# Running a training job on HyperPod Slurm
<a name="cluster-specific-configurations-run-training-job-hyperpod-slurm"></a>

SageMaker HyperPod Recipes supports submitting a training job to a GPU/Trainium slurm cluster. Before you submit the training job, update the cluster configuration. Use one of the following methods to update the cluster configuration:
+ Modify `slurm.yaml`
+ Override it through the command line

After you've updated the cluster configuration, install the environment.

## Configure the cluster
<a name="cluster-specific-configurations-configure-cluster-slurm-yaml"></a>

To submit a training job to a Slurm cluster, specify the Slurm-specific configuration. Modify `slurm.yaml` to configure the Slurm cluster. The following is an example of a Slurm cluster configuration. You can modify this file for your own training needs:

```
job_name_prefix: 'sagemaker-'
slurm_create_submission_file_only: False 
stderr_to_stdout: True
srun_args:
  # - "--no-container-mount-home"
slurm_docker_cfg:
  docker_args:
    # - "--runtime=nvidia" 
  post_launch_commands: 
container_mounts: 
  - "/fsx:/fsx"
```

1. `job_name_prefix`: Specify a job name prefix to easily identify your submissions to the Slurm cluster.

1. `slurm_create_submission_file_only`: Set this configuration to True for a dry run to help you debug.

1. `stderr_to_stdout`: Specify whether you're redirecting your standard error (stderr) to standard output (stdout).

1. `srun_args`: Customize additional srun configurations, such as excluding specific compute nodes. For more information, see the srun documentation.

1. `slurm_docker_cfg`: The SageMaker HyperPod recipe launcher launches a Docker container to run your training job. You can specify additional Docker arguments within this parameter.

1. `container_mounts`: Specify the volumes you're mounting into the container for the recipe launcher, for your training jobs to access the files in those volumes.