# aws/deep-learning-containers

One stop shop for running AI/ML on AWS.

Repository: https://github.com/aws/deep-learning-containers
Canonical: https://ross.abutalabs.com/products/deep-learning-containers
Homepage: https://aws.github.io/deep-learning-containers/
Language: Python
License: NOASSERTION
License Family: other
Topics: aws, ai, ml
Last push: 2026-09-03T01:30:56+00:00

## Health v2 (maintenance only)
Score: 96/100 (v2, computed 2026-09-03T02:39:23.370411+00:00)
- activity 100, release rhythm 88, longevity 100
- inputs: {"age_days": 2420, "days_push": 0, "days_rel": 0, "gap_med": 0, "n_releases_24m": 2692}
- flags: no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1186, forks 555 (observed 2026-09-03T02:15:09.427365+00:00)

## What it is
AWS Deep Learning Containers are pre-built, security-patched Docker images for running AI/ML workloads on AWS services like EC2, EKS, and SageMaker. The repository contains the build tooling and release pipelines for images covering PyTorch, TensorFlow, vLLM, SGLang, Ray, and more.

## Use cases
- serve large language models on AWS with vLLM or SGLang
- run PyTorch or TensorFlow training on EC2 or SageMaker
- deploy ML models with Ray Serve or TensorFlow Serving
- transcribe and diarize audio with WhisperX
- serve quantized GGUF models with llama.cpp on CPU or GPU
- run multimodal TTS and image generation models
- get security-patched deep learning environments without setup

## When to choose
- you run AI/ML workloads on AWS and want tested, pre-configured Docker images
- you need optimized LLM inference images for EC2, EKS, or SageMaker
- you want to avoid manually installing CUDA drivers and framework dependencies

## When to avoid
- you deploy outside AWS and prefer building your own images
- you need a lightweight solution without Docker or GPU infrastructure
- you require a permissive open-source license (license is custom)

## Facets
- artifact type: infra-config
- maturity: active
- function: llm-inference, machine-learning, deep-learning, container-runtime, gpu-computing
- domain: machine-learning, deep-learning, large-language-models, cloud-computing, gpu-computing
- platform: cloud, python
- tags: docker-images, aws, sagemaker, vllm, pytorch, tensorflow, ec2, eks, prebuilt-images, containers, docker, gpu, linux

## Member repositories
- aws/deep-learning-containers (main) score 96

## Provenance
- Observed fields: from GitHub, fetched 2026-09-03T02:15:09.427365+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T06:23:52.622643+00:00, confidence not recorded.
  - readme: https://github.com/aws/deep-learning-containers (fetched 2026-09-03T02:15:09.427365+00:00, sha 70ccaeefb487)
  - homepage: https://aws.github.io/deep-learning-containers/ (fetched 2026-08-29T12:31:04.543993+00:00, sha c00bfb36110b)
- Data as of 2026-08-30T08:39:29.467469+00:00.