awsome-distributed-ai: AWS reference architectures for distributed training and inference

What awsome-distributed-ai actually solves

The gap this repository addresses is not model code. It is the cluster underneath it. Distributed training on AWS means choosing between SageMaker HyperPod, AWS ParallelCluster, AWS Parallel Computing Service and Amazon EKS, then wiring a VPC, subnets, an S3 bucket for checkpoints, an identity layer for multi-user access, and a job accounting database before a single gradient step runs. The README describes the repository as reference architectures and examples for distributed AI training and inference across exactly those four compute options.

The audience is an infrastructure or ML platform engineer who has already picked a scheduler and now needs a concrete CloudFormation or Terraform starting point plus a runnable example that exercises it. The repository is organized so that each subdirectory under architectures/ is either a deployable cluster architecture or a shared building block, with categories listed as Storage, Network, Compute, Identity and Tooling. That categorization is the clearest signal of intent: vpc_network and common are prerequisites, the four compute directories are the alternatives, and ldap_server plus accounting-database are the multi-tenant glue you would otherwise write yourself.

How the repository is laid out, and why the split matters

The top level separates six concerns: architectures/ for cluster templates, ami/ for Packer and Ansible image builds, examples/ for runnable workloads, validation/ for environment and cluster health checks, observability/ for monitoring and profiling, and micro-benchmarks/ for NCCL, NCCOM and NVSHMEM level measurements. That is a deliberate ordering from infrastructure upward, and it means you can adopt the architectures without touching the examples.

The examples tree is organized along two axes, and the README is explicit about the distinction. Directories under examples/training/ and examples/inference/ are framework-centric: the engine is the subject and model variants illustrate it, so training/fsdp/ and training/megatron-lm/ are "the same example with a different model." Directories under examples/use-cases/ are use-case-centric: the model or task is the subject and the framework is incidental, so swapping the framework still leaves a recognizable demo. Each example follows a common shape, with a Dockerfile and README at the top and slurm/ and kubernetes/ subdirectories for launch scripts and manifests. If you are deciding whether an example is reusable for your own model, that layout answers the question: a framework-centric example is meant to be re-pointed at a different checkpoint, a use-case example is meant to be read as a whole.

Installing nothing: cloning the repository and reading a cluster template

There is no package to install. The repository is a collection of templates, scripts and examples that you clone and then deploy with the tooling each directory expects. The README points readers at the published workshops for guided walkthroughs rather than at an installer.

Start by cloning the default branch:

git clone https://github.com/awslabs/awsome-distributed-ai.git
cd awsome-distributed-ai

The README lists the major components as architectures/, ami/, examples/, validation/, observability/ and micro-benchmarks/, so after cloning you should see those directories at the top level alongside docs/ and assets/. From there, pick the compute architecture that matches your orchestrator. If you run Slurm on ParallelCluster, the relevant directory is architectures/aws-parallelcluster, described in the README table as cluster templates for GPU and custom silicon training. If you run Kubernetes, architectures/amazon-eks holds manifest files to train with Amazon EKS, and architectures/sagemaker-hyperpod-eks covers HyperPod with EKS orchestration. For Slurm on managed services there are separate directories for sagemaker-hyperpod-slurm and aws-pcs.

Before deploying anything, read the EFA tuning reference the README calls out:

cat docs/efa-cheatsheet.md

The README says this file covers EFA tuning and the recommended environment variables. Because interconnect configuration is where most distributed training setups fail quietly, that file is the one piece of the repository worth reading end to end before you spend money on a cluster. Beyond cloning and reading, the repository does not document a single install command for the whole collection, and the README does not document rollback for any of the architectures.

Where the documentation stops

The README is a map, not a manual. It tells you that architectures/aws-pcs contains AWS Parallel Computing Service templates with Slurm scheduler, and that validation/ holds environment and cluster health validation tools, but it does not reproduce the parameters each template requires, the IAM permissions they create, or the cost profile of standing one up. The README text available here also truncates partway through the validation and observability table, so the full inventory of those tools is only visible in the repository itself.

The release history is the second thing to weigh. The repository published v2.0.0 on 2026-09-01 with the note "repository reorganization," alongside a v2.0.0-pre-reorg snapshot from 2026-06-03 and a v2.0.1-pre-reorg pre-reorg snapshot and path migration map from the same day as v2.0.0. A reorganization release with an explicit path migration map means directory paths moved. Any bookmark, blog post, or internal wiki entry pointing at a pre-2.0 path is suspect, and the migration map exists precisely because the maintainers expect readers to be following stale links. The last push to the repository was on 2026-09-09, so the reorganization is recent and the surrounding documentation may still be catching up.

A third limitation is scope. The examples cover PyTorch DDP and FSDP, Megatron-LM and NeMo for training, and vLLM, SGLang and NVIDIA Dynamo for serving. If your stack is JAX, or a serving engine outside that list, the framework-centric examples will not map onto your code without translation work that the repository does not attempt to guide.

How it compares with a plain Terraform module or a vendor quickstart

The obvious alternative is a single-purpose infrastructure module, for example an EKS Terraform module or the ParallelCluster CLI's own configuration examples. Those give you one cluster and nothing above it. The difference here is the pairing: the same repository that ships the cluster template also ships the training example that runs on it, the validation tooling that checks the environment, and the observability stack that watches it. You are trading a narrow, well-tested module for a broad collection whose pieces are designed to fit together but are individually less documented.

A second alternative is the workshop path. The README links three workshops: AI on SageMaker HyperPod for deploying, operating and monitoring HyperPod clusters, an AWS ParallelCluster workshop described as the same journey on ParallelCluster, and an AWS PCS workshop. If you are learning the platform, the workshops are the better entry point because they sequence the steps. The repository is the better entry point once you know which service you are using and want the templates in version control next to your own code. The two are complementary, and the README treats them that way rather than presenting the repository as a replacement.

Maintenance, upgrades and the MIT-0 licence

The last push was on 2026-09-09, and the most recent tagged release is v2.0.1-pre-reorg from 2026-09-01, which the release notes describe as a pre-reorg snapshot and path migration map. That is a young 2.x line. If you forked or vendored anything before v2.0.0, budget time for the path migration rather than assuming a drop-in upgrade, and consult the migration map that shipped with v2.0.1-pre-reorg rather than diffing directories by hand. The repository is not archived, but the release cadence visible here is event-driven (a reorganization) rather than a steady stream, so pin to a tag if you depend on specific paths.

The licence is MIT-0, which is the MIT licence with the attribution requirement removed. In practice that means you can copy templates and scripts into your own repository, modify them, and redistribute them without carrying a notice, subject to whatever the underlying AWS services and third-party components in the examples require. That last clause is the one to check per example: a Dockerfile that pulls a base image or installs a framework brings its own licence terms, and MIT-0 on this repository does not speak to those. This is a description of the licence identifier, not legal advice; confirm the terms of any bundled dependency before you redistribute a built image.

What to check before you commit a team to it

Three checks are worth doing before you point a team at this repository. First, open the architecture directory for your chosen compute service and confirm it still exists at the path you intend to reference, given the v2.0.0 reorganization and the accompanying migration map. Second, open the example you want and confirm it ships the launch path for your orchestrator; the README's example structure shows slurm/ and kubernetes/ subdirectories, but not every example necessarily fills both. Third, read docs/efa-cheatsheet.md and verify the recommended environment variables match the instance family you plan to use, because EFA tuning is the difference between near-linear scaling and a cluster that looks healthy while training slowly.

If those three checks pass, the repository saves you the least interesting week of the project: assembling a VPC, a cluster template, a container image and a launch script from four separate documentation sites. If they fail, you have lost an hour and learned which parts of the collection are stale, which is a cheaper outcome than discovering it after provisioning.

Editorial conclusion

Adopt awsome-distributed-ai if you already run SageMaker HyperPod, AWS ParallelCluster, AWS PCS or Amazon EKS and want a starting cluster template plus a matching training or inference example instead of assembling one from vendor docs. Do not adopt it if you expect a supported product, a stable API, or a single script that provisions everything end to end; this is a reference collection, and the v2.0.0 release notes describe a repository reorganization, not a compatibility promise. Before you build on it, verify three things in the repository itself: whether the architecture directory you need still exists at the path you planned to reference, whether the example you want ships both a slurm/ and a kubernetes/ launch path for your orchestrator, and whether the validation tooling covers your instance type and interconnect.

Frequently asked questions

Official sources

1. awslabs/awsome-distributed-ai on GitHub
2. License: MIT-0
3. Project website
4. README
5. Releases