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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Best practices, reference architectures, and examples for distributed AI training and inference on AWS.
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Today
— stars today
This week
+2 stars this week
This month
+10 stars this month
Momentum
17.0
growth rate 0.41%/day
# Distributed AI on AWS: Reference Architectures & Examples <!-- omit from toc --> This repository contains reference architectures and examples for distributed AI training and inference on [Amazon SageMaker HyperPod](https://docs.aws.amazon.com/sagemaker/latest/dg/sagemaker-hyperpod.html), [AWS ParallelCluster](https://docs.aws.amazon.com/parallelcluster/latest/ug/what-is-aws-parallelcluster.html), [AWS Parallel Computing Service (AWS PCS)](https://aws.amazon.com/pcs/), and [Amazon Elastic Kubernetes Service (Amazon EKS)](https://docs.aws.amazon.com/eks/latest/userguide/getting-started-console.html). The examples cover different model families and sizes, training frameworks and parallel optimizations (PyTorch DDP/FSDP, Megatron-LM, NeMo), and serving engines (vLLM, SGLang, NVIDIA Dynamo). The major components of this repository are: ``` ├── architectures/ # Cluster reference architectures (CloudFormation, Terraform) ├── ami/ # Scripts to create Amazon Machine Images (Packer/Ansible) ├── examples/ # Runnable training, inference, and use-case examples ├── validation/ # Environment and cluster health validation tools ├── observability/ # Monitoring, metrics exporters, and profiling └── micro-benchmarks/ # Micro-benchmarks (NCCL, NCCOM, NVSHMEM, etc.) ``` ## Workshops You can follow the workshops below to train models on AWS. Each walks through several examples and shares practical guidance on operating a cluster for LLM training. | Name | Comments | | ---------------------------------------------------------------------------------- | --------------------------------------------------------------- | | [AI on SageMaker HyperPod](https://awslabs.github.io/ai-on-sagemaker-hyperpod/) | Deploying, operating, and monitoring SageMaker HyperPod clusters | | [AWS ParallelCluster](https://catalog.workshops.aws/ml-on-aws-parallelcluster) | The same journey on AWS ParallelCluster | | [AWS Parallel Computing Service](https://catalog.workshops.aws/ml-on-pcs) | The same journey on AWS Parallel Computing Service | ## Blog Posts about distributed AI on AWS are published at <https://awslabs.github.io/awsome-distributed-ai/>. The Hugo source lives on the [`content`](https://github.com/awslabs/awsome-distributed-ai/tree/content) branch. Blog content is editorially curated by AWS authors. Code samples in this repo (`architectures/`, `examples/`, etc.) accept external contributions as usual; see [CONTRIBUTING.md](./CONTRIBUTING.md). ## Architectures Each subdirectory under `architectures/` is a deployable cluster architecture or a shared building block. | Name | Category | Usage | | ------------------------------------------------------------------------------ | -------- | ---------------------------------------------------- | | [`common`](./architectures/common) | Storage | Common resources (S3 bucket, event notifications) | | [`vpc_network`](./architectures/vpc_network) | Network | Create a VPC with subnets and required resources | | [`aws-parallelcluster`](./architectures/aws-parallelcluster) | Compute | Cluster templates for GPU & custom silicon training | | [`amazon-eks`](./architectures/amazon-eks) | Compute | CloudFormation templates for an EKS GPU cluster with EFA, and the eksctl manifests that preceded them | | [`sagemaker-hyperpod-slurm`](./architectures/sagemaker-hyperpod-slurm) | Compute | SageMaker HyperPod with Slurm orchestration | | [`ldap_server`](./architectures/ldap_server) | Iden
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Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:7a19f436afbc1b5f, topic:gpu, topic:kubernetes, topic:slurm