# HyperPod Slurm cluster pre-training tutorial (GPU)
<a name="hyperpod-gpu-slurm-pretrain-tutorial"></a>

The following tutorial sets up Slurm environment and starts a training job on a Llama 8 billion parameter model.

**Prerequisites**  
Before you start setting up your environment to run the recipe, make sure you have:  
Set up a HyperPod GPU Slurm cluster.  
Your HyperPod Slurm cluster must have Nvidia Enroot and Pyxis enabled (these are enabled by default).
A shared storage location. It can be an Amazon FSx file system or an NFS system that's accessible from the cluster nodes.
Data in one of the following formats:  
JSON
JSONGZ (Compressed JSON)
ARROW
(Optional) You must get a HuggingFace token if you're using the model weights from HuggingFace for pre-training or fine-tuning. For more information about getting the token, see [User access tokens](https://huggingface.co/docs/hub/en/security-tokens).

## HyperPod GPU Slurm environment setup
<a name="hyperpod-gpu-slurm-environment-setup"></a>

To initiate a training job on a HyperPod GPU Slurm cluster, do the following:

1. SSH into the head node of your Slurm cluster.

1. After you log in, set up the virtual environment. Make sure you're using Python 3.9 or greater.

   ```
   #set up a virtual environment
   python3 -m venv ${PWD}/venv
   source venv/bin/activate
   ```

1. Clone the SageMaker HyperPod recipes and SageMaker HyperPod adapter repositories to a shared storage location.

   ```
   git clone https://github.com/aws/sagemaker-hyperpod-training-adapter-for-nemo.git
   git clone --recursive https://github.com/aws/sagemaker-hyperpod-recipes.git
   cd sagemaker-hyperpod-recipes
   pip3 install -r requirements.txt
   ```

1. Create a squash file using Enroot. To find the most recent release of the SMP container, see [Release notes for the SageMaker model parallelism library](model-parallel-release-notes.md). To gain a deeper understanding of how to use the Enroot file, see [Build AWS-optimized Nemo-Launcher image](https://github.com/aws-samples/awsome-distributed-training/tree/main/3.test_cases/2.nemo-launcher#2-build-aws-optimized-nemo-launcher-image).

   ```
   REGION="<region>"
   IMAGE="658645717510.dkr.ecr.${REGION}.amazonaws.com/smdistributed-modelparallel:2.4.1-gpu-py311-cu121"
   aws ecr get-login-password --region ${REGION} | docker login --username AWS --password-stdin 658645717510.dkr.ecr.${REGION}.amazonaws.com
   enroot import -o $PWD/smdistributed-modelparallel.sqsh dockerd://${IMAGE}
   mv $PWD/smdistributed-modelparallel.sqsh "/fsx/<any-path-in-the-shared-filesystem>"
   ```

1. To use the Enroot squash file to start training, use the following example to modify the `recipes_collection/config.yaml` file.

   ```
   container: /fsx/path/to/your/smdistributed-modelparallel.sqsh
   ```

## Launch the training job
<a name="hyperpod-gpu-slurm-launch-training-job"></a>

After you install the dependencies, start a training job from the `sagemaker-hyperpod-recipes/launcher_scripts` directory. You get the dependencies by cloning the [SageMaker HyperPod recipes repository](https://github.com/aws/sagemaker-hyperpod-recipes):

First, pick your training recipe from Github, the model name is specified as part of the recipe. We use the `launcher_scripts/llama/run_hf_llama3_8b_seq16k_gpu_p5x16_pretrain.sh` script to launch a Llama 8b with sequence length 8192 pre-training recipe, `llama/hf_llama3_8b_seq16k_gpu_p5x16_pretrain`, in the following example.
+ `IMAGE`: The container from the environment setup section.
+ (Optional) You can provide the HuggingFace token if you need pre-trained weights from HuggingFace by setting the following key-value pair:

  ```
  recipes.model.hf_access_token=<your_hf_token>
  ```

```
#!/bin/bash
IMAGE="${YOUR_IMAGE}"
SAGEMAKER_TRAINING_LAUNCHER_DIR="${SAGEMAKER_TRAINING_LAUNCHER_DIR:-${PWD}}"

TRAIN_DIR="${YOUR_TRAIN_DIR}" # Location of training dataset
VAL_DIR="${YOUR_VAL_DIR}" # Location of validation dataset

# experiment ouput directory
EXP_DIR="${YOUR_EXP_DIR}"

HYDRA_FULL_ERROR=1 python3 "${SAGEMAKER_TRAINING_LAUNCHER_DIR}/main.py" \
  recipes=training/llama/hf_llama3_8b_seq16k_gpu_p5x16_pretrain \
  base_results_dir="${SAGEMAKER_TRAINING_LAUNCHER_DIR}/results" \
  recipes.run.name="hf_llama3_8b" \
  recipes.exp_manager.exp_dir="$EXP_DIR" \
  recipes.model.data.train_dir="$TRAIN_DIR" \
  recipes.model.data.val_dir="$VAL_DIR" \
  container="${IMAGE}" \
  +cluster.container_mounts.0="/fsx:/fsx"
```

After you've configured all the required parameters in the launcher script, you can run the script using the following command.

```
bash launcher_scripts/llama/run_hf_llama3_8b_seq16k_gpu_p5x16_pretrain.sh
```

For more information about the Slurm cluster configuration, see [Running a training job on HyperPod Slurm](cluster-specific-configurations-run-training-job-hyperpod-slurm.md).