Distributed AI on AWS

AWS released a comprehensive guide for distributed AI training on its infrastructure, featuring reference architectures, test cases, and best practices for frameworks like PyTorch, Megatron-LM, NeMo, and JAX. The guide includes Dockerfiles, Slurm scripts, and Kubernetes manifests to help users train large-scale models on AWS compute platforms such as HyperPod, ParallelCluster, and EKS.

AWSome Distributed AI Reference architectures, test cases, and best practices for training large-scale models with PyTorch, Megatron-LM, NeMo, JAX, and more on AWS infrastructure. Training Frameworks Production-ready examples grouped by framework. Each includes Dockerfiles, Slurm scripts, and Kubernetes manifests. 🔥 FSDPDDPDeepSpeedTorchTitanPicotronvLLMTRLOpenRLHF /frameworks/pytorch PyTorch Native distributed training with DDP, FSDP, TorchTitan, DeepSpeed, and more. Covers LLMs, vision, robotics, and RLHF. ⚡ Megatron-LMNeMoNeMo RLBioNeMo /frameworks/megatron Megatron NVIDIA Megatron-LM and NeMo for large-scale LLM pre-training with tensor, pipeline, and expert parallelism. 🧬 PaxMLXLATPU/GPU /frameworks/jax JAX Google JAX with PaxML for distributed training leveraging XLA compilation and automatic parallelism. 🧠 NeuronXOptimum NeuronTrainium /frameworks/neuron AWS Neuron / Trainium NeuronX Distributed for training on AWS Trainium & Inferentia chips with optimized compilers. 🤖 Isaac LabOpenVLAV-JEPA 2nanoVLM /frameworks/physical-ai Physical AI & Robotics Embodied AI training with NVIDIA Isaac Lab, OpenVLA, V-JEPA2, and vision-language-action models. 🎯 TRLvERLSLIMEPPODPO /frameworks/reinforcement-learning Reinforcement Learning RLHF, DPO, PPO, and scalable RL frameworks for LLM alignment and post-training. 🧪 DistillationCompressionTransfer Learning /frameworks/model-customisation Model Customisation Knowledge distillation, compression, and model adaptation techniques for production. Reference Architectures CloudFormation templates and deployment guides for every AWS compute platform. Get Started in Minutes Three steps to launch your first distributed training job. Deploy Infrastructure Launch a cluster using our CloudFormation templates for HyperPod, ParallelCluster, or EKS. Build Container Use our Dockerfiles to build a training container with your framework of choice. Launch Training Submit your job with Slurm or Kubernetes using our ready-made launch scripts.