Automaker Geely’s Efficient Data Lake Architecture with Alluxio
Boost PyTorch AI/ML Model Training Performance with Alluxio
Enhance PyTorch data loading with 97%+ GPU utilization. Boost your training speed by 5-10x with improved model accuracy.
.png)
Deploy Alluxio as a high-performance data access layer co-located with PyTorch training cluster to gain up to 10x performance improvements for your machine learning and deep learning workloads. Build scalable AI/ML infrastructure with Alluxio, which helps reduce cloud storage API and egress costs while keeping your GPUs fully utilized.
Why Pytorch + Alluxio
Maximize the ROI of AI Platform with 97%+ GPU utilization
Alluxio increases your GPU utilization to 97%+ (see MLPerf benchmark for details). It brings data up to speed with GPU cycles, keeping your GPUs continuously fed with data, which significantly decreases the time spent with PyTorch DataLoader.
Efficient Data Loading Instead of Data Replication
Alluxio enables fast and on-demand data loading instead of replicating training data to local storage. Alluxio provides unified data access, removing I/O bottleneck for model training speed, improving PyTorch training performance with low latency across your AI pipeline.
Reduce cloud storage API and egress costs
Alluxio connects PyTorch with different storage systems and virtualizes the data across regions and clouds. This allows your organization to access and manage data from different sources in a unified way, reducing data transfer costs, S3 GET requests costs, etc.
Reduce AI infrastructure cost
Alluxio provides a software only solution built on your existing data lake and delivers comparable performance as expensive HPC storage.
Eliminate data engineering complexity
With a single unified access point for all PyTorch training data, Alluxio eliminates the need for managing complex data copies so that you could get fresher, more accurate models with improved data engineering team productivity.
Intelligent Caching Tailored to I/O Patterns of AI
Alluxio offers distributed cachings so PyTorch can read and write data through the high-performance Alluxio cache rather than slow data lake storage. These caching strategies are tailored to the I/O patterns of AI/ML workloads, helping you to achieve high throughput and 2-4x faster time-to-market.
Alluxio accelerates AI workloads for enterprises across the globe
.jpeg)
.jpeg)
End-to-End Machine Learning Pipeline with Alluxio
Alluxio’s Senior Solutions Engineer Tarik Bennett walks through a short end-to-end machine learning pipeline demo with Alluxio integrated. See how Alluxio can be provisioned or mounted as a local folder for the PyTorch dataloader, delivering 90%+ GPU utilization and dramatically accelerating data loading times.
- 1. Data Preparation
- 2. Setting up the Model
- 3. Setting up the PyTorch Profiler
- 4. Model Training