pytorch-deployment

What problem does it solve?

This Skill bridges the gap between developing PyTorch models and deploying them in production environments, enabling efficient inference and integration into various applications.

Core Features & Use Cases

• Model Serialization: Export models using TorchScript (JIT/Tracing) for standalone execution.
• Cross-Platform Export: Convert models to ONNX format for use with ONNX Runtime and other accelerators.
• Inference Optimization: Apply techniques like quantization to reduce model size and improve speed.
• C++ Integration: Utilize LibTorch for deploying models in C++ applications.
• Use Case: You've trained a PyTorch image classification model and need to deploy it as a high-performance API service or embed it within a C++ desktop application.

Quick Start

Use the pytorch-deployment skill to export the current PyTorch model to TorchScript format.