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v3.9.16
v3.9.16
  • 🏠Deep Lake Docs
  • List of ML Datasets
  • 🏗️SETUP
    • Installation
    • User Authentication
      • Workload Identities (Azure Only)
    • Storage and Credentials
      • Storage Options
      • Setting up Deep Lake in Your Cloud
        • Microsoft Azure
          • Configure Azure SSO on Activeloop
          • Provisioning Federated Credentials
          • Enabling CORS
        • Google Cloud
          • Provisioning Federated Credentials
          • Enabling CORS
        • Amazon Web Services
          • Provisioning Role-Based Access
          • Enabling CORS
  • 📚Examples
    • Deep Learning
      • Deep Learning Quickstart
      • Deep Learning Guide
        • Step 1: Hello World
        • Step 2: Creating Deep Lake Datasets
        • Step 3: Understanding Compression
        • Step 4: Accessing and Updating Data
        • Step 5: Visualizing Datasets
        • Step 6: Using Activeloop Storage
        • Step 7: Connecting Deep Lake Datasets to ML Frameworks
        • Step 8: Parallel Computing
        • Step 9: Dataset Version Control
        • Step 10: Dataset Filtering
      • Deep Learning Tutorials
        • Creating Datasets
          • Creating Complex Datasets
          • Creating Object Detection Datasets
          • Creating Time-Series Datasets
          • Creating Datasets with Sequences
          • Creating Video Datasets
        • Training Models
          • Splitting Datasets for Training
          • Training an Image Classification Model in PyTorch
          • Training Models Using MMDetection
          • Training Models Using PyTorch Lightning
          • Training on AWS SageMaker
          • Training an Object Detection and Segmentation Model in PyTorch
        • Updating Datasets
        • Data Processing Using Parallel Computing
      • Deep Learning Playbooks
        • Querying, Training and Editing Datasets with Data Lineage
        • Evaluating Model Performance
        • Training Reproducibility Using Deep Lake and Weights & Biases
        • Working with Videos
      • Deep Lake Dataloaders
      • API Summary
    • RAG
      • RAG Quickstart
      • RAG Tutorials
        • Vector Store Basics
        • Vector Search Options
          • LangChain API
          • Deep Lake Vector Store API
          • Managed Database REST API
        • Customizing Your Vector Store
        • Image Similarity Search
        • Improving Search Accuracy using Deep Memory
      • LangChain Integration
      • LlamaIndex Integration
      • Managed Tensor Database
        • REST API
        • Migrating Datasets to the Tensor Database
      • Deep Memory
        • How it Works
    • Tensor Query Language (TQL)
      • TQL Syntax
      • Index for ANN Search
        • Caching and Optimization
      • Sampling Datasets
  • 🔬Technical Details
    • Best Practices
      • Creating Datasets at Scale
      • Training Models at Scale
      • Storage Synchronization and "with" Context
      • Restoring Corrupted Datasets
      • Concurrent Writes
        • Concurrency Using Zookeeper Locks
    • Deep Lake Data Format
      • Tensor Relationships
      • Version Control and Querying
    • Dataset Visualization
      • Visualizer Integration
    • Shuffling in Dataloaders
    • How to Contribute
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  • How to Query Using Deep Lake Tensor Query Language (TQL)
  • Dataset Query Summary
  • Query Syntax
  • Saving and Using Views In Deep Lake

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  1. Examples

Tensor Query Language (TQL)

Deep Lake offers a performant SQL-style query engine for data analysis.

PreviousHow it WorksNextTQL Syntax

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How to Query Using Deep Lake Tensor Query Language (TQL)

Querying enables users to filter data, gather insights, and focus their work on the most relevant data. Deep Lake offers a highly-performant query engine built in C++ and optimized for the Deep Lake data format.

The Deep Lake query engine is only accessible to registered and authenticated users and cannot be used with local datasets.

Dataset Query Summary

Querying in the App

Querying in the Vector Store Python API

view = vector_store.search(query = <query_string>)

Querying in the Deep Learning Python API

Queries can also be performed in the Python API using:

view = ds.query(<query_string>)

Query Syntax

Saving and Using Views In Deep Lake

view.save_view(message = 'Samples with monarchs')

In order to maintain data lineage, Dataset Views are immutable and are connected to specific commits. Therefore, views can only be saved if the dataset has a commit and there are no uncommitted changes in the HEAD. You can check for this using ds.has_head_changes

Dataset Views can be loaded in the python API and they can passed to ML frameworks just like regular datasets:

ds_view = ds.load_view(view_id, optimize = True, num_workers = 2)

for data in ds_view.pytorch():
    # Training loop here

The optimize parameter in ds.load_view(..., optimize = True) materializes the Dataset View into a new sub-dataset that is optimized for streaming. If the original dataset uses linked tensors, the data will be copied to Deep Lake format.

Optimizing the Dataset View is critical for achieving rapid streaming.

If the saved Dataset View is no longer needed, it can be deleted using:

ds.delete_view(view_id)

The query results (Dataset Views) can be saved in the UI as shown above, or if the view is generated in Python, it can be saved using the Python API below. Full details are .

📚
TQL Syntax
available here