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

AWS Neuron Deep Learning Containers (DLCs) are a set of Docker images for training and serving models on AWS Trainium and Inferentia instances using AWS Neuron SDK.

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

Karpenter is a Kubernetes Node Autoscaler built for flexibility, performance, and simplicity.

An open and reliable container runtime

The Finch CLI an open source client for container development

Core dependencies for Finch

End-to-end tests for Finch

contaiNERD CTL - Docker-compatible CLI for containerd, with support for Compose, Rootless, eStargz, OCIcrypt, IPFS, ...

runc bindings for Go

cgroups package for Go

Infrastructure to build, test, and release Finch

Kubernetes website and documentation repo:

Homebrew formulae that allows installation of AWS tools through the Homebrew package manager.

concurrent, cache-efficient, and Dockerfile-agnostic builder toolkit

firecracker-containerd enables containerd to manage containers as Firecracker microVMs

Secure and fast microVMs for serverless computing.

Logs into Amazon ECR with the local Docker client.

An operating system designed for hosting containers

The official CLI for Amazon EKS

SageMaker specific extensions to TensorFlow.

A Spark library for Amazon SageMaker.

AWS SDK for Python

Multi Model Server is a tool for serving neural net models for inference

This is the public roadmap for AWS container services (ECS, ECR, Fargate, and EKS).

Example notebooks for working with SageMaker Studio Lab. Sign up for an account at the link below!

Example notebooks that show how to apply machine learning, deep learning and reinforcement learning in Amazon SageMaker

A continuous integration (CI) system for 📓 Jupyter notebooks, built using 🧠 Amazon SageMaker.

Experiment tracking and metric logging for Amazon SageMaker notebooks and model training.