Toolkit for running PyTorch training scripts on SageMaker. Dockerfiles used for building SageMaker Pytorch Containers are at https://github.com/aws/deep-learning-containers.

Amazon SageMaker Debugger provides functionality to save tensors during training of machine learning jobs and analyze those tensors

Train machine learning models within a 🐳 Docker container using 🧠 Amazon SageMaker.

A library for training and deploying machine learning models on Amazon SageMaker

Example 📓 Jupyter notebooks that demonstrate how to build, train, and deploy machine learning models using 🧠 Amazon SageMaker.

🤗Transformers: State-of-the-art Natural Language Processing for Pytorch and TensorFlow 2.0.

Deep Learning Examples

AWS Deep Learning Containers (DLCs) are a set of Docker images for training and serving models in TensorFlow, TensorFlow 2, PyTorch, and MXNet.

Training v0.7 results

OpenMMLab Detection Toolbox and Benchmark

Natural language processing & computer vision models optimized for AWS

Tensors and Dynamic neural networks in Python with strong GPU acceleration

🦓<->🦒 🌃<->🌆 A collection of image to image papers with code (constantly updating)

Basic Super-Resolution Toolbox, including SRResNet, SRGAN, ESRGAN, etc.

Models and examples built with TensorFlow

README文件语法解读，即Github Flavored Markdown语法介绍