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 * 🏠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
            * 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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USER AUTHENTICATION

Registration and authentication in Deep Lake.

HOW TO REGISTER AND AUTHENTICATE IN DEEP LAKE

REGISTRATION AND LOGIN

In order to use Deep Lake features that require authentication (Activeloop
storage, connecting your cloud dataset to the Deep Lake UI, etc.) you should
register and login in the Deep Lake App.

AUTHENTICATION IN PROGRAMMATIC INTERFACES

You can create an API token in the Deep Lake App (top-right corner, user
settings) and authenticate in programatic interfaces using 2 options:

ENVIRONMENTAL VARIABLE

Set the environmental variable ACTIVELOOP_TOKEN to your API token. In Python,
this can be done using:

os.environ['ACTIVELOOP_TOKEN'] = <your_token>

PASS THE TOKEN TO INDIVIDUAL METHODS

You can pass your API token to individual methods that require authentication
such as:

ds = deeplake.load('hub://org_name/dataset_name', token = <your_token>)

PreviousInstallationNextWorkload Identities (Azure Only)

On this page
 * How to Register and Authenticate in Deep Lake
 * Registration and Login
 * Authentication in Programmatic Interfaces

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