# Kubernetes cluster pre-training tutorial (GPU)
<a name="sagemaker-hyperpod-gpu-kubernetes-cluster-pretrain-tutorial"></a>

There are two ways to launch a training job in a GPU Kubernetes cluster:
+ (Recommended) [HyperPod command-line tool](https://github.com/aws/sagemaker-hyperpod-cli)
+ The NeMo style launcher

**Prerequisites**  
Before you start setting up your environment, make sure you have:  
A HyperPod GPU Kubernetes cluster is setup properly.
A shared storage location. It can be an Amazon FSx file system or 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).

## GPU Kubernetes environment setup
<a name="sagemaker-hyperpod-gpu-kubernetes-environment-setup"></a>

To set up a GPU Kubernetes environment, do the following:
+ Set up the virtual environment. Make sure you're using Python 3.9 or greater.

  ```
  python3 -m venv ${PWD}/venv
  source venv/bin/activate
  ```
+ Install dependencies using one of the following methods:
  + (Recommended): [HyperPod command-line tool](https://github.com/aws/sagemaker-hyperpod-cli) method:

    ```
    # install HyperPod command line tools
    git clone https://github.com/aws/sagemaker-hyperpod-cli
    cd sagemaker-hyperpod-cli
    pip3 install .
    ```
  + SageMaker HyperPod recipes method:

    ```
    # install SageMaker HyperPod Recipes.
    git clone --recursive git@github.com:aws/sagemaker-hyperpod-recipes.git
    cd sagemaker-hyperpod-recipes
    pip3 install -r requirements.txt
    ```
+ [Set up kubectl and eksctl](https://docs.aws.amazon.com/eks/latest/userguide/install-kubectl.html)
+ [Install Helm](https://helm.sh/docs/intro/install/)
+ Connect to your Kubernetes cluster

  ```
  aws eks update-kubeconfig --region "CLUSTER_REGION" --name "CLUSTER_NAME"
  hyperpod connect-cluster --cluster-name "CLUSTER_NAME" [--region "CLUSTER_REGION"] [--namespace <namespace>]
  ```

## Launch the training job with the SageMaker HyperPod CLI
<a name="sagemaker-hyperpod-gpu-kubernetes-launch-training-job-cli"></a>

We recommend using the SageMaker HyperPod command-line interface (CLI) tool to submit your training job with your configurations. The following example submits a training job for the `hf_llama3_8b_seq16k_gpu_p5x16_pretrain` model.
+ `your_training_container`: A Deep Learning container. To find the most recent release of the SMP container, see [Release notes for the SageMaker model parallelism library](model-parallel-release-notes.md).
+ (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>"
  ```

```
hyperpod start-job --recipe training/llama/hf_llama3_8b_seq16k_gpu_p5x16_pretrain \
--persistent-volume-claims fsx-claim:data \
--override-parameters \
'{
"recipes.run.name": "hf-llama3-8b",
"recipes.exp_manager.exp_dir": "/data/<your_exp_dir>",
"container": "658645717510.dkr.ecr.<region>.amazonaws.com/smdistributed-modelparallel:2.4.1-gpu-py311-cu121",
"recipes.model.data.train_dir": "<your_train_data_dir>",
"recipes.model.data.val_dir": "<your_val_data_dir>",
"cluster": "k8s",
"cluster_type": "k8s"
}'
```

After you've submitted a training job, you can use the following command to verify if you submitted it successfully.

```
kubectl get pods
NAME                             READY   STATUS             RESTARTS        AGE
hf-llama3-<your-alias>-worker-0   0/1     running         0               36s
```

If the `STATUS` is `PENDING` or `ContainerCreating`, run the following command to get more details.

```
kubectl describe pod name_of_pod
```

After the job `STATUS` changes to `Running`, you can examine the log by using the following command.

```
kubectl logs name_of_pod
```

The `STATUS` becomes `Completed` when you run `kubectl get pods`.

## Launch the training job with the recipes launcher
<a name="sagemaker-hyperpod-gpu-kubernetes-launch-training-job-recipes"></a>

Alternatively, you can use the SageMaker HyperPod recipes to submit your training job. Using the recipes involves updating `k8s.yaml`, `config.yaml`, and running the launch script.
+ In `k8s.yaml`, update `persistent_volume_claims`. It mounts the Amazon FSx claim to the `/data` directory of each computing pod

  ```
  persistent_volume_claims:
    - claimName: fsx-claim
      mountPath: data
  ```
+ In `config.yaml`, update `repo_url_or_path` under `git`.

  ```
  git:
    repo_url_or_path: <training_adapter_repo>
    branch: null
    commit: null
    entry_script: null
    token: null
  ```
+ Update `launcher_scripts/llama/run_hf_llama3_8b_seq16k_gpu_p5x16_pretrain.sh`
  + `your_contrainer`: A Deep Learning container. To find the most recent release of the SMP container, see [Release notes for the SageMaker model parallelism library](model-parallel-release-notes.md).
  + (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
  #Users should setup their cluster type in /recipes_collection/config.yaml
  REGION="<region>"
  IMAGE="658645717510.dkr.ecr.${REGION}.amazonaws.com/smdistributed-modelparallel:2.4.1-gpu-py311-cu121"
  SAGEMAKER_TRAINING_LAUNCHER_DIR=${SAGEMAKER_TRAINING_LAUNCHER_DIR:-"$(pwd)"}
  EXP_DIR="<your_exp_dir>" # Location to save experiment info including logging, checkpoints, ect
  TRAIN_DIR="<your_training_data_dir>" # Location of training dataset
  VAL_DIR="<your_val_data_dir>" # Location of talidation dataset
  
  HYDRA_FULL_ERROR=1 python3 "${SAGEMAKER_TRAINING_LAUNCHER_DIR}/main.py" \
      recipes=training/llama/hf_llama3_8b_seq8k_gpu_p5x16_pretrain \
      base_results_dir="${SAGEMAKER_TRAINING_LAUNCHER_DIR}/results" \
      recipes.run.name="hf-llama3" \
      recipes.exp_manager.exp_dir="$EXP_DIR" \
      cluster=k8s \
      cluster_type=k8s \
      container="${IMAGE}" \
      recipes.model.data.train_dir=$TRAIN_DIR \
      recipes.model.data.val_dir=$VAL_DIR
  ```
+ Launch the training job

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

After you've submitted the training job, you can use the following command to verify if you submitted it successfully.

```
kubectl get pods
```

```
NAME READY   STATUS             RESTARTS        AGE
hf-llama3-<your-alias>-worker-0   0/1     running         0               36s
```

If the `STATUS` is `PENDING` or `ContainerCreating`, run the following command to get more details.

```
kubectl describe pod <name-of-pod>
```

After the job `STATUS` changes to `Running`, you can examine the log by using the following command.

```
kubectl logs name_of_pod
```

The `STATUS` will turn to `Completed` when you run `kubectl get pods`.

For more information about the k8s cluster configuration, see [Running a training job on HyperPod k8s](cluster-specific-configurations-run-training-job-hyperpod-k8s.md).