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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
          • 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 Use Activeloop-Provided Storage
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  • API Tokens

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  1. Examples
  2. Deep Learning
  3. Deep Learning Guide

Step 6: Using Activeloop Storage

Storing and loading datasets from Deep Lake Storage.

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

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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 the 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

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')

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

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