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
Video:
Presentation slides:
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
Videos:
Presentation Slides:
Complete the form below to access the full overview:
.png)
Videos
Deepseek’s recent announcement of the Fire-flyer File System (3FS) has sparked excitement across the AI infra community, promising a breakthrough in how machine learning models access and process data.
In this webinar, an expert in distributed systems and AI infrastructure will take you inside Deepseek 3FS, the purpose-built file system for handling large files and high-bandwidth workloads. We’ll break down how 3FS optimizes data access and speeds up AI workloads as well as the design tradeoffs made to maximize throughput for AI workloads.
This webinar you’ll learn about how 3FS works under the hood, including:
✅ The system architecture
✅ Core software components
✅ Read/write flows
✅ Data distribution/placement algorithms
✅ Cluster/node management and disaster recovery
Whether you’re an AI researcher, ML engineer, or infrastructure architect, this deep dive will give you the technical insights you need to determine if 3FS is the right solution for you.