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v3.1.5
v3.1.5
  • Deep Lake Docs
  • List of ML Datasets
  • Quickstart
  • Dataset Visualization
  • Storage & Credentials
    • Storage Options
    • 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 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)
    • 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
    • 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
  • How Deep Lake Works
    • Data Layout
    • Version Control and Querying
    • Tensor Relationships
    • Visualizer Integration
    • Shuffling in ds.pytorch()
    • Storage Synchronization
    • How to Contribute
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  • How to Use Activeloop-Provided Storage
  • Register
  • Login
  • Tokens

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  1. Getting Started

Step 6: Using Activeloop Storage

Storing and loading datasets from Activeloop Platform Storage.

PreviousStep 5: Visualizing DatasetsNextStep 7: Connecting Deep Lake Datasets to ML Frameworks

Last updated 2 years ago

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How to Use Activeloop-Provided Storage

Register

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

activeloop register

Login

In order for the Python API to authenticate with your account, you should log in from the CLI using:

activeloop login

# Alternatively, you can directly input your username and password in the same line:
# activeloop login -u my_username -p my_password

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

import deeplake

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

When you create an account in Activeloop Platform, 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.

Tokens

ds = deeplake.load(platform_path, token = 'xyz')

Once you have an Activeloop account, you can create tokens in (Organization Details -> API Tokens) and pass them to python commands that require authentication using:

Activeloop Platform
Activeloop Platform