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Model Benchmarking
Export to ONNX

Export to ONNX

Export Hugging Face models to ONNX

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What is Export to ONNX ?

Export to ONNX is a tool designed to convert Hugging Face models into the ONNX (Open Neural Network Exchange) format. This allows users to export their models for deployment across various platforms, frameworks, and devices. By enabling this conversion, Export to ONNX enhances model portability, cross-framework compatibility, and efficiency in production environments.

Features

• Converts Hugging Face models to ONNX format for broader compatibility
• Supports multiple deep learning frameworks for inference
• Optimizes models for faster inference and reduced latency
• Enables deployment on edge devices and cloud platforms
• Simplifies model sharing and collaboration across teams
• Integrates seamlessly with the Hugging Face ecosystem

How to use Export to ONNX ?

  1. Install the required package
    Run pip install onnx to ensure you have the ONNX library installed.
  2. Load your Hugging Face model
    Use the Hugging Face transformers or torch library to load your model.
    from transformers import AutoModelForSequenceClassification, AutoTokenizer
    model_name = "bert-base-uncased"
    model = AutoModelForSequenceClassification.from_pretrained(model_name)
    tokenizer = AutoTokenizer.from_pretrained(model_name)
    
  3. Convert the model to ONNX format
    Use the torch.onnx.export function to convert the model.
    import torch
    dummy_input = "This is a sample input"
    inputs = tokenizer(dummy_input, return_tensors="pt")
    with torch.no_grad():
        torch.onnx.export(
            model,
            inputs["input_ids"],
            "model.onnx",
            opset_version=12,
            input_names=["input_ids"],
            output_names=["output"],
        )
    
  4. Verify the ONNX model (optional)
    Ensure the converted model behaves as expected by running inference with the ONNX Runtime.

Frequently Asked Questions

What models are supported for export to ONNX?
Export to ONNX supports a wide range of Hugging Face models, including BERT, RoBERTa, and other popular architectures. However, some niche or custom models may require additional configuration.

How do I optimize my ONNX model for inference?
ONNX models can be optimized using tools like ONNX Runtime or TensorRT. These tools can reduce latency and improve performance on target hardware.

What if the model fails to convert to ONNX?
If conversion fails, check for unsupported operations in your model. Some custom layers or operations may not be compatible with ONNX. You may need to modify your model architecture or update your version of ONNX.

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