ml-model-export

To convert a PyTorch model to ONNX, you export the trained model while specifying dynamic axes for flexible input sizes. This process generates a cross-platform inference graph that can be validated and optimized for faster runtime execution.

Exporting PyTorch models to TorchScript via tracing or scripting prepares them for mobile or non-Python environments. This packaging method produces a deployable artifact that runs independently of Python, ensuring cross-platform compatibility.

Yes, you can convert an ONNX model into a TensorRT engine with selectable precision including FP32, FP16, or INT8. This conversion builds an optimized engine specifically for NVIDIA hardware acceleration during downstream inference.

You can upload exported model artifacts to Hugging Face Hub and optionally log them with MLflow. This registry publishing workflow manages and shares the deployment-ready formats, ensuring traceable model management across runtimes.

Specifying dynamic axes during ONNX export allows the resulting graph to accept variable input dimensions like batch or sequence length. This satisfies format-specific conversion requirements, ensuring the model handles flexible input shapes during cross-platform inference.