In the rapidly evolving landscape of AI and machine learning, infra teams face critical challenges in managing large-scale data for AI. Performance bottlenecks, cost inefficiencies, and management complexities pose significant challenges for AI platform teams supporting large-scale model training and serving.
In this talk, Bin Fan will discuss the challenges of I/O stalls that lead to suboptimal GPU utilization during model training. He will present a reference architecture for running PyTorch jobs with Alluxio in cloud environments, demonstrating how this approach can significantly enhance GPU efficiency.
What you will learn:
- How to identify GPU utilization and I/O-related performance bottlenecks in model training
- Leverage GPU anywhere to maximize resource utilization
- Best practices for monitoring and optimizing GPU usage across training and serving pipelines
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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.