mcore-run-on-slurm

You can launch multi-node Megatron-LM training on a SLURM cluster by creating and submitting an sbatch script built from a reusable skeleton that configures torch.distributed.run across allocated GPUs.

Configuring MASTER_ADDR, MASTER_PORT, and WORLD_SIZE for SLURM distributed training involves calculating these values dynamically from SLURM environment variables to establish the torch.distributed.run rendezvous endpoints.

CUDA_DEVICE_MAX_CONNECTIONS settings for Megatron-LM depend on your specific hardware generation and parallelism modes, requiring tailored recommendations to optimize multi-node GPU communication throughput.

Yes, the SLURM sbatch skeleton supports container integration and provides monitoring guidance to track distributed GPU training jobs, alongside per-rank failure diagnosis workflows for troubleshooting.

Using a reusable sbatch skeleton for Megatron-LM provides a repeatable pattern that reduces setup time and configuration errors when orchestrating multi-node GPU training on SLURM clusters.

Yes, you can validate a new SLURM cluster by quickly spinning up a Megatron-LM job to test scaling, verify environment setup, and perform fault diagnosis across multi-node GPU hardware.