LogoLogo
API ReferenceGitHubSlackService StatusLogin
v3.8.16
v3.8.16
  • Deep Lake Docs
  • Vector Store Quickstart
  • Deep Learning Quickstart
  • Storage & Credentials
    • Storage Options
    • User Authentication
    • Storing Deep Lake Data in Your Own Cloud
      • Microsoft Azure
        • Provisioning Federated Credentials
        • Enabling CORS
      • Amazon Web Services
        • Provisioning Role-Based Access
        • Enabling CORS
  • List of ML Datasets
  • 🏢High-Performance Features
    • Introduction
    • Performant Dataloader
    • Tensor Query Language (TQL)
      • TQL Syntax
      • Sampling Datasets
    • Deep Memory
      • How it Works
    • Index for ANN Search
      • Caching and Optimization
    • Managed Tensor Database
      • REST API
      • Migrating Datasets to the Tensor Database
  • 📚EXAMPLE CODE
    • Getting Started
      • Vector Store
        • Step 1: Hello World
        • Step 2: Creating Deep Lake Vector Stores
        • Step 3: Performing Search in Vector Stores
        • Step 4: Customizing Vector Stores
      • Deep Learning
        • 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)
      • Vector Store Tutorials
        • Vector Search Options
          • Deep Lake Vector Store API
          • REST API
          • LangChain API
        • Image Similarity Search
        • Deep Lake Vector Store in LangChain
        • Deep Lake Vector Store in LlamaIndex
        • Improving Search Accuracy using Deep Memory
      • 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
      • Concurrent Writes
        • Concurrency Using Zookeeper Locks
    • Playbooks
      • Querying, Training and Editing Datasets with Data Lineage
      • Evaluating Model Performance
      • Training Reproducibility Using Deep Lake and Weights & Biases
      • Working with Videos
    • Low-Level API Summary
  • 🔬Technical Details
    • Best Practices
      • Creating Datasets at Scale
      • Training Models at Scale
      • Storage Synchronization and "with" Context
      • Restoring Corrupted Datasets
      • Concurrent Writes
    • Data Layout
    • Version Control and Querying
    • Dataset Visualization
    • Tensor Relationships
    • Visualizer Integration
    • Shuffling in dataloaders
    • How to Contribute
Powered by GitBook
On this page
  • How to Use Activeloop-Provided Storage
  • Register
  • Login
  • API Tokens

Was this helpful?

  1. EXAMPLE CODE
  2. Getting Started
  3. Deep Learning

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

Was this helpful?

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:

📚
Deep Lake App
Deep Lake App