Training Frameworks
Browse test cases and training examples organized by framework and library.
PyTorch
31 test casesPyTorch FSDP
PyTorch FSDP distributed training on AWS
PyTorch FSDP
Fully Sharded Data Parallel training for large language models
PyTorch DDP
PyTorch DDP distributed training on AWS
PyTorch DDP
Distributed Data Parallel training - the foundation for multi-GPU PyTorch
DeepSpeed
DeepSpeed distributed training on AWS
Picotron
Picotron distributed training on AWS
TorchTitan
PyTorch native distributed training framework for production LLM pre-training
OpenRLHF
OpenRLHF distributed training on AWS
NVRx
NVRx distributed training on AWS
vLLM
vLLM distributed training on AWS
NVIDIA Dynamo
Distributed LLM inference with KV cache-aware routing and disaggregated prefill/decode on HyperPod EKS
Inference Engines
Framework-centric inference serving examples on AWS, organized by engine (vLLM, SGLang, NVIDIA Dynamo)
SGLang Inference
SGLang LLM serving deployments on AWS EKS, SageMaker HyperPod, and EC2 — including prefill/decode disaggregation, MoE expert parallelism, and router-based scaling
Cosmos-3 (World Models)
Cosmos-3 (World Models) distributed training on AWS
DETR Fine-tuning
DETR Fine-tuning distributed training on AWS
DreamZero
DreamZero distributed training on AWS
ESM-2 on HyperPod
ESM-2 on HyperPod distributed training on AWS
NVIDIA Isaac Lab
NVIDIA Isaac Lab distributed training on AWS
LLM Distillation
LLM Distillation distributed training on AWS
nanoVLM
nanoVLM distributed training on AWS
OpenVLA OFT
OpenVLA OFT distributed training on AWS
OpenVLA
OpenVLA distributed training on AWS
NVIDIA Isaac Lab
Sim-to-real robot learning with NVIDIA Isaac Lab on GPU clusters
PointWorld
PointWorld distributed training on AWS
V-JEPA 2.1
V-JEPA 2.1 distributed training on AWS
V-JEPA 2
V-JEPA 2 distributed training on AWS
V-JEPA 2.1
Updated Video Joint Embedding Predictive Architecture for physical world understanding
TRL (Transformers Reinforcement Learning)
HuggingFace TRL for RLHF, DPO, PPO, and reward model training
vERL
Scalable reinforcement learning framework for LLM alignment and post-training
SLIME
Lightweight distributed training library for efficient LLM fine-tuning
Model Distillation
Knowledge distillation for compressing large models into smaller, efficient ones
Megatron / NeMo
6 test casesMegatron-LM
NVIDIA's framework for training multi-billion parameter transformer models
BioNeMo
NVIDIA's framework for biomolecular AI model training
NVIDIA NeMo
NVIDIA NeMo distributed training on AWS
NeMo RL
NeMo RL distributed training on AWS
Megatron Bridge
Megatron Bridge distributed training on AWS
miles
GRPO reinforcement learning post-training with miles on Amazon SageMaker HyperPod EKS
JAX
1 test casesReinforcement Learning
4 test casesNVIDIA Isaac Lab
Sim-to-real robot learning with NVIDIA Isaac Lab on GPU clusters
TRL (Transformers Reinforcement Learning)
HuggingFace TRL for RLHF, DPO, PPO, and reward model training
vERL
Scalable reinforcement learning framework for LLM alignment and post-training
SLIME
Lightweight distributed training library for efficient LLM fine-tuning
Model Customisation
1 test casesPhysical AI & Robotics
8 test casesNVRx
NVRx distributed training on AWS
Cosmos-3 (World Models)
Cosmos-3 (World Models) distributed training on AWS
NVIDIA Isaac Lab
NVIDIA Isaac Lab distributed training on AWS
nanoVLM
nanoVLM distributed training on AWS
OpenVLA OFT
OpenVLA OFT distributed training on AWS
OpenVLA
OpenVLA distributed training on AWS
NVIDIA Isaac Lab
Sim-to-real robot learning with NVIDIA Isaac Lab on GPU clusters
V-JEPA 2.1
Updated Video Joint Embedding Predictive Architecture for physical world understanding