在这个存储库中，我将分享一些关于在生产环境中部署基于深度学习的模型的有用的注释和参考资料。
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)
- 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)
Convert PyTorch Models to C++:
- Loading a PyTorch Model in C++ [Fantastic]
- PyTorch C++ API [Bravo]
- An Introduction To Torch (Pytorch) C++ Front-End [Very Good]
- Blogs on using PyTorch C++ API [Good]
- ATen: A TENsor library
- Important Issue about PyTorch-like C++ interface
- PyTorch C++ API Test
- PyTorch via C++ [Useful Notes]
- AUTOGRADPP
- PyTorch C++ Library
- Direct C++ Interface to PyTorch
- A Python module for compiling PyTorch graphs to C
Deploy TensorFlow Models in Production:
- How to deploy Machine Learning models with TensorFlow - Part1
- How to deploy Machine Learning models with TensorFlow - Part2
- How to deploy Machine Learning models with TensorFlow - Part3
- Neural Structured Learning (NSL) in TensorFlow [Great]
- Building Robust Production-Ready Deep Learning Vision Models
- Creating REST API for TensorFlow models
- “How to Deploy a Tensorflow Model in Production” by Siraj Raval on YouTube
- Code for the “How to Deploy a Tensorflow Model in Production” by Siraj Raval on YouTube
- How to deploy an Object Detection Model with TensorFlow serving [Very Good Tutorial]
- Freeze Tensorflow models and serve on web [Very Good Tutorial]
- How to deploy TensorFlow models to production using TF Serving [Good]
- How Zendesk Serves TensorFlow Models in Production
- TensorFlow Serving Example Projects
- Serving Models in Production with TensorFlow Serving [TensorFlow Dev Summit 2017 Video]
- Building TensorFlow as a Standalone Project
- TensorFlow C++ API Example
- TensorFlow.js
- Introducing TensorFlow.js: Machine Learning in Javascript
Convert Keras Models in Production:
- Deep learning in production with Keras, Redis, Flask, and Apache [Rank: 1st & General Usefult Tutorial]
- Deploying a Keras Deep Learning Model as a Web Application in Python [Very Good]
- Deploying a Python Web App on AWS [Very Good]
- Deploying Deep Learning Models Part 1: Preparing the Model
- Deploying your Keras model
- Deploying your Keras model using Keras.JS
- “How to Deploy a Keras Model to Production” by Siraj Raval on Youtube
- Deploy Keras Model with Flask as Web App in 10 Minutes [Good Repository]
- Deploying Keras Deep Learning Models with Flask
- keras2cpp
Deploy MXNet Models in Production:
- Model Server for Apache MXNet
- Running the Model Server
- Exporting Models for Use with MMS
- Single Shot Multi Object Detection Inference Service
- Amazon SageMaker
- How can we serve MXNet models built with gluon api
- MXNet C++ Package
- MXNet C++ Package Examples
- MXNet Image Classification Example of C++
- MXNet C++ Tutorial
- An introduction to the MXNet API [Very Good Tutorial for Learning MXNet]
- GluonCV
- GluonNLP
- [Model Quantization for Production-Level Neural Net
