AutoGineer
Generated banner for Nemo Mbridge Mlm Bridge Training
Generated logo for Nemo Mbridge Mlm Bridge Training

Nemo Mbridge Mlm Bridge Training

NVIDIAOfficialPublished by NVIDIA

  • Skill
  • Tool integration
  • Software development
  • Apache-2.0
  • Medium risk
Download ZIP

From the author

Run Megatron-LM (MLM) and Megatron Bridge training with mock or real data. Covers correlation testing, available recipes, and multi-GPU examples.
— the description by NVIDIA

AutoGineer summary

Written by AutoGineer from the skill’s own files, not by the author

This skill provides the necessary procedures and configuration patterns for running training jobs using Megatron-LM and Megatron Bridge. It focuses on ensuring consistency between the two frameworks through correlation testing, mapping CLI arguments, and managing multi-GPU environments. It also includes guidance on handling the Megatron-Core submodule and common pitfalls encountered during training setup.

What it does

  • Provides commands for running training with Megatron-LM and Megatron Bridge.
  • Outlines the procedure for loss-correlation testing between the two frameworks.
  • Lists available pre-configured training recipes for various model architectures.
  • Details how to manage the Megatron-Core submodule versions.
  • Provides multi-GPU training examples with tensor model parallelism.
  • Documents common pitfalls and environment requirements for successful training runs.

When to use it

  • When you need to run model training using Megatron-LM or Megatron Bridge.
  • When comparing loss curves between Megatron-LM and Megatron Bridge.
  • When investigating discrepancies in training results after a code commit.
  • When setting up multi-GPU training environments for large models.

Advantages

  • Ensures consistent results through standardized correlation testing.
  • Provides clear mapping between legacy Megatron-LM arguments and Bridge configurations.
  • Simplifies environment setup with specific launch wrappers and path requirements.
  • Reduces common errors by documenting specific cleanup and scheduler requirements.

Usage examples

“How do I run a correlation test between Megatron-LM and Bridge?”
The agent provides the specific CLI commands for both frameworks using the vanilla_gpt_pretrain_config, emphasizing the need to clear experiment directories and match hyperparameters.
“How can I switch the Megatron-Core version for testing?”
The agent provides the script commands to switch between main and dev versions, including the necessary synchronization steps for the submodule.

Install

  1. Download the skill as a ZIP:

    https://autogineer.com/agent-skills/nvidia/nemo-mbridge-mlm-bridge-training/download

  2. Unzip it into the cross-client skills folder of your project:

    mkdir -p .agents/skills && unzip nemo-mbridge-mlm-bridge-training.zip -d .agents/skills

Documentation by NVIDIA

MLM vs Bridge Training

For how they differ, the arg mapping tables, gotchas, and translation script, see:

  • @docs/megatron-lm-to-megatron-bridge.md

First Answer Checklist

For MLM-vs-Bridge correlation questions, always name these items up front:

  1. Bridge recipe: vanilla_gpt_pretrain_config.

  2. Bridge entry point: scripts/training/run_recipe.py.

  3. MLM entry point: 3rdparty/Megatron-LM/pretrain_gpt.py.

  4. Launch wrapper for both: uv run python -m torch.distributed.run.

  5. Fresh-run cleanup: rm -rf nemo_experiments before the Bridge run.

Also state that MLM needs PYTHONPATH=3rdparty/Megatron-LM:$PYTHONPATH, matched Bridge and MLM losses should agree within BF16 rounding, and files under 3rdparty/Megatron-LM/ should not be modified from this repo.

Correlation Testing

Use vanilla_gpt_pretrain_config for loss-correlation testing. This recipe uses bare GPTModelProvider defaults (LayerNorm, GeLU, learned_absolute position embeddings, vocab_size inherited from tokenizer) — matching MLM pretrain_gpt.py defaults with no args.

MLM Correlation Run (2L/256H, 1 GPU)
PYTHONPATH=3rdparty/Megatron-LM:$PYTHONPATH \
uv run python -m torch.distributed.run --nproc_per_node=1 \
  3rdparty/Megatron-LM/pretrain_gpt.py \
  --num-layers 2 --hidden-size 256 --num-attention-heads 4 \
  --ffn-hidden-size 1024 --seq-length 512 --max-position-embeddings 512 \
  --micro-batch-size 4 --global-batch-size 32 \
  --train-iters 10 --eval-iters 2 --eval-interval 10 \
  --mock-data --bf16 --use-mcore-models \
  --tokenizer-type NullTokenizer --vocab-size 32000 \
  --lr 3e-4 --min-lr 3e-5 --seed 1234 --log-interval 1
Bridge Correlation Run (same config, 1 GPU)
rm -rf nemo_experiments && \
uv run python -m torch.distributed.run --nproc_per_node=1 \
  scripts/training/run_recipe.py \
  --recipe vanilla_gpt_pretrain_config \
  model.num_layers=2 model.hidden_size=256 \
  model.num_attention_heads=4 model.ffn_hidden_size=1024 \
  model.seq_length=512 dataset.seq_length=512 \
  train.train_iters=10 train.global_batch_size=32 train.micro_batch_size=4 \
  validation.eval_interval=10 validation.eval_iters=2 \
  optimizer.lr=3e-4 optimizer.min_lr=3e-5 \
  scheduler.lr_warmup_iters=1 scheduler.lr_decay_iters=10 \
  rng.seed=1234 logger.log_interval=1
Verification

With matched parameters the LM losses should be nearly identical at each iteration. Compare lm loss values from both logs — they should agree to within BF16 rounding.

Multi-GPU Examples

MLM 2-GPU with TP=2
PYTHONPATH=3rdparty/Megatron-LM:$PYTHONPATH \
uv run python -m torch.distributed.run --nproc_per_node=2 \
  3rdparty/Megatron-LM/pretrain_gpt.py \
  --tensor-model-parallel-size 2 --sequence-parallel \
  --num-layers 4 --hidden-size 256 --num-attention-heads 4 \
  --seq-length 1024 --max-position-embeddings 1024 \
  --micro-batch-size 2 --global-batch-size 16 \
  --train-iters 10 --eval-iters 2 --eval-interval 10 \
  --mock-data --bf16 --use-mcore-models \
  --tokenizer-type NullTokenizer --vocab-size 1024 \
  --lr 1e-4 --log-interval 1
Bridge 2-GPU with TP=2
rm -rf nemo_experiments && \
uv run python -m torch.distributed.run --nproc_per_node=2 \
  scripts/training/run_recipe.py \
  --recipe vanilla_gpt_pretrain_config \
  model.tensor_model_parallel_size=2 model.sequence_parallel=true \
  model.num_layers=4 model.hidden_size=256 \
  model.num_attention_heads=4 model.ffn_hidden_size=1024 \
  model.seq_length=1024 dataset.seq_length=1024 \
  train.train_iters=10 train.global_batch_size=16 train.micro_batch_size=2 \
  validation.eval_interval=10 validation.eval_iters=2 \
  scheduler.lr_warmup_iters=2 scheduler.lr_decay_iters=10 \
  logger.log_interval=1

Available Recipes

Common recipes (use with --recipe):

  • vanilla_gpt_pretrain_config — Minimal GPT (bare GPTModelProvider defaults, ideal for correlation testing and custom configs)

  • llama32_1b_pretrain_config — Llama 3.2 1B (16L, 2048H, GBS=512, seq=8192)

  • llama3_8b_pretrain_config — Llama 3 8B

  • qwen3_8b_pretrain_config — Qwen3 8B

  • deepseek_v2_lite_pretrain_config — DeepSeek-V2-Lite 16B MoE

SFT/PEFT variants use _sft_config / _peft_config suffix.

Megatron-Core Submodule

For what the submodule is and why two versions exist, see @docs/megatron-lm-to-megatron-bridge.md.

Check current version
./scripts/switch_mcore.sh status
Switch to dev for testing newer MCore features
./scripts/switch_mcore.sh dev

# uv sync (without --locked) since lockfile is for main
uv sync
Switch back to main
./scripts/switch_mcore.sh main
After pulling latest main

When you pull the latest Bridge main branch, the submodule pointer may have been updated. Re-sync the submodule:

git submodule update --init 3rdparty/Megatron-LM

Pitfalls

  1. Always rm -rf nemo_experiments before a fresh correlation run. Bridge auto-resumes from stale checkpoints silently.

  2. uv run required: Always use uv run python -m torch.distributed.run (not bare torchrun or python).

  3. MLM PYTHONPATH: Must include 3rdparty/Megatron-LM so gpt_builders.py is importable.

  4. Scheduler overrides: When overriding train.train_iters to a small value, also set scheduler.lr_warmup_iters and scheduler.lr_decay_iters or you get an assertion error.

  5. Use dataset.seq_length in CLI overrides for both pretraining and fine-tuning datasets.

  6. MoE OOM: Large MoE models require full activation recomputation and typically multi-node EP. TP does NOT reduce per-GPU expert memory.

  7. uv sync --locked fails after switching to dev: The lockfile is generated against the main MCore commit. Use uv sync (without --locked) when on dev.

Files

BENCHMARK.md

Evaluation Report

Evaluation of the nemo-mbridge-mlm-bridge-training skill before publication through NVSkills-Eval.

This benchmark summarizes 3-Tier Evaluation from NVSkills-Eval results for the skill. The goal is to document whether the skill is safe, discoverable, effective, and useful for agents before it is published for broader workflow use.

Evaluation Summary

  • Skill: nemo-mbridge-mlm-bridge-training

  • Evaluation date: 2026-07-15

  • NVSkills-Eval profile: external

  • Environment: astra-sandbox

  • Dataset: 1 evaluation tasks

  • Attempts per task: 1

  • Pass threshold: 50%

  • Overall verdict: PASS

Agents Used

  • claude-code

  • codex

Metrics Used

Reported benchmark dimensions:

  • Security: checks whether skill-assisted execution avoids unsafe behavior such as secret leakage, destructive commands, or unauthorized access.

  • Correctness: checks whether the agent follows the expected workflow and produces the correct final output.

  • Discoverability: checks whether the agent loads the skill when relevant and avoids using it when irrelevant.

  • Effectiveness: checks whether the agent performs measurably better with the skill than without it.

  • Efficiency: checks whether the agent uses fewer tokens and avoids redundant work.

Underlying evaluation signals used in this run:

  • security (Security): checks for unsafe operations, secret leakage, and unauthorized access.

  • skill_execution (Skill Execution): verifies that the agent loaded the expected skill and workflow.

  • skill_efficiency (Efficiency): checks routing quality, decoy avoidance, and redundant tool usage.

  • accuracy (Accuracy): grades final-answer correctness against the reference answer.

  • goal_accuracy (Goal Accuracy): checks whether the overall user task completed successfully.

  • behavior_check (Behavior Check): verifies expected behavior steps, including safety expectations.

  • token_efficiency (Token Efficiency): compares token usage with and without the skill.

Test Tasks

The benchmark dataset contained 1 evaluation tasks:

  • Positive tasks: 1 tasks where the skill was expected to activate.

  • Negative tasks: 0 tasks where no skill was expected.

  • Unlabeled tasks: 0 tasks where positive/negative intent could not be inferred.

Task composition is derived from the evaluation dataset when possible. Entries with expected_skill set are treated as positive skill-activation cases, while entries with expected_skill: null are treated as negative activation cases.

Results

DimensionNumclaude-codecodex
Security1100% (+0%)100% (+0%)
Correctness1100% (+100%)97% (+25%)
Discoverability1100% (+100%)97% (+64%)
Effectiveness1100% (+95%)100% (+0%)
Efficiency194% (+67%)96% (+59%)

Score values show skill-assisted performance. Values in parentheses show uplift versus the no-skill baseline when baseline data is available.

Tier 1: Static Validation Summary

Tier 1 validation passed with observations. NVSkills-Eval ran 1 checks and found 4 total findings.

Top findings:

  • MEDIUM SCHEMA/body_recommended_section: Missing recommended section: '## Instructions' (skills/nemo-mbridge-mlm-bridge-training/SKILL.md)

  • MEDIUM SCHEMA/body_recommended_section: Missing recommended section: '## Examples' (skills/nemo-mbridge-mlm-bridge-training/SKILL.md)

  • MEDIUM SCHEMA/author_missing: Author not specified in metadata (skills/nemo-mbridge-mlm-bridge-training/SKILL.md)

  • LOW SCHEMA/unexpected_file: Unexpected 'card.yaml' in skill root (skills/nemo-mbridge-mlm-bridge-training/card.yaml)

Tier 2: Deduplication Summary

This tier was not run or did not produce findings in this report.

Publication Recommendation

The skill is suitable to proceed toward NVSkills-Eval publication based on this benchmark. Skill owners should keep this file with the skill and refresh it when the evaluation dataset, skill behavior, or target agents materially change.

Requirements and compatibility

Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta

“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.

Needs: Needs a command-line tool, Needs Python, Needs Node.js

Security

What our scanner noticed· Medium risk

  • Large base64-encoded blobs

A static scan is not a guarantee. Skills are third-party content: review a skill before you let an agent use it.

Attribution and licence

Published by
NVIDIA
Publisher
NVIDIA (Official)
Source
NVIDIA/skills · skills/nemo-mbridge-mlm-bridge-training · commit fce3cca
Licence
Apache License 2.0 (redistribution allowed)
  • LICENSE (added by AutoGineer from LICENSE-APACHE)
  • ATTRIBUTION.md (written by AutoGineer)
Synced

AutoGineer is not affiliated with or endorsed by NVIDIA or NVIDIA.

Download ZIP

The ZIP holds the skill’s files, its licence files and an ATTRIBUTION.md. Download nemo-mbridge-mlm-bridge-training.zip