# EXAMPLE CODE - [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. --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://docs-v3.activeloop.ai/v3.8.2/example-code.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.