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Distributed fine-tuning of Llama 3.1 8B on AWS Trainium with Ray and PyTorch Lightning

doc/source/train/examples/aws-trainium/llama3.rst

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Distributed fine-tuning of Llama 3.1 8B on AWS Trainium with Ray and PyTorch Lightning

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This example demonstrates how to fine-tune the Llama 3.1 8B <https://huggingface.co/NousResearch/Meta-Llama-3.1-8B/>__ model on AWS Trainium <https://aws.amazon.com/ai/machine-learning/trainium/>__ instances using Ray Train, PyTorch Lightning, and AWS Neuron SDK.

AWS Trainium is the machine learning (ML) chip that AWS built for deep learning (DL) training of 100B+ parameter models. AWS Neuron SDK <https://aws.amazon.com/machine-learning/neuron/>__ helps developers train models on Trainium accelerators.

Prepare the environment

See Setup EKS cluster and tools <https://github.com/aws-neuron/aws-neuron-eks-samples/tree/master/llama3.1_8B_finetune_ray_ptl_neuron#setupeksclusterandtools>__ for setting up an Amazon EKS cluster leveraging AWS Trainium instances.

Create a Docker image

When the EKS cluster is ready, create an Amazon ECR repository for building and uploading the Docker image containing artifacts for fine-tuning a Llama3.1 8B model:

  1. Clone the repo.

::

git clone https://github.com/aws-neuron/aws-neuron-eks-samples.git

  1. Go to the llama3.1_8B_finetune_ray_ptl_neuron directory.

::

cd aws-neuron-eks-samples/llama3.1_8B_finetune_ray_ptl_neuron

  1. Trigger the script.

::

chmod +x 0-kuberay-trn1-llama3-finetune-build-image.sh ./0-kuberay-trn1-llama3-finetune-build-image.sh

  1. Enter the zone your cluster is running in, for example: us-east-2.

  2. Verify in the AWS console that the Amazon ECR service has the newly created kuberay_trn1_llama3.1_pytorch2 repository.

  3. Update the ECR image ARN in the manifest file used for creating the Ray cluster.

Replace the <AWS_ACCOUNT_ID> and <REGION> placeholders with actual values in the 1-llama3-finetune-trn1-create-raycluster.yaml file using commands below to reflect the ECR image ARN created above:

::

export AWS_ACCOUNT_ID=<enter_your_aws_account_id> # for ex: 111222333444 export REGION=<enter_your_aws_region> # for ex: us-east-2 sed -i "s/<AWS_ACCOUNT_ID>/$AWS_ACCOUNT_ID/g" 1-llama3-finetune-trn1-create-raycluster.yaml sed -i "s/<REGION>/$REGION/g" 1-llama3-finetune-trn1-create-raycluster.yaml

Configuring Ray Cluster

The llama3.1_8B_finetune_ray_ptl_neuron directory in the AWS Neuron samples repository simplifies the Ray configuration. KubeRay provides a manifest that you can apply to the cluster to set up the head and worker pods.

Run the following command to set up the Ray cluster:

::

kubectl apply -f 1-llama3-finetune-trn1-create-raycluster.yaml

Accessing Ray Dashboard

Port forward from the cluster to see the state of the Ray dashboard and then view it on http://localhost:8265 <http://localhost:8265/>__. Run it in the background with the following command:

::

kubectl port-forward service/kuberay-trn1-head-svc 8265:8265 &

Launching Ray Jobs

The Ray cluster is now ready to handle workloads. Initiate the data preparation and fine-tuning Ray jobs:

  1. Launch the Ray job for downloading the dolly-15k dataset and the Llama3.1 8B model artifacts:

::

kubectl apply -f 2-llama3-finetune-trn1-rayjob-create-data.yaml

  1. When the job has executed successfully, run the following fine-tuning job:

::

kubectl apply -f 3-llama3-finetune-trn1-rayjob-submit-finetuning-job.yaml

  1. Monitor the jobs via the Ray Dashboard

For detailed information on each of the steps above, see the AWS documentation link <https://github.com/aws-neuron/aws-neuron-eks-samples/blob/master/llama3.1_8B_finetune_ray_ptl_neuron/README.md/>__.