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ml-model-export

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Export and deploy trained models fast

Education & Research#pytorch#model deployment#onnx#tensorrt#torchscript#model export#hugging face hub
Authornishide-dev
Version1.0.0
Installs0

System Documentation

What problem does it solve?

Teams need a reliable way to turn a trained PyTorch model into deployment-ready artifacts without manually re-implementing conversion pipelines for every target runtime.

Core Features & Use Cases

  • ONNX export with validation and optimization: Convert to ONNX for cross-platform inference and optimize the graph for faster runtime execution.
  • TorchScript export for production/mobile: Produce TorchScript via tracing or scripting to run without Python, including mobile deployment.
  • TensorRT conversion for NVIDIA acceleration: Convert an ONNX model into a TensorRT engine with selectable precision (FP32/FP16/INT8).
  • Registry publishing workflows: Upload exported artifacts to Hugging Face Hub and optionally log/register with MLflow for traceable model management.

Quick Start

Tell your AI to export your Lightning checkpoint into ONNX, TorchScript, and then upload the chosen artifact to Hugging Face Hub for deployment.

Dependency Matrix

Required Modules

None required

Components

Standard package

💻 Claude Code Installation

Recommended: Let Claude install automatically. Simply copy and paste the text below to Claude Code.

Please help me install this Skill:
Name: ml-model-export
Download link: https://github.com/nishide-dev/claude-code-ml-research/archive/main.zip#ml-model-export

Please download this .zip file, extract it, and install it in the .claude/skills/ directory.
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