Nemo Mbridge Multi Node Slurm

NVIDIAOfficialPublished by NVIDIA

Skill
Tool integration
Cloud and DevOps
Apache-2.0
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From the author

Convert single-node scripts to multi-node Slurm sbatch jobs and debug common multi-node failures. Covers srun-native vs uv run torch.distributed approaches, container setup, NCCL timeouts, OOM sizing for MoE models, and interactive allocation.

— the description by NVIDIA

AutoGineer summary

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

This skill provides a comprehensive framework for scaling AI model training from single-node environments to multi-node Slurm clusters. It details two primary launch approaches—srun-native and uv run torch.distributed—and offers a systematic methodology for debugging common distributed training issues such as NCCL timeouts, OOM errors in MoE models, and container configuration pitfalls. The guide emphasizes best practices for shared filesystem usage, environment variable management, and interactive cluster allocation to streamline the development-to-production lifecycle.

What it does

Provides templates for converting single-node scripts into multi-node Slurm sbatch jobs.
Compares srun-native and uv run torch.distributed launch strategies.
Offers a structured checklist for diagnosing NCCL timeouts and distributed crashes.
Details memory sizing formulas for MoE models to prevent OOM failures.
Explains how to set up interactive GPU allocations for ad-hoc testing.
Defines best practices for shared storage, environment variables, and secret management.

When to use it

When scaling training jobs from a single node to a multi-node Slurm cluster.
When encountering NCCL timeouts or distributed training crashes.
When debugging OOM errors in Mixture-of-Experts (MoE) models.
When needing to convert legacy torchrun commands to native Slurm srun patterns.
When setting up interactive debugging sessions on a compute cluster.

Advantages

Reduces configuration errors by providing standardized sbatch and srun templates.
Simplifies environment management through shared filesystem and cache strategies.
Provides clear, actionable steps for the most common distributed training failure modes.
Improves iteration speed with a two-phase srun pattern for dependency caching.
Ensures secure handling of credentials by avoiding hardcoded tokens in scripts.

Usage examples

“How do I convert my single-node training script to run on 4 nodes using Slurm?”: The agent provides the srun-native sbatch template, explains the two-phase srun pattern for caching, and details how to configure the shared filesystem for logs and data.
“My multi-node job is failing with an NCCL timeout. How do I debug this?”: The agent guides you through a specific log-analysis checklist, including filtering noise, identifying the first failed rank, and checking for common causes like save_artifacts hangs.

Install

Download the skill as a ZIP:
https://autogineer.com/agent-skills/nvidia/nemo-mbridge-multi-node-slurm/download

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

mkdir -p .agents/skills && unzip nemo-mbridge-multi-node-slurm.zip -d .agents/skills

Documentation by NVIDIA

Multi-Node Slurm

Convert single-node uv run python -m torch.distributed.run commands into multi-node Slurm sbatch scripts with Enroot container support, and debug common multi-node failures.

First Answer Checklist

When converting or debugging Bridge multi-node jobs, answer in this order:

Prefer the srun-native launch shape for Bridge scripts that reach initialize.py: #SBATCH --ntasks-per-node=8 and a direct srun ... uv run python <script> ... launch. Do not wrap these jobs in python -m torch.distributed.run.
State that Bridge derives RANK, WORLD_SIZE, LOCAL_RANK, MASTER_ADDR, and MASTER_PORT from SLURM variables during initialize.py distributed init.
Require shared paths and matching container mounts for the repo, data, logs, HF_HOME, UV_CACHE_DIR, and NEMO_HOME.
For NCCL timeout reports, do these first-log checks before speculating:
- grep for real errors while filtering warning/frame noise
- inspect Failures: to find the first failed rank and node
- grep for ncclUniqueId, timeout, or crash on rank 0

Two Approaches: srun-native vs uv run torch.distributed

Approach	`ntasks-per-node`	Process spawning	Best for
srun-native (preferred)	8	Slurm spawns 8 tasks/node	Conversion, inference, Bridge scripts
uv run torch.distributed (legacy)	1	`uv run python -m torch.distributed.run` spawns 8 procs/node	MLM pretrain_gpt.py

Prefer srun-native — simpler, avoids shell escaping issues with TRAIN_CMD. Megatron Bridge auto-derives RANK, WORLD_SIZE, LOCAL_RANK, MASTER_ADDR, MASTER_PORT from SLURM env vars (SLURM_PROCID, SLURM_NTASKS, SLURM_LOCALID, SLURM_NODELIST) via common_utils.py helpers called during initialize.py distributed init, so you never need to set them manually.

Cluster Environment

Use a shared filesystem for the repository, data, logs, HF_HOME, UV_CACHE_DIR, and NEMO_HOME. NEMO_HOME must not use the container-local default (/root/.cache/nemo) for multi-node SFT/PEFT jobs, because packed-sequence data prepared on node 0 must be visible to the other nodes.

Keep credentials out of sbatch templates and logs. Provide HF_TOKEN, GH_TOKEN, and WANDB_API_KEY through the scheduler environment or a restricted secrets file, and never hardcode token values in the script body. For copy-paste environment and sbatch templates, read references/templates.md.

Log Directory

<SHARED_FS>/logs/<job_name>_<suffix>

srun-native Approach (Preferred)

Slurm spawns all processes directly. No torch.distributed.run, no TRAIN_CMD escaping.

SBATCH Headers

#SBATCH --job-name=<model>-<task>
#SBATCH --nodes=<NNODES>
#SBATCH --ntasks-per-node=8          # Slurm spawns 8 tasks per node
#SBATCH --gpus-per-node=8
#SBATCH --time=00:30:00
#SBATCH --account=<YOUR_ACCOUNT>
#SBATCH --partition=batch
#SBATCH --output=<SHARED_FS>/logs/<job_name>_%j.log
#SBATCH --exclusive

Build and Launch

Use a two-phase srun pattern: first run a single-process uv sync to populate the shared cache, then launch the full multi-node job. The full copy-paste version lives in references/templates.md.

srun-native Key Points

Phase 1 runs uv sync once on a single node/process, building all wheels into the shared cache on Lustre
Phase 2's uv sync is a fast no-op (everything is cached) — safe to run on all ranks without sleep guards
initialize.py + common_utils.py auto-set RANK, WORLD_SIZE, LOCAL_RANK, MASTER_ADDR, MASTER_PORT from SLURM env vars
Env vars like HF_TOKEN, HF_HOME, UV_CACHE_DIR exported at sbatch level are inherited by srun tasks
Reference: examples/models/glm/glm_45v/slurm_sft.sh, examples/models/minimax/minimax_m2/slurm_conversion.sh

uv run torch.distributed Approach (Legacy)

Use when the script requires torch.distributed.run (e.g., MLM pretrain_gpt.py) or when Bridge's initialize.py is not in the call path.

1. Add SBATCH Headers

#SBATCH --job-name=<model>-<framework>
#SBATCH --nodes=<NNODES>
#SBATCH --ntasks-per-node=1          # ALWAYS 1 — torchrun handles per-node spawning
#SBATCH --gpus-per-node=8
#SBATCH --time=00:30:00
#SBATCH --account=<YOUR_ACCOUNT>
#SBATCH --partition=batch
#SBATCH --output=<SHARED_FS>/logs/<job_name>_%j.log
#SBATCH --exclusive

Critical: --ntasks-per-node=1, NOT 8. uv run python -m torch.distributed.run --nproc_per_node=8 spawns 8 processes per node. Using ntasks-per-node=8 causes EADDRINUSE port collisions (8 tasks x 8 procs = 64 per node).

2. Convert to Multi-Node

Replace single-node:

uv run python -m torch.distributed.run --nproc_per_node=8 \
  <script> <args>

With multi-node (inside TRAIN_CMD string):

uv run python -m torch.distributed.run \
  --nproc_per_node=8 \
  --nnodes=\${SLURM_JOB_NUM_NODES} \
  --node_rank=\${SLURM_NODEID} \
  <script> <args>

MASTER_ADDR and MASTER_PORT are auto-derived from SLURM env vars by initialize.py / common_utils.py — no need to set them.

3. Wrap in TRAIN_CMD + two-phase srun

Use the same two-phase pattern: first a single-process srun to warm the uv cache, then the full run.

Set runtime variables inside the container, but do not inject token values into a long bash -c string. Export credentials through the scheduler or source a restricted secrets file before the job starts. Keep HF_HOME, UV_CACHE_DIR, and NEMO_HOME on shared storage.

4. Launch (two-phase)

Use the two-phase launch template in references/templates.md, keeping #SBATCH --ntasks-per-node=1 for this legacy approach.

5. (Optional) Add Loss Extraction Footer

echo "======================================"
echo "Done. Losses:"
echo "======================================"
grep -E "iteration\s+" "$LOGDIR/<prefix>_${SLURM_JOB_ID}.log" | grep -iE "lm loss|reduced_train_loss" | head -25

Interactive GPU Allocation (`salloc` + `srun`)

For ad-hoc testing (inference, conversion debugging), always follow these 3 steps:

Step 1: Allocate the node

salloc --account <YOUR_ACCOUNT> -N 1 \
  -J <YOUR_ACCOUNT>-debug \
  -p interactive --gpus-per-node=8 -t 240

Step 2: Launch container shell

srun --mpi=pmix --no-kill \
  --container-image $CONTAINER_IMAGE \
  --container-mounts $CONTAINER_MOUNTS \
  --account <YOUR_ACCOUNT> -N 1 \
  -J <YOUR_ACCOUNT>-debug \
  --no-container-mount-home --gpus-per-node=8 \
  -p interactive --pty bash

Step 3: Set up environment inside container

export GH_TOKEN=<YOUR_GITHUB_TOKEN>
wandb login <YOUR_WANDB_KEY>
export HF_TOKEN=<YOUR_HF_TOKEN>
export HF_HOME=<SHARED_FS>/HF_HOME
export UV_CACHE_DIR="<SHARED_FS>/uv_cache"
export NEMO_HOME="<SHARED_FS>/cache/nemo"
uv sync

Then run commands with uv run (uses the synced virtualenv):

uv run python -m torch.distributed.run --nproc_per_node=8 \
  examples/conversion/hf_to_megatron_generate_text.py \
  --hf_model_path <org>/<model> --prompt "What is AI?" --max_new_tokens 50 --ep 8

Pitfalls with interactive allocation:

Error	Cause	Fix
`Cannot find GPU specification`	Missing `--gpus-per-node`	Always include `--gpus-per-node=8` in both `salloc` and `srun`
`invalid partition specified: pool0`	Wrong partition name	Use `interactive` for interactive, `batch` for sbatch. Check: `sinfo --summarize`
`Invalid account or account/partition combination`	Partition not available for account	Check combos: `sacctmgr -nP show assoc where user=$USER format=account,partition`
`Unable to create step for job... Requested node configuration is not available`	`-w <node>` conflicts with allocation	Remove `-w` flag — HF cache is on shared filesystem, accessible from any node
`uv: command not found` inside container	Container doesn't have `uv` pre-installed	Use a container with `uv` pre-installed, or `pip install uv`
`No space left on device` during `uv` or `pip`	Container's `/root/.cache/` is full	Redirect: `export UV_CACHE_DIR=<SHARED_FS>/uv_cache`
`ModuleNotFoundError: No module named 'megatron.core.activations'`	Container's pre-installed megatron-core conflicts with local `3rdparty/Megatron-LM`	Install local: `pip install -e 3rdparty/Megatron-LM --no-deps --no-build-isolation`

Debugging Multi-Node Failures

Quick Diagnosis

Check the log for these patterns (in order):

# 1. Find the actual error (filter noise)
grep -a 'Error\|OOM\|CUDA out of memory\|FAILED\|Killed' job.log \
  | grep -v 'UserWarning\|AllocatorConfig\|transformer_engine\|frame\|srun: error'

# 2. Check which rank crashed first
grep -a 'Failures:' -A 20 job.log | head -25

# 3. Check for NCCL timeout
grep -a 'ncclUniqueId\|timeout\|crash on rank 0' job.log | head -5

Debugging Checklist

When a multi-node job fails:

Check exit code: 1 = Python error, 9 = OOM killed, 143 = SIGTERM (timeout or cascade)
Find first failure: Which task/node crashed first? Others get SIGTERM (143) as cascade
grep the actual error: Filter out UserWarnings, NCCL frame dumps
Check rank 0 specifically: Most save/export errors happen on rank 0
Verify EP sizing: For MoE models, ensure num_experts / EP fits in GPU memory with headroom
Try interactive first: Use salloc -N 2 -p interactive to iterate faster than sbatch queue

NCCL Timeout at `dist.barrier()` — "crash on rank 0"

Symptom: All ranks on node 2+ show:

[rank8] is setting up NCCL communicator and retrieving ncclUniqueId from [0]
... wait timeout after 600000ms
This may indicate a possible application crash on rank 0

Root causes (check in order):

Cause	How to verify	Fix
`save_artifacts` hangs on rank 0	Error is in `save_hf_weights` → `dist.barrier()`	Increase timeout: `init_process_group("nccl", timeout=timedelta(minutes=60))`
`ImportError` in custom model code	`grep ImportError job.log`	Catch `ImportError` in `save_artifacts` (see below)
Rank 0 OOM during export	`grep 'OutOfMemory' job.log`	Increase EP or nodes
Network issue between nodes	Error only on cross-node ranks	Check `sinfo`, try different nodes

The save_artifacts problem: When trust_remote_code=True, rank 0 runs save_artifacts() (downloads tokenizer, config, custom modeling code) while all other ranks skip directly to dist.barrier(). If save_artifacts is slow or crashes, other ranks timeout.

Fix for ImportError in save_artifacts (hf_pretrained/base.py):

# Change:
except OSError:
    pass
# To:
except (OSError, ImportError):
    pass

OOM for MoE Models

Symptom: torch.OutOfMemoryError: CUDA out of memory during model loading or forward pass.

Key insight: TP does NOT reduce expert memory. Only EP splits experts across GPUs.

Sizing formula:

experts_per_gpu = num_experts / EP
expert_memory_gb ≈ experts_per_gpu * expert_params * 2 / 1e9  (bf16)
total_per_gpu ≈ expert_memory_gb + attention_memory_gb + kv_cache_gb

MiniMax-M2 example (256 experts, ~230GB fp8 → ~460GB bf16):

Config	Nodes	GPUs	Experts/GPU	Result
TP=2, EP=4	1	8	64	OOM (too many experts)
TP=2, EP=8	2	16	32	Works for roundtrip (weight-only), OOM for inference
TP=1, EP=16	2	16	16	Works for inference
TP=2, EP=32	8	64	8	Comfortable for training

Rules of thumb:

Roundtrip (weight-only): can use more experts per GPU (~60GB model params OK)
Inference (forward pass + KV cache): needs headroom (~40GB model params max)
Training (activations + optimizer): needs even more headroom (~30GB model params max)

`ModuleNotFoundError: No module named 'megatron.core.tensor_parallel'`

Cause: Container's pre-installed megatron-core conflicts with local 3rdparty/Megatron-LM.

Fix: Add uv sync before running:

CMD="if [ \"\$SLURM_LOCALID\" -eq 0 ]; then uv sync; else sleep 10; fi && "
CMD="${CMD}uv run --no-sync python <script> <args>"

FP8 Weight Mismatch in Roundtrip

Symptom: Roundtrip completes but shows ❌ for all expert weights and raises ValueError: Weight mismatch detected.

Cause: Original HF weights are FP8, Megatron stores in BF16. Exported weights are BF16. Comparison against original FP8 exceeds atol=1e-1.

This is expected for FP8 models. The conversion is correct; the comparison tolerance is insufficient for the FP8→BF16 precision gap.

`WORLD_SIZE` Not Set with srun

Symptom: Script exits with "must be launched with torchrun".

Cause: Scripts check os.environ.get("WORLD_SIZE") which torchrun sets but srun doesn't.

Fix: Also check SLURM_NTASKS:

if os.environ.get("WORLD_SIZE") is None and os.environ.get("SLURM_NTASKS") is None:
    sys.exit(1)

Bridge's common_utils.py helpers (called by initialize.py) populate env vars from SLURM:

if "RANK" not in os.environ:
    os.environ["RANK"] = str(get_rank_safe())          # uses SLURM_PROCID
if "WORLD_SIZE" not in os.environ:
    os.environ["WORLD_SIZE"] = str(get_world_size_safe())  # uses SLURM_NTASKS
if "MASTER_ADDR" not in os.environ:
    os.environ["MASTER_ADDR"] = get_master_addr_safe()     # parses SLURM_NODELIST
if "MASTER_PORT" not in os.environ:
    os.environ["MASTER_PORT"] = str(get_master_port_safe()) # derives from SLURM_JOB_ID

Key Gotchas

Two-phase srun for uv sync: Run a single-process srun first to warm the cache, then the full multi-node srun. The second uv sync is a fast no-op since everything is already cached on the shared filesystem.
--no-container-mount-home is an srun flag, NOT an #SBATCH directive.
Escaping inside TRAIN_CMD: Since TRAIN_CMD is a double-quoted string, escape inner $ for Slurm variables that must expand at runtime (not sbatch time):
- \${SLURM_PROCID}, \${SLURM_JOB_NUM_NODES}, \${SLURM_NODEID}
- Host-side variables like $GH_TOKEN, $LOGDIR, $WORKDIR expand at sbatch time — no escaping needed.
Bridge rm -rf nemo_experiments: Add before training to avoid stale checkpoint auto-resume.
MLM needs PYTHONPATH: For pretrain_gpt.py scripts, add inside TRAIN_CMD:
```
PYTHONPATH=${WORKDIR}/3rdparty/Megatron-LM:\${PYTHONPATH:-} \
```
Node count heuristic: Total GPUs = NNODES * 8. Must satisfy: TP * PP * EP * DP >= total_GPUs where DP = total_GPUs / (TP * PP * EP).
NEMO_HOME on shared filesystem for multi-node SFT: The default nemo cache (/root/.cache/nemo) is container-local. Multi-node SFT with packed sequences prepares .npy files on one node that are invisible to others. Set export NEMO_HOME=<SHARED_FS>/cache/nemo so packed data is shared. Without this, ranks on other nodes fail with TypeError: 'NoneType' object is not an iterator.

Full Templates and Command Bodies

For copyable sbatch scaffolding and Bridge/MLM-specific TRAIN_CMD bodies, read references/templates.md.

Files

ATTRIBUTION.mdAdded by AutoGineer857 B
BENCHMARK.md4.2 KiB
LICENSELicence file added by AutoGineer11.2 KiB
SKILL.md15.4 KiB
evals/evals.json1.7 KiB
references/templates.md4.6 KiB
skill-card.md3.7 KiB
skill.oms.sig4.7 KiB

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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 an API key, Needs Python, Needs Node.js

Security

What our scanner noticed· Medium risk

Large base64-encoded blobs
Instructions that use the network

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-multi-node-slurm · commit fce3cca

Licence

Apache License 2.0 (redistribution allowed)

LICENSE (added by AutoGineer from LICENSE-APACHE)
ATTRIBUTION.md (written by AutoGineer)

Synced

2026-11-09

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-multi-node-slurm.zip