Kubernetes cluster pre-training tutorial (GPU) - Amazon SageMaker AI
GPU Kubernetes environment setupLaunch the training job with the SageMaker HyperPod CLILaunch the training job with the recipes launcher

Kubernetes cluster pre-training tutorial (GPU)

There are two ways to launch a training job in a GPU Kubernetes cluster:

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.

GPU Kubernetes environment setup

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 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

  • Install Helm

  • 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

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.

  • (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

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.

    • (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 Run a training job on HyperPod k8s.