Mcore Run On Slurm
NVIDIAOfficialCreated by Philip PetrakianDistributed by NVIDIA
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Written by AutoGineer from the skill’s own files, not by the author
This skill provides a standardized approach to configuring and launching distributed Megatron-LM training jobs on SLURM-managed clusters. It covers essential environment setup, including shared filesystem requirements, dependency management with uv, and the correct configuration of distributed training variables. The guide includes a robust sbatch template and specific instructions for handling hardware-dependent environment variables like CUDA_DEVICE_MAX_CONNECTIONS, alongside best practices for monitoring, failure diagnosis, and container usage.
What it does
- Provides a minimal, reusable sbatch script template for Megatron-LM.
- Defines environment variable requirements for multi-node distributed training.
- Explains hardware-specific settings for CUDA_DEVICE_MAX_CONNECTIONS.
- Outlines prerequisites for shared filesystem access and dependency synchronization.
- Offers troubleshooting strategies for common OOM, NCCL, and import errors.
- Details monitoring and artifact collection techniques for SLURM jobs.
When to use it
- When submitting a new Megatron-LM training job to a SLURM cluster.
- When debugging sbatch scripts or multi-node connectivity issues.
- When configuring environment variables for distributed training parallelism.
- When diagnosing job failures or NCCL timeouts in a cluster environment.
Advantages
- Reduces configuration errors by providing a validated sbatch skeleton.
- Clarifies complex hardware-specific environment variable requirements.
- Promotes best practices for shared storage and dependency management.
- Simplifies failure diagnosis by distinguishing between root causes and downstream symptoms.
Usage examples
- “How do I set up a SLURM script for a multi-node Megatron-LM run?”
- The agent provides a complete sbatch template with placeholders for account, partition, and node counts, including the necessary export commands for MASTER_ADDR and WORLD_SIZE.
- “What should I set CUDA_DEVICE_MAX_CONNECTIONS to for my Hopper-based training?”
- The agent identifies the hardware and parallelism mode to recommend the correct value (e.g., 1 for non-FSDP TP/CP configurations) or warns against setting it for FSDP.
Install
Download the skill as a ZIP:
https://autogineer.com/agent-skills/nvidia/mcore-run-on-slurm/downloadUnzip it into the cross-client skills folder of your project:
mkdir -p .agents/skills && unzip mcore-run-on-slurm.zip -d .agents/skills
Documentation by Philip Petrakian
Run Megatron-LM on SLURM
Answer-First Constants
For text-only SLURM setup questions, answer with these constants before the full script:
Submit from a shared worktree path visible to every node;
cdthere in the script before launching training.Use one
sruntask per node and launch workers withuv run python -m torch.distributed.run, not baretorchrun.Set
MASTER_ADDRfromscontrol show hostnames "$SLURM_JOB_NODELIST" | head -n1, setMASTER_PORT,NNODES=${SLURM_NNODES},GPUS_PER_NODE=<GPUS_PER_NODE>, andWORLD_SIZE=$((NNODES * GPUS_PER_NODE)).Pass
--nnodes,--nproc-per-node,--node-rank,--master-addr, and--master-porttotorch.distributed.run.CUDA_DEVICE_MAX_CONNECTIONS: pre-Blackwell Hopper/Ampere with TP>1 or CP>1 and non-FSDP uses1; Blackwell/GB200 does not need it; Torch-FSDP2 or Megatron-FSDP must not use1;overlap_moe_expert_parallel_commuses32.
Prerequisites
A SLURM cluster login with submission rights to a GPU partition.
Megatron-LM checked out on a filesystem visible to all nodes in the allocation (NFS, Lustre, or similar). All nodes must reach the same paths for code, data, checkpoints, and output.
uvinstalled; runuv sync --extra training --extra dev(or--extra lts) on the worktree once before submission so the.venvis materialized and visible to every node.
Minimal sbatch script
Save as run_megatron.slurm in the worktree:
#!/bin/bash
#SBATCH --job-name=megatron
#SBATCH --account=<SLURM_ACCOUNT>
#SBATCH --partition=<SLURM_PARTITION>
#SBATCH --nodes=<NODES>
#SBATCH --ntasks-per-node=1
#SBATCH --gpus-per-node=<GPUS_PER_NODE>
#SBATCH --time=<HH:MM:SS>
#SBATCH --output=logs/%x-%j.out
#SBATCH --error=logs/%x-%j.err
set -euo pipefail
cd <MEGATRON_WORKTREE>
export MASTER_ADDR=$(scontrol show hostnames "$SLURM_JOB_NODELIST" | head -n1)
export MASTER_PORT=${MASTER_PORT:-29500}
export NNODES=${SLURM_NNODES}
export GPUS_PER_NODE=<GPUS_PER_NODE>
export WORLD_SIZE=$((NNODES * GPUS_PER_NODE))
# Set CUDA_DEVICE_MAX_CONNECTIONS only when your configuration requires it
# (see the section below). Example for pre-Blackwell with TP>1 or CP>1
# (non-FSDP):
# export CUDA_DEVICE_MAX_CONNECTIONS=1
srun --ntasks=${NNODES} --ntasks-per-node=1 bash -c '
# NODE_RANK comes from SLURM_NODEID with one task per node.
NODE_RANK=${SLURM_NODEID}
uv run python -m torch.distributed.run \
--nnodes='"${NNODES}"' \
--nproc-per-node='"${GPUS_PER_NODE}"' \
--node-rank=${NODE_RANK} \
--master-addr='"${MASTER_ADDR}"' \
--master-port='"${MASTER_PORT}"' \
pretrain_gpt.py \
<MEGATRON_ARGS>
'Submit:
mkdir -p logs && JOB_ID=$(sbatch --parsable run_megatron.slurm)
echo "Submitted ${JOB_ID}"Multi-node rules
Submit from the worktree you intend to run, or
cdto it in the script. All nodes must reach the same path on a shared filesystem (NFS, Lustre, or similar) — node-local paths will not be visible to peer ranks.Use one
torchrunworker group across all nodes; do not start independent single-node jobs.--nproc-per-nodeshould equal the number of visible GPUs per node.Write checkpoints, tensorboard data, and structured logs to shared storage.
CUDA_DEVICE_MAX_CONNECTIONS
The right value depends on your hardware and parallelism mode. Do not export it unconditionally:
Pre-Blackwell (Hopper, Ampere) with TP>1 or CP>1, non-FSDP: set to
1. The relevant code path asserts on this — you will get an assertion error if it is not1, not a silent deadlock.Blackwell: not required; setting it has no effect.
Torch-FSDP2 or Megatron-FSDP: must NOT be
1. Leave the env var unset, or set it to a value greater than1.overlap_moe_expert_parallel_commenabled: set to32.
Set it explicitly in the sbatch script when your configuration calls for it.
Containers
Many sites run Megatron-LM inside a container (enroot/pyxis on some clusters, singularity on others). If you do, the uv-managed .venv must live on a path that is visible from inside the container, and the container image must provide the CUDA / NCCL / torch versions the repo expects (see docker/.ngc_version.dev and .ngc_version.lts). The skeleton above stays the same; wrap the srun invocation with your scheduler's container flags (--container-image=…, --container-mounts=…, etc.).
Monitor and collect
squeue -j "$JOB_ID" -o "%.10i %.8T %.10M %.6D %R"
sacct -j "$JOB_ID" --format=JobID,State,ExitCode,Elapsed
scancel "$JOB_ID"If your training script writes a result artifact (a JSON metrics file from rank 0, a final checkpoint, etc.), poll for the artifact rather than waiting only on squeue state. Useful output usually appears before SLURM marks the job complete, and polling on the artifact lets you cancel the job as soon as it lands instead of holding the allocation until the timeout.
Failure diagnosis
Scan stderr from every rank, not just rank 0. The earliest non-NCCL Python traceback is usually the root cause; later NCCL timeouts on other ranks are downstream symptoms of the first crash.
Classify quickly:
OOM: record rank, phase (forward / backward / optimizer), batch size, sequence length, parallelism (TP/DP/CP/PP), and peak memory before adjusting.
Shape / divisibility error: check
WORLD_SIZE = TP × DP × CP × PPand head-count divisibility (num_attention_heads % TP == 0).Import error: wrong worktree, missing
uv sync, or stalePYTHONPATH. Confirmcd <MEGATRON_WORKTREE>before launch.NCCL failure with no Python traceback: verify allocation, port reachability,
MASTER_ADDRresolution, and command consistency across ranks.
Common pitfalls
Forgetting
uv syncbefore the first submission. If the venv is missing, every job rebuilds it from insidesrun, costing minutes per job.Writing logs to a node-local path that disappears at job exit. Always write to the shared filesystem.
Setting
CUDA_DEVICE_MAX_CONNECTIONS=1blindly. The right value depends on hardware and parallelism mode (see the dedicated section above). Setting it to1with FSDP causes a different problem; on Blackwell it has no effect; on pre-Blackwell with TP>1 or CP>1 (non-FSDP) the code asserts, it does not deadlock.Running bare
torchruninstead ofuv run python -m torch.distributed.run. Baretorchrunmay dispatch through a python interpreter that does not see venv packages, depending on how the venv is set up.
Files
- ATTRIBUTION.mdAdded by AutoGineer922 B
- BENCHMARK.md2.7 KiB
- LICENSELicence file added by AutoGineer11.2 KiB
- SKILL.md7.1 KiB
- evals/evals.json3 B
- skill-card.md2.8 KiB
- skill.oms.sig4.5 KiB
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Attribution and licence
- Created by
- Philip Petrakian
- Publisher
- NVIDIA (Official)
- Distributed by
- NVIDIA
- Source
- NVIDIA/skills · skills/mcore-run-on-slurm · commit fce3cca
- Licence
- Apache License 2.0 (redistribution allowed)
- LICENSE (added by AutoGineer from LICENSE-APACHE)
- ATTRIBUTION.md (written by AutoGineer)
- Synced
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