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v3.4.0
v3.4.0
  • Deep Lake Docs
  • List of ML Datasets
  • Quickstart
  • Dataset Visualization
  • Storage & Credentials
    • Storage Options
    • User Authentication
    • Managed Credentials
      • Enabling CORS
      • Provisioning Role-Based Access
  • API Reference
  • Enterprise Features
    • Querying Datasets
      • Sampling Datasets
    • Performant Dataloader
  • EXAMPLE CODE
  • Getting Started
    • 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
  • Tutorials (w Colab)
    • Deep Lake Vector Store in LangChain
    • Creating Datasets
      • Creating Complex Datasets
      • Creating Object Detection Datasets
      • Creating Time-Series Datasets
      • Creating Datasets with Sequences
      • Creating Video Datasets
    • Training Models
      • 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
  • Playbooks
    • Querying, Training and Editing Datasets with Data Lineage
    • Evaluating Model Performance
    • Training Reproducibility Using Deep Lake and Weights & Biases
    • Working with Videos
  • API Summary
  • Technical Details
    • Best Practices
      • Creating Datasets at Scale
      • Training Models at Scale
      • Storage Synchronization and "with" Context
      • Restoring Corrupted Datasets
    • Data Layout
    • Version Control and Querying
    • Tensor Relationships
    • Visualizer Integration
    • Shuffling in dataloaders
    • How to Contribute
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On this page
  • How to Register and Authenticate in Deep Lake
  • Registration
  • Authentication in Programmatic Interfaces

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  1. Storage & Credentials

User Authentication

Registration and authentication in Deep Lake.

How to Register and Authenticate in Deep Lake

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 with Deep Lake.

Registration

You can register in the Deep Lake App, or in the CLI using:

activeloop register -e <email> -u <username> -p <password>

Authentication in Programmatic Interfaces

After registering, you can create an API token in the Deep Lake UI (top-right corner, user settings) and authenticate in programatic interfaces using 3 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>

CLI Login

Login in the CLI using two options:

  • activeloop login -u <username> -p <password>

  • activeloop login -t <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>)

PreviousStorage OptionsNextManaged Credentials

Last updated 2 years ago

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