Managing resources and workflows for large language model (LLM) training can be a significant challenge. Automating tasks such as resource provisioning, scaling, and workflow management is vital for optimizing resource usage and streamlining complex workflows.
Combining AWS’s machine learning acceleration tool Trainium with AWS Batch can simplify these processes. Trainium provides massive scalability and cost-effective access to computational power, while AWS Batch manages tasks like infrastructure management and job scheduling.
AWS offers a step-by-step guide on how to set up and use these tools for LLM training. The process starts by creating a Docker image and pushing it to the Amazon Elastic Container Registry (ECR). Users then submit the training task to AWS Batch, which provisions and manages the necessary resources.
Scripts are provided for different parts of the process, like tokenizing the data set, building and uploading the Docker image, and starting the training job. The guide also explains how to monitor the model’s training through Amazon CloudWatch and save checkpoints to Amazon’s Simple Storage Service (S3).
AWS asserts that the combination of Trainium’s capabilities and AWS Batch’s orchestration functionalities can bring numerous benefits to machine learning (ML) training. These include massive scalability, cost-effective access to computational power, and the ability to focus on building applications and analyzing results rather than managing infrastructure.
The authors of this post, Scott Perry and Sadaf Rasool from AWS’s Annapurna ML accelerator team, encourage users to use these tools for their next deep learning job. They believe users will appreciate the efficiency and cost-effectiveness of this integrated approach to resource and workflow management.
