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Awsome Distributed AI

Awsome Distributed AI is a public repository of reference architectures, best practices and examples for distributed AI training and inference on Amazon SageMaker HyperPod, AWS ParallelCluster, AWS Parallel Computing Service and Amazon EKS.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Awsome Distributed AI — OpenViST project profile in AI Infrastructure & Serving.
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Understand the project

Practical overview

When it fits

Use it when planning an AWS distributed AI environment or when you need a starting example tied to a training framework, inference engine, or use case.

Who it is for

  • Teams planning distributed AI training or inference on AWS.
  • Practitioners comparing cluster patterns or looking for framework-specific workload examples.
  • Operators seeking reference material for cluster setup, validation, monitoring or profiling.

Documented uses

  • Review cluster architecture patterns and infrastructure templates for an AWS compute environment.
  • Explore training examples using PyTorch DDP/FSDP, Megatron-LM or NeMo.
  • Browse inference examples using engines such as vLLM, SGLang or NVIDIA Dynamo.
  • Consult validation, observability and micro-benchmark resources alongside workload examples.
Before you choose

Requirements & limits

The repository is public and identified as open source under the MIT-0 license.

  • Choose examples according to the AWS service and orchestration path; the repository covers several distinct environments.
  • Architecture resources include CloudFormation and Terraform, while custom image workflows use Packer and Ansible.
  • Example setup and launch materials vary by workload and may include Dockerfiles, configuration, Slurm scripts or Kubernetes manifests.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Choose and browse a distributed AI example

Find a training, inference, or use-case example suited to a supported AWS platform and framework.

Before you start

  • A target platform among Amazon SageMaker HyperPod, AWS ParallelCluster, AWS PCS, or Amazon EKS.
  • A training or inference framework/engine or a specific use case to explore.

Steps

  1. Review the repository’s architecture directories to identify the cluster reference architectures and building blocks for your platform.

  2. Browse examples/training/ or examples/inference/ for framework-centric examples, or examples/use-cases/ for task-centric demos.

  3. Open the chosen example’s top-level README for its introduction and environment setup, then consult its service-specific subdirectory for launch instructions.

Expected outcome

A selected example with its environment setup and platform-specific launch instructions identified.

Guide limitations

  • The available guidance describes repository organization and where to find launch instructions; it does not provide the steps to deploy or run a particular example.
  • Examples and architectures differ by platform, framework, and use case.
Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
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Sources checked
13 October 2026
Original website title
awslabs/awsome-distributed-ai · Official homepage title · retained in its source language
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