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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
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On this page
  • How to embed the Activeloop visualizer into your own web applications
  • HTML iframe (Alpha)
  • Javascript API (Alpha)

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  1. Technical Details

Visualizer Integration

How to embed our visualizer in your application.

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How to embed the Activeloop visualizer into your own web applications

Visualization engine allows the user to visualize, explore, and interact with Deep Lake datasets. In addition to using through the or , the Activeloop visualizer can also be embedded into your application.

HTML iframe (Alpha)

To embed into your html page, you can use our iframe integration:

<iframe src="https://app.activeloop.ai/visualizer/iframe?url=hub://activeloop/imagenet-train" width="800px" height="600px">

iframe URL: https://app.activeloop.ai/visualizer/iframe?url=hub://$org/$ds&{checkpoint=$checkpoint}&{vs=$visualizer_state}&{token=$token}

Params:

url - The url of the dataset vs - Visualizer state, which can be obtained from the platform url token - User token, for private datasets. If the value is ask then the UI will be populated for entering the token checkpoint - Dataset checkpoint query - Query string to apply on the dataset

Javascript API (Alpha)

To have more fine grained control, you can embed the visualizer using Javascript:

<div id='container'></div>
<script src="https://app.activeloop.ai/visualizer/vis.js"></script>
<script>
  let container = document.getElementById('container')
  window.vis.visualize("hub://activeloop/imagenet-train", null, null, container, null)
</script>

or to visualize private datasets with authentication

<div id='container'></div>
<script src="https://app.activeloop.ai/visualizer/vis.js"></script>
<script>
  let container = document.getElementById('container')
  window.vis.visualize("hub://org/private", null, null, container, {
		requireSignin: true
	})
</script>

Interface

Below you can find definitions of the arguments.

/// ds - Dataset url
/// commit - optional commit id
/// state - optional initial state of the visualizer
/// container - HTML element serving as container for visualizer elements
/// options - optional Visualization options
static visualize(
  ds: string,
  commit: string | null = null,
  state: string | null = null,
  container: HTMLElement,
  options: VisOptions | null
): Promise<Vis>;

/// backlink - Show backlink to platform button
/// singleSampleView - Enable single sample view through enter key
/// requireSignin - Requires signin to get access token
/// token - Token id
/// gridMode - Canvas vs Grid
/// queryString - Query to apply on the iframe
export type VisOptions = {
  backlink?: Boolean
  singleSampleView?: Boolean
  requireSignin?: Boolean
  token: string | null
  gridMode?: "canvas" | "grid"
  queryString?: string
}

This visualize returns Promise<Vis> which can be used to dynamically change the visualizer state. Vis supports only query functions for now

class Vis
{
	/// Asynchronously runs a query and resolves the promise when query completed.
  /// In case of error in query, rejects the promise.
	query(queryString: string): Promise<void>
}

🔬
Activeloop UI
in Python