wehub-resource-sync
110 lines
4.0 KiB
ReStructuredText
110 lines
4.0 KiB
ReStructuredText
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Distributed fine-tuning of Llama 3.1 8B on AWS Trainium with Ray and PyTorch Lightning
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======================================================================================
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.. raw:: html
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<a id="try-anyscale-quickstart-aws-trainium-llama3" target="_blank" href="https://console.anyscale.com/register/ha?render_flow=ray&utm_source=ray_docs&utm_medium=docs&utm_campaign=aws-trainium-llama3">
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<img src="../../../_static/img/run-on-anyscale.svg" alt="Run on Anyscale" />
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<br/><br/>
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</a>
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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
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Trainium <https://aws.amazon.com/ai/machine-learning/trainium/>`__ instances using Ray Train, PyTorch Lightning, and AWS Neuron SDK.
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AWS Trainium is the machine learning (ML) chip that AWS built for deep
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learning (DL) training of 100B+ parameter models. `AWS Neuron
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SDK <https://aws.amazon.com/machine-learning/neuron/>`__ helps
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developers train models on Trainium accelerators.
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Prepare the environment
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-----------------------
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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.
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Create a Docker image
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---------------------
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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:
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1. Clone the repo.
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::
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git clone https://github.com/aws-neuron/aws-neuron-eks-samples.git
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2. Go to the ``llama3.1_8B_finetune_ray_ptl_neuron`` directory.
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::
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cd aws-neuron-eks-samples/llama3.1_8B_finetune_ray_ptl_neuron
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3. Trigger the script.
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::
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chmod +x 0-kuberay-trn1-llama3-finetune-build-image.sh
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./0-kuberay-trn1-llama3-finetune-build-image.sh
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4. Enter the zone your cluster is running in, for example: us-east-2.
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5. Verify in the AWS console that the Amazon ECR service has the newly
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created ``kuberay_trn1_llama3.1_pytorch2`` repository.
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6. Update the ECR image ARN in the manifest file used for creating the Ray cluster.
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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:
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::
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export AWS_ACCOUNT_ID=<enter_your_aws_account_id> # for ex: 111222333444
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export REGION=<enter_your_aws_region> # for ex: us-east-2
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sed -i "s/<AWS_ACCOUNT_ID>/$AWS_ACCOUNT_ID/g" 1-llama3-finetune-trn1-create-raycluster.yaml
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sed -i "s/<REGION>/$REGION/g" 1-llama3-finetune-trn1-create-raycluster.yaml
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Configuring Ray Cluster
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-----------------------
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The ``llama3.1_8B_finetune_ray_ptl_neuron`` directory in the AWS Neuron samples repository simplifies the
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Ray configuration. KubeRay provides a manifest that you can apply
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to the cluster to set up the head and worker pods.
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Run the following command to set up the Ray cluster:
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::
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kubectl apply -f 1-llama3-finetune-trn1-create-raycluster.yaml
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Accessing Ray Dashboard
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-----------------------
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Port forward from the cluster to see the state of the Ray dashboard and
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then view it on `http://localhost:8265 <http://localhost:8265/>`__.
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Run it in the background with the following command:
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::
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kubectl port-forward service/kuberay-trn1-head-svc 8265:8265 &
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Launching Ray Jobs
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------------------
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The Ray cluster is now ready to handle workloads. Initiate the data preparation and fine-tuning Ray jobs:
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1. Launch the Ray job for downloading the dolly-15k dataset and the Llama3.1 8B model artifacts:
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::
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kubectl apply -f 2-llama3-finetune-trn1-rayjob-create-data.yaml
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2. When the job has executed successfully, run the following fine-tuning job:
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::
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kubectl apply -f 3-llama3-finetune-trn1-rayjob-submit-finetuning-job.yaml
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3. Monitor the jobs via the Ray Dashboard
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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/>`__.
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