Official agent skill

Nemo Mbridge Perf Megatron Fsdp

Operational guide for enabling Megatron FSDP in Megatron-Bridge, including config knobs, code anchors, pitfalls, and verification.

OfficialApache-2.0Auto-check passedAI & LLM Engineering

Install Nemo Mbridge Perf Megatron Fsdp

skills CLI

$ npx skills add NVIDIA/skills --skill nemo-mbridge-perf-megatron-fsdp -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI

$ gh skill install NVIDIA/skills nemo-mbridge-perf-megatron-fsdp --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy

$ git clone --depth 1 https://github.com/NVIDIA/skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/nemo-mbridge-perf-megatron-fsdp .claude/skills/nemo-mbridge-perf-megatron-fsdp && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Paste into Claude Code

Install the "nemo-mbridge-perf-megatron-fsdp" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/nemo-mbridge-perf-megatron-fsdp into .claude/skills/nemo-mbridge-perf-megatron-fsdp/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "nemo-mbridge-perf-megatron-fsdp", then confirm the skill loads.

Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

$skill-installer

$skill-installer install https://github.com/NVIDIA/skills/tree/main/skills/nemo-mbridge-perf-megatron-fsdp

Type this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.

skills CLI

$ npx skills add NVIDIA/skills --skill nemo-mbridge-perf-megatron-fsdp -a codex

Project install goes to .agents/skills/; add -g for ~/.codex/skills/.

GitHub CLI

$ gh skill install NVIDIA/skills nemo-mbridge-perf-megatron-fsdp --agent codex

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/NVIDIA/skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/nemo-mbridge-perf-megatron-fsdp .agents/skills/nemo-mbridge-perf-megatron-fsdp && rm -rf skills-src

Use ~/.agents/skills/ instead of .agents/skills for a personal install.

Codex skills documentation · loads skills from .agents/skills/

Paste into Codex

Install the "nemo-mbridge-perf-megatron-fsdp" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/nemo-mbridge-perf-megatron-fsdp into .agents/skills/nemo-mbridge-perf-megatron-fsdp/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "nemo-mbridge-perf-megatron-fsdp", then confirm the skill loads.

Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

skills CLI

$ npx skills add NVIDIA/skills --skill nemo-mbridge-perf-megatron-fsdp -a cursor

Project install goes to .agents/skills/; add -g for ~/.cursor/skills/.

GitHub CLI

$ gh skill install NVIDIA/skills nemo-mbridge-perf-megatron-fsdp --agent cursor

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/NVIDIA/skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/nemo-mbridge-perf-megatron-fsdp .cursor/skills/nemo-mbridge-perf-megatron-fsdp && rm -rf skills-src

Use ~/.cursor/skills/ instead of .cursor/skills for a personal install.

Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/

Paste into Cursor

Install the "nemo-mbridge-perf-megatron-fsdp" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/nemo-mbridge-perf-megatron-fsdp into .cursor/skills/nemo-mbridge-perf-megatron-fsdp/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "nemo-mbridge-perf-megatron-fsdp", then confirm the skill loads.

Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

gemini skills install

$ gemini skills install https://github.com/NVIDIA/skills.git --path skills/nemo-mbridge-perf-megatron-fsdp

--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.

skills CLI

$ npx skills add NVIDIA/skills --skill nemo-mbridge-perf-megatron-fsdp -a gemini-cli

Project install goes to .agents/skills/; add -g for ~/.gemini/skills/.

GitHub CLI

$ gh skill install NVIDIA/skills nemo-mbridge-perf-megatron-fsdp --agent gemini-cli

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/NVIDIA/skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/nemo-mbridge-perf-megatron-fsdp .gemini/skills/nemo-mbridge-perf-megatron-fsdp && rm -rf skills-src

Use ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.

Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/

Paste into Gemini CLI

Install the "nemo-mbridge-perf-megatron-fsdp" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/nemo-mbridge-perf-megatron-fsdp into .gemini/skills/nemo-mbridge-perf-megatron-fsdp/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "nemo-mbridge-perf-megatron-fsdp", then confirm the skill loads.

Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

GitHub CLI

$ gh skill install NVIDIA/skills nemo-mbridge-perf-megatron-fsdp

Installs for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).

skills CLI

$ npx skills add NVIDIA/skills --skill nemo-mbridge-perf-megatron-fsdp -a github-copilot

Project install goes to .agents/skills/; add -g for ~/.copilot/skills/.

Manual copy

$ git clone --depth 1 https://github.com/NVIDIA/skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/nemo-mbridge-perf-megatron-fsdp .github/skills/nemo-mbridge-perf-megatron-fsdp && rm -rf skills-src

Use ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.

GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/

Paste into GitHub Copilot

Install the "nemo-mbridge-perf-megatron-fsdp" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/nemo-mbridge-perf-megatron-fsdp into .github/skills/nemo-mbridge-perf-megatron-fsdp/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "nemo-mbridge-perf-megatron-fsdp", then confirm the skill loads.

GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

skills CLI

$ npx skills add NVIDIA/skills --skill nemo-mbridge-perf-megatron-fsdp -a opencode

OpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.

GitHub CLI

$ gh skill install NVIDIA/skills nemo-mbridge-perf-megatron-fsdp --agent opencode

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/NVIDIA/skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/nemo-mbridge-perf-megatron-fsdp .opencode/skills/nemo-mbridge-perf-megatron-fsdp && rm -rf skills-src

Use ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.

OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/

Paste into OpenCode

Install the "nemo-mbridge-perf-megatron-fsdp" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/nemo-mbridge-perf-megatron-fsdp into .opencode/skills/nemo-mbridge-perf-megatron-fsdp/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "nemo-mbridge-perf-megatron-fsdp", then confirm the skill loads.

OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

Facts

Skill name: nemo-mbridge-perf-megatron-fsdp
GitHub stars: 3.6k
Token cost: ~973 tokens
SKILL.md length: 143 words
Files: 6
Skills in repo: 390
Repo updated: Oct 9, 2026
First seen: Oct 7, 2026
Licence: Apache-2.0

At a glance

Operational guide for enabling Megatron FSDP in Megatron-Bridge, including config knobs, code anchors, pitfalls, and verification.

Works in 5 steps: Public recipes often expose… → use_torch_fsdp2 exists, but on the… → CPU offloading is only valid when… → …
Tasks that involve Deep learning
SKILL.md covers Enablement, Code Anchors, Pitfalls and Verification
Calls python and uv

What it does

Nemo Mbridge Perf Megatron Fsdp is an agent skill from NVIDIA/skills, published by the product's own GitHub organization. Operational guide for enabling Megatron FSDP in Megatron-Bridge, including config knobs, code anchors, pitfalls, and verification.

Its SKILL.md is about 970 tokens, which your agent loads only when the skill is triggered. The skill folder holds 6 other files (for example `BENCHMARK.md`, `card.yaml` and `evals/evals.json`).

It sits in AI & LLM Engineering, covering Deep learning. It works with NVIDIA AI Platform and CUDA. The repository describes itself as: Agent Skills for NVIDIA products — install into Claude Code, Codex, and other coding agents to run Physical AI, robotics, simulation, CUDA, and RAG workflows end to end. The licence is Apache-2.0.

When your agent uses it

Tasks that involve Deep learning

Example prompts

“/nemo-mbridge-perf-megatron-fsdp”

Requirements

Python 3

Workflow steps

5 steps, taken from the first numbered list in SKILL.md.

Public recipes often expose use_megatron_fsdp but still default to ckpt_format="torch_dist". If save/load is enabled, switch to…
use_torch_fsdp2 exists, but on the validated branch Bridge still fails before training because _ddp_wrap passes pg_collection.
CPU offloading is only valid when pipeline_model_parallel_size == 1 and activation recomputation is disabled.
Upstream warns that FSDP and TP/CP can want different CUDA_DEVICE_MAX_CONNECTIONS settings on Hopper and earlier.
Megatron FSDP and FSDP2 are mutually exclusive.

What it can do on your machine

Read from SKILL.md and the folder at commit 14a98ae. It shows what the files ask for, not the result of running them.

Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Runs code
Shell commands in SKILL.md call:
- python
- uv
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md. Its commands use uv, which can reach the network depending on how they are called.
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Nemo Mbridge Perf Megatron Fsdp loads about 973 tokens when it runs. Until then it costs about 41 tokens; SKILL.md has 143 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it: ~41
When it runs · the whole SKILL.md, loaded when a task matches: ~973

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from NVIDIA/skills at commit 14a98ae, republished under its Apache-2.0 licence (© NVIDIA). 143 words, ~973 tokens.

Download SKILL.mdSave it as .claude/skills/nemo-mbridge-perf-megatron-fsdp/SKILL.md (or your agent's skills folder). This skill also uses 5 other files; get the full folder from GitHub.

name: nemo-mbridge-perf-megatron-fsdp
description: Operational guide for enabling Megatron FSDP in Megatron-Bridge, including config knobs, code anchors, pitfalls, and verification.
license: Apache-2.0
when_to_use: Using FSDP-based data parallelism instead of DDP, or tracing an OOM or regression to a FSDP config change; 'use_megatron_fsdp'…

Megatron FSDP Skill

For stable background and recommendation level, see:

@docs/training/megatron-fsdp.md
@skills/nemo-mbridge-perf-megatron-fsdp/card.yaml

Enablement

Minimal Megatron FSDP override in Bridge:

python

cfg.dist.use_megatron_fsdp = True
cfg.ddp.use_megatron_fsdp = True
cfg.ddp.data_parallel_sharding_strategy = "optim_grads_params"
cfg.ddp.average_in_collective = False
cfg.checkpoint.ckpt_format = "fsdp_dtensor"

Example recipe fixup:

python

cfg = llama3_8b_pretrain_config()
cfg.dist.use_megatron_fsdp = True
cfg.ddp.use_megatron_fsdp = True
cfg.ddp.data_parallel_sharding_strategy = "optim_grads_params"
cfg.ddp.average_in_collective = False
cfg.checkpoint.ckpt_format = "fsdp_dtensor"
cfg.checkpoint.save = "/tmp/fsdp_ckpts"
cfg.checkpoint.load = None

Performance harness note:

bash

python scripts/performance/launch.py --use_megatron_fsdp true

Code Anchors

Bridge config definition:

148154srcmegatronbri

use_megatron_fsdp: bool = False
"""Use Megatron's Fully Sharded Data Parallel. Cannot be used together with use_torch_fsdp2."""

use_torch_fsdp2: bool = False
"""Use the torch FSDP2 implementation. FSDP2 is not currently working with Pipeline Parallel.
It is still not in a stable release stage, and may therefore contain bugs or other
potential issues."""

Bridge validation:

15331578srcmegatronb

if self.dist.use_megatron_fsdp and self.dist.use_torch_fsdp2:
    raise ValueError(...)
...
assert not self.dist.use_tp_pp_dp_mapping, "use_tp_pp_dp_mapping is not supported with Megatron FSDP"
...
assert self.checkpoint.ckpt_format == "fsdp_dtensor", (
    "Megatron FSDP only supports fsdp_dtensor checkpoint format"
)

Runtime wrapper selection:

217243srcmegatronbri

if use_megatron_fsdp:
    DP = FullyShardedDataParallel
elif use_torch_fsdp2:
    DP = TorchFullyShardedDataParallel
else:
    DP = DistributedDataParallel
...
DP(
    config=get_model_config(model_chunk),
    ddp_config=ddp_config,
    module=model_chunk,
    ...
    pg_collection=pg_collection,
)

Perf harness overrides:

7498scriptsperforman

recipe.ddp.use_megatron_fsdp = True
recipe.ddp.data_parallel_sharding_strategy = "optim_grads_params"
recipe.ddp.keep_fp8_transpose_cache = False
recipe.ddp.average_in_collective = False
...
recipe.checkpoint.load = None

Pitfalls

Public recipes often expose use_megatron_fsdp but still default to ckpt_format="torch_dist". If save/load is enabled, switch to fsdp_dtensor.
use_torch_fsdp2 exists, but on the validated branch Bridge still fails before training because _ddp_wrap passes pg_collection.
CPU offloading is only valid when pipeline_model_parallel_size == 1 and activation recomputation is disabled.
Upstream warns that FSDP and TP/CP can want different CUDA_DEVICE_MAX_CONNECTIONS settings on Hopper and earlier.
Megatron FSDP and FSDP2 are mutually exclusive.

Verification

Use the existing 2-GPU functional smoke test:

bash

CUDA_VISIBLE_DEVICES=0,1 uv run python -m torch.distributed.run --nproc_per_node=2 \
  -m pytest tests/functional_tests/training/test_megatron_fsdp.py::TestMegatronFSDP::test_fsdp_pretrain_basic -v -s

Success criteria:

Pytest reports 1 passed
The log shows finite loss at the last iteration
The run finishes without a checkpoint format assertion

Files

SKILL.md and 5 other files in skills/nemo-mbridge-perf-megatron-fsdp of NVIDIA/skills.

SKILL.md
BENCHMARK.md
card.yaml
evals/evals.json
skill-card.md
skill.oms.sig

Open the folder on GitHubat commit 14a98ae

Compare with similar skills

Nemo Mbridge Perf Megatron Fsdp next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

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Hyperpod Version Checkerawslabs/agent-plugins	916	—	~910	Automated safety check: Pass	Apache-2.0	today
Spark Environment Setupwshobson/agents	40k	—	~2k	Automated safety check: Pass	MIT	5 days ago
Oob Perf Analysisintel/torch-xpu-ops	115	—	~681	Automated safety check: Pass	Apache-2.0	today

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Works with

Questions about Nemo Mbridge Perf Megatron Fsdp

What does Nemo Mbridge Perf Megatron Fsdp do?

Operational guide for enabling Megatron FSDP in Megatron-Bridge, including config knobs, code anchors, pitfalls, and verification. Nemo Mbridge Perf Megatron Fsdp is an agent skill from NVIDIA/skills, published by the product's own GitHub organization. Operational guide for enabling Megatron FSDP in Megatron-Bridge, including config knobs, code anchors, pitfalls, and verification.

When should I use Nemo Mbridge Perf Megatron Fsdp?

Nemo Mbridge Perf Megatron Fsdp fits situations like: tasks that involve Deep learning.

How do I install Nemo Mbridge Perf Megatron Fsdp in Claude Code?

Run `npx skills add NVIDIA/skills --skill nemo-mbridge-perf-megatron-fsdp -a claude-code`. Or copy the skill folder (skills/nemo-mbridge-perf-megatron-fsdp in NVIDIA/skills) into .claude/skills/nemo-mbridge-perf-megatron-fsdp in your project. Claude Code loads it when a task matches its description.

How do I install Nemo Mbridge Perf Megatron Fsdp in Codex?

Run `npx skills add NVIDIA/skills --skill nemo-mbridge-perf-megatron-fsdp -a codex`. Or copy the skill folder (skills/nemo-mbridge-perf-megatron-fsdp in NVIDIA/skills) into .agents/skills/nemo-mbridge-perf-megatron-fsdp in your project. Codex loads it when a task matches its description.

Can I use Nemo Mbridge Perf Megatron Fsdp in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add NVIDIA/skills --skill nemo-mbridge-perf-megatron-fsdp -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/nemo-mbridge-perf-megatron-fsdp, .gemini/skills/nemo-mbridge-perf-megatron-fsdp, .github/skills/nemo-mbridge-perf-megatron-fsdp and .opencode/skills/nemo-mbridge-perf-megatron-fsdp in your project.

What does Nemo Mbridge Perf Megatron Fsdp need to run?

Going by SKILL.md and its folder, Nemo Mbridge Perf Megatron Fsdp needs the command-line tools its instructions call (python and uv). Our summary lists: Python 3.

Does Nemo Mbridge Perf Megatron Fsdp access the network?

SKILL.md contains no URLs. Its commands use uv, which can reach the network depending on how they are called. This is read from the text; nothing was executed.

Is Nemo Mbridge Perf Megatron Fsdp safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Nemo Mbridge Perf Megatron Fsdp use?

Nemo Mbridge Perf Megatron Fsdp is published under the Apache-2.0 licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Nemo Mbridge Perf Megatron Fsdp use?

About 973 tokens (SKILL.md is roughly 3.9k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Nemo Mbridge Perf Megatron Fsdp?

Skills that share tags, products or a category with Nemo Mbridge Perf Megatron Fsdp: Graphsignal (graphsignal/graphsignal, 257 stars), Quark Env Preflight (amd/Quark, 182 stars), Hyperpod Version Checker (awslabs/agent-plugins, 916 stars) and Spark Environment Setup (wshobson/agents, 40k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Nemo Mbridge Perf Megatron Fsdp?

NVIDIA (a GitHub organization, an official publisher) maintains it in NVIDIA/skills, which has 3,555 GitHub stars. The repository holds 390 skills in this directory. The repository was last updated on October 9, 2026.

Source: NVIDIA/skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.

Install Nemo Mbridge Perf Megatron Fsdp

Facts

At a glance

What it does

When your agent uses it

Example prompts

Requirements

Workflow steps

What it can do on your machine

Tool permissions

Runs code

Network

Credentials

Context cost

Safety

SKILL.md

Megatron FSDP Skill

Enablement

Code Anchors

Pitfalls

Verification

Files

Compare with similar skills

Similar skills

More from NVIDIA/skills

Related

Questions about Nemo Mbridge Perf Megatron Fsdp