README
Deep-Learning-in-Production
In this repository, I will share some useful notes and references about deploying deep learning-based models in production.
Convert PyTorch Models in Production:
- PyTorch Production Level Tutorials [Fantastic]
- The road to 1.0: production ready PyTorch
- PyTorch 1.0 tracing JIT and LibTorch C++ API to integrate PyTorch into NodeJS [Good Article]
- Model Serving in PyTorch
- PyTorch Summer Hackathon [Very Important]
- Deploying PyTorch and Building a REST API using Flask [Important]
- PyTorch model recognizing hotdogs and not-hotdogs deployed on flask
- Serving PyTorch 1.0 Models as a Web Server in C++ [Useful Example]
- PyTorch Internals [Interesting & Useful Article]
- Flask application to support pytorch model prediction
- Serving PyTorch Model on Flask Thread-Safety
- Serving PyTorch Models on AWS Lambda with Caffe2 & ONNX
- Serving PyTorch Models on AWS Lambda with Caffe2 & ONNX (Another Version)
- Deep Dive into ONNX Runtime
- EuclidesDB - multi-model machine learning feature database with PyTorch
- EuclidesDB - GitHub
- WebDNN: Fastest DNN Execution Framework on Web Browser
- FastAI PyTorch Serverless API (with AWS Lambda)
- FastAI PyTorch in Production (discussion)
- OpenMMLab Model Deployment Framework
- PyTorch Native Architecture Optimization: torchao
- TorchServe [Great Tool]