Schedule a Slurm job on a SageMaker HyperPod cluster - Amazon SageMaker
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Schedule a Slurm job on a SageMaker HyperPod cluster

You can launch training jobs using the standard Slurm sbatch or srun commands. For example, to launch an 8-node training job, you can run srun -N 8 --exclusive train.sh SageMaker HyperPod supports training in a range of environments, including conda, venv, docker, and enroot. You can configure an ML environment by running lifecycle scripts on your SageMaker HyperPod clusters. You also have an option to attach a shared file system such as Amazon FSx, which can also be used as a virtual environment.

The following example shows how to run a job for training Llama-2 with the Fully Sharded Data Parallelism (FSDP) technique on a SageMaker HyperPod cluster with an Amazon FSx shared file system. You can also find more examples from the Awsome Distributed Training GitHub repository.

Tip

All SageMaker HyperPod examples are available in the 3.test_cases folder of the Awsome Distributed Training GitHub repository.

  1. Clone the Awsome Distributed Training GitHub repository, and copy the training job examples to your Amazon FSx file system. 

    $ TRAINING_DIR=/fsx/users/my-user/fsdp
$ git clone https://github.com/aws-samples/awsome-distributed-training/

  2. Run the create_conda_env.sh script. This creates a conda environment on your Amazon FSx file system. Make sure that the file system is accessible to all nodes in the cluster.

  3. Build the virtual Conda environment by launching a single node slurm job as follows.

    $ srun -N 1 /path_to/create_conda_env.sh

  4. After the environment is built, you can launch a training job by pointing to the environment path on the shared volume. You can launch both single-node and multi-node training jobs with the same setup. To launch a job, create a job launcher script (also called an entry point script) as follows.

    #!/usr/bin/env bash
set -ex

ENV_PATH=/fsx/users/my_user/pytorch_env
TORCHRUN=$ENV_PATH/bin/torchrun
TRAINING_SCRIPT=/fsx/users/my_user/pt_train.py

WORLD_SIZE_JOB=$SLURM_NTASKS
RANK_NODE=$SLURM_NODEID
PROC_PER_NODE=8
MASTER_ADDR=(`scontrol show hostnames \$SLURM_JOB_NODELIST | head -n 1`)
MASTER_PORT=$(expr 10000 + $(echo -n $SLURM_JOBID | tail -c 4))

DIST_ARGS="--nproc_per_node=$PROC_PER_NODE \
           --nnodes=$WORLD_SIZE_JOB \
           --node_rank=$RANK_NODE \
           --master_addr=$MASTER_ADDR \
           --master_port=$MASTER_PORT \
          "
          
$TORCHRUN $DIST_ARGS $TRAINING_SCRIPT
    Tip

    If you want to make your training job more resilient against hardware failures by using the auto-resume capability of SageMaker HyperPod, you need to properly set up the environment variable MASTER_ADDR in the entrypoint script. To learn more, see Auto-resume.

    This tutorial assumes that this script is saved as /fsx/users/my_user/train.sh.

  5. With this script in the shared volume at /fsx/users/my_user/train.sh, run the following srun command to schedule the Slurm job.

    $ cd /fsx/users/my_user/
$ srun -N 8 train.sh