As enterprises race to roll out artificial intelligence, often overlookModel training requires extensive computational and GPU resources. When training models on AWS, loading data from S3 often becomes a major bottleneck, wasting valuable GPU cycles. Optimizing data loading can greatly reduce GPU idle time and increase GPU utilization.
In this webinar, Greg Palmer will discuss best practices for efficient data loading during model training on AWS. He will demonstrate how to use Alluxio on EKS as a distributed cache to accelerate PyTorch training jobs that read datasets from S3. This architecture significantly improves the utilization of GPUs from 30% to 90%+, archives ~5x faster training, and lower cloud storage costs.
What you will learn:
- The challenges of feeding data-hungry GPUs in the cloud
- How to accelerate model training by optimizing data loading on AWS
- The reference architecture for running PyTorch jobs with Alluxio cache on EKS while reading data from S3, with benchmark results of training ResNet50 and BERT
- How to use TensorBoard to identify bottlenecks in GPU utilization
As enterprises race to roll out artificial intelligence, often overlookModel training requires extensive computational and GPU resources. When training models on AWS, loading data from S3 often becomes a major bottleneck, wasting valuable GPU cycles. Optimizing data loading can greatly reduce GPU idle time and increase GPU utilization.
In this webinar, Greg Palmer will discuss best practices for efficient data loading during model training on AWS. He will demonstrate how to use Alluxio on EKS as a distributed cache to accelerate PyTorch training jobs that read datasets from S3. This architecture significantly improves the utilization of GPUs from 30% to 90%+, archives ~5x faster training, and lower cloud storage costs.
What you will learn:
- The challenges of feeding data-hungry GPUs in the cloud
- How to accelerate model training by optimizing data loading on AWS
- The reference architecture for running PyTorch jobs with Alluxio cache on EKS while reading data from S3, with benchmark results of training ResNet50 and BERT
- How to use TensorBoard to identify bottlenecks in GPU utilization
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