWS SageMaker using Deep Lake datasets
- [Training an Object Detection and Segmentation Model in PyTorch](https://docs-v3.activeloop.ai/v3.7.1/tutorials/deep-learning/training-models/training-an-object-detection-and-segmentation-model-in-pytorch.md): Training an object detection and segmentation model is a great way to learn about complex data preprocessing for training models.
- [Updating Datasets](https://docs-v3.activeloop.ai/v3.7.1/tutorials/deep-learning/updating-datasets.md): Updating Deep Lake datasets
- [Data Processing Using Parallel Computing](https://docs-v3.activeloop.ai/v3.7.1/tutorials/deep-learning/data-processing-using-parallel-computing.md): Deeplake offers built-in methods for parallelizing dataset computations in order to achieve faster data processing.
- [Concurrent Writes](https://docs-v3.activeloop.ai/v3.7.1/tutorials/concurrent-writes.md): Concurrent writes in Deep Lake
- [Concurrency Using Zookeeper Locks](https://docs-v3.activeloop.ai/v3.7.1/tutorials/concurrent-writes/concurrency-using-zookeeper-locks.md): Using Zookeeper for locking Deep Lake datasets.
- [Playbooks](https://docs-v3.activeloop.ai/v3.7.1/playbooks.md): How to perform complex workflows using Deep Lake.
- [Querying, Training and Editing Datasets with Data Lineage](https://docs-v3.activeloop.ai/v3.7.1/playbooks/training-with-lineage.md): How to use queries and version control while training models.
- [Evaluating Model Performance](https://docs-v3.activeloop.ai/v3.7.1/playbooks/evaluating-model-performance.md): How to compare ground-truth annotations with model predictions
- [Training Reproducibility Using Deep Lake and Weights & Biases](https://docs-v3.activeloop.ai/v3.7.1/playbooks/training-reproducibility-with-wandb.md): How to achieve full reproducibility of model training using Deep Lake and W\&B
- [Working with Videos](https://docs-v3.activeloop.ai/v3.7.1/playbooks/working-with-videos.md): How manage video datasets and train models using Deep Lake.
- [Low-Level API Summary](https://docs-v3.activeloop.ai/v3.7.1/api-basics.md): Summary of the most important low-level Deep Lake commands.
- [Best Practices](https://docs-v3.activeloop.ai/v3.7.1/technical-details/best-practices.md): How to use Deep Lake at scale with best practices.
- [Creating Datasets at Scale](https://docs-v3.activeloop.ai/v3.7.1/technical-details/best-practices/creating-datasets-at-scale.md): Creating large Deep Lake datasets with high performance and reliability
- [Training Models at Scale](https://docs-v3.activeloop.ai/v3.7.1/technical-details/best-practices/training-models-at-scale.md): Train models at scale using Deep Lake
- [Storage Synchronization and "with" Context](https://docs-v3.activeloop.ai/v3.7.1/technical-details/best-practices/storage-synchronization.md): Synchronizing data with long-term storage and achieving optimal performance using Deep Lake.
- [Restoring Corrupted Datasets](https://docs-v3.activeloop.ai/v3.7.1/technical-details/best-practices/restoring-corrupted-datasets.md): Restoring Deep Lake datasets that may be corrupted.
- [Data Layout](https://docs-v3.activeloop.ai/v3.7.1/technical-details/data-layout.md): Understanding the data layout in Deep Lake
- [Version Control and Querying](https://docs-v3.activeloop.ai/v3.7.1/technical-details/version-control-and-querying.md): Understanding Deep Lake's Version control and Querying Layout
- [Dataset Visualization](https://docs-v3.activeloop.ai/v3.7.1/technical-details/dataset-visualization.md): How to visualize Deep Lake datasets
- [Tensor Relationships](https://docs-v3.activeloop.ai/v3.7.1/technical-details/tensor-relationships.md): Understanding the correct data layout for successful visualization.
- [Visualizer Integration](https://docs-v3.activeloop.ai/v3.7.1/technical-details/visualizer-integration.md): How to embed our visualizer in your application.
- [Shuffling in dataloaders](https://docs-v3.activeloop.ai/v3.7.1/technical-details/shuffling-in-dataloaders.md): Understanding data shuffling in Deep Lake's pytorch dataloader
- [How to Contribute](https://docs-v3.activeloop.ai/v3.7.1/technical-details/how-to-contribute.md): Guidelines for open source enthusiasts to contribute to our open-source data format.

## v3.7.0

- [Deep Lake Docs](https://docs-v3.activeloop.ai/v3.7.0/master.md): We hope you enjoy Docs for Deep Lake.
- [Vector Store Quickstart](https://docs-v3.activeloop.ai/v3.7.0/quickstart.md): A jump-start guide to using Deep Lake for Vector Search.
- [Deep Learning Quickstart](https://docs-v3.activeloop.ai/v3.7.0/quickstart-dl.md): A jump-start guide to using Deep Lake for Deep Learning.
- [Storage & Credentials](https://docs-v3.activeloop.ai/v3.7.0/storage-and-credentials.md): How to access datasets in other clouds and manage their credentials.
- [Storage Options](https://docs-v3.activeloop.ai/v3.7.0/storage-and-credentials/storage-options.md): How to authenticate using Activeloop storage, AWS S3, and Google Cloud Storage.
- [User Authentication](https://docs-v3.activeloop.ai/v3.7.0/storage-and-credentials/user-authentication.md): Registration and authentication in Deep Lake.
- [Storing Deep Lake Data in Your Own Cloud](https://docs-v3.activeloop.ai/v3.7.0/storage-and-credentials/managed-credentials.md): How to store Deep Lake data in your own cloud and manage credentials with Deep Lake
- [Microsoft Azure](https://docs-v3.activeloop.ai/v3.7.0/storage-and-credentials/managed-credentials/microsoft-azure.md): Azure-specific information for connecting data to Deep Lake
- [Provisioning Federated Credentials](https://docs-v3.activeloop.ai/v3.7.0/storage-and-credentials/managed-credentials/microsoft-azure/provisioning-federated-credentials.md): How to setup Federated Credentials in Azure
- [Enabling CORS](https://docs-v3.activeloop.ai/v3.7.0/storage-and-credentials/managed-credentials/microsoft-azure/enabling-cors.md): How to enable Cross-Origin Resource Sharing in your Azure account.
- [Amazon Web Services](https://docs-v3.activeloop.ai/v3.7.0/storage-and-credentials/managed-credentials/amazon-web-services.md): AWS-specific information for connecting data to Deep Lake
- [Provisioning Role-Based Access](https://docs-v3.activeloop.ai/v3.7.0/storage-and-credentials/managed-credentials/amazon-web-services/provisioning-role-based-access.md): How to provision Role-Based Access in S3
- [Enabling CORS](https://docs-v3.activeloop.ai/v3.7.0/storage-and-credentials/managed-credentials/amazon-web-services/enabling-cors.md): How to enable Cross-Origin Resource Sharing in your AWS S3 buckets.
- [Introduction](https://docs-v3.activeloop.ai/v3.7.0/performance-features/introduction.md): C++ implementations of Deep Lake optimized for faster data fetching and computations
- [Performant Dataloader](https://docs-v3.activeloop.ai/v3.7.0/performance-features/performant-dataloader.md): Overview of Deep Lake's dataloader built and optimized in C++
- [Tensor Query Language (TQL)](https://docs-v3.activeloop.ai/v3.7.0/performance-features/querying-datasets.md): Deep Lake offers a highly-performant SQL-style query engine for filtering your data.
- [TQL Syntax](https://docs-v3.activeloop.ai/v3.7.0/performance-features/querying-datasets/query-syntax.md): How to properly format TQL queries
- [Sampling Datasets](https://docs-v3.activeloop.ai/v3.7.0/performance-features/querying-datasets/sampling-datasets.md): Implementation of samplers in TQL
- [Index for ANN Search](https://docs-v3.activeloop.ai/v3.7.0/performance-features/index-for-ann-search.md): Overview of Deep Lake's Index implementation for ANN search.
- [Managed Tensor Database](https://docs-v3.activeloop.ai/v3.7.0/performance-features/managed-database.md): Deep Lake Managed Database
- [REST API](https://docs-v3.activeloop.ai/v3.7.0/performance-features/managed-database/rest-api.md): How to Use the Deep Lake REST API
- [Migrating Datasets to the Tensor Database](https://docs-v3.activeloop.ai/v3.7.0/performance-features/managed-database/migrating-datasets-to-the-tensor-database.md): Migrating datasets to the Tensor Database
- [Getting Started](https://docs-v3.activeloop.ai/v3.7.0/getting-started.md): Comprehensive guides for getting started with Deep Lake
- [Vector Store](https://docs-v3.activeloop.ai/v3.7.0/getting-started/vector-store.md): The comprehensive guide for Deep Lake in Vector Storage and Search applications.
- [Step 1: Hello World](https://docs-v3.activeloop.ai/v3.7.0/getting-started/vector-store/step-1-hello-world.md): Installing Deep Lake
- [Step 2: Creating Deep Lake Vector Stores](https://docs-v3.activeloop.ai/v3.7.0/getting-started/vector-store/step-2-creating-deep-lake-vector-stores.md): Creating the Deep Lake Vector Store
- [Step 3: Performing Search in Vector Stores](https://docs-v3.activeloop.ai/v3.7.0/getting-started/vector-store/step-3-performing-search-in-the-vector-store.md): Running search in the Deep Lake Vector Store
- [Step 4: Customizing Vector Stores](https://docs-v3.activeloop.ai/v3.7.0/getting-started/vector-store/step-4-customizing-vector-stores.md): Customizing the Deep Lake Vector Store
- [Deep Learning](https://docs-v3.activeloop.ai/v3.7.0/getting-started/deep-learning.md): The comprehensive guide for Deep Lake in Deep Learning applications.
- [Step 1: Hello World](https://docs-v3.activeloop.ai/v3.7.0/getting-started/deep-learning/hello-world.md): Installing Deep Lake and accessing your first Deep Lake Dataset.
- [Step 2: Creating Deep Lake Datasets](https://docs-v3.activeloop.ai/v3.7.0/getting-started/deep-learning/creating-datasets-manually.md): Creating and storing Deep Lake Datasets.
- [Step 3: Understanding Compression](https://docs-v3.activeloop.ai/v3.7.0/getting-started/deep-learning/understanding-compression.md): Using compression to achieve optimal performance in Deep Lake.
- [Step 4: Accessing and Updating Data](https://docs-v3.activeloop.ai/v3.7.0/getting-started/deep-learning/accessing-datasets.md): Learn how Deep Lake Datasets can be accessed or loaded from a variety of storage locations.
- [Step 5: Visualizing Datasets](https://docs-v3.activeloop.ai/v3.7.0/getting-started/deep-learning/visualizing-datasets.md): Visualizing and inspecting your datasets.
- [Step 6: Using Activeloop Storage](https://docs-v3.activeloop.ai/v3.7.0/getting-started/deep-learning/using-activeloop-storage.md): Storing and loading datasets from Deep Lake Storage.
- [Step 7: Connecting Deep Lake Datasets to ML Frameworks](https://docs-v3.activeloop.ai/v3.7.0/getting-started/deep-learning/connecting-to-ml-frameworks.md): Connecting Deep Lake Datasets to machine learning frameworks such as PyTorch and TensorFlow.
- [Step 8: Parallel Computing](https://docs-v3.activeloop.ai/v3.7.0/getting-started/deep-learning/parallel-computing.md): Running computations and processing data in parallel.
- [Step 9: Dataset Version Control](https://docs-v3.activeloop.ai/v3.7.0/getting-started/deep-learning/dataset-version-control.md): Managing changes to your datasets using Version Control.
- [Step 10: Dataset Filtering](https://docs-v3.activeloop.ai/v3.7.0/getting-started/deep-learning/dataset-filtering.md): Filtering datasets using user-defined-functions or SQL-style queries.
- [Tutorials (w Colab)](https://docs-v3.activeloop.ai/v3.7.0/tutorials.md): Common workflows with Deep Lake (includes Colab notebooks)
- [Vector Store Tutorials](https://docs-v3.activeloop.ai/v3.7.0/tutorials/vector-store.md): Tutorials for using Deep Lake in Vector Store applications
- [Vector Search Options](https://docs-v3.activeloop.ai/v3.7.0/tutorials/vector-store/vector-search-options.md): Overview of Vector Search Options in Deep Lake
- [Deep Lake Vector Store API](https://docs-v3.activeloop.ai/v3.7.0/tutorials/vector-store/vector-search-options/deep-lake-vector-store-api.md): Running Vector Search in the Deep Lake Vector Store module.
- [REST API](https://docs-v3.activeloop.ai/v3.7.0/tutorials/vector-store/vector-search-options/rest-api.md): Running Vector Search in the Deep Lake Tensor Database using the REST API
- [LangChain API](https://docs-v3.activeloop.ai/v3.7.0/tutorials/vector-store/vector-search-options/langchain-api.md): Vector Search using Deep Lake in LangChain
- [Image Similarity Search](https://docs-v3.activeloop.ai/v3.7.0/tutorials/vector-store/image-similarity-search.md): Using Deep Lake for image similarity search
- [Deep Lake Vector Store in LangChain](https://docs-v3.activeloop.ai/v3.7.0/tutorials/vector-store/deep-lake-vector-store-in-langchain.md): Using Deep Lake as a Vector Store in LangChain
- [Deep Learning Tutorials](https://docs-v3.activeloop.ai/v3.7.0/tutorials/deep-learning.md): Tutorials for using Deep Lake in deep-learning applications.
- [Creating Datasets](https://docs-v3.activeloop.ai/v3.7.0/tutorials/deep-learning/creating-datasets.md): Workflows for creating Deep Lake datasets
- [Creating Complex Datasets](https://docs-v3.activeloop.ai/v3.7.0/tutorials/deep-learning/creating-datasets/creating-complex-datasets.md): Converting a multi-annotation dataset to Deep Lake format is helpful for understanding how to use Deep Lake with rich data.
- [Creating Object Detection Datasets](https://docs-v3.activeloop.ai/v3.7.0/tutorials/deep-learning/creating-datasets/creating-object-detection-datasets.md): Converting an object detection dataset to Deep Lake format is a great way to get started with datasets of increasing complexity.
- [Creating Time-Series Datasets](https://docs-v3.activeloop.ai/v3.7.0/tutorials/deep-learning/creating-datasets/creating-time-series-datasets.md): Deep Lake is a powerful tool for easily storing and sharing time-series datasets with your team.
- [Creating Datasets with Sequences](https://docs-v3.activeloop.ai/v3.7.0/tutorials/deep-learning/creating-datasets/creating-datasets-with-sequences.md): Deep Lake sequences are a powerful tool for storing temporal annotations such as bounding boxes in each frame of a video.
- [Creating Video Datasets](https://docs-v3.activeloop.ai/v3.7.0/tutorials/deep-learning/creating-datasets/creating-video-datasets.md): Get started with video datasets using Deep Lake.
- [Training Models](https://docs-v3.activeloop.ai/v3.7.0/tutorials/deep-learning/training-models.md): Workflows for training models using Deep Lake datasets
- [Training an Image Classification Model in PyTorch](https://docs-v3.activeloop.ai/v3.7.0/tutorials/deep-learning/training-models/training-an-image-classification-model-in-pytorch.md): Training an image classification model is a great way to get started with model training using Deep Lake datasets.
- [Training Models Using MMDetection](https://docs-v3.activeloop.ai/v3.7.0/tutorials/deep-learning/training-models/training-models-using-mmdetection.md): How to Train Deep Learning models using Deep Lake's integration with MMDetection
- [Training Models Using PyTorch Lightning](https://docs-v3.activeloop.ai/v3.7.0/tutorials/deep-learning/training-models/training-models-using-pytorch-lightning.md): How to Train models using Deep Lake and PyTorch Lightning
- [Training on AWS SageMaker](https://docs-v3.activeloop.ai/v3.7.0/tutorials/deep-learning/training-models/training-on-aws-sagemaker.md): How to Train models on AWS SageMaker using Deep Lake datasets
- [Training an Object Detection and Segmentation Model in PyTorch](https://docs-v3.activeloop.ai/v3.7.0/tutorials/deep-learning/training-models/training-an-object-detection-and-segmentation-model-in-pytorch.md): Training an object detection and segmentation model is a great way to learn about complex data preprocessing for training models.
- [Updating Datasets](https://docs-v3.activeloop.ai/v3.7.0/tutorials/deep-learning/updating-datasets.md): Updating Deep Lake datasets
- [Data Processing Using Parallel Computing](https://docs-v3.activeloop.ai/v3.7.0/tutorials/deep-learning/data-processing-using-parallel-computing.md): Deeplake offers built-in methods for parallelizing dataset computations in order to achieve faster data processing.
- [Concurrent Writes](https://docs-v3.activeloop.ai/v3.7.0/tutorials/concurrent-writes.md): Concurrent writes in Deep Lake
- [Concurrency Using Zookeeper Locks](https://docs-v3.activeloop.ai/v3.7.0/tutorials/concurrent-writes/concurrency-using-zookeeper-locks.md): Using Zookeeper for locking Deep Lake datasets.
- [Playbooks](https://docs-v3.activeloop.ai/v3.7.0/playbooks.md): How to perform complex workflows using Deep Lake.
- [Querying, Training and Editing Datasets with Data Lineage](https://docs-v3.activeloop.ai/v3.7.0/playbooks/training-with-lineage.md): How to use queries and version control while training models.
- [Evaluating Model Performance](https://docs-v3.activeloop.ai/v3.7.0/playbooks/evaluating-model-performance.md): How to compare ground-truth annotations with model predictions
- [Training Reproducibility Using Deep Lake and Weights & Biases](https://docs-v3.activeloop.ai/v3.7.0/playbooks/training-reproducibility-with-wandb.md): How to achieve full reproducibility of model training using Deep Lake and W\&B
- [Working with Videos](https://docs-v3.activeloop.ai/v3.7.0/playbooks/working-with-videos.md): How manage video datasets and train models using Deep Lake.
- [Low-Level API Summary](https://docs-v3.activeloop.ai/v3.7.0/api-basics.md): Summary of the most important low-level Deep Lake commands.
- [Best Practices](https://docs-v3.activeloop.ai/v3.7.0/technical-details/best-practices.md): How to use Deep Lake at scale with best practices.
- [Creating Datasets at Scale](https://docs-v3.activeloop.ai/v3.7.0/technical-details/best-practices/creating-datasets-at-scale.md): Creating large Deep Lake datasets with high performance and reliability
- [Training Models at Scale](https://docs-v3.activeloop.ai/v3.7.0/technical-details/best-practices/training-models-at-scale.md): Train models at scale using Deep Lake
- [Storage Synchronization and "with" Context](https://docs-v3.activeloop.ai/v3.7.0/technical-details/best-practices/storage-synchronization.md): Synchronizing data with long-term storage and achieving optimal performance using Deep Lake.
- [Restoring Corrupted Datasets](https://docs-v3.activeloop.ai/v3.7.0/technical-details/best-practices/restoring-corrupted-datasets.md): Restoring Deep Lake datasets that may be corrupted.
- [Data Layout](https://docs-v3.activeloop.ai/v3.7.0/technical-details/data-layout.md): Understanding the data layout in Deep Lake
- [Version Control and Querying](https://docs-v3.activeloop.ai/v3.7.0/technical-details/version-control-and-querying.md): Understanding Deep Lake's Version control and Querying Layout
- [Dataset Visualization](https://docs-v3.activeloop.ai/v3.7.0/technical-details/dataset-visualization.md): How to visualize Deep Lake datasets
- [Tensor Relationships](https://docs-v3.activeloop.ai/v3.7.0/technical-details/tensor-relationships.md): Understanding the correct data layout for successful visualization.
- [Visualizer Integration](https://docs-v3.activeloop.ai/v3.7.0/technical-details/visualizer-integration.md): How to embed our visualizer in your application.
- [Shuffling in dataloaders](https://docs-v3.activeloop.ai/v3.7.0/technical-details/shuffling-in-dataloaders.md): Understanding data shuffling in Deep Lake's pytorch dataloader
- [How to Contribute](https://docs-v3.activeloop.ai/v3.7.0/technical-details/how-to-contribute.md): Guidelines for open source enthusiasts to contribute to our open-source data format.

## v3.6.18

- [Deep Lake Docs](https://docs-v3.activeloop.ai/v3.6.18/master.md): We hope you enjoy Docs for Deep Lake.
- [Vector Store Quickstart](https://docs-v3.activeloop.ai/v3.6.18/quickstart.md): A jump-start guide to using Deep Lake for Vector Search.
- [Deep Learning Quickstart](https://docs-v3.activeloop.ai/v3.6.18/quickstart-dl.md): A jump-start guide to using Deep Lake for Deep Learning.
- [Storage & Credentials](https://docs-v3.activeloop.ai/v3.6.18/storage-and-credentials.md): How to access datasets in other clouds and manage their credentials.
- [Storage Options](https://docs-v3.activeloop.ai/v3.6.18/storage-and-credentials/storage-options.md): How to authenticate using Activeloop storage, AWS S3, and Google Cloud Storage.
- [User Authentication](https://docs-v3.activeloop.ai/v3.6.18/storage-and-credentials/user-authentication.md): Registration and authentication in Deep Lake.
- [Storing Deep Lake Data in Your Own Cloud](https://docs-v3.activeloop.ai/v3.6.18/storage-and-credentials/managed-credentials.md): How to store Deep Lake data in your own cloud and manage credentials with Deep Lake
- [Microsoft Azure](https://docs-v3.activeloop.ai/v3.6.18/storage-and-credentials/managed-credentials/microsoft-azure.md): Azure-specific information for connecting data to Deep Lake
- [Provisioning Federated Credentials](https://docs-v3.activeloop.ai/v3.6.18/storage-and-credentials/managed-credentials/microsoft-azure/provisioning-federated-credentials.md): How to setup Federated Credentials in Azure
- [Enabling CORS](https://docs-v3.activeloop.ai/v3.6.18/storage-and-credentials/managed-credentials/microsoft-azure/enabling-cors.md): How to enable Cross-Origin Resource Sharing in your Azure account.
- [Amazon Web Services](https://docs-v3.activeloop.ai/v3.6.18/storage-and-credentials/managed-credentials/amazon-web-services.md): AWS-specific information for connecting data to Deep Lake
- [Provisioning Role-Based Access](https://docs-v3.activeloop.ai/v3.6.18/storage-and-credentials/managed-credentials/amazon-web-services/provisioning-role-based-access.md): How to provision Role-Based Access in S3
- [Enabling CORS](https://docs-v3.activeloop.ai/v3.6.18/storage-and-credentials/managed-credentials/amazon-web-services/enabling-cors.md): How to enable Cross-Origin Resource Sharing in your AWS S3 buckets.
- [Compute Engine](https://docs-v3.activeloop.ai/v3.6.18/enterprise-features/compute-engine.md): C++ part of the Deep Lake codebase optimized for faster data fetching and computations
- [Tensor Query Language (TQL)](https://docs-v3.activeloop.ai/v3.6.18/enterprise-features/compute-engine/querying-datasets.md): Deep Lake offers a highly-performant SQL-style query engine for filtering your data.
- [TQL Syntax](https://docs-v3.activeloop.ai/v3.6.18/enterprise-features/compute-engine/querying-datasets/query-syntax.md): How to properly format TQL queries
- [Sampling Datasets](https://docs-v3.activeloop.ai/v3.6.18/enterprise-features/compute-engine/querying-datasets/sampling-datasets.md): Implementation of samplers in TQL
- [Performant Dataloader](https://docs-v3.activeloop.ai/v3.6.18/enterprise-features/compute-engine/performant-dataloader.md): How to use Deep Lake's new dataloader built and optimized in C++
- [Tensor Database](https://docs-v3.activeloop.ai/v3.6.18/enterprise-features/managed-database.md): Deep Lake Managed Database
- [REST API](https://docs-v3.activeloop.ai/v3.6.18/enterprise-features/managed-database/rest-api.md): How to Use the Deep Lake REST API
- [Migrating Datasets to the Tensor Database](https://docs-v3.activeloop.ai/v3.6.18/enterprise-features/managed-database/migrating-datasets-to-the-tensor-database.md): Migrating datasets to the Tensor Database
- [Getting Started](https://docs-v3.activeloop.ai/v3.6.18/getting-started.md): Comprehensive guides for getting started with Deep Lake
- [Vector Store](https://docs-v3.activeloop.ai/v3.6.18/getting-started/vector-store.md): The comprehensive guide for Deep Lake in Vector Storage and Search applications.
- [Step 1: Hello World](https://docs-v3.activeloop.ai/v3.6.18/getting-started/vector-store/step-1-hello-world.md): Installing Deep Lake
- [Step 2: Creating Deep Lake Vector Stores](https://docs-v3.activeloop.ai/v3.6.18/getting-started/vector-store/step-2-creating-deep-lake-vector-stores.md): Creating the Deep Lake Vector Store
- [Step 3: Performing Search in Vector Stores](https://docs-v3.activeloop.ai/v3.6.18/getting-started/vector-store/step-3-performing-search-in-the-vector-store.md): Running search in the Deep Lake Vector Store
- [Step 4: Customizing Vector Stores](https://docs-v3.activeloop.ai/v3.6.18/getting-started/vector-store/step-4-customizing-vector-stores.md): Customizing the Deep Lake Vector Store
- [Deep Learning](https://docs-v3.activeloop.ai/v3.6.18/getting-started/deep-learning.md): The comprehensive guide for Deep Lake in Deep Learning applications.
- [Step 1: Hello World](https://docs-v3.activeloop.ai/v3.6.18/getting-started/deep-learning/hello-world.md): Installing Deep Lake and accessing your first Deep Lake Dataset.
- [Step 2: Creating Deep Lake Datasets](https://docs-v3.activeloop.ai/v3.6.18/getting-started/deep-learning/creating-datasets-manually.md): Creating and storing Deep Lake Datasets.
- [Step 3: Understanding Compression](https://docs-v3.activeloop.ai/v3.6.18/getting-started/deep-learning/understanding-compression.md): Using compression to achieve optimal performance in Deep Lake.
- [Step 4: Accessing and Updating Data](https://docs-v3.activeloop.ai/v3.6.18/getting-started/deep-learning/accessing-datasets.md): Learn how Deep Lake Datasets can be accessed or loaded from a variety of storage locations.
- [Step 5: Visualizing Datasets](https://docs-v3.activeloop.ai/v3.6.18/getting-started/deep-learning/visualizing-datasets.md): Visualizing and inspecting your datasets.
- [Step 6: Using Activeloop Storage](https://docs-v3.activeloop.ai/v3.6.18/getting-started/deep-learning/using-activeloop-storage.md): Storing and loading datasets from Deep Lake Storage.
- [Step 7: Connecting Deep Lake Datasets to ML Frameworks](https://docs-v3.activeloop.ai/v3.6.18/getting-started/deep-learning/connecting-to-ml-frameworks.md): Connecting Deep Lake Datasets to machine learning frameworks such as PyTorch and TensorFlow.
- [Step 8: Parallel Computing](https://docs-v3.activeloop.ai/v3.6.18/getting-started/deep-learning/parallel-computing.md): Running computations and processing data in parallel.
- [Step 9: Dataset Version Control](https://docs-v3.activeloop.ai/v3.6.18/getting-started/deep-learning/dataset-version-control.md): Managing changes to your datasets using Version Control.
- [Step 10: Dataset Filtering](https://docs-v3.activeloop.ai/v3.6.18/getting-started/deep-learning/dataset-filtering.md): Filtering datasets using user-defined-functions or SQL-style queries.
- [Tutorials (w Colab)](https://docs-v3.activeloop.ai/v3.6.18/tutorials.md): Common workflows with Deep Lake (includes Colab notebooks)
- [Vector Store Tutorials](https://docs-v3.activeloop.ai/v3.6.18/tutorials/vector-store.md): Tutorials for using Deep Lake in Vector Store applications
- [Vector Search Options](https://docs-v3.activeloop.ai/v3.6.18/tutorials/vector-store/vector-search-options.md): Overview of Vector Search Options in Deep Lake
- [Deep Lake Vector Store API](https://docs-v3.activeloop.ai/v3.6.18/tutorials/vector-store/vector-search-options/deep-lake-vector-store-api.md): Running Vector Search in the Deep Lake Vector Store module.
- [REST API](https://docs-v3.activeloop.ai/v3.6.18/tutorials/vector-store/vector-search-options/rest-api.md): Running Vector Search in the Deep Lake Tensor Database using the REST API
- [LangChain API](https://docs-v3.activeloop.ai/v3.6.18/tutorials/vector-store/vector-search-options/langchain-api.md): Vector Search using Deep Lake in LangChain
- [Image Similarity Search](https://docs-v3.activeloop.ai/v3.6.18/tutorials/vector-store/image-similarity-search.md): Using Deep Lake for image similarity search
- [Deep Lake Vector Store in LangChain](https://docs-v3.activeloop.ai/v3.6.18/tutorials/vector-store/deep-lake-vector-store-in-langchain.md): Using Deep Lake as a Vector Store in LangChain
- [Deep Learning Tutorials](https://docs-v3.activeloop.ai/v3.6.18/tutorials/deep-learning.md): Tutorials for using Deep Lake in deep-learning applications.
- [Creating Datasets](https://docs-v3.activeloop.ai/v3.6.18/tutorials/deep-learning/creating-datasets.md): Workflows for creating Deep Lake datasets
- [Creating Complex Datasets](https://docs-v3.activeloop.ai/v3.6.18/tutorials/deep-learning/creating-datasets/creating-complex-datasets.md): Converting a multi-annotation dataset to Deep Lake format is helpful for understanding how to use Deep Lake with rich data.
- [Creating Object Detection Datasets](https://docs-v3.activeloop.ai/v3.6.18/tutorials/deep-learning/creating-datasets/creating-object-detection-datasets.md): Converting an object detection dataset to Deep Lake format is a great way to get started with datasets of increasing complexity.
- [Creating Time-Series Datasets](https://docs-v3.activeloop.ai/v3.6.18/tutorials/deep-learning/creating-datasets/creating-time-series-datasets.md): Deep Lake is a powerful tool for easily storing and sharing time-series datasets with your team.
- [Creating Datasets with Sequences](https://docs-v3.activeloop.ai/v3.6.18/tutorials/deep-learning/creating-datasets/creating-datasets-with-sequences.md): Deep Lake sequences are a powerful tool for storing temporal annotations such as bounding boxes in each frame of a video.
- [Creating Video Datasets](https://docs-v3.activeloop.ai/v3.6.18/tutorials/deep-learning/creating-datasets/creating-video-datasets.md): Get started with video datasets using Deep Lake.
- [Training Models](https://docs-v3.activeloop.ai/v3.6.18/tutorials/deep-learning/training-models.md): Workflows for training models using Deep Lake datasets
- [Training an Image Classification Model in PyTorch](https://docs-v3.activeloop.ai/v3.6.18/tutorials/deep-learning/training-models/training-an-image-classification-model-in-pytorch.md): Training an image classification model is a great way to get started with model training using Deep Lake datasets.
- [Training Models Using MMDetection](https://docs-v3.activeloop.ai/v3.6.18/tutorials/deep-learning/training-models/training-models-using-mmdetection.md): How to Train Deep Learning models using Deep Lake's integration with MMDetection
- [Training Models Using PyTorch Lightning](https://docs-v3.activeloop.ai/v3.6.18/tutorials/deep-learning/training-models/training-models-using-pytorch-lightning.md): How to Train models using Deep Lake and PyTorch Lightning
- [Training on AWS SageMaker](https://docs-v3.activeloop.ai/v3.6.18/tutorials/deep-learning/training-models/training-on-aws-sagemaker.md): How to Train models on AWS SageMaker using Deep Lake datasets
- [Training an Object Detection and Segmentation Model in PyTorch](https://docs-v3.activeloop.ai/v3.6.18/tutorials/deep-learning/training-models/training-an-object-detection-and-segmentation-model-in-pytorch.md): Training an object detection and segmentation model is a great way to learn about complex data preprocessing for training models.
- [Updating Datasets](https://docs-v3.activeloop.ai/v3.6.18/tutorials/deep-learning/updating-datasets.md): Updating Deep Lake datasets
- [Data Processing Using Parallel Computing](https://docs-v3.activeloop.ai/v3.6.18/tutorials/deep-learning/data-processing-using-parallel-computing.md): Deeplake offers built-in methods for parallelizing dataset computations in order to achieve faster data processing.
- [Concurrent Writes](https://docs-v3.activeloop.ai/v3.6.18/tutorials/concurrent-writes.md): Concurrent writes in Deep Lake
- [Concurrency Using Zookeeper Locks](https://docs-v3.activeloop.ai/v3.6.18/tutorials/concurrent-writes/concurrency-using-zookeeper-locks.md): Using Zookeeper for locking Deep Lake datasets.
- [Playbooks](https://docs-v3.activeloop.ai/v3.6.18/playbooks.md): How to perform complex workflows using Deep Lake.
- [Querying, Training and Editing Datasets with Data Lineage](https://docs-v3.activeloop.ai/v3.6.18/playbooks/training-with-lineage.md): How to use queries and version control while training models.
- [Evaluating Model Performance](https://docs-v3.activeloop.ai/v3.6.18/playbooks/evaluating-model-performance.md): How to compare ground-truth annotations with model predictions
- [Training Reproducibility Using Deep Lake and Weights & Biases](https://docs-v3.activeloop.ai/v3.6.18/playbooks/training-reproducibility-with-wandb.md): How to achieve full reproducibility of model training using Deep Lake and W\&B
- [Working with Videos](https://docs-v3.activeloop.ai/v3.6.18/playbooks/working-with-videos.md): How manage video datasets and train models using Deep Lake.
- [Low-Level API Summary](https://docs-v3.activeloop.ai/v3.6.18/api-basics.md): Summary of the most important low-level Deep Lake commands.
- [Best Practices](https://docs-v3.activeloop.ai/v3.6.18/technical-details/best-practices.md): How to use Deep Lake at scale with best practices.
- [Creating Datasets at Scale](https://docs-v3.activeloop.ai/v3.6.18/technical-details/best-practices/creating-datasets-at-scale.md): Creating large Deep Lake datasets with high performance and reliability
- [Training Models at Scale](https://docs-v3.activeloop.ai/v3.6.18/technical-details/best-practices/training-models-at-scale.md): Train models at scale using Deep Lake
- [Storage Synchronization and "with" Context](https://docs-v3.activeloop.ai/v3.6.18/technical-details/best-practices/storage-synchronization.md): Synchronizing data with long-term storage and achieving optimal performance using Deep Lake.
- [Restoring Corrupted Datasets](https://docs-v3.activeloop.ai/v3.6.18/technical-details/best-practices/restoring-corrupted-datasets.md): Restoring Deep Lake datasets that may be corrupted.
- [Data Layout](https://docs-v3.activeloop.ai/v3.6.18/technical-details/data-layout.md): Understanding the data layout in Deep Lake
- [Version Control and Querying](https://docs-v3.activeloop.ai/v3.6.18/technical-details/version-control-and-querying.md): Understanding Deep Lake's Version control and Querying Layout
- [Dataset Visualization](https://docs-v3.activeloop.ai/v3.6.18/technical-details/dataset-visualization.md): How to visualize Deep Lake datasets
- [Tensor Relationships](https://docs-v3.activeloop.ai/v3.6.18/technical-details/tensor-relationships.md): Understanding the correct data layout for successful visualization.
- [Visualizer Integration](https://docs-v3.activeloop.ai/v3.6.18/technical-details/visualizer-integration.md): How to embed our visualizer in your application.
- [Shuffling in dataloaders](https://docs-v3.activeloop.ai/v3.6.18/technical-details/shuffling-in-dataloaders.md): Understanding data shuffling in Deep Lake's pytorch dataloader
- [How to Contribute](https://docs-v3.activeloop.ai/v3.6.18/technical-details/how-to-contribute.md): Guidelines for open source enthusiasts to contribute to our open-source data format.

## v3.6.10

- [Deep Lake Docs](https://docs-v3.activeloop.ai/v3.6.9/master.md): We hope you enjoy Docs for Deep Lake.
- [Vector Store Quickstart](https://docs-v3.activeloop.ai/v3.6.9/quickstart.md): A jump-start guide to using Deep Lake for Vector Search.
- [Deep Learning Quickstart](https://docs-v3.activeloop.ai/v3.6.9/quickstart-dl.md): A jump-start guide to using Deep Lake for Deep Learning.
- [Storage & Credentials](https://docs-v3.activeloop.ai/v3.6.9/storage-and-credentials.md): How to access datasets in other clouds and manage their credentials.
- [Storage Options](https://docs-v3.activeloop.ai/v3.6.9/storage-and-credentials/storage-options.md): How to authenticate using Activeloop storage, AWS S3, and Google Cloud Storage.
- [User Authentication](https://docs-v3.activeloop.ai/v3.6.9/storage-and-credentials/user-authentication.md): Registration and authentication in Deep Lake.
- [Storing Deep Lake Data in Your Own Cloud](https://docs-v3.activeloop.ai/v3.6.9/storage-and-credentials/managed-credentials.md): How to store Deep Lake data in your own cloud and manage credentials with Deep Lake
- [Microsoft Azure](https://docs-v3.activeloop.ai/v3.6.9/storage-and-credentials/managed-credentials/microsoft-azure.md): Azure-specific information for connecting data to Deep Lake
- [Provisioning Federated Credentials](https://docs-v3.activeloop.ai/v3.6.9/storage-and-credentials/managed-credentials/microsoft-azure/provisioning-federated-credentials.md): How to setup Federated Credentials in Azure
- [Enabling CORS](https://docs-v3.activeloop.ai/v3.6.9/storage-and-credentials/managed-credentials/microsoft-azure/enabling-cors.md): How to enable Cross-Origin Resource Sharing in your Azure account.
- [Amazon Web Services](https://docs-v3.activeloop.ai/v3.6.9/storage-and-credentials/managed-credentials/amazon-web-services.md): AWS-specific information for connecting data to Deep Lake
- [Provisioning Role-Based Access](https://docs-v3.activeloop.ai/v3.6.9/storage-and-credentials/managed-credentials/amazon-web-services/provisioning-role-based-access.md): How to provision Role-Based Access in S3
- [Enabling CORS](https://docs-v3.activeloop.ai/v3.6.9/storage-and-credentials/managed-credentials/amazon-web-services/enabling-cors.md): How to enable Cross-Origin Resource Sharing in your AWS S3 buckets.
- [Compute Engine](https://docs-v3.activeloop.ai/v3.6.9/enterprise-features/compute-engine.md): C++ part of the Deep Lake codebase optimized for faster data fetching and computations
- [Tensor Query Language (TQL)](https://docs-v3.activeloop.ai/v3.6.9/enterprise-features/compute-engine/querying-datasets.md): Deep Lake offers a highly-performant SQL-style query engine for filtering your data.
- [TQL Syntax](https://docs-v3.activeloop.ai/v3.6.9/enterprise-features/compute-engine/querying-datasets/query-syntax.md): How to properly format TQL queries
- [Sampling Datasets](https://docs-v3.activeloop.ai/v3.6.9/enterprise-features/compute-engine/querying-datasets/sampling-datasets.md): Implementation of samplers in TQL
- [Performant Dataloader](https://docs-v3.activeloop.ai/v3.6.9/enterprise-features/compute-engine/performant-dataloader.md): How to use Deep Lake's new dataloader built and optimized in C++
- [Tensor Database](https://docs-v3.activeloop.ai/v3.6.9/enterprise-features/managed-database.md): Deep Lake Managed Database
- [REST API](https://docs-v3.activeloop.ai/v3.6.9/enterprise-features/managed-database/rest-api.md): How to Use the Deep Lake REST API
- [Migrating Datasets to the Tensor Database](https://docs-v3.activeloop.ai/v3.6.9/enterprise-features/managed-database/migrating-datasets-to-the-tensor-database.md): Migrating datasets to the Tensor Database
- [Getting Started](https://docs-v3.activeloop.ai/v3.6.9/getting-started.md): Comprehensive guides for getting started with Deep Lake
- [Vector Store](https://docs-v3.activeloop.ai/v3.6.9/getting-started/vector-store.md): The comprehensive guide for Deep Lake in Vector Storage and Search applications.
- [Step 1: Hello World](https://docs-v3.activeloop.ai/v3.6.9/getting-started/vector-store/step-1-hello-world.md): Installing Deep Lake
- [Step 2: Creating Deep Lake Vector Stores](https://docs-v3.activeloop.ai/v3.6.9/getting-started/vector-store/step-2-creating-deep-lake-vector-stores.md): Creating the Deep Lake Vector Store
- [Step 3: Performing Search in Vector Stores](https://docs-v3.activeloop.ai/v3.6.9/getting-started/vector-store/step-3-performing-search-in-the-vector-store.md): Running search in the Deep Lake Vector Store
- [Step 4: Customizing Vector Stores](https://docs-v3.activeloop.ai/v3.6.9/getting-started/vector-store/step-4-customizing-vector-stores.md): Customizing the Deep Lake Vector Store
- [Deep Learning](https://docs-v3.activeloop.ai/v3.6.9/getting-started/deep-learning.md): The comprehensive guide for Deep Lake in Deep Learning applications.
- [Step 1: Hello World](https://docs-v3.activeloop.ai/v3.6.9/getting-started/deep-learning/hello-world.md): Installing Deep Lake and accessing your first Deep Lake Dataset.
- [Step 2: Creating Deep Lake Datasets](https://docs-v3.activeloop.ai/v3.6.9/getting-started/deep-learning/creating-datasets-manually.md): Creating and storing Deep Lake Datasets.
- [Step 3: Understanding Compression](https://docs-v3.activeloop.ai/v3.6.9/getting-started/deep-learning/understanding-compression.md): Using compression to achieve optimal performance in Deep Lake.
- [Step 4: Accessing and Updating Data](https://docs-v3.activeloop.ai/v3.6.9/getting-started/deep-learning/accessing-datasets.md): Learn how Deep Lake Datasets can be accessed or loaded from a variety of storage locations.
- [Step 5: Visualizing Datasets](https://docs-v3.activeloop.ai/v3.6.9/getting-started/deep-learning/visualizing-datasets.md): Visualizing and inspecting your datasets.
- [Step 6: Using Activeloop Storage](https://docs-v3.activeloop.ai/v3.6.9/getting-started/deep-learning/using-activeloop-storage.md): Storing and loading datasets from Deep Lake Storage.
- [Step 7: Connecting Deep Lake Datasets to ML Frameworks](https://docs-v3.activeloop.ai/v3.6.9/getting-started/deep-learning/connecting-to-ml-frameworks.md): Connecting Deep Lake Datasets to machine learning frameworks such as PyTorch and TensorFlow.
- [Step 8: Parallel Computing](https://docs-v3.activeloop.ai/v3.6.9/getting-started/deep-learning/parallel-computing.md): Running computations and processing data in parallel.
- [Step 9: Dataset Version Control](https://docs-v3.activeloop.ai/v3.6.9/getting-started/deep-learning/dataset-version-control.md): Managing changes to your datasets using Version Control.
- [Step 10: Dataset Filtering](https://docs-v3.activeloop.ai/v3.6.9/getting-started/deep-learning/dataset-filtering.md): Filtering datasets using user-defined-functions or SQL-style queries.
- [Tutorials (w Colab)](https://docs-v3.activeloop.ai/v3.6.9/tutorials.md): Common workflows with Deep Lake (includes Colab notebooks)
- [Vector Store Tutorials](https://docs-v3.activeloop.ai/v3.6.9/tutorials/vector-store.md): Tutorials for using Deep Lake in Vector Store applications
- [Vector Search Options](https://docs-v3.activeloop.ai/v3.6.9/tutorials/vector-store/vector-search-options.md): Overview of Vector Search Options in Deep Lake
- [Deep Lake Vector Store API](https://docs-v3.activeloop.ai/v3.6.9/tutorials/vector-store/vector-search-options/deep-lake-vector-store-api.md): Running Vector Search in the Deep Lake Vector Store module.
- [REST API](https://docs-v3.activeloop.ai/v3.6.9/tutorials/vector-store/vector-search-options/rest-api.md): Running Vector Search in the Deep Lake Tensor Database using the REST API
- [LangChain API](https://docs-v3.activeloop.ai/v3.6.9/tutorials/vector-store/vector-search-options/langchain-api.md): Vector Search using Deep Lake in LangChain
- [Image Similarity Search](https://docs-v3.activeloop.ai/v3.6.9/tutorials/vector-store/image-similarity-search.md): Using Deep Lake for image similarity search
- [Deep Lake Vector Store in LangChain](https://docs-v3.activeloop.ai/v3.6.9/tutorials/vector-store/deep-lake-vector-store-in-langchain.md): Using Deep Lake as a Vector Store in LangChain
- [Deep Learning Tutorials](https://docs-v3.activeloop.ai/v3.6.9/tutorials/deep-learning.md): Tutorials for using Deep Lake in deep-learning applications.
- [Creating Datasets](https://docs-v3.activeloop.ai/v3.6.9/tutorials/deep-learning/creating-datasets.md): Workflows for creating Deep Lake datasets
- [Creating Complex Datasets](https://docs-v3.activeloop.ai/v3.6.9/tutorials/deep-learning/creating-datasets/creating-complex-datasets.md): Converting a multi-annotation dataset to Deep Lake format is helpful for understanding how to use Deep Lake with rich data.
- [Creating Object Detection Datasets](https://docs-v3.activeloop.ai/v3.6.9/tutorials/deep-learning/creating-datasets/creating-object-detection-datasets.md): Converting an object detection dataset to Deep Lake format is a great way to get started with datasets of increasing complexity.
- [Creating Time-Series Datasets](https://docs-v3.activeloop.ai/v3.6.9/tutorials/deep-learning/creating-datasets/creating-time-series-datasets.md): Deep Lake is a powerful tool for easily storing and sharing time-series datasets with your team.
- [Creating Datasets with Sequences](https://docs-v3.activeloop.ai/v3.6.9/tutorials/deep-learning/creating-datasets/creating-datasets-with-sequences.md): Deep Lake sequences are a powerful tool for storing temporal annotations such as bounding boxes in each frame of a video.
- [Creating Video Datasets](https://docs-v3.activeloop.ai/v3.6.9/tutorials/deep-learning/creating-datasets/creating-video-datasets.md): Get started with video datasets using Deep Lake.
- [Training Models](https://docs-v3.activeloop.ai/v3.6.9/tutorials/deep-learning/training-models.md): Workflows for training models using Deep Lake datasets
- [Training an Image Classification Model in PyTorch](https://docs-v3.activeloop.ai/v3.6.9/tutorials/deep-learning/training-models/training-an-image-classification-model-in-pytorch.md): Training an image classification model is a great way to get started with model training using Deep Lake datasets.
- [Training Models Using MMDetection](https://docs-v3.activeloop.ai/v3.6.9/tutorials/deep-learning/training-models/training-models-using-mmdetection.md): How to Train Deep Learning models using Deep Lake's integration with MMDetection
- [Training Models Using PyTorch Lightning](https://docs-v3.activeloop.ai/v3.6.9/tutorials/deep-learning/training-models/training-models-using-pytorch-lightning.md): How to Train models using Deep Lake and PyTorch Lightning
- [Training on AWS SageMaker](https://docs-v3.activeloop.ai/v3.6.9/tutorials/deep-learning/training-models/training-on-aws-sagemaker.md): How to Train models on AWS SageMaker using Deep Lake datasets
- [Training an Object Detection and Segmentation Model in PyTorch](https://docs-v3.activeloop.ai/v3.6.9/tutorials/deep-learning/training-models/training-an-object-detection-and-segmentation-model-in-pytorch.md): Training an object detection and segmentation model is a great way to learn about complex data preprocessing for training models.
- [Updating Datasets](https://docs-v3.activeloop.ai/v3.6.9/tutorials/deep-learning/updating-datasets.md): Updating Deep Lake datasets
- [Data Processing Using Parallel Computing](https://docs-v3.activeloop.ai/v3.6.9/tutorials/deep-learning/data-processing-using-parallel-computing.md): Deeplake offers built-in methods for parallelizing dataset computations in order to achieve faster data processing.
- [Concurrent Writes](https://docs-v3.activeloop.ai/v3.6.9/tutorials/concurrent-writes.md): Concurrent writes in Deep Lake
- [Concurrency Using Zookeeper Locks](https://docs-v3.activeloop.ai/v3.6.9/tutorials/concurrent-writes/concurrency-using-zookeeper-locks.md): Using Zookeeper for locking Deep Lake datasets.
- [Playbooks](https://docs-v3.activeloop.ai/v3.6.9/playbooks.md): How to perform complex workflows using Deep Lake.
- [Querying, Training and Editing Datasets with Data Lineage](https://docs-v3.activeloop.ai/v3.6.9/playbooks/training-with-lineage.md): How to use queries and version control while training models.
- [Evaluating Model Performance](https://docs-v3.activeloop.ai/v3.6.9/playbooks/evaluating-model-performance.md): How to compare ground-truth annotations with model predictions
- [Training Reproducibility Using Deep Lake and Weights & Biases](https://docs-v3.activeloop.ai/v3.6.9/playbooks/training-reproducibility-with-wandb.md): How to achieve full reproducibility of model training using Deep Lake and W\&B
- [Working with Videos](https://docs-v3.activeloop.ai/v3.6.9/playbooks/working-with-videos.md): How manage video datasets and train models using Deep Lake.
- [Low-Level API Summary](https://docs-v3.activeloop.ai/v3.6.9/api-basics.md): Summary of the most important low-level Deep Lake commands.
- [Best Practices](https://docs-v3.activeloop.ai/v3.6.9/how-it-works/best-practices.md): How to use Deep Lake at scale with best practices.
- [Creating Datasets at Scale](https://docs-v3.activeloop.ai/v3.6.9/how-it-works/best-practices/creating-datasets-at-scale.md): Creating large Deep Lake datasets with high performance and reliability
- [Training Models at Scale](https://docs-v3.activeloop.ai/v3.6.9/how-it-works/best-practices/training-models-at-scale.md): Train models at scale using Deep Lake
- [Storage Synchronization and "with" Context](https://docs-v3.activeloop.ai/v3.6.9/how-it-works/best-practices/storage-synchronization.md): Synchronizing data with long-term storage and achieving optimal performance using Deep Lake.
- [Restoring Corrupted Datasets](https://docs-v3.activeloop.ai/v3.6.9/how-it-works/best-practices/restoring-corrupted-datasets.md): Restoring Deep Lake datasets that may be corrupted.
- [Data Layout](https://docs-v3.activeloop.ai/v3.6.9/how-it-works/data-layout.md): Understanding the data layout in Deep Lake
- [Version Control and Querying](https://docs-v3.activeloop.ai/v3.6.9/how-it-works/version-control-and-querying.md): Understanding Deep Lake's Version control and Querying Layout
- [Dataset Visualization](https://docs-v3.activeloop.ai/v3.6.9/how-it-works/dataset-visualization.md): How to visualize Deep Lake datasets
- [Tensor Relationships](https://docs-v3.activeloop.ai/v3.6.9/how-it-works/tensor-relationships.md): Understanding the correct data layout for successful visualization.
- [Visualizer Integration](https://docs-v3.activeloop.ai/v3.6.9/how-it-works/visualizer-integration.md): How to embed our visualizer in your application.
- [Shuffling in dataloaders](https://docs-v3.activeloop.ai/v3.6.9/how-it-works/shuffling-in-dataloaders.md): Understanding data shuffling in Deep Lake's pytorch dataloader
- [How to Contribute](https://docs-v3.activeloop.ai/v3.6.9/how-it-works/how-to-contribute.md): Guidelines for open source enthusiasts to contribute to our open-source data format.

## v3.6.8

- [Deep Lake Docs](https://docs-v3.activeloop.ai/v3.6.8/master.md): We hope you enjoy Docs for Deep Lake.
- [Vector Store Quickstart](https://docs-v3.activeloop.ai/v3.6.8/quickstart.md): A jump-start guide to using Deep Lake for Vector Search.
- [Deep Learning Quickstart](https://docs-v3.activeloop.ai/v3.6.8/quickstart-dl.md): A jump-start guide to using Deep Lake for Deep Learning.
- [Storage & Credentials](https://docs-v3.activeloop.ai/v3.6.8/storage-and-credentials.md): How to access datasets in other clouds and manage their credentials.
- [Storage Options](https://docs-v3.activeloop.ai/v3.6.8/storage-and-credentials/storage-options.md): How to authenticate using Activeloop storage, AWS S3, and Google Cloud Storage.
- [User Authentication](https://docs-v3.activeloop.ai/v3.6.8/storage-and-credentials/user-authentication.md): Registration and authentication in Deep Lake.
- [Storing Deep Lake Data in Your Own Cloud](https://docs-v3.activeloop.ai/v3.6.8/storage-and-credentials/managed-credentials.md): How to store Deep Lake data in your own cloud and manage credentials with Deep Lake
- [Microsoft Azure](https://docs-v3.activeloop.ai/v3.6.8/storage-and-credentials/managed-credentials/microsoft-azure.md): Azure-specific information for connecting data to Deep Lake
- [Provisioning Federated Credentials](https://docs-v3.activeloop.ai/v3.6.8/storage-and-credentials/managed-credentials/microsoft-azure/provisioning-federated-credentials.md): How to setup Federated Credentials in Azure
- [Enabling CORS](https://docs-v3.activeloop.ai/v3.6.8/storage-and-credentials/managed-credentials/microsoft-azure/enabling-cors.md): How to enable Cross-Origin Resource Sharing in your Azure account.
- [Amazon Web Services](https://docs-v3.activeloop.ai/v3.6.8/storage-and-credentials/managed-credentials/amazon-web-services.md): AWS-specific information for connecting data to Deep Lake
- [Provisioning Role-Based Access](https://docs-v3.activeloop.ai/v3.6.8/storage-and-credentials/managed-credentials/amazon-web-services/provisioning-role-based-access.md): How to provision Role-Based Access in S3
- [Enabling CORS](https://docs-v3.activeloop.ai/v3.6.8/storage-and-credentials/managed-credentials/amazon-web-services/enabling-cors.md): How to enable Cross-Origin Resource Sharing in your AWS S3 buckets.
- [Compute Engine](https://docs-v3.activeloop.ai/v3.6.8/enterprise-features/compute-engine.md): C++ part of the Deep Lake codebase optimized for faster data fetching and computations
- [Tensor Query Language (TQL)](https://docs-v3.activeloop.ai/v3.6.8/enterprise-features/compute-engine/querying-datasets.md): Deep Lake offers a highly-performant SQL-style query engine for filtering your data.
- [TQL Syntax](https://docs-v3.activeloop.ai/v3.6.8/enterprise-features/compute-engine/querying-datasets/query-syntax.md): How to properly format TQL queries
- [Sampling Datasets](https://docs-v3.activeloop.ai/v3.6.8/enterprise-features/compute-engine/querying-datasets/sampling-datasets.md): Implementation of samplers in TQL
- [Performant Dataloader](https://docs-v3.activeloop.ai/v3.6.8/enterprise-features/compute-engine/performant-dataloader.md): How to use Deep Lake's new dataloader built and optimized in C++
- [Tensor Database](https://docs-v3.activeloop.ai/v3.6.8/enterprise-features/managed-database.md): Deep Lake Managed Database
- [REST API](https://docs-v3.activeloop.ai/v3.6.8/enterprise-features/managed-database/rest-api.md): How to Use the Deep Lake REST API
- [Migrating Datasets to the Tensor Database](https://docs-v3.activeloop.ai/v3.6.8/enterprise-features/managed-database/migrating-datasets-to-the-tensor-database.md): Migrating datasets to the Tensor Database
- [Getting Started](https://docs-v3.activeloop.ai/v3.6.8/getting-started.md): Comprehensive guides for getting started with Deep Lake
- [Vector Store](https://docs-v3.activeloop.ai/v3.6.8/getting-started/vector-store.md): The comprehensive guide for Deep Lake in Vector Storage and Search applications.
- [Step 1: Hello World](https://docs-v3.activeloop.ai/v3.6.8/getting-started/vector-store/step-1-hello-world.md): Installing Deep Lake
- [Step 2: Creating Deep Lake Vector Stores](https://docs-v3.activeloop.ai/v3.6.8/getting-started/vector-store/step-2-creating-deep-lake-vector-stores.md): Creating the Deep Lake Vector Store
- [Step 3: Performing Search in Vector Stores](https://docs-v3.activeloop.ai/v3.6.8/getting-started/vector-store/step-3-performing-search-in-the-vector-store.md): Running search in the Deep Lake Vector Store
- [Step 4: Customizing Vector Stores](https://docs-v3.activeloop.ai/v3.6.8/getting-started/vector-store/step-4-customizing-vector-stores.md): Customizing the Deep Lake Vector Store
- [Deep Learning](https://docs-v3.activeloop.ai/v3.6.8/getting-started/deep-learning.md): The comprehensive guide for Deep Lake in Deep Learning applications.
- [Step 1: Hello World](https://docs-v3.activeloop.ai/v3.6.8/getting-started/deep-learning/hello-world.md): Installing Deep Lake and accessing your first Deep Lake Dataset.
- [Step 2: Creating Deep Lake Datasets](https://docs-v3.activeloop.ai/v3.6.8/getting-started/deep-learning/creating-datasets-manually.md): Creating and storing Deep Lake Datasets.
- [Step 3: Understanding Compression](https://docs-v3.activeloop.ai/v3.6.8/getting-started/deep-learning/understanding-compression.md): Using compression to achieve optimal performance in Deep Lake.
- [Step 4: Accessing and Updating Data](https://docs-v3.activeloop.ai/v3.6.8/getting-started/deep-learning/accessing-datasets.md): Learn how Deep Lake Datasets can be accessed or loaded from a variety of storage locations.
- [Step 5: Visualizing Datasets](https://docs-v3.activeloop.ai/v3.6.8/getting-started/deep-learning/visualizing-datasets.md): Visualizing and inspecting your datasets.
- [Step 6: Using Activeloop Storage](https://docs-v3.activeloop.ai/v3.6.8/getting-started/deep-learning/using-activeloop-storage.md): Storing and loading datasets from Deep Lake Storage.
- [Step 7: Connecting Deep Lake Datasets to ML Frameworks](https://docs-v3.activeloop.ai/v3.6.8/getting-started/deep-learning/connecting-to-ml-frameworks.md): Connecting Deep Lake Datasets to machine learning frameworks such as PyTorch and TensorFlow.
- [Step 8: Parallel Computing](https://docs-v3.activeloop.ai/v3.6.8/getting-started/deep-learning/parallel-computing.md): Running computations and processing data in parallel.
- [Step 9: Dataset Version Control](https://docs-v3.activeloop.ai/v3.6.8/getting-started/deep-learning/dataset-version-control.md): Managing changes to your datasets using Version Control.
- [Step 10: Dataset Filtering](https://docs-v3.activeloop.ai/v3.6.8/getting-started/deep-learning/dataset-filtering.md): Filtering datasets using user-defined-functions or SQL-style queries.
- [Tutorials (w Colab)](https://docs-v3.activeloop.ai/v3.6.8/tutorials.md): Common workflows with Deep Lake (includes Colab notebooks)
- [Vector Store Tutorials](https://docs-v3.activeloop.ai/v3.6.8/tutorials/vector-store.md): Tutorials for using Deep Lake in Vector Store applications
- [Vector Search Options](https://docs-v3.activeloop.ai/v3.6.8/tutorials/vector-store/vector-search-options.md): Overview of Vector Search Options in Deep Lake
- [Deep Lake Vector Store API](https://docs-v3.activeloop.ai/v3.6.8/tutorials/vector-store/vector-search-options/deep-lake-vector-store-api.md): Running Vector Search in the Deep Lake Vector Store module.
- [REST API](https://docs-v3.activeloop.ai/v3.6.8/tutorials/vector-store/vector-search-options/rest-api.md): Running Vector Search in the Deep Lake Tensor Database using the REST API
- [LangChain API](https://docs-v3.activeloop.ai/v3.6.8/tutorials/vector-store/vector-search-options/langchain-api.md)
- [Image Similarity Search](https://docs-v3.activeloop.ai/v3.6.8/tutorials/vector-store/image-similarity-search.md): Using Deep Lake for image similarity search
- [Deep Lake Vector Store in LangChain](https://docs-v3.activeloop.ai/v3.6.8/tutorials/vector-store/deep-lake-vector-store-in-langchain.md): Using Deep Lake as a Vector Store in LangChain
- [Deep Learning Tutorials](https://docs-v3.activeloop.ai/v3.6.8/tutorials/deep-learning.md): Tutorials for using Deep Lake in deep-learning applications.
- [Creating Datasets](https://docs-v3.activeloop.ai/v3.6.8/tutorials/deep-learning/creating-datasets.md): Workflows for creating Deep Lake datasets
- [Creating Complex Datasets](https://docs-v3.activeloop.ai/v3.6.8/tutorials/deep-learning/creating-datasets/creating-complex-datasets.md): Converting a multi-annotation dataset to Deep Lake format is helpful for understanding how to use Deep Lake with rich data.
- [Creating Object Detection Datasets](https://docs-v3.activeloop.ai/v3.6.8/tutorials/deep-learning/creating-datasets/creating-object-detection-datasets.md): Converting an object detection dataset to Deep Lake format is a great way to get started with datasets of increasing complexity.
- [Creating Time-Series Datasets](https://docs-v3.activeloop.ai/v3.6.8/tutorials/deep-learning/creating-datasets/creating-time-series-datasets.md): Deep Lake is a powerful tool for easily storing and sharing time-series datasets with your team.
- [Creating Datasets with Sequences](https://docs-v3.activeloop.ai/v3.6.8/tutorials/deep-learning/creating-datasets/creating-datasets-with-sequences.md): Deep Lake sequences are a powerful tool for storing temporal annotations such as bounding boxes in each frame of a video.
- [Creating Video Datasets](https://docs-v3.activeloop.ai/v3.6.8/tutorials/deep-learning/creating-datasets/creating-video-datasets.md): Get started with video datasets using Deep Lake.
- [Training Models](https://docs-v3.activeloop.ai/v3.6.8/tutorials/deep-learning/training-models.md): Workflows for training models using Deep Lake datasets
- [Training an Image Classification Model in PyTorch](https://docs-v3.activeloop.ai/v3.6.8/tutorials/deep-learning/training-models/training-an-image-classification-model-in-pytorch.md): Training an image classification model is a great way to get started with model training using Deep Lake datasets.
- [Training Models Using MMDetection](https://docs-v3.activeloop.ai/v3.6.8/tutorials/deep-learning/training-models/training-models-using-mmdetection.md): How to Train Deep Learning models using Deep Lake's integration with MMDetection
- [Training Models Using PyTorch Lightning](https://docs-v3.activeloop.ai/v3.6.8/tutorials/deep-learning/training-models/training-models-using-pytorch-lightning.md): How to Train models using Deep Lake and PyTorch Lightning
- [Training on AWS SageMaker](https://docs-v3.activeloop.ai/v3.6.8/tutorials/deep-learning/training-models/training-on-aws-sagemaker.md): How to Train models on AWS SageMaker using Deep Lake datasets
- [Training an Object Detection and Segmentation Model in PyTorch](https://docs-v3.activeloop.ai/v3.6.8/tutorials/deep-learning/training-models/training-an-object-detection-and-segmentation-model-in-pytorch.md): Training an object detection and segmentation model is a great way to learn about complex data preprocessing for training models.
- [Updating Datasets](https://docs-v3.activeloop.ai/v3.6.8/tutorials/deep-learning/updating-datasets.md): Updating Deep Lake datasets
- [Data Processing Using Parallel Computing](https://docs-v3.activeloop.ai/v3.6.8/tutorials/deep-learning/data-processing-using-parallel-computing.md): Deeplake offers built-in methods for parallelizing dataset computations in order to achieve faster data processing.
- [Playbooks](https://docs-v3.activeloop.ai/v3.6.8/playbooks.md): How to perform complex workflows using Deep Lake.
- [Querying, Training and Editing Datasets with Data Lineage](https://docs-v3.activeloop.ai/v3.6.8/playbooks/training-with-lineage.md): How to use queries and version control while training models.
- [Evaluating Model Performance](https://docs-v3.activeloop.ai/v3.6.8/playbooks/evaluating-model-performance.md): How to compare ground-truth annotations with model predictions
- [Training Reproducibility Using Deep Lake and Weights & Biases](https://docs-v3.activeloop.ai/v3.6.8/playbooks/training-reproducibility-with-wandb.md): How to achieve full reproducibility of model training using Deep Lake and W\&B
- [Working with Videos](https://docs-v3.activeloop.ai/v3.6.8/playbooks/working-with-videos.md): How manage video datasets and train models using Deep Lake.
- [Low-Level API Summary](https://docs-v3.activeloop.ai/v3.6.8/api-basics.md): Summary of the most important low-level Deep Lake commands.
- [Best Practices](https://docs-v3.activeloop.ai/v3.6.8/how-it-works/best-practices.md): How to use Deep Lake at scale with best practices.
- [Creating Datasets at Scale](https://docs-v3.activeloop.ai/v3.6.8/how-it-works/best-practices/creating-datasets-at-scale.md): Creating large Deep Lake datasets with high performance and reliability
- [Training Models at Scale](https://docs-v3.activeloop.ai/v3.6.8/how-it-works/best-practices/training-models-at-scale.md): Train models at scale using Deep Lake
- [Storage Synchronization and "with" Context](https://docs-v3.activeloop.ai/v3.6.8/how-it-works/best-practices/storage-synchronization.md): Synchronizing data with long-term storage and achieving optimal performance using Deep Lake.
- [Restoring Corrupted Datasets](https://docs-v3.activeloop.ai/v3.6.8/how-it-works/best-practices/restoring-corrupted-datasets.md): Restoring Deep Lake datasets that may be corrupted.
- [Data Layout](https://docs-v3.activeloop.ai/v3.6.8/how-it-works/data-layout.md): Understanding the data layout in Deep Lake
- [Version Control and Querying](https://docs-v3.activeloop.ai/v3.6.8/how-it-works/version-control-and-querying.md): Understanding Deep Lake's Version control and Querying Layout
- [Dataset Visualization](https://docs-v3.activeloop.ai/v3.6.8/how-it-works/dataset-visualization.md): How to visualize Deep Lake datasets
- [Tensor Relationships](https://docs-v3.activeloop.ai/v3.6.8/how-it-works/tensor-relationships.md): Understanding the correct data layout for successful visualization.
- [Visualizer Integration](https://docs-v3.activeloop.ai/v3.6.8/how-it-works/visualizer-integration.md): How to embed our visualizer in your application.
- [Shuffling in dataloaders](https://docs-v3.activeloop.ai/v3.6.8/how-it-works/shuffling-in-dataloaders.md): Understanding data shuffling in Deep Lake's pytorch dataloader
- [How to Contribute](https://docs-v3.activeloop.ai/v3.6.8/how-it-works/how-to-contribute.md): Guidelines for open source enthusiasts to contribute to our open-source data format.

## v3.6.3

- [Deep Lake Docs](https://docs-v3.activeloop.ai/v3.6.3/master.md): We hope you enjoy Docs for Deep Lake.
- [Vector Store Quickstart](https://docs-v3.activeloop.ai/v3.6.3/quickstart.md): A jump-start guide to using Deep Lake for Vector Search.
- [Deep Learning Quickstart](https://docs-v3.activeloop.ai/v3.6.3/quickstart-dl.md): A jump-start guide to using Deep Lake for Deep Learning.
- [Storage & Credentials](https://docs-v3.activeloop.ai/v3.6.3/storage-and-credentials.md): How to access datasets in other clouds and manage their credentials.
- [Storage Options](https://docs-v3.activeloop.ai/v3.6.3/storage-and-credentials/storage-options.md): How to authenticate using Activeloop storage, AWS S3, and Google Cloud Storage.
- [User Authentication](https://docs-v3.activeloop.ai/v3.6.3/storage-and-credentials/user-authentication.md): Registration and authentication in Deep Lake.
- [Storing Deep Lake Data in Your Own Cloud](https://docs-v3.activeloop.ai/v3.6.3/storage-and-credentials/managed-credentials.md): How to store Deep Lake data in your own cloud and manage credentials with Deep Lake
- [Microsoft Azure](https://docs-v3.activeloop.ai/v3.6.3/storage-and-credentials/managed-credentials/microsoft-azure.md): Azure-specific information for connecting data to Deep Lake
- [Provisioning Federated Credentials](https://docs-v3.activeloop.ai/v3.6.3/storage-and-credentials/managed-credentials/microsoft-azure/provisioning-federated-credentials.md): How to setup Federated Credentials in Azure
- [Enabling CORS](https://docs-v3.activeloop.ai/v3.6.3/storage-and-credentials/managed-credentials/microsoft-azure/enabling-cors.md): How to enable Cross-Origin Resource Sharing in your Azure account.
- [Amazon Web Services](https://docs-v3.activeloop.ai/v3.6.3/storage-and-credentials/managed-credentials/amazon-web-services.md): AWS-specific information for connecting data to Deep Lake
- [Provisioning Role-Based Access](https://docs-v3.activeloop.ai/v3.6.3/storage-and-credentials/managed-credentials/amazon-web-services/provisioning-role-based-access.md): How to provision Role-Based Access in S3
- [Enabling CORS](https://docs-v3.activeloop.ai/v3.6.3/storage-and-credentials/managed-credentials/amazon-web-services/enabling-cors.md): How to enable Cross-Origin Resource Sharing in your AWS S3 buckets.
- [Compute Engine](https://docs-v3.activeloop.ai/v3.6.3/enterprise-features/compute-engine.md): C++ part of the Deep Lake codebase optimized for faster data fetching and computations
- [Tensor Query Language (TQL)](https://docs-v3.activeloop.ai/v3.6.3/enterprise-features/compute-engine/querying-datasets.md): Deep Lake offers a highly-performant SQL-style query engine for filtering your data.
- [TQL Syntax](https://docs-v3.activeloop.ai/v3.6.3/enterprise-features/compute-engine/querying-datasets/query-syntax.md): How to properly format TQL queries
- [Sampling Datasets](https://docs-v3.activeloop.ai/v3.6.3/enterprise-features/compute-engine/querying-datasets/sampling-datasets.md)
- [Performant Dataloader](https://docs-v3.activeloop.ai/v3.6.3/enterprise-features/compute-engine/performant-dataloader.md): How to use Deep Lake's new dataloader built and optimized in C++
- [Tensor Database](https://docs-v3.activeloop.ai/v3.6.3/enterprise-features/managed-database.md): Deep Lake Managed Database
- [REST API](https://docs-v3.activeloop.ai/v3.6.3/enterprise-features/managed-database/rest-api.md): How to Use the Deep Lake REST API
- [Migrating Datasets to the Tensor Database](https://docs-v3.activeloop.ai/v3.6.3/enterprise-features/managed-database/migrating-datasets-to-the-tensor-database.md): Migrating datasets to the Tensor Database
- [Getting Started](https://docs-v3.activeloop.ai/v3.6.3/getting-started.md): Comprehensive guides for getting started with Deep Lake
- [Vector Store](https://docs-v3.activeloop.ai/v3.6.3/getting-started/vector-store.md): The comprehensive guide for Deep Lake in Vector Storage and Search applications.
- [Step 1: Hello World](https://docs-v3.activeloop.ai/v3.6.3/getting-started/vector-store/step-1-hello-world.md): Installing Deep Lake
- [Step 2: Creating Deep Lake Vector Stores](https://docs-v3.activeloop.ai/v3.6.3/getting-started/vector-store/step-2-creating-deep-lake-vector-stores.md): Creating the Deep Lake Vector Store
- [Step 3: Performing Search in Vector Stores](https://docs-v3.activeloop.ai/v3.6.3/getting-started/vector-store/step-3-performing-search-in-the-vector-store.md): Running search in the Deep Lake Vector Store
- [Step 4: Customizing Vector Stores](https://docs-v3.activeloop.ai/v3.6.3/getting-started/vector-store/step-4-customizing-vector-stores.md): Customizing the Deep Lake Vector Store
- [Deep Learning](https://docs-v3.activeloop.ai/v3.6.3/getting-started/deep-learning.md): The comprehensive guide for Deep Lake in Deep Learning applications.
- [Step 1: Hello World](https://docs-v3.activeloop.ai/v3.6.3/getting-started/deep-learning/hello-world.md): Installing Deep Lake and accessing your first Deep Lake Dataset.
- [Step 2: Creating Deep Lake Datasets](https://docs-v3.activeloop.ai/v3.6.3/getting-started/deep-learning/creating-datasets-manually.md): Creating and storing Deep Lake Datasets.
- [Step 3: Understanding Compression](https://docs-v3.activeloop.ai/v3.6.3/getting-started/deep-learning/understanding-compression.md): Using compression to achieve optimal performance in Deep Lake.
- [Step 4: Accessing and Updating Data](https://docs-v3.activeloop.ai/v3.6.3/getting-started/deep-learning/accessing-datasets.md): Learn how Deep Lake Datasets can be accessed or loaded from a variety of storage locations.
- [Step 5: Visualizing Datasets](https://docs-v3.activeloop.ai/v3.6.3/getting-started/deep-learning/visualizing-datasets.md): Visualizing and inspecting your datasets.
- [Step 6: Using Activeloop Storage](https://docs-v3.activeloop.ai/v3.6.3/getting-started/deep-learning/using-activeloop-storage.md): Storing and loading datasets from Deep Lake Storage.
- [Step 7: Connecting Deep Lake Datasets to ML Frameworks](https://docs-v3.activeloop.ai/v3.6.3/getting-started/deep-learning/connecting-to-ml-frameworks.md): Connecting Deep Lake Datasets to machine learning frameworks such as PyTorch and TensorFlow.
- [Step 8: Parallel Computing](https://docs-v3.activeloop.ai/v3.6.3/getting-started/deep-learning/parallel-computing.md): Running computations and processing data in parallel.
- [Step 9: Dataset Version Control](https://docs-v3.activeloop.ai/v3.6.3/getting-started/deep-learning/dataset-version-control.md): Managing changes to your datasets using Version Control.
- [Step 10: Dataset Filtering](https://docs-v3.activeloop.ai/v3.6.3/getting-started/deep-learning/dataset-filtering.md): Filtering datasets using user-defined-functions or SQL-style queries.
- [Tutorials (w Colab)](https://docs-v3.activeloop.ai/v3.6.3/tutorials.md): Common workflows with Deep Lake (includes Colab notebooks)
- [Vector Store Tutorials](https://docs-v3.activeloop.ai/v3.6.3/tutorials/vector-store.md): Tutorials for using Deep Lake in Vector Store applications
- [Vector Search Options](https://docs-v3.activeloop.ai/v3.6.3/tutorials/vector-store/vector-search-options.md): Overview of Vector Search Options in Deep Lake
- [Deep Lake Vector Store API](https://docs-v3.activeloop.ai/v3.6.3/tutorials/vector-store/vector-search-options/deep-lake-vector-store-api.md): Running Vector Search in the Deep Lake Vector Store module.
- [REST API](https://docs-v3.activeloop.ai/v3.6.3/tutorials/vector-store/vector-search-options/rest-api.md): Running Vector Search in the Deep Lake Tensor Database using the REST API
- [LangChain API](https://docs-v3.activeloop.ai/v3.6.3/tutorials/vector-store/vector-search-options/langchain-api.md)
- [Image Similarity Search](https://docs-v3.activeloop.ai/v3.6.3/tutorials/vector-store/image-similarity-search.md): Using Deep Lake for image similarity search
- [Deep Lake Vector Store in LangChain](https://docs-v3.activeloop.ai/v3.6.3/tutorials/vector-store/deep-lake-vector-store-in-langchain.md): Using Deep Lake as a Vector Store in LangChain
- [Deep Learning Tutorials](https://docs-v3.activeloop.ai/v3.6.3/tutorials/deep-learning.md): Tutorials for using Deep Lake in deep-learning applications.
- [Creating Datasets](https://docs-v3.activeloop.ai/v3.6.3/tutorials/deep-learning/creating-datasets.md): Workflows for creating Deep Lake datasets
- [Creating Complex Datasets](https://docs-v3.activeloop.ai/v3.6.3/tutorials/deep-learning/creating-datasets/creating-complex-datasets.md): Converting a multi-annotation dataset to Deep Lake format is helpful for understanding how to use Deep Lake with rich data.
- [Creating Object Detection Datasets](https://docs-v3.activeloop.ai/v3.6.3/tutorials/deep-learning/creating-datasets/creating-object-detection-datasets.md): Converting an object detection dataset to Deep Lake format is a great way to get started with datasets of increasing complexity.
- [Creating Time-Series Datasets](https://docs-v3.activeloop.ai/v3.6.3/tutorials/deep-learning/creating-datasets/creating-time-series-datasets.md): Deep Lake is a powerful tool for easily storing and sharing time-series datasets with your team.
- [Creating Datasets with Sequences](https://docs-v3.activeloop.ai/v3.6.3/tutorials/deep-learning/creating-datasets/creating-datasets-with-sequences.md): Deep Lake sequences are a powerful tool for storing temporal annotations such as bounding boxes in each frame of a video.
- [Creating Video Datasets](https://docs-v3.activeloop.ai/v3.6.3/tutorials/deep-learning/creating-datasets/creating-video-datasets.md): Get started with video datasets using Deep Lake.
- [Training Models](https://docs-v3.activeloop.ai/v3.6.3/tutorials/deep-learning/training-models.md): Workflows for training models using Deep Lake datasets
- [Training an Image Classification Model in PyTorch](https://docs-v3.activeloop.ai/v3.6.3/tutorials/deep-learning/training-models/training-an-image-classification-model-in-pytorch.md): Training an image classification model is a great way to get started with model training using Deep Lake datasets.
- [Training Models Using MMDetection](https://docs-v3.activeloop.ai/v3.6.3/tutorials/deep-learning/training-models/training-models-using-mmdetection.md): How to Train Deep Learning models using Deep Lake's integration with MMDetection
- [Training Models Using PyTorch Lightning](https://docs-v3.activeloop.ai/v3.6.3/tutorials/deep-learning/training-models/training-models-using-pytorch-lightning.md): How to Train models using Deep Lake and PyTorch Lightning
- [Training on AWS SageMaker](https://docs-v3.activeloop.ai/v3.6.3/tutorials/deep-learning/training-models/training-on-aws-sagemaker.md): How to Train models on AWS SageMaker using Deep Lake datasets
- [Training an Object Detection and Segmentation Model in PyTorch](https://docs-v3.activeloop.ai/v3.6.3/tutorials/deep-learning/training-models/training-an-object-detection-and-segmentation-model-in-pytorch.md): Training an object detection and segmentation model is a great way to learn about complex data preprocessing for training models.
- [Updating Datasets](https://docs-v3.activeloop.ai/v3.6.3/tutorials/deep-learning/updating-datasets.md): Updating Deep Lake datasets
- [Data Processing Using Parallel Computing](https://docs-v3.activeloop.ai/v3.6.3/tutorials/deep-learning/data-processing-using-parallel-computing.md): Deeplake offers built-in methods for parallelizing dataset computations in order to achieve faster data processing.
- [Playbooks](https://docs-v3.activeloop.ai/v3.6.3/playbooks.md): How to perform complex workflows using Deep Lake.
- [Querying, Training and Editing Datasets with Data Lineage](https://docs-v3.activeloop.ai/v3.6.3/playbooks/training-with-lineage.md): How to use queries and version control while training models.
- [Evaluating Model Performance](https://docs-v3.activeloop.ai/v3.6.3/playbooks/evaluating-model-performance.md): How to compare ground-truth annotations with model predictions
- [Training Reproducibility Using Deep Lake and Weights & Biases](https://docs-v3.activeloop.ai/v3.6.3/playbooks/training-reproducibility-with-wandb.md): How to achieve full reproducibility of model training using Deep Lake and W\&B
- [Working with Videos](https://docs-v3.activeloop.ai/v3.6.3/playbooks/working-with-videos.md): How manage video datasets and train models using Deep Lake.
- [Low-Level API Summary](https://docs-v3.activeloop.ai/v3.6.3/api-basics.md): Summary of the most important low-level Deep Lake commands.
- [Best Practices](https://docs-v3.activeloop.ai/v3.6.3/how-it-works/best-practices.md): How to use Deep Lake at scale with best practices.
- [Creating Datasets at Scale](https://docs-v3.activeloop.ai/v3.6.3/how-it-works/best-practices/creating-datasets-at-scale.md): Creating large Deep Lake datasets with high performance and reliability
- [Training Models at Scale](https://docs-v3.activeloop.ai/v3.6.3/how-it-works/best-practices/training-models-at-scale.md): Train models at scale using Deep Lake
- [Storage Synchronization and "with" Context](https://docs-v3.activeloop.ai/v3.6.3/how-it-works/best-practices/storage-synchronization.md): Synchronizing data with long-term storage and achieving optimal performance using Deep Lake.
- [Restoring Corrupted Datasets](https://docs-v3.activeloop.ai/v3.6.3/how-it-works/best-practices/restoring-corrupted-datasets.md): Restoring Deep Lake datasets that may be corrupted.
- [Data Layout](https://docs-v3.activeloop.ai/v3.6.3/how-it-works/data-layout.md): Understanding the data layout in Deep Lake
- [Version Control and Querying](https://docs-v3.activeloop.ai/v3.6.3/how-it-works/version-control-and-querying.md): Understanding Deep Lake's Version control and Querying Layout
- [Dataset Visualization](https://docs-v3.activeloop.ai/v3.6.3/how-it-works/dataset-visualization.md): How to visualize Deep Lake datasets
- [Tensor Relationships](https://docs-v3.activeloop.ai/v3.6.3/how-it-works/tensor-relationships.md): Understanding the correct data layout for successful visualization.
- [Visualizer Integration](https://docs-v3.activeloop.ai/v3.6.3/how-it-works/visualizer-integration.md): How to embed our visualizer in your application.
- [Shuffling in dataloaders](https://docs-v3.activeloop.ai/v3.6.3/how-it-works/shuffling-in-dataloaders.md): Understanding data shuffling in Deep Lake's pytorch dataloader
- [How to Contribute](https://docs-v3.activeloop.ai/v3.6.3/how-it-works/how-to-contribute.md): Guidelines for open source enthusiasts to contribute to our open-source data format.

## v3.6.2

- [Deep Lake Docs](https://docs-v3.activeloop.ai/v3.6.2/master.md): We hope you enjoy Docs for Deep Lake.
- [Vector Store Quickstart](https://docs-v3.activeloop.ai/v3.6.2/quickstart.md): A jump-start guide to using Deep Lake for Vector Search.
- [Deep Learning Quickstart](https://docs-v3.activeloop.ai/v3.6.2/quickstart-dl.md): A jump-start guide to using Deep Lake for Deep Learning.
- [Storage & Credentials](https://docs-v3.activeloop.ai/v3.6.2/storage-and-credentials.md): How to access datasets in other clouds and manage their credentials.
- [Storage Options](https://docs-v3.activeloop.ai/v3.6.2/storage-and-credentials/storage-options.md): How to authenticate using Activeloop storage, AWS S3, and Google Cloud Storage.
- [User Authentication](https://docs-v3.activeloop.ai/v3.6.2/storage-and-credentials/user-authentication.md): Registration and authentication in Deep Lake.
- [Storing Deep Lake Data in Your Own Cloud](https://docs-v3.activeloop.ai/v3.6.2/storage-and-credentials/managed-credentials.md): How to store Deep Lake data in your own cloud and manage credentials with Deep Lake
- [Microsoft Azure](https://docs-v3.activeloop.ai/v3.6.2/storage-and-credentials/managed-credentials/microsoft-azure.md): Azure-specific information for connecting data to Deep Lake
- [Provisioning Federated Credentials](https://docs-v3.activeloop.ai/v3.6.2/storage-and-credentials/managed-credentials/microsoft-azure/provisioning-federated-credentials.md): How to setup Federated Credentials in Azure
- [Amazon Web Services](https://docs-v3.activeloop.ai/v3.6.2/storage-and-credentials/managed-credentials/amazon-web-services.md): AWS-specific information for connecting data to Deep Lake
- [Provisioning Role-Based Access](https://docs-v3.activeloop.ai/v3.6.2/storage-and-credentials/managed-credentials/amazon-web-services/provisioning-role-based-access.md): How to provision Role-Based Access in S3
- [Enabling CORS](https://docs-v3.activeloop.ai/v3.6.2/storage-and-credentials/managed-credentials/amazon-web-services/enabling-cors.md): How to enable Cross-Origin Resource Sharing in your cloud buckets.
- [Compute Engine](https://docs-v3.activeloop.ai/v3.6.2/enterprise-features/compute-engine.md): C++ part of the Deep Lake codebase optimized for faster data fetching and computations
- [Tensor Query Language (TQL)](https://docs-v3.activeloop.ai/v3.6.2/enterprise-features/compute-engine/querying-datasets.md): Deep Lake offers a highly-performant SQL-style query engine for filtering your data.
- [TQL Syntax](https://docs-v3.activeloop.ai/v3.6.2/enterprise-features/compute-engine/querying-datasets/query-syntax.md): How to properly format TQL queries
- [Sampling Datasets](https://docs-v3.activeloop.ai/v3.6.2/enterprise-features/compute-engine/querying-datasets/sampling-datasets.md)
- [Performant Dataloader](https://docs-v3.activeloop.ai/v3.6.2/enterprise-features/compute-engine/performant-dataloader.md): How to use Deep Lake's new dataloader built and optimized in C++
- [Tensor Database](https://docs-v3.activeloop.ai/v3.6.2/enterprise-features/managed-database.md): Deep Lake Managed Database
- [REST API](https://docs-v3.activeloop.ai/v3.6.2/enterprise-features/managed-database/rest-api.md): How to Use the Deep Lake REST API
- [Migrating Datasets to the Tensor Database](https://docs-v3.activeloop.ai/v3.6.2/enterprise-features/managed-database/migrating-datasets-to-the-tensor-database.md): Migrating datasets to the Tensor Database
- [Getting Started](https://docs-v3.activeloop.ai/v3.6.2/getting-started.md): Comprehensive guides for getting started with Deep Lake
- [Vector Store](https://docs-v3.activeloop.ai/v3.6.2/getting-started/vector-store.md): The comprehensive guide for Deep Lake in Vector Storage and Search applications.
- [Step 1: Hello World](https://docs-v3.activeloop.ai/v3.6.2/getting-started/vector-store/step-1-hello-world.md): Installing Deep Lake
- [Step 2: Creating Deep Lake Vector Stores](https://docs-v3.activeloop.ai/v3.6.2/getting-started/vector-store/step-2-creating-deep-lake-vector-stores.md)
- [Step 3: Performing Search in the Vector Store](https://docs-v3.activeloop.ai/v3.6.2/getting-started/vector-store/step-3-performing-search-in-the-vector-store.md)
- [Step 4: Customizing Vector Stores](https://docs-v3.activeloop.ai/v3.6.2/getting-started/vector-store/step-4-customizing-vector-stores.md)
- [Deep Learning](https://docs-v3.activeloop.ai/v3.6.2/getting-started/deep-learning.md): The comprehensive guide for Deep Lake in Deep Learning applications.
- [Step 1: Hello World](https://docs-v3.activeloop.ai/v3.6.2/getting-started/deep-learning/hello-world.md): Installing Deep Lake and accessing your first Deep Lake Dataset.
- [Step 2: Creating Deep Lake Datasets](https://docs-v3.activeloop.ai/v3.6.2/getting-started/deep-learning/creating-datasets-manually.md): Creating and storing Deep Lake Datasets.
- [Step 3: Understanding Compression](https://docs-v3.activeloop.ai/v3.6.2/getting-started/deep-learning/understanding-compression.md): Using compression to achieve optimal performance in Deep Lake.
- [Step 4: Accessing and Updating Data](https://docs-v3.activeloop.ai/v3.6.2/getting-started/deep-learning/accessing-datasets.md): Learn how Deep Lake Datasets can be accessed or loaded from a variety of storage locations.
- [Step 5: Visualizing Datasets](https://docs-v3.activeloop.ai/v3.6.2/getting-started/deep-learning/visualizing-datasets.md): Visualizing and inspecting your datasets.
- [Step 6: Using Activeloop Storage](https://docs-v3.activeloop.ai/v3.6.2/getting-started/deep-learning/using-activeloop-storage.md): Storing and loading datasets from Deep Lake Storage.
- [Step 7: Connecting Deep Lake Datasets to ML Frameworks](https://docs-v3.activeloop.ai/v3.6.2/getting-started/deep-learning/connecting-to-ml-frameworks.md): Connecting Deep Lake Datasets to machine learning frameworks such as PyTorch and TensorFlow.
- [Step 8: Parallel Computing](https://docs-v3.activeloop.ai/v3.6.2/getting-started/deep-learning/parallel-computing.md): Running computations and processing data in parallel.
- [Step 9: Dataset Version Control](https://docs-v3.activeloop.ai/v3.6.2/getting-started/deep-learning/dataset-version-control.md): Managing changes to your datasets using Version Control.
- [Step 10: Dataset Filtering](https://docs-v3.activeloop.ai/v3.6.2/getting-started/deep-learning/dataset-filtering.md): Filtering datasets using user-defined-functions or SQL-style queries.
- [Tutorials (w Colab)](https://docs-v3.activeloop.ai/v3.6.2/tutorials.md): Common workflows with Deep Lake (includes Colab notebooks)
- [Vector Store Tutorials](https://docs-v3.activeloop.ai/v3.6.2/tutorials/vector-store.md): Tutorials for using Deep Lake in Vector Store applications
- [Vector Search Options](https://docs-v3.activeloop.ai/v3.6.2/tutorials/vector-store/vector-search-options.md): Overview of Vector Search Options in Deep Lake
- [Deep Lake Vector Store API](https://docs-v3.activeloop.ai/v3.6.2/tutorials/vector-store/vector-search-options/deep-lake-vector-store-api.md): Running Vector Search in the Deep Lake Vector Store module.
- [REST API](https://docs-v3.activeloop.ai/v3.6.2/tutorials/vector-store/vector-search-options/rest-api.md): Running Vector Search in the Deep Lake Tensor Database using the REST API
- [LangChain API](https://docs-v3.activeloop.ai/v3.6.2/tutorials/vector-store/vector-search-options/langchain-api.md)
- [Deep Lake Vector Store in LangChain](https://docs-v3.activeloop.ai/v3.6.2/tutorials/vector-store/deep-lake-vector-store-in-langchain.md): Using Deep Lake as a Vector Store in LangChain
- [Deep Learning Tutorials](https://docs-v3.activeloop.ai/v3.6.2/tutorials/deep-learning.md): Tutorials for using Deep Lake in deep-learning applications.
- [Creating Datasets](https://docs-v3.activeloop.ai/v3.6.2/tutorials/deep-learning/creating-datasets.md): Workflows for creating Deep Lake datasets
- [Creating Complex Datasets](https://docs-v3.activeloop.ai/v3.6.2/tutorials/deep-learning/creating-datasets/creating-complex-datasets.md): Converting a multi-annotation dataset to Deep Lake format is helpful for understanding how to use Deep Lake with rich data.
- [Creating Object Detection Datasets](https://docs-v3.activeloop.ai/v3.6.2/tutorials/deep-learning/creating-datasets/creating-object-detection-datasets.md): Converting an object detection dataset to Deep Lake format is a great way to get started with datasets of increasing complexity.
- [Creating Time-Series Datasets](https://docs-v3.activeloop.ai/v3.6.2/tutorials/deep-learning/creating-datasets/creating-time-series-datasets.md): Deep Lake is a powerful tool for easily storing and sharing time-series datasets with your team.
- [Creating Datasets with Sequences](https://docs-v3.activeloop.ai/v3.6.2/tutorials/deep-learning/creating-datasets/creating-datasets-with-sequences.md): Deep Lake sequences are a powerful tool for storing temporal annotations such as bounding boxes in each frame of a video.
- [Creating Video Datasets](https://docs-v3.activeloop.ai/v3.6.2/tutorials/deep-learning/creating-datasets/creating-video-datasets.md): Get started with video datasets using Deep Lake.
- [Training Models](https://docs-v3.activeloop.ai/v3.6.2/tutorials/deep-learning/training-models.md): Workflows for training models using Deep Lake datasets
- [Training an Image Classification Model in PyTorch](https://docs-v3.activeloop.ai/v3.6.2/tutorials/deep-learning/training-models/training-an-image-classification-model-in-pytorch.md): Training an image classification model is a great way to get started with model training using Deep Lake datasets.
- [Training Models Using MMDetection](https://docs-v3.activeloop.ai/v3.6.2/tutorials/deep-learning/training-models/training-models-using-mmdetection.md): How to Train Deep Learning models using Deep Lake's integration with MMDetection
- [Training Models Using PyTorch Lightning](https://docs-v3.activeloop.ai/v3.6.2/tutorials/deep-learning/training-models/training-models-using-pytorch-lightning.md): How to Train models using Deep Lake and PyTorch Lightning
- [Training on AWS SageMaker](https://docs-v3.activeloop.ai/v3.6.2/tutorials/deep-learning/training-models/training-on-aws-sagemaker.md): How to Train models on AWS SageMaker using Deep Lake datasets
- [Training an Object Detection and Segmentation Model in PyTorch](https://docs-v3.activeloop.ai/v3.6.2/tutorials/deep-learning/training-models/training-an-object-detection-and-segmentation-model-in-pytorch.md): Training an object detection and segmentation model is a great way to learn about complex data preprocessing for training models.
- [Updating Datasets](https://docs-v3.activeloop.ai/v3.6.2/tutorials/deep-learning/updating-datasets.md): Updating Deep Lake datasets
- [Data Processing Using Parallel Computing](https://docs-v3.activeloop.ai/v3.6.2/tutorials/deep-learning/data-processing-using-parallel-computing.md): Deeplake offers built-in methods for parallelizing dataset computations in order to achieve faster data processing.
- [Playbooks](https://docs-v3.activeloop.ai/v3.6.2/playbooks.md): How to perform complex workflows using Deep Lake.
- [Querying, Training and Editing Datasets with Data Lineage](https://docs-v3.activeloop.ai/v3.6.2/playbooks/training-with-lineage.md): How to use queries and version control while training models.
- [Evaluating Model Performance](https://docs-v3.activeloop.ai/v3.6.2/playbooks/evaluating-model-performance.md): How to compare ground-truth annotations with model predictions
- [Training Reproducibility Using Deep Lake and Weights & Biases](https://docs-v3.activeloop.ai/v3.6.2/playbooks/training-reproducibility-with-wandb.md): How to achieve full reproducibility of model training using Deep Lake and W\&B
- [Working with Videos](https://docs-v3.activeloop.ai/v3.6.2/playbooks/working-with-videos.md): How manage video datasets and train models using Deep Lake.
- [API Summary](https://docs-v3.activeloop.ai/v3.6.2/api-basics.md): Summary of the most important Deep Lake commands.
- [Best Practices](https://docs-v3.activeloop.ai/v3.6.2/how-it-works/best-practices.md): How to use Deep Lake at scale with best practices.
- [Creating Datasets at Scale](https://docs-v3.activeloop.ai/v3.6.2/how-it-works/best-practices/creating-datasets-at-scale.md): Creating large Deep Lake datasets with high performance and reliability
- [Training Models at Scale](https://docs-v3.activeloop.ai/v3.6.2/how-it-works/best-practices/training-models-at-scale.md): Train models at scale using Deep Lake
- [Storage Synchronization and "with" Context](https://docs-v3.activeloop.ai/v3.6.2/how-it-works/best-practices/storage-synchronization.md): Synchronizing data with long-term storage and achieving optimal performance using Deep Lake.
- [Restoring Corrupted Datasets](https://docs-v3.activeloop.ai/v3.6.2/how-it-works/best-practices/restoring-corrupted-datasets.md): Restoring Deep Lake datasets that may be corrupted.
- [Data Layout](https://docs-v3.activeloop.ai/v3.6.2/how-it-works/data-layout.md): Understanding the data layout in Deep Lake
- [Version Control and Querying](https://docs-v3.activeloop.ai/v3.6.2/how-it-works/version-control-and-querying.md): Understanding Deep Lake's Version control and Querying Layout
- [Dataset Visualization](https://docs-v3.activeloop.ai/v3.6.2/how-it-works/dataset-visualization.md): How to visualize Deep Lake datasets
- [Tensor Relationships](https://docs-v3.activeloop.ai/v3.6.2/how-it-works/tensor-relationships.md): Understanding the correct data layout for successful visualization.
- [Visualizer Integration](https://docs-v3.activeloop.ai/v3.6.2/how-it-works/visualizer-integration.md): How to embed our visualizer in your application.
- [Shuffling in dataloaders](https://docs-v3.activeloop.ai/v3.6.2/how-it-works/shuffling-in-dataloaders.md): Understanding data shuffling in Deep Lake's pytorch dataloader
- [How to Contribute](https://docs-v3.activeloop.ai/v3.6.2/how-it-works/how-to-contribute.md): Guidelines for open source enthusiasts to contribute to our open-source data format.

## v3.6.1

- [Deep Lake Docs](https://docs-v3.activeloop.ai/v3.6.1/master.md): We hope you enjoy Docs for Deep Lake.
- [Vector Store Quickstart](https://docs-v3.activeloop.ai/v3.6.1/quickstart.md): A jump-start guide to using Deep Lake for Vector Search.
- [Deep Learning Quickstart](https://docs-v3.activeloop.ai/v3.6.1/quickstart-dl.md): A jump-start guide to using Deep Lake for Deep Learning.
- [Storage & Credentials](https://docs-v3.activeloop.ai/v3.6.1/storage-and-credentials.md): How to access datasets in other clouds and manage their credentials.
- [Storage Options](https://docs-v3.activeloop.ai/v3.6.1/storage-and-credentials/storage-options.md): How to authenticate using Activeloop storage, AWS S3, and Google Cloud Storage.
- [User Authentication](https://docs-v3.activeloop.ai/v3.6.1/storage-and-credentials/user-authentication.md): Registration and authentication in Deep Lake.
- [Storing Deep Lake Data in Your Own Cloud](https://docs-v3.activeloop.ai/v3.6.1/storage-and-credentials/managed-credentials.md): How to store Deep Lake data in your own cloud and manage credentials with Deep Lake
- [Microsoft Azure](https://docs-v3.activeloop.ai/v3.6.1/storage-and-credentials/managed-credentials/microsoft-azure.md): Azure-specific information for connecting data to Deep Lake
- [Provisioning Federated Credentials](https://docs-v3.activeloop.ai/v3.6.1/storage-and-credentials/managed-credentials/microsoft-azure/provisioning-federated-credentials.md): How to setup Federated Credentials in Azure
- [Amazon Web Services](https://docs-v3.activeloop.ai/v3.6.1/storage-and-credentials/managed-credentials/amazon-web-services.md): AWS-specific information for connecting data to Deep Lake
- [Provisioning Role-Based Access](https://docs-v3.activeloop.ai/v3.6.1/storage-and-credentials/managed-credentials/amazon-web-services/provisioning-role-based-access.md): How to provision Role-Based Access in S3
- [Enabling CORS](https://docs-v3.activeloop.ai/v3.6.1/storage-and-credentials/managed-credentials/amazon-web-services/enabling-cors.md): How to enable Cross-Origin Resource Sharing in your cloud buckets.
- [Compute Engine](https://docs-v3.activeloop.ai/v3.6.1/enterprise-features/compute-engine.md): C++ part of the Deep Lake codebase optimized for faster data fetching and computations
- [Tensor Query Language (TQL)](https://docs-v3.activeloop.ai/v3.6.1/enterprise-features/compute-engine/querying-datasets.md): Deep Lake offers a highly-performant SQL-style query engine for filtering your data.
- [TQL Syntax](https://docs-v3.activeloop.ai/v3.6.1/enterprise-features/compute-engine/querying-datasets/query-syntax.md): How to properly format TQL queries
- [Sampling Datasets](https://docs-v3.activeloop.ai/v3.6.1/enterprise-features/compute-engine/querying-datasets/sampling-datasets.md)
- [Performant Dataloader](https://docs-v3.activeloop.ai/v3.6.1/enterprise-features/compute-engine/performant-dataloader.md): How to use Deep Lake's new dataloader built and optimized in C++
- [Tensor Database](https://docs-v3.activeloop.ai/v3.6.1/enterprise-features/managed-database.md): Deep Lake Managed Database
- [REST API](https://docs-v3.activeloop.ai/v3.6.1/enterprise-features/managed-database/rest-api.md): How to Use the Deep Lake REST API
- [Migrating Datasets to the Tensor Database](https://docs-v3.activeloop.ai/v3.6.1/enterprise-features/managed-database/migrating-datasets-to-the-tensor-database.md): Migrating datasets to the Tensor Database
- [Getting Started](https://docs-v3.activeloop.ai/v3.6.1/getting-started.md): Comprehensive guides for getting started with Deep Lake
- [Vector Store](https://docs-v3.activeloop.ai/v3.6.1/getting-started/vector-store.md): The comprehensive guide for Deep Lake in Vector Storage and Search applications.
- [Step 1: Hello World](https://docs-v3.activeloop.ai/v3.6.1/getting-started/vector-store/step-1-hello-world.md): Installing Deep Lake
- [Step 2: Creating Deep Lake Vector Stores](https://docs-v3.activeloop.ai/v3.6.1/getting-started/vector-store/step-2-creating-deep-lake-vector-stores.md)
- [Step 3: Performing Search in the Vector Store](https://docs-v3.activeloop.ai/v3.6.1/getting-started/vector-store/step-3-performing-search-in-the-vector-store.md)
- [Step 4: Customizing Vector Stores](https://docs-v3.activeloop.ai/v3.6.1/getting-started/vector-store/step-4-customizing-vector-stores.md)
- [Deep Learning](https://docs-v3.activeloop.ai/v3.6.1/getting-started/deep-learning.md): The comprehensive guide for Deep Lake in Deep Learning applications.
- [Step 1: Hello World](https://docs-v3.activeloop.ai/v3.6.1/getting-started/deep-learning/hello-world.md): Installing Deep Lake and accessing your first Deep Lake Dataset.
- [Step 2: Creating Deep Lake Datasets](https://docs-v3.activeloop.ai/v3.6.1/getting-started/deep-learning/creating-datasets-manually.md): Creating and storing Deep Lake Datasets.
- [Step 3: Understanding Compression](https://docs-v3.activeloop.ai/v3.6.1/getting-started/deep-learning/understanding-compression.md): Using compression to achieve optimal performance in Deep Lake.
- [Step 4: Accessing and Updating Data](https://docs-v3.activeloop.ai/v3.6.1/getting-started/deep-learning/accessing-datasets.md): Learn how Deep Lake Datasets can be accessed or loaded from a variety of storage locations.
- [Step 5: Visualizing Datasets](https://docs-v3.activeloop.ai/v3.6.1/getting-started/deep-learning/visualizing-datasets.md): Visualizing and inspecting your datasets.
- [Step 6: Using Activeloop Storage](https://docs-v3.activeloop.ai/v3.6.1/getting-started/deep-learning/using-activeloop-storage.md): Storing and loading datasets from Deep Lake Storage.
- [Step 7: Connecting Deep Lake Datasets to ML Frameworks](https://docs-v3.activeloop.ai/v3.6.1/getting-started/deep-learning/connecting-to-ml-frameworks.md): Connecting Deep Lake Datasets to machine learning frameworks such as PyTorch and TensorFlow.
- [Step 8: Parallel Computing](https://docs-v3.activeloop.ai/v3.6.1/getting-started/deep-learning/parallel-computing.md): Running computations and processing data in parallel.
- [Step 9: Dataset Version Control](https://docs-v3.activeloop.ai/v3.6.1/getting-started/deep-learning/dataset-version-control.md): Managing changes to your datasets using Version Control.
- [Step 10: Dataset Filtering](https://docs-v3.activeloop.ai/v3.6.1/getting-started/deep-learning/dataset-filtering.md): Filtering datasets using user-defined-functions or SQL-style queries.
- [Tutorials (w Colab)](https://docs-v3.activeloop.ai/v3.6.1/tutorials.md): Common workflows with Deep Lake (includes Colab notebooks)
- [Vector Store Tutorials](https://docs-v3.activeloop.ai/v3.6.1/tutorials/vector-store.md): Using Deep Lake as a Vector Store for LLM applications
- [Vector Search Options](https://docs-v3.activeloop.ai/v3.6.1/tutorials/vector-store/vector-search-options.md): Overview of Vector Search Options in Deep Lake
- [Deep Lake Vector Store API](https://docs-v3.activeloop.ai/v3.6.1/tutorials/vector-store/vector-search-options/deep-lake-vector-store-api.md): Running Vector Search in the Deep Lake Vector Store module.
- [REST API](https://docs-v3.activeloop.ai/v3.6.1/tutorials/vector-store/vector-search-options/rest-api.md): Running Vector Search in the Deep Lake Tensor Database using the REST API
- [LangChain API](https://docs-v3.activeloop.ai/v3.6.1/tutorials/vector-store/vector-search-options/langchain-api.md)
- [Deep Lake Vector Store in LangChain](https://docs-v3.activeloop.ai/v3.6.1/tutorials/vector-store/deep-lake-vector-store-in-langchain.md): Using Deep Lake as a Vector Store in LangChain
- [Creating Datasets](https://docs-v3.activeloop.ai/v3.6.1/tutorials/creating-datasets.md): Workflows for creating Hub datasets (includes Colab notebooks)
- [Creating Complex Datasets](https://docs-v3.activeloop.ai/v3.6.1/tutorials/creating-datasets/creating-complex-datasets.md): Converting a multi-annotation dataset to Deep Lake format is helpful for understanding how to use Deep Lake with rich data.
- [Creating Object Detection Datasets](https://docs-v3.activeloop.ai/v3.6.1/tutorials/creating-datasets/creating-object-detection-datasets.md): Converting an object detection dataset to Deep Lake format is a great way to get started with datasets of increasing complexity.
- [Creating Time-Series Datasets](https://docs-v3.activeloop.ai/v3.6.1/tutorials/creating-datasets/creating-time-series-datasets.md): Deep Lake is a powerful tool for easily storing and sharing time-series datasets with your team.
- [Creating Datasets with Sequences](https://docs-v3.activeloop.ai/v3.6.1/tutorials/creating-datasets/creating-datasets-with-sequences.md): Deep Lake sequences are a powerful tool for storing temporal annotations such as bounding boxes in each frame of a video.
- [Creating Video Datasets](https://docs-v3.activeloop.ai/v3.6.1/tutorials/creating-datasets/creating-video-datasets.md): Get started with video datasets using Deep Lake.
- [Training Models](https://docs-v3.activeloop.ai/v3.6.1/tutorials/training-models.md): Workflows for training models using Deep Lake datasets (includes Colab notebooks)
- [Training an Image Classification Model in PyTorch](https://docs-v3.activeloop.ai/v3.6.1/tutorials/training-models/training-an-image-classification-model-in-pytorch.md): Training an image classification model is a great way to get started with model training using Deep Lake datasets.
- [Training Models Using MMDetection](https://docs-v3.activeloop.ai/v3.6.1/tutorials/training-models/training-models-using-mmdetection.md): How to Train Deep Learning models using Deep Lake's integration with MMDetection
- [Training Models Using PyTorch Lightning](https://docs-v3.activeloop.ai/v3.6.1/tutorials/training-models/training-models-using-pytorch-lightning.md): How to Train models using Deep Lake and PyTorch Lightning
- [Training on AWS SageMaker](https://docs-v3.activeloop.ai/v3.6.1/tutorials/training-models/training-on-aws-sagemaker.md): How to Train models on AWS SageMaker using Deep Lake datasets
- [Training an Object Detection and Segmentation Model in PyTorch](https://docs-v3.activeloop.ai/v3.6.1/tutorials/training-models/training-an-object-detection-and-segmentation-model-in-pytorch.md): Training an object detection and segmentation model is a great way to learn about complex data preprocessing for training models.
- [Updating Datasets](https://docs-v3.activeloop.ai/v3.6.1/tutorials/updating-datasets.md): Updating Deep Lake datasets
- [Data Processing Using Parallel Computing](https://docs-v3.activeloop.ai/v3.6.1/tutorials/data-processing-using-parallel-computing.md): Deeplake offers built-in methods for parallelizing dataset computations in order to achieve faster data processing.
- [Playbooks](https://docs-v3.activeloop.ai/v3.6.1/playbooks.md): How to perform complex workflows using Deep Lake.
- [Querying, Training and Editing Datasets with Data Lineage](https://docs-v3.activeloop.ai/v3.6.1/playbooks/training-with-lineage.md): How to use queries and version control while training models.
- [Evaluating Model Performance](https://docs-v3.activeloop.ai/v3.6.1/playbooks/evaluating-model-performance.md): How to compare ground-truth annotations with model predictions
- [Training Reproducibility Using Deep Lake and Weights & Biases](https://docs-v3.activeloop.ai/v3.6.1/playbooks/training-reproducibility-with-wandb.md): How to achieve full reproducibility of model training using Deep Lake and W\&B
- [Working with Videos](https://docs-v3.activeloop.ai/v3.6.1/playbooks/working-with-videos.md): How manage video datasets and train models using Deep Lake.
- [API Summary](https://docs-v3.activeloop.ai/v3.6.1/api-basics.md): Summary of the most important Deep Lake commands.
- [Best Practices](https://docs-v3.activeloop.ai/v3.6.1/how-it-works/best-practices.md): How to use Deep Lake at scale with best practices.
- [Creating Datasets at Scale](https://docs-v3.activeloop.ai/v3.6.1/how-it-works/best-practices/creating-datasets-at-scale.md): Creating large Deep Lake datasets with high performance and reliability
- [Training Models at Scale](https://docs-v3.activeloop.ai/v3.6.1/how-it-works/best-practices/training-models-at-scale.md): Train models at scale using Deep Lake
- [Storage Synchronization and "with" Context](https://docs-v3.activeloop.ai/v3.6.1/how-it-works/best-practices/storage-synchronization.md): Synchronizing data with long-term storage and achieving optimal performance using Deep Lake.
- [Restoring Corrupted Datasets](https://docs-v3.activeloop.ai/v3.6.1/how-it-works/best-practices/restoring-corrupted-datasets.md): Restoring Deep Lake datasets that may be corrupted.
- [Data Layout](https://docs-v3.activeloop.ai/v3.6.1/how-it-works/data-layout.md): Understanding the data layout in Deep Lake
- [Version Control and Querying](https://docs-v3.activeloop.ai/v3.6.1/how-it-works/version-control-and-querying.md): Understanding Deep Lake's Version control and Querying Layout
- [Dataset Visualization](https://docs-v3.activeloop.ai/v3.6.1/how-it-works/dataset-visualization.md): How to visualize Deep Lake datasets
- [Tensor Relationships](https://docs-v3.activeloop.ai/v3.6.1/how-it-works/tensor-relationships.md): Understanding the correct data layout for successful visualization.
- [Visualizer Integration](https://docs-v3.activeloop.ai/v3.6.1/how-it-works/visualizer-integration.md): How to embed our visualizer in your application.
- [Shuffling in dataloaders](https://docs-v3.activeloop.ai/v3.6.1/how-it-works/shuffling-in-dataloaders.md): Understanding data shuffling in Deep Lake's pytorch dataloader
- [How to Contribute](https://docs-v3.activeloop.ai/v3.6.1/how-it-works/how-to-contribute.md): Guidelines for open source enthusiasts to contribute to our open-source data format.

## v3.6.0

- [Deep Lake Docs](https://docs-v3.activeloop.ai/v3.6.0/master.md): We hope you enjoy Docs for Deep Lake.
- [Vector Store Quickstart](https://docs-v3.activeloop.ai/v3.6.0/quickstart.md): A jump-start guide to using Deep Lake for Vector Search.
- [Deep Learning Quickstart](https://docs-v3.activeloop.ai/v3.6.0/quickstart-dl.md): A jump-start guide to using Deep Lake for Deep Learning.
- [Storage & Credentials](https://docs-v3.activeloop.ai/v3.6.0/storage-and-credentials.md): How to access datasets in other clouds and manage their credentials.
- [Storage Options](https://docs-v3.activeloop.ai/v3.6.0/storage-and-credentials/storage-options.md): How to authenticate using Activeloop storage, AWS S3, and Google Cloud Storage.
- [User Authentication](https://docs-v3.activeloop.ai/v3.6.0/storage-and-credentials/user-authentication.md): Registration and authentication in Deep Lake.
- [Storing Deep Lake Data in Your Own Cloud](https://docs-v3.activeloop.ai/v3.6.0/storage-and-credentials/managed-credentials.md): How to store Deep Lake data in your own cloud and manage credentials with Deep Lake
- [Microsoft Azure](https://docs-v3.activeloop.ai/v3.6.0/storage-and-credentials/managed-credentials/microsoft-azure.md): Azure-specific information for connecting data to Deep Lake
- [Provisioning Federated Credentials](https://docs-v3.activeloop.ai/v3.6.0/storage-and-credentials/managed-credentials/microsoft-azure/provisioning-federated-credentials.md): How to setup Federated Credentials in Azure
- [Amazon Web Services](https://docs-v3.activeloop.ai/v3.6.0/storage-and-credentials/managed-credentials/amazon-web-services.md): AWS-specific information for connecting data to Deep Lake
- [Provisioning Role-Based Access](https://docs-v3.activeloop.ai/v3.6.0/storage-and-credentials/managed-credentials/amazon-web-services/provisioning-role-based-access.md): How to provision Role-Based Access in S3
- [Enabling CORS](https://docs-v3.activeloop.ai/v3.6.0/storage-and-credentials/managed-credentials/amazon-web-services/enabling-cors.md): How to enable Cross-Origin Resource Sharing in your cloud buckets.
- [Compute Engine](https://docs-v3.activeloop.ai/v3.6.0/enterprise-features/compute-engine.md): C++ part of the Deep Lake codebase optimized for faster data fetching and computations
- [Tensor Query Language (TQL)](https://docs-v3.activeloop.ai/v3.6.0/enterprise-features/compute-engine/querying-datasets.md): Deep Lake offers a highly-performant SQL-style query engine for filtering your data.
- [TQL Syntax](https://docs-v3.activeloop.ai/v3.6.0/enterprise-features/compute-engine/querying-datasets/query-syntax.md): How to properly format TQL queries
- [Sampling Datasets](https://docs-v3.activeloop.ai/v3.6.0/enterprise-features/compute-engine/querying-datasets/sampling-datasets.md)
- [Performant Dataloader](https://docs-v3.activeloop.ai/v3.6.0/enterprise-features/compute-engine/performant-dataloader.md): How to use Deep Lake's new dataloader built and optimized in C++
- [Tensor Database](https://docs-v3.activeloop.ai/v3.6.0/enterprise-features/managed-database.md): Deep Lake Managed Database
- [REST API](https://docs-v3.activeloop.ai/v3.6.0/enterprise-features/managed-database/rest-api.md): How to Use the Deep Lake REST API
- [Migrating Datasets to the Tensor Database](https://docs-v3.activeloop.ai/v3.6.0/enterprise-features/managed-database/migrating-datasets-to-the-tensor-database.md): Migrating datasets to the Tensor Database
- [Getting Started](https://docs-v3.activeloop.ai/v3.6.0/getting-started.md): Comprehensive guides for getting started with Deep Lake
- [Vector Store](https://docs-v3.activeloop.ai/v3.6.0/getting-started/vector-store.md): The comprehensive guide for Deep Lake in Vector Storage and Search applications.
- [Step 1: Hello World](https://docs-v3.activeloop.ai/v3.6.0/getting-started/vector-store/step-1-hello-world.md): Installing Deep Lake
- [Step 2: Creating Deep Lake Vector Stores](https://docs-v3.activeloop.ai/v3.6.0/getting-started/vector-store/step-2-creating-deep-lake-vector-stores.md)
- [Step 3: Performing Search in the Vector Store](https://docs-v3.activeloop.ai/v3.6.0/getting-started/vector-store/step-3-performing-search-in-the-vector-store.md)
- [Step 4: Customizing Vector Stores](https://docs-v3.activeloop.ai/v3.6.0/getting-started/vector-store/step-4-customizing-vector-stores.md)
- [Deep Learning](https://docs-v3.activeloop.ai/v3.6.0/getting-started/deep-learning.md): The comprehensive guide for Deep Lake in Deep Learning applications.
- [Step 1: Hello World](https://docs-v3.activeloop.ai/v3.6.0/getting-started/deep-learning/hello-world.md): Installing Deep Lake and accessing your first Deep Lake Dataset.
- [Step 2: Creating Deep Lake Datasets](https://docs-v3.activeloop.ai/v3.6.0/getting-started/deep-learning/creating-datasets-manually.md): Creating and storing Deep Lake Datasets.
- [Step 3: Understanding Compression](https://docs-v3.activeloop.ai/v3.6.0/getting-started/deep-learning/understanding-compression.md): Using compression to achieve optimal performance in Deep Lake.
- [Step 4: Accessing and Updating Data](https://docs-v3.activeloop.ai/v3.6.0/getting-started/deep-learning/accessing-datasets.md): Learn how Deep Lake Datasets can be accessed or loaded from a variety of storage locations.
- [Step 5: Visualizing Datasets](https://docs-v3.activeloop.ai/v3.6.0/getting-started/deep-learning/visualizing-datasets.md): Visualizing and inspecting your datasets.
- [Step 6: Using Activeloop Storage](https://docs-v3.activeloop.ai/v3.6.0/getting-started/deep-learning/using-activeloop-storage.md): Storing and loading datasets from Deep Lake Storage.
- [Step 7: Connecting Deep Lake Datasets to ML Frameworks](https://docs-v3.activeloop.ai/v3.6.0/getting-started/deep-learning/connecting-to-ml-frameworks.md): Connecting Deep Lake Datasets to machine learning frameworks such as PyTorch and TensorFlow.
- [Step 8: Parallel Computing](https://docs-v3.activeloop.ai/v3.6.0/getting-started/deep-learning/parallel-computing.md): Running computations and processing data in parallel.
- [Step 9: Dataset Version Control](https://docs-v3.activeloop.ai/v3.6.0/getting-started/deep-learning/dataset-version-control.md): Managing changes to your datasets using Version Control.
- [Step 10: Dataset Filtering](https://docs-v3.activeloop.ai/v3.6.0/getting-started/deep-learning/dataset-filtering.md): Filtering datasets using user-defined-functions or SQL-style queries.
- [Tutorials (w Colab)](https://docs-v3.activeloop.ai/v3.6.0/tutorials.md): Common workflows with Deep Lake (includes Colab notebooks)
- [Vector Store Tutorials](https://docs-v3.activeloop.ai/v3.6.0/tutorials/vector-store.md): Using Deep Lake as a Vector Store for LLM applications
- [Vector Search Options](https://docs-v3.activeloop.ai/v3.6.0/tutorials/vector-store/vector-search-options.md): Overview of Vector Search Options in Deep Lake
- [Deep Lake Vector Store API](https://docs-v3.activeloop.ai/v3.6.0/tutorials/vector-store/vector-search-options/deep-lake-vector-store-api.md): Running Vector Search in the Deep Lake Vector Store module.
- [REST API](https://docs-v3.activeloop.ai/v3.6.0/tutorials/vector-store/vector-search-options/rest-api.md): Running Vector Search in the Deep Lake Tensor Database using the REST API
- [LangChain API](https://docs-v3.activeloop.ai/v3.6.0/tutorials/vector-store/vector-search-options/langchain-api.md)
- [Deep Lake Vector Store in LangChain](https://docs-v3.activeloop.ai/v3.6.0/tutorials/vector-store/deep-lake-vector-store-in-langchain.md): Using Deep Lake as a Vector Store in LangChain
- [Creating Datasets](https://docs-v3.activeloop.ai/v3.6.0/tutorials/creating-datasets.md): Workflows for creating Hub datasets (includes Colab notebooks)
- [Creating Complex Datasets](https://docs-v3.activeloop.ai/v3.6.0/tutorials/creating-datasets/creating-complex-datasets.md): Converting a multi-annotation dataset to Deep Lake format is helpful for understanding how to use Deep Lake with rich data.
- [Creating Object Detection Datasets](https://docs-v3.activeloop.ai/v3.6.0/tutorials/creating-datasets/creating-object-detection-datasets.md): Converting an object detection dataset to Deep Lake format is a great way to get started with datasets of increasing complexity.
- [Creating Time-Series Datasets](https://docs-v3.activeloop.ai/v3.6.0/tutorials/creating-datasets/creating-time-series-datasets.md): Deep Lake is a powerful tool for easily storing and sharing time-series datasets with your team.
- [Creating Datasets with Sequences](https://docs-v3.activeloop.ai/v3.6.0/tutorials/creating-datasets/creating-datasets-with-sequences.md): Deep Lake sequences are a powerful tool for storing temporal annotations such as bounding boxes in each frame of a video.
- [Creating Video Datasets](https://docs-v3.activeloop.ai/v3.6.0/tutorials/creating-datasets/creating-video-datasets.md): Get started with video datasets using Deep Lake.
- [Training Models](https://docs-v3.activeloop.ai/v3.6.0/tutorials/training-models.md): Workflows for training models using Deep Lake datasets (includes Colab notebooks)
- [Training an Image Classification Model in PyTorch](https://docs-v3.activeloop.ai/v3.6.0/tutorials/training-models/training-an-image-classification-model-in-pytorch.md): Training an image classification model is a great way to get started with model training using Deep Lake datasets.
- [Training Models Using MMDetection](https://docs-v3.activeloop.ai/v3.6.0/tutorials/training-models/training-models-using-mmdetection.md): How to Train Deep Learning models using Deep Lake's integration with MMDetection
- [Training Models Using PyTorch Lightning](https://docs-v3.activeloop.ai/v3.6.0/tutorials/training-models/training-models-using-pytorch-lightning.md): How to Train models using Deep Lake and PyTorch Lightning
- [Training on AWS SageMaker](https://docs-v3.activeloop.ai/v3.6.0/tutorials/training-models/training-on-aws-sagemaker.md): How to Train models on AWS SageMaker using Deep Lake datasets
- [Training an Object Detection and Segmentation Model in PyTorch](https://docs-v3.activeloop.ai/v3.6.0/tutorials/training-models/training-an-object-detection-and-segmentation-model-in-pytorch.md): Training an object detection and segmentation model is a great way to learn about complex data preprocessing for training models.
- [Updating Datasets](https://docs-v3.activeloop.ai/v3.6.0/tutorials/updating-datasets.md): Updating Deep Lake datasets
- [Data Processing Using Parallel Computing](https://docs-v3.activeloop.ai/v3.6.0/tutorials/data-processing-using-parallel-computing.md): Deeplake offers built-in methods for parallelizing dataset computations in order to achieve faster data processing.
- [Playbooks](https://docs-v3.activeloop.ai/v3.6.0/playbooks.md): How to perform complex workflows using Deep Lake.
- [Querying, Training and Editing Datasets with Data Lineage](https://docs-v3.activeloop.ai/v3.6.0/playbooks/training-with-lineage.md): How to use queries and version control while training models.
- [Evaluating Model Performance](https://docs-v3.activeloop.ai/v3.6.0/playbooks/evaluating-model-performance.md): How to compare ground-truth annotations with model predictions
- [Training Reproducibility Using Deep Lake and Weights & Biases](https://docs-v3.activeloop.ai/v3.6.0/playbooks/training-reproducibility-with-wandb.md): How to achieve full reproducibility of model training using Deep Lake and W\&B
- [Working with Videos](https://docs-v3.activeloop.ai/v3.6.0/playbooks/working-with-videos.md): How manage video datasets and train models using Deep Lake.
- [API Summary](https://docs-v3.activeloop.ai/v3.6.0/api-basics.md): Summary of the most important Deep Lake commands.
- [Best Practices](https://docs-v3.activeloop.ai/v3.6.0/how-it-works/best-practices.md): How to use Deep Lake at scale with best practices.
- [Creating Datasets at Scale](https://docs-v3.activeloop.ai/v3.6.0/how-it-works/best-practices/creating-datasets-at-scale.md): Creating large Deep Lake datasets with high performance and reliability
- [Training Models at Scale](https://docs-v3.activeloop.ai/v3.6.0/how-it-works/best-practices/training-models-at-scale.md): Train models at scale using Deep Lake
- [Storage Synchronization and "with" Context](https://docs-v3.activeloop.ai/v3.6.0/how-it-works/best-practices/storage-synchronization.md): Synchronizing data with long-term storage and achieving optimal performance using Deep Lake.
- [Restoring Corrupted Datasets](https://docs-v3.activeloop.ai/v3.6.0/how-it-works/best-practices/restoring-corrupted-datasets.md): Restoring Deep Lake datasets that may be corrupted.
- [Data Layout](https://docs-v3.activeloop.ai/v3.6.0/how-it-works/data-layout.md): Understanding the data layout in Deep Lake
- [Version Control and Querying](https://docs-v3.activeloop.ai/v3.6.0/how-it-works/version-control-and-querying.md): Understanding Deep Lake's Version control and Querying Layout
- [Dataset Visualization](https://docs-v3.activeloop.ai/v3.6.0/how-it-works/dataset-visualization.md): How to visualize Deep Lake datasets
- [Tensor Relationships](https://docs-v3.activeloop.ai/v3.6.0/how-it-works/tensor-relationships.md): Understanding the correct data layout for successful visualization.
- [Visualizer Integration](https://docs-v3.activeloop.ai/v3.6.0/how-it-works/visualizer-integration.md): How to embed our visualizer in your application.
- [Shuffling in dataloaders](https://docs-v3.activeloop.ai/v3.6.0/how-it-works/shuffling-in-dataloaders.md): Understanding data shuffling in Deep Lake's pytorch dataloader
- [How to Contribute](https://docs-v3.activeloop.ai/v3.6.0/how-it-works/how-to-contribute.md): Guidelines for open source enthusiasts to contribute to our open-source data format.

## v3.5.0

- [Deep Lake Docs](https://docs-v3.activeloop.ai/v3.4.1/master.md): We hope you enjoy Docs for Deep Lake.
- [Vector Store Quickstart](https://docs-v3.activeloop.ai/v3.4.1/quickstart.md): A jump-start guide to using Deep Lake for Vector Search.
- [Deep Learning Quickstart](https://docs-v3.activeloop.ai/v3.4.1/quickstart-dl.md): A jump-start guide to using Deep Lake for Deep Learning.
- [Storage & Credentials](https://docs-v3.activeloop.ai/v3.4.1/storage-and-credentials.md): How to access datasets in other clouds and manage their credentials.
- [Storage Options](https://docs-v3.activeloop.ai/v3.4.1/storage-and-credentials/storage-options.md): How to authenticate using Activeloop storage, AWS S3, and Google Cloud Storage.
- [User Authentication](https://docs-v3.activeloop.ai/v3.4.1/storage-and-credentials/user-authentication.md): Registration and authentication in Deep Lake.
- [Managed Credentials](https://docs-v3.activeloop.ai/v3.4.1/storage-and-credentials/managed-credentials.md): How to manage your credentials with Deep Lake
- [Enabling CORS](https://docs-v3.activeloop.ai/v3.4.1/storage-and-credentials/managed-credentials/enabling-cors.md): How to enable Cross-Origin Resource Sharing in your cloud buckets.
- [Provisioning Role-Based Access](https://docs-v3.activeloop.ai/v3.4.1/storage-and-credentials/managed-credentials/provisioning-role-based-access.md)
- [Compute Engine](https://docs-v3.activeloop.ai/v3.4.1/enterprise-features/compute-engine.md): C++ part of the Deep Lake codebase optimized for faster data fetching and computations
- [Querying Datasets](https://docs-v3.activeloop.ai/v3.4.1/enterprise-features/compute-engine/querying-datasets.md): Deep Lake offers a highly-performant SQL-style query engine for filtering your data.
- [Query Syntax](https://docs-v3.activeloop.ai/v3.4.1/enterprise-features/compute-engine/querying-datasets/query-syntax.md): How to properly format TQL queries
- [Sampling Datasets](https://docs-v3.activeloop.ai/v3.4.1/enterprise-features/compute-engine/querying-datasets/sampling-datasets.md)
- [Performant Dataloader](https://docs-v3.activeloop.ai/v3.4.1/enterprise-features/compute-engine/performant-dataloader.md): How to use Deep Lake's new dataloader built and optimized in C++
- [Tensor Database](https://docs-v3.activeloop.ai/v3.4.1/enterprise-features/managed-database.md): Deep Lake Managed Database
- [REST API](https://docs-v3.activeloop.ai/v3.4.1/enterprise-features/managed-database/rest-api.md): How to Use the Deep Lake REST API
- [Migrating Datasets to the Tensor Database](https://docs-v3.activeloop.ai/v3.4.1/enterprise-features/managed-database/migrating-datasets-to-the-tensor-database.md): Migrating datasets to the Tensor Database
- [Getting Started](https://docs-v3.activeloop.ai/v3.4.1/getting-started.md): Comprehensive guides for getting started with Deep Lake
- [Vector Store](https://docs-v3.activeloop.ai/v3.4.1/getting-started/vector-search.md): The comprehensive guide for learning Deep Lake for Deep Learning applications.
- [Deep Learning](https://docs-v3.activeloop.ai/v3.4.1/getting-started/deep-learning.md): The comprehensive guide for learning Deep Lake for Deep Learning applications.
- [Step 1: Hello World](https://docs-v3.activeloop.ai/v3.4.1/getting-started/deep-learning/hello-world.md): Installing Deep Lake and accessing your first Deep Lake Dataset.
- [Step 2: Creating Deep Lake Datasets](https://docs-v3.activeloop.ai/v3.4.1/getting-started/deep-learning/creating-datasets-manually.md): Creating and storing Deep Lake Datasets.
- [Step 3: Understanding Compression](https://docs-v3.activeloop.ai/v3.4.1/getting-started/deep-learning/understanding-compression.md): Using compression to achieve optimal performance in Deep Lake.
- [Step 4: Accessing and Updating Data](https://docs-v3.activeloop.ai/v3.4.1/getting-started/deep-learning/accessing-datasets.md): Learn how Deep Lake Datasets can be accessed or loaded from a variety of storage locations.
- [Step 5: Visualizing Datasets](https://docs-v3.activeloop.ai/v3.4.1/getting-started/deep-learning/visualizing-datasets.md): Visualizing and inspecting your datasets.
- [Step 6: Using Activeloop Storage](https://docs-v3.activeloop.ai/v3.4.1/getting-started/deep-learning/using-activeloop-storage.md): Storing and loading datasets from Deep Lake Storage.
- [Step 7: Connecting Deep Lake Datasets to ML Frameworks](https://docs-v3.activeloop.ai/v3.4.1/getting-started/deep-learning/connecting-to-ml-frameworks.md): Connecting Deep Lake Datasets to machine learning frameworks such as PyTorch and TensorFlow.
- [Step 8: Parallel Computing](https://docs-v3.activeloop.ai/v3.4.1/getting-started/deep-learning/parallel-computing.md): Running computations and processing data in parallel.
- [Step 9: Dataset Version Control](https://docs-v3.activeloop.ai/v3.4.1/getting-started/deep-learning/dataset-version-control.md): Managing changes to your datasets using Version Control.
- [Step 10: Dataset Filtering](https://docs-v3.activeloop.ai/v3.4.1/getting-started/deep-learning/dataset-filtering.md): Filtering datasets using user-defined-functions or SQL-style queries.
- [Tutorials (w Colab)](https://docs-v3.activeloop.ai/v3.4.1/tutorials.md): Common workflows with Deep Lake (includes Colab notebooks)
- [Vector Store Tutorials](https://docs-v3.activeloop.ai/v3.4.1/tutorials/vector-store.md): Using Deep Lake as a Vector Store for LLM applications
- [Deep Lake Vector Store in LangChain](https://docs-v3.activeloop.ai/v3.4.1/tutorials/vector-store/deep-lake-vector-store-in-langchain.md): Using Deep Lake as a Vector Store in LangChain
- [Vector Search Using the Deep Lake Tensor Database](https://docs-v3.activeloop.ai/v3.4.1/tutorials/vector-store/vector-search-using-the-deep-lake-managed-database.md): Running Vector Search in the Deep Lake Tensor Database
- [Creating Datasets](https://docs-v3.activeloop.ai/v3.4.1/tutorials/creating-datasets.md): Workflows for creating Hub datasets (includes Colab notebooks)
- [Creating Complex Datasets](https://docs-v3.activeloop.ai/v3.4.1/tutorials/creating-datasets/creating-complex-datasets.md): Converting a multi-annotation dataset to Deep Lake format is helpful for understanding how to use Deep Lake with rich data.
- [Creating Object Detection Datasets](https://docs-v3.activeloop.ai/v3.4.1/tutorials/creating-datasets/creating-object-detection-datasets.md): Converting an object detection dataset to Deep Lake format is a great way to get started with datasets of increasing complexity.
- [Creating Time-Series Datasets](https://docs-v3.activeloop.ai/v3.4.1/tutorials/creating-datasets/creating-time-series-datasets.md): Deep Lake is a powerful tool for easily storing and sharing time-series datasets with your team.
- [Creating Datasets with Sequences](https://docs-v3.activeloop.ai/v3.4.1/tutorials/creating-datasets/creating-datasets-with-sequences.md): Deep Lake sequences are a powerful tool for storing temporal annotations such as bounding boxes in each frame of a video.
- [Creating Video Datasets](https://docs-v3.activeloop.ai/v3.4.1/tutorials/creating-datasets/creating-video-datasets.md): Get started with video datasets using Deep Lake.
- [Training Models](https://docs-v3.activeloop.ai/v3.4.1/tutorials/training-models.md): Workflows for training models using Deep Lake datasets (includes Colab notebooks)
- [Training an Image Classification Model in PyTorch](https://docs-v3.activeloop.ai/v3.4.1/tutorials/training-models/training-an-image-classification-model-in-pytorch.md): Training an image classification model is a great way to get started with model training using Deep Lake datasets.
- [Training Models Using MMDetection](https://docs-v3.activeloop.ai/v3.4.1/tutorials/training-models/training-models-using-mmdetection.md): How to Train Deep Learning models using Deep Lake's integration with MMDetection
- [Training Models Using PyTorch Lightning](https://docs-v3.activeloop.ai/v3.4.1/tutorials/training-models/training-models-using-pytorch-lightning.md): How to Train models using Deep Lake and PyTorch Lightning
- [Training on AWS SageMaker](https://docs-v3.activeloop.ai/v3.4.1/tutorials/training-models/training-on-aws-sagemaker.md): How to Train models on AWS SageMaker using Deep Lake datasets
- [Training an Object Detection and Segmentation Model in PyTorch](https://docs-v3.activeloop.ai/v3.4.1/tutorials/training-models/training-an-object-detection-and-segmentation-model-in-pytorch.md): Training an object detection and segmentation model is a great way to learn about complex data preprocessing for training models.
- [Updating Datasets](https://docs-v3.activeloop.ai/v3.4.1/tutorials/updating-datasets.md): Updating Deep Lake datasets
- [Data Processing Using Parallel Computing](https://docs-v3.activeloop.ai/v3.4.1/tutorials/data-processing-using-parallel-computing.md): Deeplake offers built-in methods for parallelizing dataset computations in order to achieve faster data processing.
- [Playbooks](https://docs-v3.activeloop.ai/v3.4.1/playbooks.md): How to perform complex workflows using Deep Lake.
- [Querying, Training and Editing Datasets with Data Lineage](https://docs-v3.activeloop.ai/v3.4.1/playbooks/training-with-lineage.md): How to use queries and version control while training models.
- [Evaluating Model Performance](https://docs-v3.activeloop.ai/v3.4.1/playbooks/evaluating-model-performance.md): How to compare ground-truth annotations with model predictions
- [Training Reproducibility Using Deep Lake and Weights & Biases](https://docs-v3.activeloop.ai/v3.4.1/playbooks/training-reproducibility-with-wandb.md): How to achieve full reproducibility of model training using Deep Lake and W\&B
- [Working with Videos](https://docs-v3.activeloop.ai/v3.4.1/playbooks/working-with-videos.md): How manage video datasets and train models using Deep Lake.
- [API Summary](https://docs-v3.activeloop.ai/v3.4.1/api-basics.md): Summary of the most important Deep Lake commands.
- [Best Practices](https://docs-v3.activeloop.ai/v3.4.1/how-it-works/best-practices.md): How to use Deep Lake at scale with best practices.
- [Creating Datasets at Scale](https://docs-v3.activeloop.ai/v3.4.1/how-it-works/best-practices/creating-datasets-at-scale.md): Creating large Deep Lake datasets with high performance and reliability
- [Training Models at Scale](https://docs-v3.activeloop.ai/v3.4.1/how-it-works/best-practices/training-models-at-scale.md): Train models at scale using Deep Lake
- [Storage Synchronization and "with" Context](https://docs-v3.activeloop.ai/v3.4.1/how-it-works/best-practices/storage-synchronization.md): Synchronizing data with long-term storage and achieving optimal performance using Deep Lake.
- [Restoring Corrupted Datasets](https://docs-v3.activeloop.ai/v3.4.1/how-it-works/best-practices/restoring-corrupted-datasets.md): Restoring Deep Lake datasets that may be corrupted.
- [Data Layout](https://docs-v3.activeloop.ai/v3.4.1/how-it-works/data-layout.md): Understanding the data layout in Deep Lake
- [Version Control and Querying](https://docs-v3.activeloop.ai/v3.4.1/how-it-works/version-control-and-querying.md): Understanding Deep Lake's Version control and Querying Layout
- [Dataset Visualization](https://docs-v3.activeloop.ai/v3.4.1/how-it-works/dataset-visualization.md): How to visualize Deep Lake datasets
- [Tensor Relationships](https://docs-v3.activeloop.ai/v3.4.1/how-it-works/tensor-relationships.md): Understanding the correct data layout for successful visualization.
- [Visualizer Integration](https://docs-v3.activeloop.ai/v3.4.1/how-it-works/visualizer-integration.md): How to embed our visualizer in your application.
- [Shuffling in dataloaders](https://docs-v3.activeloop.ai/v3.4.1/how-it-works/shuffling-in-dataloaders.md): Understanding data shuffling in Deep Lake's pytorch dataloader
- [How to Contribute](https://docs-v3.activeloop.ai/v3.4.1/how-it-works/how-to-contribute.md): Guidelines for open source enthusiasts to contribute to our open-source data format.

## v3.4.0

- [Deep Lake Docs](https://docs-v3.activeloop.ai/v3.4.0/master.md): We hope you enjoy Docs for Deep Lake.
- [Quickstart](https://docs-v3.activeloop.ai/v3.4.0/quickstart.md): A jump-start guide to using Deep Lake.
- [Dataset Visualization](https://docs-v3.activeloop.ai/v3.4.0/dataset-visualization.md): How to connect Deep Lake datasets to Activeloop Platform
- [Storage & Credentials](https://docs-v3.activeloop.ai/v3.4.0/storage-and-credentials.md): How to access datasets in other clouds and manage their credentials.
- [Storage Options](https://docs-v3.activeloop.ai/v3.4.0/storage-and-credentials/storage-options.md): How to authenticate using Activeloop storage, AWS S3, and Google Cloud Storage.
- [User Authentication](https://docs-v3.activeloop.ai/v3.4.0/storage-and-credentials/user-authentication.md): Registration and authentication in Deep Lake.
- [Managed Credentials](https://docs-v3.activeloop.ai/v3.4.0/storage-and-credentials/managed-credentials.md): How to manage your credentials with Deep Lake
- [Enabling CORS](https://docs-v3.activeloop.ai/v3.4.0/storage-and-credentials/managed-credentials/enabling-cors.md): How to enable Cross-Origin Resource Sharing in your cloud buckets.
- [Provisioning Role-Based Access](https://docs-v3.activeloop.ai/v3.4.0/storage-and-credentials/managed-credentials/provisioning-role-based-access.md)
- [Querying Datasets](https://docs-v3.activeloop.ai/v3.4.0/enterprise-features/querying-datasets.md): Deep Lake offers a highly-performant SQL-style query engine for filtering your data.
- [Sampling Datasets](https://docs-v3.activeloop.ai/v3.4.0/enterprise-features/querying-datasets/sampling-datasets.md)
- [Performant Dataloader](https://docs-v3.activeloop.ai/v3.4.0/enterprise-features/performant-dataloader.md): How to use Deep Lake's new dataloader built and optimized in C++
- [Getting Started](https://docs-v3.activeloop.ai/v3.4.0/getting-started.md): The comprehensive guide for learning to use Hub.
- [Step 1: Hello World](https://docs-v3.activeloop.ai/v3.4.0/getting-started/hello-world.md): Installing Deep Lake and accessing your first Deep Lake Dataset.
- [Step 2: Creating Deep Lake Datasets](https://docs-v3.activeloop.ai/v3.4.0/getting-started/creating-datasets-manually.md): Creating and storing Deep Lake Datasets.
- [Step 3: Understanding Compression](https://docs-v3.activeloop.ai/v3.4.0/getting-started/understanding-compression.md): Using compression to achieve optimal performance in Deep Lake.
- [Step 4: Accessing and Updating Data](https://docs-v3.activeloop.ai/v3.4.0/getting-started/accessing-datasets.md): Learn how Deep Lake Datasets can be accessed or loaded from a variety of storage locations.
- [Step 5: Visualizing Datasets](https://docs-v3.activeloop.ai/v3.4.0/getting-started/visualizing-datasets.md): Visualizing and inspecting your datasets.
- [Step 6: Using Activeloop Storage](https://docs-v3.activeloop.ai/v3.4.0/getting-started/using-activeloop-storage.md): Storing and loading datasets from Deep Lake Storage.
- [Step 7: Connecting Deep Lake Datasets to ML Frameworks](https://docs-v3.activeloop.ai/v3.4.0/getting-started/connecting-to-ml-frameworks.md): Connecting Deep Lake Datasets to machine learning frameworks such as PyTorch and TensorFlow.
- [Step 8: Parallel Computing](https://docs-v3.activeloop.ai/v3.4.0/getting-started/parallel-computing.md): Running computations and processing data in parallel.
- [Step 9: Dataset Version Control](https://docs-v3.activeloop.ai/v3.4.0/getting-started/dataset-version-control.md): Managing changes to your datasets using Version Control.
- [Step 10: Dataset Filtering](https://docs-v3.activeloop.ai/v3.4.0/getting-started/dataset-filtering.md): Filtering datasets using user-defined-functions or SQL-style queries.
- [Tutorials (w Colab)](https://docs-v3.activeloop.ai/v3.4.0/tutorials.md): Common workflows with Deep Lake (includes Colab notebooks)
- [Deep Lake Vector Store in LangChain](https://docs-v3.activeloop.ai/v3.4.0/tutorials/deep-lake-vector-store-in-langchain.md): Using Deep Lake as a Vector Store in LangChain
- [Creating Datasets](https://docs-v3.activeloop.ai/v3.4.0/tutorials/creating-datasets.md): Workflows for creating Hub datasets (includes Colab notebooks)
- [Creating Complex Datasets](https://docs-v3.activeloop.ai/v3.4.0/tutorials/creating-datasets/creating-complex-datasets.md): Converting a multi-annotation dataset to Deep Lake format is helpful for understanding how to use Deep Lake with rich data.
- [Creating Object Detection Datasets](https://docs-v3.activeloop.ai/v3.4.0/tutorials/creating-datasets/creating-object-detection-datasets.md): Converting an object detection dataset to Deep Lake format is a great way to get started with datasets of increasing complexity.
- [Creating Time-Series Datasets](https://docs-v3.activeloop.ai/v3.4.0/tutorials/creating-datasets/creating-time-series-datasets.md): Deep Lake is a powerful tool for easily storing and sharing time-series datasets with your team.
- [Creating Datasets with Sequences](https://docs-v3.activeloop.ai/v3.4.0/tutorials/creating-datasets/creating-datasets-with-sequences.md): Deep Lake sequences are a powerful tool for storing temporal annotations such as bounding boxes in each frame of a video.
- [Creating Video Datasets](https://docs-v3.activeloop.ai/v3.4.0/tutorials/creating-datasets/creating-video-datasets.md): Get started with video datasets using Deep Lake.
- [Training Models](https://docs-v3.activeloop.ai/v3.4.0/tutorials/training-models.md): Workflows for training models using Deep Lake datasets (includes Colab notebooks)
- [Training an Image Classification Model in PyTorch](https://docs-v3.activeloop.ai/v3.4.0/tutorials/training-models/training-an-image-classification-model-in-pytorch.md): Training an image classification model is a great way to get started with model training using Deep Lake datasets.
- [Training Models Using MMDetection](https://docs-v3.activeloop.ai/v3.4.0/tutorials/training-models/training-models-using-mmdetection.md): How to Train Deep Learning models using Deep Lake's integration with MMDetection
- [Training Models Using PyTorch Lightning](https://docs-v3.activeloop.ai/v3.4.0/tutorials/training-models/training-models-using-pytorch-lightning.md): How to Train models using Deep Lake and PyTorch Lightning
- [Training on AWS SageMaker](https://docs-v3.activeloop.ai/v3.4.0/tutorials/training-models/training-on-aws-sagemaker.md): How to Train models on AWS SageMaker using Deep Lake datasets
- [Training an Object Detection and Segmentation Model in PyTorch](https://docs-v3.activeloop.ai/v3.4.0/tutorials/training-models/training-an-object-detection-and-segmentation-model-in-pytorch.md): Training an object detection and segmentation model is a great way to learn about complex data preprocessing for training models.
- [Updating Datasets](https://docs-v3.activeloop.ai/v3.4.0/tutorials/updating-datasets.md): Updating Deep Lake datasets
- [Data Processing Using Parallel Computing](https://docs-v3.activeloop.ai/v3.4.0/tutorials/data-processing-using-parallel-computing.md): Deeplake offers built-in methods for parallelizing dataset computations in order to achieve faster data processing.
- [Playbooks](https://docs-v3.activeloop.ai/v3.4.0/playbooks.md): How to perform complex workflows using Deep Lake.
- [Querying, Training and Editing Datasets with Data Lineage](https://docs-v3.activeloop.ai/v3.4.0/playbooks/training-with-lineage.md): How to use queries and version control while training models.
- [Evaluating Model Performance](https://docs-v3.activeloop.ai/v3.4.0/playbooks/evaluating-model-performance.md): How to compare ground-truth annotations with model predictions
- [Training Reproducibility Using Deep Lake and Weights & Biases](https://docs-v3.activeloop.ai/v3.4.0/playbooks/training-reproducibility-with-wandb.md): How to achieve full reproducibility of model training using Deep Lake and W\&B
- [Working with Videos](https://docs-v3.activeloop.ai/v3.4.0/playbooks/working-with-videos.md): How manage video datasets and train models using Deep Lake.
- [API Summary](https://docs-v3.activeloop.ai/v3.4.0/api-basics.md): Summary of the most important Deep Lake commands.
- [Best Practices](https://docs-v3.activeloop.ai/v3.4.0/how-it-works/best-practices.md): How to use Deep Lake at scale with best practices.
- [Creating Datasets at Scale](https://docs-v3.activeloop.ai/v3.4.0/how-it-works/best-practices/creating-datasets-at-scale.md): Creating large Deep Lake datasets with high performance and reliability
- [Training Models at Scale](https://docs-v3.activeloop.ai/v3.4.0/how-it-works/best-practices/training-models-at-scale.md): Train models at scale using Deep Lake
- [Storage Synchronization and "with" Context](https://docs-v3.activeloop.ai/v3.4.0/how-it-works/best-practices/storage-synchronization.md): Synchronizing data with long-term storage and achieving optimal performance using Deep Lake.
- [Restoring Corrupted Datasets](https://docs-v3.activeloop.ai/v3.4.0/how-it-works/best-practices/restoring-corrupted-datasets.md): Restoring Deep Lake datasets that may be corrupted.
- [Data Layout](https://docs-v3.activeloop.ai/v3.4.0/how-it-works/data-layout.md): Understanding the data layout in Deep Lake
- [Version Control and Querying](https://docs-v3.activeloop.ai/v3.4.0/how-it-works/version-control-and-querying.md): Understanding Deep Lake's Version control and Querying Layout
- [Tensor Relationships](https://docs-v3.activeloop.ai/v3.4.0/how-it-works/tensor-relationships.md): Understanding the correct data layout for successful visualization.
- [Visualizer Integration](https://docs-v3.activeloop.ai/v3.4.0/how-it-works/visualizer-integration.md): How to embed our visualizer in your application.
- [Shuffling in dataloaders](https://docs-v3.activeloop.ai/v3.4.0/how-it-works/shuffling-in-dataloaders.md): Understanding data shuffling in Deep Lake's pytorch dataloader
- [How to Contribute](https://docs-v3.activeloop.ai/v3.4.0/how-it-works/how-to-contribute.md): Guidelines for open source enthusiasts to contribute to our open-source data format.

## v3.2.22

- [Deep Lake Docs](https://docs-v3.activeloop.ai/v3.2.22/master.md): We hope you enjoy Docs for Deep Lake.
- [Quickstart](https://docs-v3.activeloop.ai/v3.2.22/quickstart.md): A jump-start guide to using Deep Lake.
- [Dataset Visualization](https://docs-v3.activeloop.ai/v3.2.22/dataset-visualization.md): How to connect Deep Lake datasets to Activeloop Platform
- [Storage & Credentials](https://docs-v3.activeloop.ai/v3.2.22/storage-and-credentials.md): How to access datasets in other clouds and manage their credentials.
- [Storage Options](https://docs-v3.activeloop.ai/v3.2.22/storage-and-credentials/storage-options.md): How to authenticate using Activeloop storage, AWS S3, and Google Cloud Storage.
- [User Authentication](https://docs-v3.activeloop.ai/v3.2.22/storage-and-credentials/user-authentication.md): Registration and authentication in Deep Lake.
- [Managed Credentials](https://docs-v3.activeloop.ai/v3.2.22/storage-and-credentials/managed-credentials.md): How to manage your credentials with Deep Lake
- [Enabling CORS](https://docs-v3.activeloop.ai/v3.2.22/storage-and-credentials/managed-credentials/enabling-cors.md): How to enable Cross-Origin Resource Sharing in your cloud buckets.
- [Provisioning Role-Based Access](https://docs-v3.activeloop.ai/v3.2.22/storage-and-credentials/managed-credentials/provisioning-role-based-access.md)
- [Querying Datasets](https://docs-v3.activeloop.ai/v3.2.22/enterprise-features/querying-datasets.md): Deep Lake offers a highly-performant SQL-style query engine for filtering your data.
- [Sampling Datasets](https://docs-v3.activeloop.ai/v3.2.22/enterprise-features/querying-datasets/sampling-datasets.md)
- [Performant Dataloader](https://docs-v3.activeloop.ai/v3.2.22/enterprise-features/performant-dataloader.md): How to use Deep Lake's new dataloader built and optimized in C++
- [Getting Started](https://docs-v3.activeloop.ai/v3.2.22/getting-started.md): The comprehensive guide for learning to use Hub.
- [Step 1: Hello World](https://docs-v3.activeloop.ai/v3.2.22/getting-started/hello-world.md): Installing Deep Lake and accessing your first Deep Lake Dataset.
- [Step 2: Creating Deep Lake Datasets](https://docs-v3.activeloop.ai/v3.2.22/getting-started/creating-datasets-manually.md): Creating and storing Deep Lake Datasets.
- [Step 3: Understanding Compression](https://docs-v3.activeloop.ai/v3.2.22/getting-started/understanding-compression.md): Using compression to achieve optimal performance in Deep Lake.
- [Step 4: Accessing and Updating Data](https://docs-v3.activeloop.ai/v3.2.22/getting-started/accessing-datasets.md): Learn how Deep Lake Datasets can be accessed or loaded from a variety of storage locations.
- [Step 5: Visualizing Datasets](https://docs-v3.activeloop.ai/v3.2.22/getting-started/visualizing-datasets.md): Visualizing and inspecting your datasets.
- [Step 6: Using Activeloop Storage](https://docs-v3.activeloop.ai/v3.2.22/getting-started/using-activeloop-storage.md): Storing and loading datasets from Deep Lake Storage.
- [Step 7: Connecting Deep Lake Datasets to ML Frameworks](https://docs-v3.activeloop.ai/v3.2.22/getting-started/connecting-to-ml-frameworks.md): Connecting Deep Lake Datasets to machine learning frameworks such as PyTorch and TensorFlow.
- [Step 8: Parallel Computing](https://docs-v3.activeloop.ai/v3.2.22/getting-started/parallel-computing.md): Running computations and processing data in parallel.
- [Step 9: Dataset Version Control](https://docs-v3.activeloop.ai/v3.2.22/getting-started/dataset-version-control.md): Managing changes to your datasets using Version Control.
- [Step 10: Dataset Filtering](https://docs-v3.activeloop.ai/v3.2.22/getting-started/dataset-filtering.md): Filtering datasets using user-defined-functions or SQL-style queries.
- [Tutorials (w Colab)](https://docs-v3.activeloop.ai/v3.2.22/tutorials.md): Common workflows with Deep Lake (includes Colab notebooks)
- [Deep Lake Vector Store in LangChain](https://docs-v3.activeloop.ai/v3.2.22/tutorials/deep-lake-vector-store-in-langchain.md): Using Deep Lake as a Vector Store in LangChain
- [Creating Datasets](https://docs-v3.activeloop.ai/v3.2.22/tutorials/creating-datasets.md): Workflows for creating Hub datasets (includes Colab notebooks)
- [Creating Complex Datasets](https://docs-v3.activeloop.ai/v3.2.22/tutorials/creating-datasets/creating-complex-datasets.md): Converting a multi-annotation dataset to Deep Lake format is helpful for understanding how to use Deep Lake with rich data.
- [Creating Object Detection Datasets](https://docs-v3.activeloop.ai/v3.2.22/tutorials/creating-datasets/creating-object-detection-datasets.md): Converting an object detection dataset to Deep Lake format is a great way to get started with datasets of increasing complexity.
- [Creating Time-Series Datasets](https://docs-v3.activeloop.ai/v3.2.22/tutorials/creating-datasets/creating-time-series-datasets.md): Deep Lake is a powerful tool for easily storing and sharing time-series datasets with your team.
- [Creating Datasets with Sequences](https://docs-v3.activeloop.ai/v3.2.22/tutorials/creating-datasets/creating-datasets-with-sequences.md): Deep Lake sequences are a powerful tool for storing temporal annotations such as bounding boxes in each frame of a video.
- [Creating Video Datasets](https://docs-v3.activeloop.ai/v3.2.22/tutorials/creating-datasets/creating-video-datasets.md): Get started with video datasets using Deep Lake.
- [Training Models](https://docs-v3.activeloop.ai/v3.2.22/tutorials/training-models.md): Workflows for training models using Deep Lake datasets (includes Colab notebooks)
- [Training an Image Classification Model in PyTorch](https://docs-v3.activeloop.ai/v3.2.22/tutorials/training-models/training-an-image-classification-model-in-pytorch.md): Training an image classification model is a great way to get started with model training using Deep Lake datasets.
- [Training Models Using MMDetection](https://docs-v3.activeloop.ai/v3.2.22/tutorials/training-models/training-models-using-mmdetection.md): How to Train Deep Learning models using Deep Lake's integration with MMDetection
- [Training Models Using PyTorch Lightning](https://docs-v3.activeloop.ai/v3.2.22/tutorials/training-models/training-models-using-pytorch-lightning.md): How to Train models using Deep Lake and PyTorch Lightning
- [Training on AWS SageMaker](https://docs-v3.activeloop.ai/v3.2.22/tutorials/training-models/training-on-aws-sagemaker.md): How to Train models on AWS SageMaker using Deep Lake datasets
- [Training an Object Detection and Segmentation Model in PyTorch](https://docs-v3.activeloop.ai/v3.2.22/tutorials/training-models/training-an-object-detection-and-segmentation-model-in-pytorch.md): Training an object detection and segmentation model is a great way to learn about complex data preprocessing for training models.
- [Updating Datasets](https://docs-v3.activeloop.ai/v3.2.22/tutorials/updating-datasets.md): Updating Deep Lake datasets
- [Data Processing Using Parallel Computing](https://docs-v3.activeloop.ai/v3.2.22/tutorials/data-processing-using-parallel-computing.md): Deeplake offers built-in methods for parallelizing dataset computations in order to achieve faster data processing.
- [Playbooks](https://docs-v3.activeloop.ai/v3.2.22/playbooks.md): How to perform complex workflows using Deep Lake.
- [Querying, Training and Editing Datasets with Data Lineage](https://docs-v3.activeloop.ai/v3.2.22/playbooks/training-with-lineage.md): How to use queries and version control while training models.
- [Evaluating Model Performance](https://docs-v3.activeloop.ai/v3.2.22/playbooks/evaluating-model-performance.md): How to compare ground-truth annotations with model predictions
- [Training Reproducibility Using Deep Lake and Weights & Biases](https://docs-v3.activeloop.ai/v3.2.22/playbooks/training-reproducibility-with-wandb.md): How to achieve full reproducibility of model training using Deep Lake and W\&B
- [Working with Videos](https://docs-v3.activeloop.ai/v3.2.22/playbooks/working-with-videos.md): How manage video datasets and train models using Deep Lake.
- [API Summary](https://docs-v3.activeloop.ai/v3.2.22/api-basics.md): Summary of the most important Deep Lake commands.
- [Best Practices](https://docs-v3.activeloop.ai/v3.2.22/how-it-works/best-practices.md): How to use Deep Lake at scale with best practices.
- [Creating Datasets at Scale](https://docs-v3.activeloop.ai/v3.2.22/how-it-works/best-practices/creating-datasets-at-scale.md): Creating large Deep Lake datasets with high performance and reliability
- [Training Models at Scale](https://docs-v3.activeloop.ai/v3.2.22/how-it-works/best-practices/training-models-at-scale.md): Train models at scale using Deep Lake
- [Storage Synchronization and "with" Context](https://docs-v3.activeloop.ai/v3.2.22/how-it-works/best-practices/storage-synchronization.md): Synchronizing data with long-term storage and achieving optimal performance using Deep Lake.
- [Restoring Corrupted Datasets](https://docs-v3.activeloop.ai/v3.2.22/how-it-works/best-practices/restoring-corrupted-datasets.md): Restoring Deep Lake datasets that may be corrupted.
- [Data Layout](https://docs-v3.activeloop.ai/v3.2.22/how-it-works/data-layout.md): Understanding the data layout in Deep Lake
- [Version Control and Querying](https://docs-v3.activeloop.ai/v3.2.22/how-it-works/version-control-and-querying.md): Understanding Deep Lake's Version control and Querying Layout
- [Tensor Relationships](https://docs-v3.activeloop.ai/v3.2.22/how-it-works/tensor-relationships.md): Understanding the correct data layout for successful visualization.
- [Visualizer Integration](https://docs-v3.activeloop.ai/v3.2.22/how-it-works/visualizer-integration.md): How to embed our visualizer in your application.
- [Shuffling in dataloaders](https://docs-v3.activeloop.ai/v3.2.22/how-it-works/shuffling-in-dataloaders.md): Understanding data shuffling in Deep Lake's pytorch dataloader
- [How to Contribute](https://docs-v3.activeloop.ai/v3.2.22/how-it-works/how-to-contribute.md): Guidelines for open source enthusiasts to contribute to our open-source data format.

## v3.2.21

- [Deep Lake Docs](https://docs-v3.activeloop.ai/v3.2.20/master.md): We hope you enjoy Docs for Deep Lake, the open-source Data Lake for Deep Learning by Activeloop. Deep Lake was formerly known as Activeloop Hub.
- [Quickstart](https://docs-v3.activeloop.ai/v3.2.20/quickstart.md): A jump-start guide to using Deep Lake.
- [Dataset Visualization](https://docs-v3.activeloop.ai/v3.2.20/dataset-visualization.md): How to connect Deep Lake datasets to Activeloop Platform
- [Storage & Credentials](https://docs-v3.activeloop.ai/v3.2.20/storage-and-credentials.md): How to access datasets in other clouds and manage their credentials.
- [Storage Options](https://docs-v3.activeloop.ai/v3.2.20/storage-and-credentials/storage-options.md): How to authenticate using Activeloop storage, AWS S3, and Google Cloud Storage.
- [Managed Credentials](https://docs-v3.activeloop.ai/v3.2.20/storage-and-credentials/managed-credentials.md): How to manage your credentials with Deep Lake
- [Enabling CORS](https://docs-v3.activeloop.ai/v3.2.20/storage-and-credentials/managed-credentials/enabling-cors.md): How to enable Cross-Origin Resource Sharing in your cloud buckets.
- [Provisioning Role-Based Access](https://docs-v3.activeloop.ai/v3.2.20/storage-and-credentials/managed-credentials/provisioning-role-based-access.md)
- [Querying Datasets](https://docs-v3.activeloop.ai/v3.2.20/enterprise-features/querying-datasets.md): Deep Lake offers a highly-performant SQL-style query engine for filtering your data.
- [Sampling Datasets](https://docs-v3.activeloop.ai/v3.2.20/enterprise-features/querying-datasets/sampling-datasets.md)
- [Performant Dataloader](https://docs-v3.activeloop.ai/v3.2.20/enterprise-features/performant-dataloader.md): How to use Deep Lake's new dataloader built and optimized in C++
- [Getting Started](https://docs-v3.activeloop.ai/v3.2.20/getting-started.md): The comprehensive guide for learning to use Hub.
- [Step 1: Hello World](https://docs-v3.activeloop.ai/v3.2.20/getting-started/hello-world.md): Installing Deep Lake and accessing your first Deep Lake Dataset.
- [Step 2: Creating Deep Lake Datasets](https://docs-v3.activeloop.ai/v3.2.20/getting-started/creating-datasets-manually.md): Creating and storing Deep Lake Datasets.
- [Step 3: Understanding Compression](https://docs-v3.activeloop.ai/v3.2.20/getting-started/understanding-compression.md): Using compression to achieve optimal performance in Deep Lake.
- [Step 4: Accessing and Updating Data](https://docs-v3.activeloop.ai/v3.2.20/getting-started/accessing-datasets.md): Learn how Deep Lake Datasets can be accessed or loaded from a variety of storage locations.
- [Step 5: Visualizing Datasets](https://docs-v3.activeloop.ai/v3.2.20/getting-started/visualizing-datasets.md): Visualizing and inspecting your datasets.
- [Step 6: Using Activeloop Storage](https://docs-v3.activeloop.ai/v3.2.20/getting-started/using-activeloop-storage.md): Storing and loading datasets from Deep Lake Storage.
- [Step 7: Connecting Deep Lake Datasets to ML Frameworks](https://docs-v3.activeloop.ai/v3.2.20/getting-started/connecting-to-ml-frameworks.md): Connecting Deep Lake Datasets to machine learning frameworks such as PyTorch and TensorFlow.
- [Step 8: Parallel Computing](https://docs-v3.activeloop.ai/v3.2.20/getting-started/parallel-computing.md): Running computations and processing data in parallel.
- [Step 9: Dataset Version Control](https://docs-v3.activeloop.ai/v3.2.20/getting-started/dataset-version-control.md): Managing changes to your datasets using Version Control.
- [Step 10: Dataset Filtering](https://docs-v3.activeloop.ai/v3.2.20/getting-started/dataset-filtering.md): Filtering datasets using user-defined-functions or SQL-style queries.
- [Tutorials (w Colab)](https://docs-v3.activeloop.ai/v3.2.20/tutorials.md): Common workflows with Deep Lake (includes Colab notebooks)
- [Creating Datasets](https://docs-v3.activeloop.ai/v3.2.20/tutorials/creating-datasets.md): Workflows for creating Hub datasets (includes Colab notebooks)
- [Creating Complex Datasets](https://docs-v3.activeloop.ai/v3.2.20/tutorials/creating-datasets/creating-complex-datasets.md): Converting a multi-annotation dataset to Deep Lake format is helpful for understanding how to use Deep Lake with rich data.
- [Creating Object Detection Datasets](https://docs-v3.activeloop.ai/v3.2.20/tutorials/creating-datasets/creating-object-detection-datasets.md): Converting an object detection dataset to Deep Lake format is a great way to get started with datasets of increasing complexity.
- [Creating Time-Series Datasets](https://docs-v3.activeloop.ai/v3.2.20/tutorials/creating-datasets/creating-time-series-datasets.md): Deep Lake is a powerful tool for easily storing and sharing time-series datasets with your team.
- [Creating Datasets with Sequences](https://docs-v3.activeloop.ai/v3.2.20/tutorials/creating-datasets/creating-datasets-with-sequences.md): Deep Lake sequences are a powerful tool for storing temporal annotations such as bounding boxes in each frame of a video.
- [Creating Video Datasets](https://docs-v3.activeloop.ai/v3.2.20/tutorials/creating-datasets/creating-video-datasets.md): Get started with video datasets using Deep Lake.
- [Training Models](https://docs-v3.activeloop.ai/v3.2.20/tutorials/training-models.md): Workflows for training models using Hub datasets (includes Colab notebooks)
- [Training an Image Classification Model in PyTorch](https://docs-v3.activeloop.ai/v3.2.20/tutorials/training-models/training-an-image-classification-model-in-pytorch.md): Training an image classification model is a great way to get started with model training using Deep Lake datasets.
- [Training Models Using MMDetection](https://docs-v3.activeloop.ai/v3.2.20/tutorials/training-models/training-models-using-mmdetection.md): How to Train Deep Learning models using Deep Lake's integration with MMDetection
- [Training Models Using PyTorch Lightning](https://docs-v3.activeloop.ai/v3.2.20/tutorials/training-models/training-models-using-pytorch-lightning.md): How to Train models using Deep Lake and PyTorch Lightning
- [Training on AWS SageMaker](https://docs-v3.activeloop.ai/v3.2.20/tutorials/training-models/training-on-aws-sagemaker.md): How to Train models on AWS SageMaker using Deep Lake datasets
- [Training an Object Detection and Segmentation Model in PyTorch](https://docs-v3.activeloop.ai/v3.2.20/tutorials/training-models/training-an-object-detection-and-segmentation-model-in-pytorch.md): Training an object detection and segmentation model is a great way to learn about complex data preprocessing for training models.
- [Data Processing Using Parallel Computing](https://docs-v3.activeloop.ai/v3.2.20/tutorials/data-processing-using-parallel-computing.md): Deeplake offers built-in methods for parallelizing dataset computations in order to achieve faster data processing.
- [Playbooks](https://docs-v3.activeloop.ai/v3.2.20/playbooks.md): How to perform complex workflows using Deep Lake.
- [Querying, Training and Editing Datasets with Data Lineage](https://docs-v3.activeloop.ai/v3.2.20/playbooks/training-with-lineage.md): How to use queries and version control while training models.
- [Evaluating Model Performance](https://docs-v3.activeloop.ai/v3.2.20/playbooks/evaluating-model-performance.md): How to compare ground-truth annotations with model predictions
- [Training Reproducibility Using Deep Lake and Weights & Biases](https://docs-v3.activeloop.ai/v3.2.20/playbooks/training-reproducibility-with-wandb.md): How to achieve full reproducibility of model training using Deep Lake and W\&B
- [Working with Videos](https://docs-v3.activeloop.ai/v3.2.20/playbooks/working-with-videos.md): How manage video datasets and train models using Deep Lake.
- [API Summary](https://docs-v3.activeloop.ai/v3.2.20/api-basics.md): Summary of the most important Deep Lake commands.
- [Best Practices](https://docs-v3.activeloop.ai/v3.2.20/how-it-works/best-practices.md): How to use Deep Lake at scale with best practices.
- [Creating Datasets at Scale](https://docs-v3.activeloop.ai/v3.2.20/how-it-works/best-practices/creating-datasets-at-scale.md): Creating large Deep Lake datasets with high performance and reliability
- [Training Models at Scale](https://docs-v3.activeloop.ai/v3.2.20/how-it-works/best-practices/training-models-at-scale.md): Train models at scale using Deep Lake
- [Storage Synchronization and "with" Context](https://docs-v3.activeloop.ai/v3.2.20/how-it-works/best-practices/storage-synchronization.md): Synchronizing data with long-term storage and achieving optimal performance using Deep Lake.
- [Restoring Corrupted Datasets](https://docs-v3.activeloop.ai/v3.2.20/how-it-works/best-practices/restoring-corrupted-datasets.md): Restoring Deep Lake datasets that may be corrupted.
- [Data Layout](https://docs-v3.activeloop.ai/v3.2.20/how-it-works/data-layout.md): Understanding the data layout in Deep Lake
- [Version Control and Querying](https://docs-v3.activeloop.ai/v3.2.20/how-it-works/version-control-and-querying.md): Understanding Deep Lake's Version control and Querying Layout
- [Tensor Relationships](https://docs-v3.activeloop.ai/v3.2.20/how-it-works/tensor-relationships.md): Understanding the correct data layout for successful visualization.
- [Visualizer Integration](https://docs-v3.activeloop.ai/v3.2.20/how-it-works/visualizer-integration.md): How to embed our visualizer in your application.
- [Shuffling in dataloaders](https://docs-v3.activeloop.ai/v3.2.20/how-it-works/shuffling-in-dataloaders.md): Understanding data shuffling in Deep Lake's pytorch dataloader
- [How to Contribute](https://docs-v3.activeloop.ai/v3.2.20/how-it-works/how-to-contribute.md): Guidelines for open source enthusiasts to contribute to our open-source data format.

## v3.2.9

- [Deep Lake Docs](https://docs-v3.activeloop.ai/v3.2.9/master.md): We hope you enjoy Docs for Deep Lake, the open-source Data Lake for Deep Learning by Activeloop. Deep Lake was formerly known as Activeloop Hub.
- [Quickstart](https://docs-v3.activeloop.ai/v3.2.9/quickstart.md): A jump-start guide to using Deep Lake.
- [Dataset Visualization](https://docs-v3.activeloop.ai/v3.2.9/dataset-visualization.md): How to connect Deep Lake datasets to Activeloop Platform
- [Storage & Credentials](https://docs-v3.activeloop.ai/v3.2.9/storage-and-credentials.md): How to access datasets in other clouds and manage their credentials.
- [Storage Options](https://docs-v3.activeloop.ai/v3.2.9/storage-and-credentials/storage-options.md): How to authenticate using Activeloop storage, AWS S3, and Google Cloud Storage.
- [Managed Credentials](https://docs-v3.activeloop.ai/v3.2.9/storage-and-credentials/managed-credentials.md): How to manage your credentials with Deep Lake
- [Enabling CORS](https://docs-v3.activeloop.ai/v3.2.9/storage-and-credentials/managed-credentials/enabling-cors.md): How to enable Cross-Origin Resource Sharing in your cloud buckets.
- [Provisioning Role-Based Access](https://docs-v3.activeloop.ai/v3.2.9/storage-and-credentials/managed-credentials/provisioning-role-based-access.md)
- [Querying Datasets](https://docs-v3.activeloop.ai/v3.2.9/enterprise-features/querying-datasets.md): Deep Lake offers a highly-performant SQL-style query engine for filtering your data.
- [Sampling Datasets](https://docs-v3.activeloop.ai/v3.2.9/enterprise-features/querying-datasets/sampling-datasets.md)
- [Performant Dataloader](https://docs-v3.activeloop.ai/v3.2.9/enterprise-features/performant-dataloader.md): How to use Deep Lake's new dataloader built and optimized in C++
- [Getting Started](https://docs-v3.activeloop.ai/v3.2.9/getting-started.md): The comprehensive guide for learning to use Hub.
- [Step 1: Hello World](https://docs-v3.activeloop.ai/v3.2.9/getting-started/hello-world.md): Installing Deep Lake and accessing your first Deep Lake Dataset.
- [Step 2: Creating Deep Lake Datasets](https://docs-v3.activeloop.ai/v3.2.9/getting-started/creating-datasets-manually.md): Creating and storing Deep Lake Datasets.
- [Step 3: Understanding Compression](https://docs-v3.activeloop.ai/v3.2.9/getting-started/understanding-compression.md): Using compression to achieve optimal performance in Deep Lake.
- [Step 4: Accessing and Updating Data](https://docs-v3.activeloop.ai/v3.2.9/getting-started/accessing-datasets.md): Learn how Deep Lake Datasets can be accessed or loaded from a variety of storage locations.
- [Step 5: Visualizing Datasets](https://docs-v3.activeloop.ai/v3.2.9/getting-started/visualizing-datasets.md): Visualizing and inspecting your datasets.
- [Step 6: Using Activeloop Storage](https://docs-v3.activeloop.ai/v3.2.9/getting-started/using-activeloop-storage.md): Storing and loading datasets from Deep Lake Storage.
- [Step 7: Connecting Deep Lake Datasets to ML Frameworks](https://docs-v3.activeloop.ai/v3.2.9/getting-started/connecting-to-ml-frameworks.md): Connecting Deep Lake Datasets to machine learning frameworks such as PyTorch and TensorFlow.
- [Step 8: Parallel Computing](https://docs-v3.activeloop.ai/v3.2.9/getting-started/parallel-computing.md): Running computations and processing data in parallel.
- [Step 9: Dataset Version Control](https://docs-v3.activeloop.ai/v3.2.9/getting-started/dataset-version-control.md): Managing changes to your datasets using Version Control.
- [Step 10: Dataset Filtering](https://docs-v3.activeloop.ai/v3.2.9/getting-started/dataset-filtering.md): Filtering datasets using user-defined-functions or SQL-style queries.
- [Tutorials (w Colab)](https://docs-v3.activeloop.ai/v3.2.9/tutorials.md): Common workflows with Deep Lake (includes Colab notebooks)
- [Creating Datasets](https://docs-v3.activeloop.ai/v3.2.9/tutorials/creating-datasets.md): Workflows for creating Hub datasets (includes Colab notebooks)
- [Creating Complex Datasets](https://docs-v3.activeloop.ai/v3.2.9/tutorials/creating-datasets/creating-complex-datasets.md): Converting a multi-annotation dataset to Deep Lake format is helpful for understanding how to use Deep Lake with rich data.
- [Creating Object Detection Datasets](https://docs-v3.activeloop.ai/v3.2.9/tutorials/creating-datasets/creating-object-detection-datasets.md): Converting an object detection dataset to Deep Lake format is a great way to get started with datasets of increasing complexity.
- [Creating Time-Series Datasets](https://docs-v3.activeloop.ai/v3.2.9/tutorials/creating-datasets/creating-time-series-datasets.md): Deep Lake is a powerful tool for easily storing and sharing time-series datasets with your team.
- [Creating Datasets with Sequences](https://docs-v3.activeloop.ai/v3.2.9/tutorials/creating-datasets/creating-datasets-with-sequences.md): Deep Lake sequences are a powerful tool for storing temporal annotations such as bounding boxes in each frame of a video.
- [Creating Video Datasets](https://docs-v3.activeloop.ai/v3.2.9/tutorials/creating-datasets/creating-video-datasets.md): Get started with video datasets using Deep Lake.
- [Training Models](https://docs-v3.activeloop.ai/v3.2.9/tutorials/training-models.md): Workflows for training models using Hub datasets (includes Colab notebooks)
- [Training an Image Classification Model in PyTorch](https://docs-v3.activeloop.ai/v3.2.9/tutorials/training-models/training-an-image-classification-model-in-pytorch.md): Training an image classification model is a great way to get started with model training using Deep Lake datasets.
- [Training Models Using MMDetection](https://docs-v3.activeloop.ai/v3.2.9/tutorials/training-models/training-models-using-mmdetection.md): How to Train Deep Learning models using Deep Lake's integration with MMDetection
- [Training Models Using PyTorch Lightning](https://docs-v3.activeloop.ai/v3.2.9/tutorials/training-models/training-models-using-pytorch-lightning.md): How to Train models using Deep Lake and PyTorch Lightning
- [Training on AWS SageMaker](https://docs-v3.activeloop.ai/v3.2.9/tutorials/training-models/training-on-aws-sagemaker.md): How to Train models on AWS SageMaker using Deep Lake datasets
- [Training an Object Detection and Segmentation Model in PyTorch](https://docs-v3.activeloop.ai/v3.2.9/tutorials/training-models/training-an-object-detection-and-segmentation-model-in-pytorch.md): Training an object detection and segmentation model is a great way to learn about complex data preprocessing for training models.
- [Data Processing Using Parallel Computing](https://docs-v3.activeloop.ai/v3.2.9/tutorials/data-processing-using-parallel-computing.md): Deeplake offers built-in methods for parallelizing dataset computations in order to achieve faster data processing.
- [Playbooks](https://docs-v3.activeloop.ai/v3.2.9/playbooks.md): How to perform complex workflows using Deep Lake.
- [Querying, Training and Editing Datasets with Data Lineage](https://docs-v3.activeloop.ai/v3.2.9/playbooks/training-with-lineage.md): How to use queries and version control while training models.
- [Evaluating Model Performance](https://docs-v3.activeloop.ai/v3.2.9/playbooks/evaluating-model-performance.md): How to compare ground-truth annotations with model predictions
- [Training Reproducibility Using Deep Lake and Weights & Biases](https://docs-v3.activeloop.ai/v3.2.9/playbooks/training-reproducibility-with-wandb.md): How to achieve full reproducibility of model training using Deep Lake and W\&B
- [Working with Videos](https://docs-v3.activeloop.ai/v3.2.9/playbooks/working-with-videos.md): How manage video datasets and train models using Deep Lake.
- [API Summary](https://docs-v3.activeloop.ai/v3.2.9/api-basics.md): Summary of the most important Deep Lake commands.
- [Data Layout](https://docs-v3.activeloop.ai/v3.2.9/how-it-works/data-layout.md): Understanding the data layout in Deep Lake
- [Version Control and Querying](https://docs-v3.activeloop.ai/v3.2.9/how-it-works/version-control-and-querying.md): Understanding Deep Lake's Version control and Querying Layout
- [Tensor Relationships](https://docs-v3.activeloop.ai/v3.2.9/how-it-works/tensor-relationships.md): Understanding the correct data layout for successful visualization.
- [Visualizer Integration](https://docs-v3.activeloop.ai/v3.2.9/how-it-works/visualizer-integration.md): How to embed our visualizer in your application.
- [Shuffling in ds.pytorch()](https://docs-v3.activeloop.ai/v3.2.9/how-it-works/shuffling-in-ds.pytorch.md): Understanding data shuffling in Deep Lake's pytorch dataloader
- [Storage Synchronization](https://docs-v3.activeloop.ai/v3.2.9/how-it-works/storage-synchronization.md): Synchronizing data with long-term storage and achieving optimal performance using Deep Lake.
- [How to Contribute](https://docs-v3.activeloop.ai/v3.2.9/how-it-works/how-to-contribute.md): Guidelines for open source enthusiasts to contribute to our open-source data format.

## v3.2.0

- [Deep Lake Docs](https://docs-v3.activeloop.ai/v3.2.0/master.md): We hope you enjoy Docs for Deep Lake, the open-source Data Lake for Deep Learning by Activeloop. Deep Lake was formerly known as Activeloop Hub.
- [Quickstart](https://docs-v3.activeloop.ai/v3.2.0/quickstart.md): A jump-start guide to using Deep Lake.
- [Dataset Visualization](https://docs-v3.activeloop.ai/v3.2.0/dataset-visualization.md): How to connect Deep Lake datasets to Activeloop Platform
- [Storage & Credentials](https://docs-v3.activeloop.ai/v3.2.0/storage-and-credentials.md): How to access datasets in other clouds and manage their credentials.
- [Storage Options](https://docs-v3.activeloop.ai/v3.2.0/storage-and-credentials/storage-options.md): How to authenticate using Activeloop storage, AWS S3, and Google Cloud Storage.
- [Managed Credentials](https://docs-v3.activeloop.ai/v3.2.0/storage-and-credentials/managed-credentials.md): How to manage your credentials with Deep Lake
- [Enabling CORS](https://docs-v3.activeloop.ai/v3.2.0/storage-and-credentials/managed-credentials/enabling-cors.md): How to enable Cross-Origin Resource Sharing in your cloud buckets.
- [Provisioning Role-Based Access](https://docs-v3.activeloop.ai/v3.2.0/storage-and-credentials/managed-credentials/provisioning-role-based-access.md)
- [Querying Datasets](https://docs-v3.activeloop.ai/v3.2.0/enterprise-features/querying-datasets.md): Deep Lake offers a highly-performant SQL-style query engine for filtering your data.
- [Sampling Datasets](https://docs-v3.activeloop.ai/v3.2.0/enterprise-features/querying-datasets/sampling-datasets.md)
- [Performant Dataloader](https://docs-v3.activeloop.ai/v3.2.0/enterprise-features/performant-dataloader.md): How to use Deep Lake's new dataloader built and optimized in C++
- [Getting Started](https://docs-v3.activeloop.ai/v3.2.0/getting-started.md): The comprehensive guide for learning to use Hub.
- [Step 1: Hello World](https://docs-v3.activeloop.ai/v3.2.0/getting-started/hello-world.md): Installing Deep Lake and accessing your first Deep Lake Dataset.
- [Step 2: Creating Deep Lake Datasets](https://docs-v3.activeloop.ai/v3.2.0/getting-started/creating-datasets-manually.md): Creating and storing Deep Lake Datasets.
- [Step 3: Understanding Compression](https://docs-v3.activeloop.ai/v3.2.0/getting-started/understanding-compression.md): Using compression to achieve optimal performance in Deep Lake.
- [Step 4: Accessing and Updating Data](https://docs-v3.activeloop.ai/v3.2.0/getting-started/accessing-datasets.md): Learn how Deep Lake Datasets can be accessed or loaded from a variety of storage locations.
- [Step 5: Visualizing Datasets](https://docs-v3.activeloop.ai/v3.2.0/getting-started/visualizing-datasets.md): Visualizing and inspecting your datasets.
- [Step 6: Using Activeloop Storage](https://docs-v3.activeloop.ai/v3.2.0/getting-started/using-activeloop-storage.md): Storing and loading datasets from Deep Lake Storage.
- [Step 7: Connecting Deep Lake Datasets to ML Frameworks](https://docs-v3.activeloop.ai/v3.2.0/getting-started/connecting-to-ml-frameworks.md): Connecting Deep Lake Datasets to machine learning frameworks such as PyTorch and TensorFlow.
- [Step 8: Parallel Computing](https://docs-v3.activeloop.ai/v3.2.0/getting-started/parallel-computing.md): Running computations and processing data in parallel.
- [Step 9: Dataset Version Control](https://docs-v3.activeloop.ai/v3.2.0/getting-started/dataset-version-control.md): Managing changes to your datasets using Version Control.
- [Step 10: Dataset Filtering](https://docs-v3.activeloop.ai/v3.2.0/getting-started/dataset-filtering.md): Filtering datasets using user-defined-functions or SQL-style queries.
- [Tutorials (w Colab)](https://docs-v3.activeloop.ai/v3.2.0/tutorials.md): Common workflows with Deep Lake (includes Colab notebooks)
- [Creating Datasets](https://docs-v3.activeloop.ai/v3.2.0/tutorials/creating-datasets.md): Workflows for creating Hub datasets (includes Colab notebooks)
- [Creating Complex Datasets](https://docs-v3.activeloop.ai/v3.2.0/tutorials/creating-datasets/creating-complex-datasets.md): Converting a multi-annotation dataset to Deep Lake format is helpful for understanding how to use Deep Lake with rich data.
- [Creating Object Detection Datasets](https://docs-v3.activeloop.ai/v3.2.0/tutorials/creating-datasets/creating-object-detection-datasets.md): Converting an object detection dataset to Deep Lake format is a great way to get started with datasets of increasing complexity.
- [Creating Time-Series Datasets](https://docs-v3.activeloop.ai/v3.2.0/tutorials/creating-datasets/creating-time-series-datasets.md): Deep Lake is a powerful tool for easily storing and sharing time-series datasets with your team.
- [Creating Datasets with Sequences](https://docs-v3.activeloop.ai/v3.2.0/tutorials/creating-datasets/creating-datasets-with-sequences.md): Deep Lake sequences are a powerful tool for storing temporal annotations such as bounding boxes in each frame of a video.
- [Creating Video Datasets](https://docs-v3.activeloop.ai/v3.2.0/tutorials/creating-datasets/creating-video-datasets.md): Get started with video datasets using Deep Lake.
- [Training Models](https://docs-v3.activeloop.ai/v3.2.0/tutorials/training-models.md): Workflows for training models using Hub datasets (includes Colab notebooks)
- [Training an Image Classification Model in PyTorch](https://docs-v3.activeloop.ai/v3.2.0/tutorials/training-models/training-an-image-classification-model-in-pytorch.md): Training an image classification model is a great way to get started with model training using Deep Lake datasets.
- [Training Models Using MMDetection](https://docs-v3.activeloop.ai/v3.2.0/tutorials/training-models/training-models-using-mmdetection.md): How to Train Deep Learning models using Deep Lake's integration with MMDetection
- [Training Models Using PyTorch Lightning](https://docs-v3.activeloop.ai/v3.2.0/tutorials/training-models/training-models-using-pytorch-lightning.md): How to Train models using Deep Lake and PyTorch Lightning
- [Training on AWS SageMaker](https://docs-v3.activeloop.ai/v3.2.0/tutorials/training-models/training-on-aws-sagemaker.md): How to Train models on AWS SageMaker using Deep Lake datasets
- [Training an Object Detection and Segmentation Model in PyTorch](https://docs-v3.activeloop.ai/v3.2.0/tutorials/training-models/training-an-object-detection-and-segmentation-model-in-pytorch.md): Training an object detection and segmentation model is a great way to learn about complex data preprocessing for training models.
- [Data Processing Using Parallel Computing](https://docs-v3.activeloop.ai/v3.2.0/tutorials/data-processing-using-parallel-computing.md): Deeplake offers built-in methods for parallelizing dataset computations in order to achieve faster data processing.
- [Playbooks](https://docs-v3.activeloop.ai/v3.2.0/playbooks.md): How to perform complex workflows using Deep Lake.
- [Querying, Training and Editing Datasets with Data Lineage](https://docs-v3.activeloop.ai/v3.2.0/playbooks/training-with-lineage.md): How to use queries and version control while training models.
- [Evaluating Model Performance](https://docs-v3.activeloop.ai/v3.2.0/playbooks/evaluating-model-performance.md): How to compare ground-truth annotations with model predictions
- [Training Reproducibility Using Deep Lake and Weights & Biases](https://docs-v3.activeloop.ai/v3.2.0/playbooks/training-reproducibility-with-wandb.md): How to achieve full reproducibility of model training using Deep Lake and W\&B
- [Working with Videos](https://docs-v3.activeloop.ai/v3.2.0/playbooks/working-with-videos.md): How manage video datasets and train models using Deep Lake.
- [API Summary](https://docs-v3.activeloop.ai/v3.2.0/api-basics.md): Summary of the most important Deep Lake commands.
- [Data Layout](https://docs-v3.activeloop.ai/v3.2.0/how-it-works/data-layout.md): Understanding the data layout in Deep Lake
- [Version Control and Querying](https://docs-v3.activeloop.ai/v3.2.0/how-it-works/version-control-and-querying.md): Understanding Deep Lake's Version control and Querying Layout
- [Tensor Relationships](https://docs-v3.activeloop.ai/v3.2.0/how-it-works/tensor-relationships.md): Understanding the correct data layout for successful visualization.
- [Visualizer Integration](https://docs-v3.activeloop.ai/v3.2.0/how-it-works/visualizer-integration.md): How to embed our visualizer in your application.
- [Shuffling in ds.pytorch()](https://docs-v3.activeloop.ai/v3.2.0/how-it-works/shuffling-in-ds.pytorch.md): Understanding data shuffling in Deep Lake's pytorch dataloader
- [Storage Synchronization](https://docs-v3.activeloop.ai/v3.2.0/how-it-works/storage-synchronization.md): Synchronizing data with long-term storage and achieving optimal performance using Deep Lake.
- [How to Contribute](https://docs-v3.activeloop.ai/v3.2.0/how-it-works/how-to-contribute.md): Guidelines for open source enthusiasts to contribute to our open-source data format.

## v3.1.5

- [Deep Lake Docs](https://docs-v3.activeloop.ai/v3.1.5/master.md): We hope you enjoy Docs for Deep Lake, the open-source Data Lake for Deep Learning by Activeloop. Deep Lake was formerly known as Activeloop Hub.
- [Quickstart](https://docs-v3.activeloop.ai/v3.1.5/quickstart.md): A jump-start guide to using Deep Lake.
- [Dataset Visualization](https://docs-v3.activeloop.ai/v3.1.5/dataset-visualization.md): How to connect Deep Lake datasets to Activeloop Platform
- [Storage & Credentials](https://docs-v3.activeloop.ai/v3.1.5/storage-and-credentials.md): How to access datasets in other clouds and manage their credentials.
- [Storage Options](https://docs-v3.activeloop.ai/v3.1.5/storage-and-credentials/storage-options.md): How to authenticate using Activeloop storage, AWS S3, and Google Cloud Storage.
- [Managed Credentials](https://docs-v3.activeloop.ai/v3.1.5/storage-and-credentials/managed-credentials.md): How to manage your credentials with Activeloop Platform
- [Enabling CORS](https://docs-v3.activeloop.ai/v3.1.5/storage-and-credentials/managed-credentials/enabling-cors.md): How to enable Cross-Origin Resource Sharing in your cloud buckets.
- [Provisioning Role-Based Access](https://docs-v3.activeloop.ai/v3.1.5/storage-and-credentials/managed-credentials/provisioning-role-based-access.md)
- [Querying Datasets](https://docs-v3.activeloop.ai/v3.1.5/enterprise-features/querying-datasets.md): Deep Lake offers a highly-performant SQL-style query engine for filtering your data.
- [Sampling Datasets](https://docs-v3.activeloop.ai/v3.1.5/enterprise-features/querying-datasets/sampling-datasets.md)
- [Performant Dataloader](https://docs-v3.activeloop.ai/v3.1.5/enterprise-features/performant-dataloader.md): How to use Deep Lake's new dataloader built and optimized in C++
- [Getting Started](https://docs-v3.activeloop.ai/v3.1.5/getting-started.md): The comprehensive guide for learning to use Hub.
- [Step 1: Hello World](https://docs-v3.activeloop.ai/v3.1.5/getting-started/hello-world.md): Installing Deep Lake and accessing your first Deep Lake Dataset.
- [Step 2: Creating Deep Lake Datasets](https://docs-v3.activeloop.ai/v3.1.5/getting-started/creating-datasets-manually.md): Creating and storing Deep Lake Datasets.
- [Step 3: Understanding Compression](https://docs-v3.activeloop.ai/v3.1.5/getting-started/understanding-compression.md): Using compression to achieve optimal performance in Deep Lake.
- [Step 4: Accessing Data](https://docs-v3.activeloop.ai/v3.1.5/getting-started/accessing-datasets.md): Learn how Deep Lake Datasets can be accessed or loaded from a variety of storage locations.
- [Step 5: Visualizing Datasets](https://docs-v3.activeloop.ai/v3.1.5/getting-started/visualizing-datasets.md): Visualizing and inspecting your datasets.
- [Step 6: Using Activeloop Storage](https://docs-v3.activeloop.ai/v3.1.5/getting-started/using-activeloop-storage.md): Storing and loading datasets from Activeloop Platform Storage.
- [Step 7: Connecting Deep Lake Datasets to ML Frameworks](https://docs-v3.activeloop.ai/v3.1.5/getting-started/connecting-to-ml-frameworks.md): Connecting Deep Lake Datasets to machine learning frameworks such as PyTorch and TensorFlow.
- [Step 8: Parallel Computing](https://docs-v3.activeloop.ai/v3.1.5/getting-started/parallel-computing.md): Running computations and processing data in parallel.
- [Step 9: Dataset Version Control](https://docs-v3.activeloop.ai/v3.1.5/getting-started/dataset-version-control.md): Managing changes to your datasets using Version Control.
- [Step 10: Dataset Filtering](https://docs-v3.activeloop.ai/v3.1.5/getting-started/dataset-filtering.md): Filtering datasets using user-defined-functions or SQL-style queries.
- [Tutorials (w Colab)](https://docs-v3.activeloop.ai/v3.1.5/tutorials.md): Common workflows with Deep Lake (includes Colab notebooks)
- [Creating Datasets](https://docs-v3.activeloop.ai/v3.1.5/tutorials/creating-datasets.md): Workflows for creating Hub datasets (includes Colab notebooks)
- [Creating Complex Datasets](https://docs-v3.activeloop.ai/v3.1.5/tutorials/creating-datasets/creating-complex-datasets.md): Converting a multi-annotation dataset to Deep Lake format is helpful for understanding how to use Deep Lake with rich data.
- [Creating Object Detection Datasets](https://docs-v3.activeloop.ai/v3.1.5/tutorials/creating-datasets/creating-object-detection-datasets.md): Converting an object detection dataset to Deep Lake format is a great way to get started with datasets of increasing complexity.
- [Creating Time-Series Datasets](https://docs-v3.activeloop.ai/v3.1.5/tutorials/creating-datasets/creating-time-series-datasets.md): Deep Lake is a powerful tool for easily storing and sharing time-series datasets with your team.
- [Creating Datasets with Sequences](https://docs-v3.activeloop.ai/v3.1.5/tutorials/creating-datasets/creating-datasets-with-sequences.md): Deep Lake sequences are a powerful tool for storing temporal annotations such as bounding boxes in each frame of a video.
- [Creating Video Datasets](https://docs-v3.activeloop.ai/v3.1.5/tutorials/creating-datasets/creating-video-datasets.md): Get started with video datasets using Deep Lake.
- [Training Models](https://docs-v3.activeloop.ai/v3.1.5/tutorials/training-models.md): Workflows for training models using Hub datasets (includes Colab notebooks)
- [Training an Image Classification Model in PyTorch](https://docs-v3.activeloop.ai/v3.1.5/tutorials/training-models/training-an-image-classification-model-in-pytorch.md): Training an image classification model is a great way to get started with model training using Deep Lake datasets.
- [Training Models Using MMDetection](https://docs-v3.activeloop.ai/v3.1.5/tutorials/training-models/training-models-using-mmdetection.md): How to Train Deep Learning models using Deep Lake's integration with MMDetection
- [Training Models Using PyTorch Lightning](https://docs-v3.activeloop.ai/v3.1.5/tutorials/training-models/training-models-using-pytorch-lightning.md): How to Train models using Deep Lake and PyTorch Lightning
- [Training on AWS SageMaker](https://docs-v3.activeloop.ai/v3.1.5/tutorials/training-models/training-on-aws-sagemaker.md): How to Train models on AWS SageMaker using Deep Lake datasets
- [Training an Object Detection and Segmentation Model in PyTorch](https://docs-v3.activeloop.ai/v3.1.5/tutorials/training-models/training-an-object-detection-and-segmentation-model-in-pytorch.md): Training an object detection and segmentation model is a great way to learn about complex data preprocessing for training models.
- [Data Processing Using Parallel Computing](https://docs-v3.activeloop.ai/v3.1.5/tutorials/data-processing-using-parallel-computing.md): Deeplake offers built-in methods for parallelizing dataset computations in order to achieve faster data processing.
- [Playbooks](https://docs-v3.activeloop.ai/v3.1.5/playbooks.md): How to perform complex workflows using Deep Lake.
- [Querying, Training and Editing Datasets with Data Lineage](https://docs-v3.activeloop.ai/v3.1.5/playbooks/training-with-lineage.md): How to use queries and version control while training models.
- [Evaluating Model Performance](https://docs-v3.activeloop.ai/v3.1.5/playbooks/evaluating-model-performance.md): How to compare ground-truth annotations with model predictions
- [Training Reproducibility Using Deep Lake and Weights & Biases](https://docs-v3.activeloop.ai/v3.1.5/playbooks/training-reproducibility-with-wandb.md): How to achieve full reproducibility of model training using Deep Lake and W\&B
- [Working with Videos](https://docs-v3.activeloop.ai/v3.1.5/playbooks/working-with-videos.md): How manage video datasets and train models using Deep Lake.
- [API Summary](https://docs-v3.activeloop.ai/v3.1.5/api-basics.md): Summary of the most important Deep Lake commands.
- [Data Layout](https://docs-v3.activeloop.ai/v3.1.5/how-it-works/data-layout.md): Understanding the data layout in Deep Lake
- [Version Control and Querying](https://docs-v3.activeloop.ai/v3.1.5/how-it-works/version-control-and-querying.md): Understanding Deep Lake's Version control and Querying Layout
- [Tensor Relationships](https://docs-v3.activeloop.ai/v3.1.5/how-it-works/tensor-relationships.md): Understanding the correct data layout for successful visualization.
- [Visualizer Integration](https://docs-v3.activeloop.ai/v3.1.5/how-it-works/visualizer-integration.md): How to embed our visualizer in your application.
- [Shuffling in ds.pytorch()](https://docs-v3.activeloop.ai/v3.1.5/how-it-works/shuffling-in-ds.pytorch.md): Understanding data shuffling in Deep Lake's pytorch dataloader
- [Storage Synchronization](https://docs-v3.activeloop.ai/v3.1.5/how-it-works/storage-synchronization.md): Synchronizing data with long-term storage and achieving optimal performance using Deep Lake.
- [How to Contribute](https://docs-v3.activeloop.ai/v3.1.5/how-it-works/how-to-contribute.md): Guidelines for open source enthusiasts to contribute to our open-source data format.

## v3.1.1

- [Deep Lake Docs](https://docs-v3.activeloop.ai/3.1.1/master.md): We hope you enjoy Docs for Deep Lake, the open-source Data Lake for Deep Learning by Activeloop. Deep Lake was formerly known as Activeloop Hub.
- [Quickstart](https://docs-v3.activeloop.ai/3.1.1/quickstart.md): A jump-start guide to using Deep Lake.
- [Dataset Visualization](https://docs-v3.activeloop.ai/3.1.1/dataset-visualization.md): How to connect Deep Lake datasets to Activeloop Platform
- [Storage & Credentials](https://docs-v3.activeloop.ai/3.1.1/storage-and-credentials.md): How to access datasets in other clouds and manage their credentials.
- [Storage Options](https://docs-v3.activeloop.ai/3.1.1/storage-and-credentials/storage-options.md): How to authenticate using Activeloop storage, AWS S3, and Google Cloud Storage.
- [Managed Credentials](https://docs-v3.activeloop.ai/3.1.1/storage-and-credentials/managed-credentials.md): How to manage your credentials with Activeloop Platform
- [Enabling CORS](https://docs-v3.activeloop.ai/3.1.1/storage-and-credentials/managed-credentials/enabling-cors.md): How to enable Cross-Origin Resource Sharing in your cloud buckets.
- [Provisioning Role-Based Access](https://docs-v3.activeloop.ai/3.1.1/storage-and-credentials/managed-credentials/provisioning-role-based-access.md)
- [Querying Datasets](https://docs-v3.activeloop.ai/3.1.1/enterprise-features/querying-datasets.md): Activeloop Platform offer a highly-performant SQL-style query engine for filtering your data.
- [Sampling Datasets](https://docs-v3.activeloop.ai/3.1.1/enterprise-features/querying-datasets/sampling-datasets.md)
- [Performant Dataloader](https://docs-v3.activeloop.ai/3.1.1/enterprise-features/performant-dataloader.md): How to use Deep Lake's new dataloader built and optimized in C++
- [Getting Started](https://docs-v3.activeloop.ai/3.1.1/getting-started.md): The comprehensive guide for learning to use Hub.
- [Step 1: Hello World](https://docs-v3.activeloop.ai/3.1.1/getting-started/hello-world.md): Installing Deep Lake and accessing your first Deep Lake Dataset.
- [Step 2: Creating Deep Lake Datasets](https://docs-v3.activeloop.ai/3.1.1/getting-started/creating-datasets-manually.md): Creating and storing Deep Lake Datasets.
- [Step 3: Understanding Compression](https://docs-v3.activeloop.ai/3.1.1/getting-started/understanding-compression.md): Using compression to achieve optimal performance in Deep Lake.
- [Step 4: Accessing Data](https://docs-v3.activeloop.ai/3.1.1/getting-started/accessing-datasets.md): Learn how Deep Lake Datasets can be accessed or loaded from a variety of storage locations.
- [Step 5: Visualizing Datasets](https://docs-v3.activeloop.ai/3.1.1/getting-started/visualizing-datasets.md): Visualizing and inspecting your datasets.
- [Step 6: Using Activeloop Storage](https://docs-v3.activeloop.ai/3.1.1/getting-started/using-activeloop-storage.md): Storing and loading datasets from Activeloop Platform Storage.
- [Step 7: Connecting Deep Lake Datasets to ML Frameworks](https://docs-v3.activeloop.ai/3.1.1/getting-started/connecting-to-ml-frameworks.md): Connecting Deep Lake Datasets to machine learning frameworks such as PyTorch and TensorFlow.
- [Step 8: Parallel Computing](https://docs-v3.activeloop.ai/3.1.1/getting-started/parallel-computing.md): Running computations and processing data in parallel.
- [Step 9: Dataset Version Control](https://docs-v3.activeloop.ai/3.1.1/getting-started/dataset-version-control.md): Managing changes to your datasets using Version Control.
- [Step 10: Dataset Filtering](https://docs-v3.activeloop.ai/3.1.1/getting-started/dataset-filtering.md): Filtering datasets using user-defined-functions or SQL-style queries.
- [Tutorials (w Colab)](https://docs-v3.activeloop.ai/3.1.1/tutorials.md): Common workflows with Deep Lake (includes Colab notebooks)
- [Creating Datasets](https://docs-v3.activeloop.ai/3.1.1/tutorials/creating-datasets.md): Workflows for creating Hub datasets (includes Colab notebooks)
- [Creating Complex Datasets](https://docs-v3.activeloop.ai/3.1.1/tutorials/creating-datasets/creating-complex-datasets.md): Converting a multi-annotation dataset to Deep Lake format is helpful for understanding how to use Deep Lake with rich data.
- [Creating Object Detection Datasets](https://docs-v3.activeloop.ai/3.1.1/tutorials/creating-datasets/creating-object-detection-datasets.md): Converting an object detection dataset to Deep Lake format is a great way to get started with datasets of increasing complexity.
- [Creating Time-Series Datasets](https://docs-v3.activeloop.ai/3.1.1/tutorials/creating-datasets/creating-time-series-datasets.md): Deep Lake is a powerful tool for easily storing and sharing time-series datasets with your team.
- [Creating Datasets with Sequences](https://docs-v3.activeloop.ai/3.1.1/tutorials/creating-datasets/creating-datasets-with-sequences.md): Deep Lake sequences are a powerful tool for storing temporal annotations such as bounding boxes in each frame of a video.
- [Creating Video Datasets](https://docs-v3.activeloop.ai/3.1.1/tutorials/creating-datasets/creating-video-datasets.md): Get started with video datasets using Deep Lake.
- [Training Models](https://docs-v3.activeloop.ai/3.1.1/tutorials/training-models.md): Workflows for training models using Hub datasets (includes Colab notebooks)
- [Training an Image Classification Model in PyTorch](https://docs-v3.activeloop.ai/3.1.1/tutorials/training-models/training-an-image-classification-model-in-pytorch.md): Training an image classification model is a great way to get started with model training using Deep Lake datasets.
- [Training Models Using MMDetection](https://docs-v3.activeloop.ai/3.1.1/tutorials/training-models/training-models-using-mmdetection.md): How to Train Deep Learning models using Deep Lake's integration with MMDetection
- [Training on AWS SageMaker Using Deep Lake Datasets](https://docs-v3.activeloop.ai/3.1.1/tutorials/training-models/training-on-aws-sagemaker-using-deep-lake-datasets.md): How to Train models on AWS SageMaker using Deep Lake datasets
- [Training an Object Detection and Segmentation Model in PyTorch](https://docs-v3.activeloop.ai/3.1.1/tutorials/training-models/training-an-object-detection-and-segmentation-model-in-pytorch.md): Training an object detection and segmentation model is a great way to learn about complex data preprocessing for training models.
- [Data Processing Using Parallel Computing](https://docs-v3.activeloop.ai/3.1.1/tutorials/data-processing-using-parallel-computing.md): Deeplake offers built-in methods for parallelizing dataset computations in order to achieve faster data processing.
- [Playbooks](https://docs-v3.activeloop.ai/3.1.1/playbooks.md): How to perform complex workflows using Deep Lake.
- [Querying, Training and Editing Datasets with Data Lineage](https://docs-v3.activeloop.ai/3.1.1/playbooks/training-with-lineage.md): How to use queries and version control while training models.
- [Evaluating Model Performance](https://docs-v3.activeloop.ai/3.1.1/playbooks/evaluating-model-performance.md): How to compare ground-truth annotations with model predictions
- [Training Reproducibility Using Deep Lake and Weights & Biases](https://docs-v3.activeloop.ai/3.1.1/playbooks/training-reproducibility-with-wandb.md): How to achieve full reproducibility of model training using Deep Lake and W\&B
- [Working with Videos](https://docs-v3.activeloop.ai/3.1.1/playbooks/working-with-videos.md): How manage video datasets and train models using Deep Lake.
- [API Summary](https://docs-v3.activeloop.ai/3.1.1/api-basics.md): Summary of the most important Deep Lake commands.
- [Data Layout](https://docs-v3.activeloop.ai/3.1.1/how-it-works/data-layout.md): Understanding the data layout in Deep Lake
- [Version Control and Querying](https://docs-v3.activeloop.ai/3.1.1/how-it-works/version-control-and-querying.md): Understanding Deep Lake's Version control and Querying Layout
- [Tensor Relationships](https://docs-v3.activeloop.ai/3.1.1/how-it-works/tensor-relationships.md): Understanding the correct data layout for successful visualization.
- [Visualizer Integration](https://docs-v3.activeloop.ai/3.1.1/how-it-works/visualizer-integration.md): How to embed our visualizer in your application.
- [Shuffling in ds.pytorch()](https://docs-v3.activeloop.ai/3.1.1/how-it-works/shuffling-in-ds.pytorch.md): Understanding data shuffling in Deep Lake's pytorch dataloader
- [Storage Synchronization](https://docs-v3.activeloop.ai/3.1.1/how-it-works/storage-synchronization.md): Synchronizing data with long-term storage and achieving optimal performance using Deep Lake.
- [How to Contribute](https://docs-v3.activeloop.ai/3.1.1/how-it-works/how-to-contribute.md): Guidelines for open source enthusiasts to contribute to our open-source data format.

## v3.1.0

- [Deep Lake Docs](https://docs-v3.activeloop.ai/3.1.0/master.md): We hope you enjoy Docs for Deep Lake, the open-source Data Lake for Deep Learning by Activeloop. Deep Lake was formerly known as Activeloop Hub.
- [Quickstart](https://docs-v3.activeloop.ai/3.1.0/quickstart.md): A jump-start guide to using Deep Lake.
- [Dataset Visualization](https://docs-v3.activeloop.ai/3.1.0/dataset-visualization.md): How to connect Deep Lake datasets to Activeloop Platform
- [Storage & Credentials](https://docs-v3.activeloop.ai/3.1.0/storage-and-credentials.md): How to access datasets in other clouds and manage their credentials.
- [Storage Options](https://docs-v3.activeloop.ai/3.1.0/storage-and-credentials/storage-options.md): How to authenticate using Activeloop storage, AWS S3, and Google Cloud Storage.
- [Managed Credentials](https://docs-v3.activeloop.ai/3.1.0/storage-and-credentials/managed-credentials.md): How to manage your credentials with Activeloop Platform
- [Enabling CORS](https://docs-v3.activeloop.ai/3.1.0/storage-and-credentials/managed-credentials/enabling-cors.md): How to enable Cross-Origin Resource Sharing in your cloud buckets.
- [Provisioning Role-Based Access](https://docs-v3.activeloop.ai/3.1.0/storage-and-credentials/managed-credentials/provisioning-role-based-access.md)
- [Querying Datasets](https://docs-v3.activeloop.ai/3.1.0/enterprise-features/querying-datasets.md): Activeloop Platform offer a highly-performant SQL-style query engine for filtering your data.
- [Sampling Datasets](https://docs-v3.activeloop.ai/3.1.0/enterprise-features/querying-datasets/sampling-datasets.md)
- [Performant Dataloader](https://docs-v3.activeloop.ai/3.1.0/enterprise-features/performant-dataloader.md): How to use Deep Lake's new dataloader built and optimized in C++
- [Getting Started](https://docs-v3.activeloop.ai/3.1.0/getting-started.md): The comprehensive guide for learning to use Hub.
- [Step 1: Hello World](https://docs-v3.activeloop.ai/3.1.0/getting-started/hello-world.md): Installing Deep Lake and accessing your first Deep Lake Dataset.
- [Step 2: Creating Deep Lake Datasets](https://docs-v3.activeloop.ai/3.1.0/getting-started/creating-datasets-manually.md): Creating and storing Deep Lake Datasets.
- [Step 3: Understanding Compression](https://docs-v3.activeloop.ai/3.1.0/getting-started/understanding-compression.md): Using compression to achieve optimal performance in Deep Lake.
- [Step 4: Accessing Data](https://docs-v3.activeloop.ai/3.1.0/getting-started/accessing-datasets.md): Learn how Deep Lake Datasets can be accessed or loaded from a variety of storage locations.
- [Step 5: Visualizing Datasets](https://docs-v3.activeloop.ai/3.1.0/getting-started/visualizing-datasets.md): Visualizing and inspecting your datasets.
- [Step 6: Using Activeloop Storage](https://docs-v3.activeloop.ai/3.1.0/getting-started/using-activeloop-storage.md): Storing and loading datasets from Activeloop Platform Storage.
- [Step 7: Connecting Deep Lake Datasets to ML Frameworks](https://docs-v3.activeloop.ai/3.1.0/getting-started/connecting-to-ml-frameworks.md): Connecting Deep Lake Datasets to machine learning frameworks such as PyTorch and TensorFlow.
- [Step 8: Parallel Computing](https://docs-v3.activeloop.ai/3.1.0/getting-started/parallel-computing.md): Running computations and processing data in parallel.
- [Step 9: Dataset Version Control](https://docs-v3.activeloop.ai/3.1.0/getting-started/dataset-version-control.md): Managing changes to your datasets using Version Control.
- [Step 10: Dataset Filtering](https://docs-v3.activeloop.ai/3.1.0/getting-started/dataset-filtering.md): Filtering datasets using user-defined-functions or SQL-style queries.
- [Tutorials (w Colab)](https://docs-v3.activeloop.ai/3.1.0/tutorials.md): Common workflows with Deep Lake (includes Colab notebooks)
- [Creating Datasets](https://docs-v3.activeloop.ai/3.1.0/tutorials/creating-datasets.md): Workflows for creating Hub datasets (includes Colab notebooks)
- [Creating Complex Datasets](https://docs-v3.activeloop.ai/3.1.0/tutorials/creating-datasets/creating-complex-datasets.md): Converting a multi-annotation dataset to Deep Lake format is helpful for understanding how to use Deep Lake with rich data.
- [Creating Object Detection Datasets](https://docs-v3.activeloop.ai/3.1.0/tutorials/creating-datasets/creating-object-detection-datasets.md): Converting an object detection dataset to Deep Lake format is a great way to get started with datasets of increasing complexity.
- [Creating Time-Series Datasets](https://docs-v3.activeloop.ai/3.1.0/tutorials/creating-datasets/creating-time-series-datasets.md): Deep Lake is a powerful tool for easily storing and sharing time-series datasets with your team.
- [Creating Datasets with Sequences](https://docs-v3.activeloop.ai/3.1.0/tutorials/creating-datasets/creating-datasets-with-sequences.md): Deep Lake sequences are a powerful tool for storing temporal annotations such as bounding boxes in each frame of a video.
- [Creating Video Datasets](https://docs-v3.activeloop.ai/3.1.0/tutorials/creating-datasets/creating-video-datasets.md): Get started with video datasets using Deep Lake.
- [Training Models](https://docs-v3.activeloop.ai/3.1.0/tutorials/training-models.md): Workflows for training models using Hub datasets (includes Colab notebooks)
- [Training an Image Classification Model in PyTorch](https://docs-v3.activeloop.ai/3.1.0/tutorials/training-models/training-an-image-classification-model-in-pytorch.md): Training an image classification model is a great way to get started with model training using Deep Lake datasets.
- [Training an Object Detection and Segmentation Model in PyTorch](https://docs-v3.activeloop.ai/3.1.0/tutorials/training-models/training-an-object-detection-and-segmentation-model-in-pytorch.md): Training an object detection and segmentation model is a great way to learn about complex data preprocessing for training models.
- [Data Processing Using Parallel Computing](https://docs-v3.activeloop.ai/3.1.0/tutorials/data-processing-using-parallel-computing.md): Deeplake offers built-in methods for parallelizing dataset computations in order to achieve faster data data processing.
- [Playbooks](https://docs-v3.activeloop.ai/3.1.0/playbooks.md): How to perform complex workflows using Deep Lake.
- [Querying, Training and Editing Datasets with Data Lineage](https://docs-v3.activeloop.ai/3.1.0/playbooks/training-with-lineage.md): How to use queries and version control while training models.
- [Evaluating Model Performance](https://docs-v3.activeloop.ai/3.1.0/playbooks/evaluating-model-performance.md): How to compare ground-truth annotations with model predictions
- [Training Reproducibility Using Deep Lake and Weights & Biases](https://docs-v3.activeloop.ai/3.1.0/playbooks/training-reproducibility-with-wandb.md): How to achieve full reproducibility of model training using Deep Lake and W\&B
- [Working with Videos](https://docs-v3.activeloop.ai/3.1.0/playbooks/working-with-videos.md): How manage video datasets and train models using Deep Lake.
- [API Summary](https://docs-v3.activeloop.ai/3.1.0/api-basics.md): Summary of the most important Deep Lake commands.
- [Data Layout](https://docs-v3.activeloop.ai/3.1.0/how-it-works/data-layout.md): Understanding the data layout in Deep Lake
- [Version Control and Querying](https://docs-v3.activeloop.ai/3.1.0/how-it-works/version-control-and-querying.md): Understanding Deep Lake's Version control and Querying Layout
- [Tensor Relationships](https://docs-v3.activeloop.ai/3.1.0/how-it-works/tensor-relationships.md): Understanding the correct data layout for successful visualization.
- [Visualizer Integration](https://docs-v3.activeloop.ai/3.1.0/how-it-works/visualizer-integration.md): How to embed our visualizer in your application.
- [Shuffling in ds.pytorch()](https://docs-v3.activeloop.ai/3.1.0/how-it-works/shuffling-in-ds.pytorch.md): Understanding data shuffling in Deep Lake's pytorch dataloader
- [Storage Synchronization](https://docs-v3.activeloop.ai/3.1.0/how-it-works/storage-synchronization.md): Synchronizing data with long-term storage and achieving optimal performance using Deep Lake.
- [How to Contribute](https://docs-v3.activeloop.ai/3.1.0/how-it-works/how-to-contribute.md): Guidelines for open source enthusiasts to contribute to our open-source data format.

## v3.0.16

- [Deep Lake Docs](https://docs-v3.activeloop.ai/v3.0.15/master.md): We hope you enjoy Docs for Deep Lake, the open-source Data Lake for Deep Learning by Activeloop. Deep Lake was formerly known as Activeloop Hub.
- [Quickstart](https://docs-v3.activeloop.ai/v3.0.15/quickstart.md): A jump-start guide to using Deep Lake.
- [Dataset Visualization](https://docs-v3.activeloop.ai/v3.0.15/dataset-visualization.md): How to connect Deep Lake datasets to Activeloop Platform
- [Storage & Credentials](https://docs-v3.activeloop.ai/v3.0.15/storage-and-credentials.md): How to access datasets in other clouds and manage their credentials.
- [Storage Options](https://docs-v3.activeloop.ai/v3.0.15/storage-and-credentials/storage-options.md): How to authenticate using Activeloop storage, AWS S3, and Google Cloud Storage.
- [Managed Credentials](https://docs-v3.activeloop.ai/v3.0.15/storage-and-credentials/managed-credentials.md): How to manage your credentials with Activeloop Platform
- [Enabling CORS](https://docs-v3.activeloop.ai/v3.0.15/storage-and-credentials/managed-credentials/enabling-cors.md): How to enable Cross-Origin Resource Sharing in your cloud buckets.
- [Provisioning Role-Based Access](https://docs-v3.activeloop.ai/v3.0.15/storage-and-credentials/managed-credentials/provisioning-role-based-access.md)
- [Getting Started](https://docs-v3.activeloop.ai/v3.0.15/getting-started.md): The comprehensive guide for learning to use Hub.
- [Step 1: Hello World](https://docs-v3.activeloop.ai/v3.0.15/getting-started/hello-world.md): Installing Deep Lake and accessing your first Deep Lake Dataset.
- [Step 2: Creating Deep Lake Datasets](https://docs-v3.activeloop.ai/v3.0.15/getting-started/creating-datasets-manually.md): Creating and storing Deep Lake Datasets.
- [Step 3: Understanding Compression](https://docs-v3.activeloop.ai/v3.0.15/getting-started/understanding-compression.md): Using compression to achieve optimal performance in Deep Lake.
- [Step 4: Accessing Data](https://docs-v3.activeloop.ai/v3.0.15/getting-started/accessing-datasets.md): Learn how Deep Lake Datasets can be accessed or loaded from a variety of storage locations.
- [Step 5: Visualizing Datasets](https://docs-v3.activeloop.ai/v3.0.15/getting-started/visualizing-datasets.md): Visualizing and inspecting your datasets.
- [Step 6: Using Activeloop Storage](https://docs-v3.activeloop.ai/v3.0.15/getting-started/using-activeloop-storage.md): Storing and loading datasets from Activeloop Platform Storage.
- [Step 7: Connecting Deep Lake Datasets to ML Frameworks](https://docs-v3.activeloop.ai/v3.0.15/getting-started/connecting-to-ml-frameworks.md): Connecting Deep Lake Datasets to machine learning frameworks such as PyTorch and TensorFlow.
- [Step 8: Parallel Computing](https://docs-v3.activeloop.ai/v3.0.15/getting-started/parallel-computing.md): Running computations and processing data in parallel.
- [Step 9: Dataset Version Control](https://docs-v3.activeloop.ai/v3.0.15/getting-started/dataset-version-control.md): Managing changes to your datasets using Version Control.
- [Step 10: Dataset Filtering](https://docs-v3.activeloop.ai/v3.0.15/getting-started/dataset-filtering.md): Filtering datasets using user-defined-functions or SQL-style queries.
- [Tutorials (w Colab)](https://docs-v3.activeloop.ai/v3.0.15/tutorials.md): Common workflows with Deep Lake (includes Colab notebooks)
- [Creating Datasets](https://docs-v3.activeloop.ai/v3.0.15/tutorials/creating-datasets.md): Workflows for creating Hub datasets (includes Colab notebooks)
- [Creating Complex Datasets](https://docs-v3.activeloop.ai/v3.0.15/tutorials/creating-datasets/creating-complex-datasets.md): Converting a multi-annotation dataset to Deep Lake format is helpful for understanding how to use Deep Lake with rich data.
- [Creating Object Detection Datasets](https://docs-v3.activeloop.ai/v3.0.15/tutorials/creating-datasets/creating-object-detection-datasets.md): Converting an object detection dataset to Deep Lake format is a great way to get started with datasets of increasing complexity.
- [Creating Time-Series Datasets](https://docs-v3.activeloop.ai/v3.0.15/tutorials/creating-datasets/creating-time-series-datasets.md): Deep Lake is a powerful tool for easily storing and sharing time-series datasets with your team.
- [Creating Datasets with Sequences](https://docs-v3.activeloop.ai/v3.0.15/tutorials/creating-datasets/creating-datasets-with-sequences.md): Deep Lake sequences are a powerful tool for storing temporal annotations such as bounding boxes in each frame of a video.
- [Creating Video Datasets](https://docs-v3.activeloop.ai/v3.0.15/tutorials/creating-datasets/creating-video-datasets.md): Get started with video datasets using Deep Lake.
- [Training Models](https://docs-v3.activeloop.ai/v3.0.15/tutorials/training-models.md): Workflows for training models using Hub datasets (includes Colab notebooks)
- [Training an Image Classification Model in PyTorch](https://docs-v3.activeloop.ai/v3.0.15/tutorials/training-models/training-an-image-classification-model-in-pytorch.md): Training an image classification model is a great way to get started with model training using Deep Lake datasets.
- [Training an Object Detection and Segmentation Model in PyTorch](https://docs-v3.activeloop.ai/v3.0.15/tutorials/training-models/training-an-object-detection-and-segmentation-model-in-pytorch.md): Training an object detection and segmentation model is a great way to learn about complex data preprocessing for training models.
- [Querying Datasets](https://docs-v3.activeloop.ai/v3.0.15/tutorials/querying-datasets.md): Activeloop Platform offer a highly-performant SQL-style query engine for filtering your data.
- [Sampling Datasets](https://docs-v3.activeloop.ai/v3.0.15/tutorials/querying-datasets/sampling-datasets.md)
- [Data Processing Using Parallel Computing](https://docs-v3.activeloop.ai/v3.0.15/tutorials/data-processing-using-parallel-computing.md): Deeplake offers built-in methods for parallelizing dataset computations in order to achieve faster data data processing.
- [Playbooks](https://docs-v3.activeloop.ai/v3.0.15/playbooks.md): How to perform complex workflows using Deep Lake.
- [Querying, Training and Editing Datasets with Data Lineage](https://docs-v3.activeloop.ai/v3.0.15/playbooks/training-with-lineage.md): How to use queries and version control while training models.
- [Evaluating Model Performance](https://docs-v3.activeloop.ai/v3.0.15/playbooks/evaluating-model-performance.md): How to compare ground-truth annotations with model predictions
- [Training Reproducibility Using Deep Lake and Weights & Biases](https://docs-v3.activeloop.ai/v3.0.15/playbooks/training-reproducibility-with-wandb.md): How to achieve full reproducibility of model training using Deep Lake and W\&B
- [Working with Videos](https://docs-v3.activeloop.ai/v3.0.15/playbooks/working-with-videos.md): How manage video datasets and train models using Deep Lake.
- [Performant Dataloader (Beta)](https://docs-v3.activeloop.ai/v3.0.15/performant-dataloader.md): How to use Deep Lake's new dataloader built and optimized in C++
- [API Summary](https://docs-v3.activeloop.ai/v3.0.15/api-basics.md): Summary of the most important Deep Lake commands.
- [Data Layout](https://docs-v3.activeloop.ai/v3.0.15/how-it-works/data-layout.md): Understanding the data layout in Deep Lake
- [Tensor Relationships](https://docs-v3.activeloop.ai/v3.0.15/how-it-works/tensor-relationships.md): Understanding the correct data layout for successful visualization.
- [Visualizer Integration](https://docs-v3.activeloop.ai/v3.0.15/how-it-works/visualizer-integration.md): How to embed our visualizer in your application.
- [Shuffling in ds.pytorch()](https://docs-v3.activeloop.ai/v3.0.15/how-it-works/shuffling-in-ds.pytorch.md): Understanding data shuffling in Deep Lake's pytorch dataloader
- [Storage Synchronization](https://docs-v3.activeloop.ai/v3.0.15/how-it-works/storage-synchronization.md): Synchronizing data with long-term storage and achieving optimal performance using Deep Lake.
- [How to Contribute](https://docs-v3.activeloop.ai/v3.0.15/how-it-works/how-to-contribute.md): Guidelines for open source enthusiasts to contribute to our open-source data format.

## v3.0.10

- [Deep Lake Docs](https://docs-v3.activeloop.ai/v3.0.x/master.md): We hope you enjoy Docs for Deep Lake, the open-source Data Lake for Deep Learning by Activeloop. Deep Lake was formerly known as Activeloop Hub.
- [Quickstart](https://docs-v3.activeloop.ai/v3.0.x/quickstart.md): A jump-start guide to using Deep Lake.
- [Dataset Visualization](https://docs-v3.activeloop.ai/v3.0.x/dataset-visualization.md): How to connect Deep Lake datasets to Activeloop Platform
- [Storage & Credentials](https://docs-v3.activeloop.ai/v3.0.x/storage-and-credentials.md): How to access datasets in other clouds and manage their credentials.
- [Storage Options](https://docs-v3.activeloop.ai/v3.0.x/storage-and-credentials/storage-options.md): How to authenticate using Activeloop storage, AWS S3, and Google Cloud Storage.
- [Managed Credentials](https://docs-v3.activeloop.ai/v3.0.x/storage-and-credentials/managed-credentials.md): How to manage your credentials with Activeloop Platform
- [Enabling CORS](https://docs-v3.activeloop.ai/v3.0.x/storage-and-credentials/managed-credentials/enabling-cors.md): How to enable Cross Origin Resource Sharing in your cloud buckets.
- [Provisioning Role-Based Access](https://docs-v3.activeloop.ai/v3.0.x/storage-and-credentials/managed-credentials/provisioning-role-based-access.md)
- [Getting Started](https://docs-v3.activeloop.ai/v3.0.x/getting-started.md): The comprehensive guide for learning to use Hub.
- [Step 1: Hello World](https://docs-v3.activeloop.ai/v3.0.x/getting-started/hello-world.md): Installing Deep Lake and accessing your first Deep Lake Dataset.
- [Step 2: Creating Deep Lake Datasets](https://docs-v3.activeloop.ai/v3.0.x/getting-started/creating-datasets-manually.md): Creating and storing Deep Lake Datasets.
- [Step 3: Understanding Compression](https://docs-v3.activeloop.ai/v3.0.x/getting-started/understanding-compression.md): Using compression to achieve optimal performance in Deep Lake.
- [Step 4: Accessing Data](https://docs-v3.activeloop.ai/v3.0.x/getting-started/accessing-datasets.md): Learn how Deep Lake Datasets can be accessed or loaded from a variety of storage locations.
- [Step 5: Visualizing Datasets](https://docs-v3.activeloop.ai/v3.0.x/getting-started/visualizing-datasets.md): Visualizing and inspecting your datasets.
- [Step 6: Using Activeloop Storage](https://docs-v3.activeloop.ai/v3.0.x/getting-started/using-activeloop-storage.md): Storing and loading datasets from Activeloop Platform Storage.
- [Step 7: Connecting Deep Lake Datasets to ML Frameworks](https://docs-v3.activeloop.ai/v3.0.x/getting-started/connecting-to-ml-frameworks.md): Connecting Deep Lake Datasets to machine learning frameworks such as PyTorch and TensorFlow.
- [Step 8: Parallel Computing](https://docs-v3.activeloop.ai/v3.0.x/getting-started/parallel-computing.md): Running computations and processing data in parallel.
- [Step 9: Dataset Version Control](https://docs-v3.activeloop.ai/v3.0.x/getting-started/dataset-version-control.md): Managing changes to your datasets using Version Control.
- [Step 10: Dataset Filtering](https://docs-v3.activeloop.ai/v3.0.x/getting-started/dataset-filtering.md): Filtering datasets using user-defined-functions or SQL-style queries.
- [Tutorials (w Colab)](https://docs-v3.activeloop.ai/v3.0.x/tutorials.md): Common workflows using hub (includes Colab notebooks)
- [Creating Datasets](https://docs-v3.activeloop.ai/v3.0.x/tutorials/creating-datasets.md): Workflows for creating Hub datasets (includes Colab notebooks)
- [Creating Complex Datasets](https://docs-v3.activeloop.ai/v3.0.x/tutorials/creating-datasets/creating-complex-datasets.md): Converting a multi-annotation dataset to Deep Lake format is helpful for understanding how to use Deep Lake with rich data.
- [Creating Object Detection Datasets](https://docs-v3.activeloop.ai/v3.0.x/tutorials/creating-datasets/creating-object-detection-datasets.md): Converting an object detection dataset to Deep Lake format is a great way to get started with datasets of increasing complexity.
- [Creating Time-Series Datasets](https://docs-v3.activeloop.ai/v3.0.x/tutorials/creating-datasets/creating-time-series-datasets.md): Deep Lake is a powerful tool for easily storing and sharing time-series datasets with your team.
- [Creating Datasets with Sequences](https://docs-v3.activeloop.ai/v3.0.x/tutorials/creating-datasets/creating-datasets-with-sequences.md): Deep Lake sequences are a powerful tool for storing temporal annotations such as bounding boxes in each frame of a video.
- [Creating Video Datasets](https://docs-v3.activeloop.ai/v3.0.x/tutorials/creating-datasets/creating-video-datasets.md): Get started with video datasets using Deep Lake.
- [Training Models](https://docs-v3.activeloop.ai/v3.0.x/tutorials/training-models.md): Workflows for training models using Hub datasets (includes Colab notebooks)
- [Training an Image Classification Model in PyTorch](https://docs-v3.activeloop.ai/v3.0.x/tutorials/training-models/training-an-image-classification-model-in-pytorch.md): Training an image classification model is a great way to get started with model training using Deep Lake datasets.
- [Training an Object Detection and Segmentation Model in PyTorch](https://docs-v3.activeloop.ai/v3.0.x/tutorials/training-models/training-an-object-detection-and-segmentation-model-in-pytorch.md): Training an object detection and segmentation model is a great way to learn about complex data preprocessing for training models.
- [Querying Datasets](https://docs-v3.activeloop.ai/v3.0.x/tutorials/querying-datasets.md): Activeloop Platform offer a highly-performant SQL-style query engine for filtering your data
- [Data Processing Using Parallel Computing](https://docs-v3.activeloop.ai/v3.0.x/tutorials/data-processing-using-parallel-computing.md): Deeplake offers built-in methods for parallelizing dataset computations in order to achieve faster data data processing.
- [Playbooks](https://docs-v3.activeloop.ai/v3.0.x/playbooks.md): How to perform complex workflows using Hub and Platform.
- [Querying, Training and Editing Datasets with Data Lineage](https://docs-v3.activeloop.ai/v3.0.x/playbooks/training-with-lineage.md): How to use queries and version control while training models.
- [Evaluating Model Performance](https://docs-v3.activeloop.ai/v3.0.x/playbooks/evaluating-model-performance.md): How to compare ground-truth annotations with model predictions
- [Training Reproducibility Using Deep Lake and Weights & Biases](https://docs-v3.activeloop.ai/v3.0.x/playbooks/training-reproducibility-with-wandb.md): How to achieve full reproducibility of model training using Deep Lake and W\&B
- [Working with Videos](https://docs-v3.activeloop.ai/v3.0.x/playbooks/working-with-videos.md): How manage video datasets and train models using Deep Lake.
- [Performant Dataloader (Beta)](https://docs-v3.activeloop.ai/v3.0.x/performant-dataloader.md): How to use Deep Lake's new dataloader built and optimized in C++
- [API Summary](https://docs-v3.activeloop.ai/v3.0.x/api-basics.md): Summary of the most important Deep Lake commands.
- [Data Layout](https://docs-v3.activeloop.ai/v3.0.x/how-it-works/data-layout.md): Understanding the data layout in Deep Lake
- [Tensor Relationships](https://docs-v3.activeloop.ai/v3.0.x/how-it-works/tensor-relationships.md): Understanding the correct data layout for successful visualization.
- [Visualizer Integration](https://docs-v3.activeloop.ai/v3.0.x/how-it-works/visualizer-integration.md): How to embed our visualizer in your application.
- [Shuffling in ds.pytorch()](https://docs-v3.activeloop.ai/v3.0.x/how-it-works/shuffling-in-ds.pytorch.md): Understanding data shuffling in Deep Lake's pytorch dataloader
- [Storage Synchronization](https://docs-v3.activeloop.ai/v3.0.x/how-it-works/storage-synchronization.md): Synchronizing data with long-term storage and achieving optimal performance using Deep Lake.
- [How to Contribute](https://docs-v3.activeloop.ai/v3.0.x/how-it-works/how-to-contribute.md): Guidelines for open source enthusiasts to contribute to our open-source data format.

## v3.0.0

- [Deep Lake Docs](https://docs-v3.activeloop.ai/v3.0.0/master.md): We hope you enjoy Docs for Deep Lake, the open-source Data Lake for Deep Learning by Activeloop. Deep Lake was formerly known as Activeloop Hub.
- [Quickstart](https://docs-v3.activeloop.ai/v3.0.0/quickstart.md): A jump-start guide to using Deep Lake.
- [Dataset Visualization](https://docs-v3.activeloop.ai/v3.0.0/dataset-visualization.md): How to connect Deep Lake datasets to Activeloop Platform
- [Storage & Credentials](https://docs-v3.activeloop.ai/v3.0.0/storage-and-credentials.md): How to access datasets in other clouds and manage their credentials.
- [Storage Options](https://docs-v3.activeloop.ai/v3.0.0/storage-and-credentials/storage-options.md): How to authenticate using Activeloop storage, AWS S3, and Google Cloud Storage.
- [Managed Credentials](https://docs-v3.activeloop.ai/v3.0.0/storage-and-credentials/managed-credentials.md): How to manage your credentials with Activeloop Platform
- [Getting Started](https://docs-v3.activeloop.ai/v3.0.0/getting-started.md): The comprehensive guide for learning to use Hub.
- [Step 1: Hello World](https://docs-v3.activeloop.ai/v3.0.0/getting-started/hello-world.md): Installing Deep Lake and accessing your first Deep Lake Dataset.
- [Step 2: Creating Deep Lake Datasets](https://docs-v3.activeloop.ai/v3.0.0/getting-started/creating-datasets-manually.md): Creating and storing Deep Lake Datasets.
- [Step 3: Understanding Compression](https://docs-v3.activeloop.ai/v3.0.0/getting-started/understanding-compression.md): Using compression to achieve optimal performance in Deep Lake.
- [Step 4: Accessing Data](https://docs-v3.activeloop.ai/v3.0.0/getting-started/accessing-datasets.md): Learn how Deep Lake Datasets can be accessed or loaded from a variety of storage locations.
- [Step 5: Visualizing Datasets](https://docs-v3.activeloop.ai/v3.0.0/getting-started/visualizing-datasets.md): Visualizing and inspecting your datasets.
- [Step 6: Using Activeloop Storage](https://docs-v3.activeloop.ai/v3.0.0/getting-started/using-activeloop-storage.md): Storing and loading datasets from Activeloop Platform Storage.
- [Step 7: Connecting Deep Lake Datasets to ML Frameworks](https://docs-v3.activeloop.ai/v3.0.0/getting-started/connecting-to-ml-frameworks.md): Connecting Deep Lake Datasets to machine learning frameworks such as PyTorch and TensorFlow.
- [Step 8: Parallel Computing](https://docs-v3.activeloop.ai/v3.0.0/getting-started/parallel-computing.md): Running computations and processing data in parallel.
- [Step 9: Dataset Version Control](https://docs-v3.activeloop.ai/v3.0.0/getting-started/dataset-version-control.md): Managing changes to your datasets using Version Control.
- [Step 10: Dataset Filtering](https://docs-v3.activeloop.ai/v3.0.0/getting-started/dataset-filtering.md): Filtering datasets using user-defined-functions or SQL-style queries.
- [Tutorials (w Colab)](https://docs-v3.activeloop.ai/v3.0.0/tutorials.md): Common workflows using hub (includes Colab notebooks)
- [Creating Datasets](https://docs-v3.activeloop.ai/v3.0.0/tutorials/creating-datasets.md): Workflows for creating Hub datasets (includes Colab notebooks)
- [Creating Complex Datasets](https://docs-v3.activeloop.ai/v3.0.0/tutorials/creating-datasets/creating-complex-datasets.md): Converting a multi-annotation dataset to Deep Lake format is helpful for understanding how to use Deep Lake with rich data.
- [Creating Object Detection Datasets](https://docs-v3.activeloop.ai/v3.0.0/tutorials/creating-datasets/creating-object-detection-datasets.md): Converting an object detection dataset to Deep Lake format is a great way to get started with datasets of increasing complexity.
- [Creating Time-Series Datasets](https://docs-v3.activeloop.ai/v3.0.0/tutorials/creating-datasets/creating-time-series-datasets.md): Deep Lake is a powerful tool for easily storing and sharing time-series datasets with your team.
- [Creating Datasets with Sequences](https://docs-v3.activeloop.ai/v3.0.0/tutorials/creating-datasets/creating-datasets-with-sequences.md): Deep Lake sequences are a powerful tool for storing temporal annotations such as bounding boxes in each frame of a video.
- [Creating Video Datasets](https://docs-v3.activeloop.ai/v3.0.0/tutorials/creating-datasets/creating-video-datasets.md): Get started with video datasets using Deep Lake.
- [Training Models](https://docs-v3.activeloop.ai/v3.0.0/tutorials/training-models.md): Workflows for training models using Hub datasets (includes Colab notebooks)
- [Training an Image Classification Model in PyTorch](https://docs-v3.activeloop.ai/v3.0.0/tutorials/training-models/training-an-image-classification-model-in-pytorch.md): Training an image classification model is a great way to get started with model training using Deep Lake datasets.
- [Training an Object Detection and Segmentation Model in PyTorch](https://docs-v3.activeloop.ai/v3.0.0/tutorials/training-models/training-an-object-detection-and-segmentation-model-in-pytorch.md): Training an object detection and segmentation model is a great way to learn about complex data preprocessing for training models.
- [Querying Datasets](https://docs-v3.activeloop.ai/v3.0.0/tutorials/querying-datasets.md): Activeloop Platform offer a highly-performant SQL-style query engine for filtering your data
- [Data Processing Using Parallel Computing](https://docs-v3.activeloop.ai/v3.0.0/tutorials/data-processing-using-parallel-computing.md): Deeplake offers built-in methods for parallelizing dataset computations in order to achieve faster data data processing.
- [Playbooks](https://docs-v3.activeloop.ai/v3.0.0/playbooks.md): How to perform complex workflows using Hub and Platform.
- [Querying, Training and Editing Datasets with Data Lineage](https://docs-v3.activeloop.ai/v3.0.0/playbooks/training-with-lineage.md): How to use queries and version control while training models.
- [Evaluating Model Performance](https://docs-v3.activeloop.ai/v3.0.0/playbooks/evaluating-model-performance.md): How to compare ground-truth annotations with model predictions
- [Training Reproducibility Using Deep Lake and Weights & Biases](https://docs-v3.activeloop.ai/v3.0.0/playbooks/training-reproducibility-with-wandb.md): How to achieve full reproducibility of model training using Deep Lake and W\&B
- [Working with Videos](https://docs-v3.activeloop.ai/v3.0.0/playbooks/working-with-videos.md): How manage video datasets and train models using Deep Lake.
- [Performant Dataloader (Alpha)](https://docs-v3.activeloop.ai/v3.0.0/performant-dataloader.md): How to use Deep Lake's new dataloader built and optimized in C++
- [API Summary](https://docs-v3.activeloop.ai/v3.0.0/api-basics.md): Summary of the most important Deep Lake commands.
- [Data Layout](https://docs-v3.activeloop.ai/v3.0.0/how-it-works/data-layout.md): Understanding the data layout in Deep Lake
- [Tensor Relationships](https://docs-v3.activeloop.ai/v3.0.0/how-it-works/tensor-relationships.md): Understanding the correct data layout for successful visualization.
- [Visualizer Integration](https://docs-v3.activeloop.ai/v3.0.0/how-it-works/visualizer-integration.md): How to embed our visualizer in your application.
- [Shuffling in ds.pytorch()](https://docs-v3.activeloop.ai/v3.0.0/how-it-works/shuffling-in-ds.pytorch.md): Understanding data shuffling in Deep Lake's pytorch dataloader
- [Storage Synchronization](https://docs-v3.activeloop.ai/v3.0.0/how-it-works/storage-synchronization.md): Synchronizing data with long-term storage and achieving optimal performance using Deep Lake.
- [How to Contribute](https://docs-v3.activeloop.ai/v3.0.0/how-it-works/how-to-contribute.md): Guidelines for open source enthusiasts to contribute to our open-source data format.