nemo-automodel-distributed-training

Configure FSDP2, Megatron FSDP, and DDP parallelism strategies for NeMo AutoModel training.

960|316|Updated May 21, 2025
One-click install
npx skills add https://github.com/NVIDIA-NeMo/Automodel --skill nemo-automodel-distributed-training-nvidia-nemo
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: nemo-automodel-distributed-training
Source: https://github.com/NVIDIA-NeMo/Automodel/tree/main/skills/nemo-automodel-distributed-training
Command: npx skills add https://github.com/NVIDIA-NeMo/Automodel --skill nemo-automodel-distributed-training-nvidia-nemo

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve? Choosing and configuring the right distributed training strategy in NeMo AutoModel is error-prone: tensor, pipeline, context, and expert parallelism each have sizing constraints, hardware topology rules, and strategy-specific limitations that are easy to get wrong. ## Core Features & Use Cases - Strategy Selection Guidance: Decide between FSDP2, Megatron FSDP, and DDP based on model size, MoE architecture, sequence length, and cluster topology. - YAML Configuration Patterns: Ready-to-adapt distributed section examples covering TP, PP, CP, EP, HSDP, activation checkpointing, and sequence packing. - Constraint and Pitfall Reference: Sizing rules such as dp_size = world_size / (tp_size * pp_size * cp_size), EP divisibility requirements, and MegatronFSDP limitations (no PP, EP, or sequence_parallel). - Use Case: You are training a 70B model across 8 nodes and need tensor plus pipeline parallelism; the skill recommends strategy: fsdp2 with the correct tp_size, pp_size, and pipeline sub-config. ## Quick Start Ask which distributed strategy and YAML settings to use for training your model on a given number of GPUs with tensor, pipeline, context, or expert parallelism.

Frequently Asked Questions about nemo-automodel-distributed-training

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
Which distributed strategy should I use in NeMo AutoModel for tensor and pipeline parallelism?▼

Use strategy fsdp2, which is the recommended default and the only strategy supporting TP plus PP. Set tp_size and pp_size, add a pipeline sub-config with pp_schedule and pp_microbatch_size, and let dp_size be inferred as world_size divided by tp_size times pp_size times cp_size.

How do I configure expert parallelism for MoE models in NeMo AutoModel?▼

Set strategy fsdp2 with ep_size greater than 1, which creates a separate moe_mesh alongside the main device mesh. Optionally add a moe sub-config mapping to MoEParallelizerConfig. Note that ep_size must evenly divide dp_size times cp_size.

Does megatron_fsdp support pipeline parallelism or sequence_parallel?▼

No. In NeMo AutoModel, megatron_fsdp does not support pipeline parallelism, expert parallelism, or sequence_parallel, and MeshContext validation raises on those combinations. Use fsdp2 when you need PP, EP, or sequence_parallel; DDP is simple data parallelism only.

What are the requirements for using context parallelism with long sequences?▼

Context parallelism requires SDPA with Flash Attention or Efficient Attention backend, or Transformer Engine attention; SDPBackend.MATH is incompatible with DTensor. Set cp_size greater than 1, and when combining CP with sequence packing, packed_sequence_size must be divisible by cp_size.

Why does tensor parallelism across nodes hurt training throughput?▼

TP is communication-intensive and must stay within a single NVLink domain, typically one node of up to 8 GPUs. Running TP across InfiniBand links degrades throughput severely, so use pipeline parallelism or data parallelism for cross-node scaling instead.