Speeding up first ICR operation by predownloading models

Use warmup to pre-download vision models before processing documents.

Common use cases include:

• Removing first-request latency in user-facing apps
• Preparing batch jobs before processing starts
• Marking containers ready only after dependencies are available
• Preloading models before offline operation
• Meeting latency targets for production APIs

This guide shows how to warm up ICR models so ExtractContent() runs without initial download delays.

How Nutrient helps

Nutrient .NET SDK handles model download orchestration and cache management.

The SDK handles:

• Model downloads and cache storage details
• Engine-specific model dependencies
• Download retries and transient failure handling
• Readiness checks for model availability

Complete implementation

This example warms up ICR models and then runs extraction:

using Nutrient;

Warming up Vision API

Open a document with a using statement(opens in a new tab), set the engine to ICR, create a vision instance, and call Warmup().

In this sample:

• Setting Engine to VisionEngine.Icr selects ICR mode.
• vision.Warmup() downloads required models.
• Models are cached for subsequent requests.
• Console output shows progress.

try
{
    using Document document = Document.Open("input.png");
    // Configure ICR engine
    document.Settings.VisionSettings.Engine = VisionEngine.Icr;

    // Create Vision instance
    var vision = Vision.Set(document);

    // Pre-download all required models
    // This ensures subsequent ExtractContent() calls are fast
    Console.WriteLine("Downloading Vision models...");
    vision.Warmup();
    Console.WriteLine("Models ready!");

Processing documents after warmup

After warmup, run ExtractContent() without download latency.

In this sample:

• ExtractContent() returns a JSON string.
• The JSON output is written to output.json.
• The using statement releases the document handle.

    // Now ExtractContent() won't need to download anything
    string contentJson = vision.ExtractContent();

    File.WriteAllText("output.json", contentJson);
}
catch (NutrientException e)
{
    Console.Error.WriteLine($"Error: {e.Message}");
    Environment.Exit(1);
}

Best practices

Apply these patterns for using warmup effectively in production environments:

• Application startup — Run warmup before accepting requests.
• Background task — Run warmup asynchronously during initialization.
• Health checks — Expose warmup status in readiness probes.
• Deployment pipelines — Validate model availability during deployment.
• Offline environments — Download models while connected, then process offline.

What gets downloaded?

Warmup downloads model sets based on the configured vision engine:

• ICR mode (VisionEngine.Icr) — Layout, text, tables, equations, and key-value detection models
• OCR mode (VisionEngine.AdaptiveOcr) — OCR language and text recognition resources
• VLM-enhanced mode (VisionEngine.VlmEnhancedIcr) — ICR resources plus VLM-related resources

Downloaded models are cached locally and reused across restarts until the cache is cleared or models are updated.

Conclusion

Use this workflow to pre-download ICR requirements:

1. Open a document with a using statement for automatic resource cleanup after warmup and processing complete.
2. The SDK supports multiple document formats, including PNG, JPEG, PDF, and TIFF for vision operations.
3. Configure the vision engine with Settings.VisionSettings.Engine.
4. Set the engine to ICR with VisionEngine.Icr to enable advanced document understanding with layout detection, text recognition, table extraction, equation recognition, and key-value pair detection.
5. Alternative engines include OCR mode for basic text extraction and VLM-enhanced mode for semantic understanding with vision language models.
6. Create a vision instance with Vision.Set() bound to the document with configured engine settings.
7. Call vision.Warmup() to trigger pre-download of all AI models required for the configured vision engine, fetching models from the SDK’s model repository and caching them locally.
8. Warmup downloads different model sets based on engine configuration — ICR downloads comprehensive document understanding models, OCR downloads text recognition models, and VLM downloads ICR models plus semantic understanding resources.
9. Console output provides feedback during model downloads, informing users about download progress and completion status for potentially multi-second operations.
10. After warmup completes, call vision.ExtractContent() to perform ICR operations without model download delays, ensuring predictable and fast processing for all subsequent requests.
11. The ExtractContent() method returns extracted content as JSON, including document structure (headings, paragraphs, tables, lists), textual content, table structures, equations, and key-value pairs.
12. Write the extracted JSON to a file for downstream processing.
13. Handle NutrientException failures for vision processing issues, including model download errors, processing failures, or configuration issues.

For related image extraction workflows, refer to the .NET SDK guides.

Download this ready-to-use sample package to integrate warmup into application startup.