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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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  • How to Use Activeloop-Provided Storage
  • Register
  • Login
  • API Tokens

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  1. Getting Started

Step 6: Using Activeloop Storage

Storing and loading datasets from Deep Lake Storage.

How to Use Activeloop-Provided Storage

Register

You can store your Deep Lake Datasets with Activeloop by first creating an account in the Deep Lake App or in the CLI using:

activeloop register

Login

In order for the Python API to authenticate with your account, you can use API tokens (see below), or log in from the CLI using:

!activeloop login

# Alternatively, you can directly input your username and password in the same line:
# activeloop login -u <your_username> -p <your_password>

You can then access or create Deep Lake Datasets by passing the Deep Lake path to deeplake.dataset()

import deeplake

deeplake_path = 'hub://organization_name/dataset_name'
               #'hub://jane_smith/my_awesome_dataset'
               
ds = deeplake.dataset(deeplake_path)

When you create an account in Deep Lake, a default organization is created that has the same name as your username. You can also create other organizations that represent companies, teams, or other collections of multiple users.

Public datasets such as 'hub://activeloop/mnist-train' can be accessed without logging in.

API Tokens

Once you have an Activeloop account, you can create tokens in the Deep Lake App (Organization Details -> API Tokens) and authenticate by setting the environmental variable:

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

Or login in the CLI using the token:

!activeloop login --token <your_token>

If you are not logged in through the CLI, you may also pass the token to python commands that require authentication:

ds = deeplake.load(deeplake_path, token = 'xyz')
PreviousStep 5: Visualizing DatasetsNextStep 7: Connecting Deep Lake Datasets to ML Frameworks

Last updated 2 years ago

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