Driven by strong interests from our open source community, the Alluxio core engineering team re-designed things to come up with a more efficient and transparent way for users to leverage data orchestration through the POSIX interface. This enables much better performance for ML workloads where data is accessed via the POSIX interface.
In this 20 minute community session, you’ll hear from Lu Qiu, one of Alluxio’s lead engineers on the POSIX implementation project.
In this session, you’ll learn:
- How Alluxio’s new JNI-based FUSE implementation supports more efficient POSIX data access
- How improvements to multiple data operations, including distributedLoad, optimizations on listing or calculating directories with a massive amounts of files, etc., improve performance. In model training
- How these latest enhancements improve performance on TensorFlow and PyTorch training workloads, even with GPU-based training and compute
ALLUXIO WEBINAR
Driven by strong interests from our open source community, the Alluxio core engineering team re-designed things to come up with a more efficient and transparent way for users to leverage data orchestration through the POSIX interface. This enables much better performance for ML workloads where data is accessed via the POSIX interface.
In this 20 minute community session, you’ll hear from Lu Qiu, one of Alluxio’s lead engineers on the POSIX implementation project.
In this session, you’ll learn:
- How Alluxio’s new JNI-based FUSE implementation supports more efficient POSIX data access
- How improvements to multiple data operations, including distributedLoad, optimizations on listing or calculating directories with a massive amounts of files, etc., improve performance. In model training
- How these latest enhancements improve performance on TensorFlow and PyTorch training workloads, even with GPU-based training and compute
Video:
Slack with speakers, experts, and community members.
Join the Alluxio Global Online Meetup Group.
Driven by strong interests from our open source community, the Alluxio core engineering team re-designed things to come up with a more efficient and transparent way for users to leverage data orchestration through the POSIX interface. This enables much better performance for ML workloads where data is accessed via the POSIX interface.
In this 20 minute community session, you’ll hear from Lu Qiu, one of Alluxio’s lead engineers on the POSIX implementation project.
In this session, you’ll learn:
- How Alluxio’s new JNI-based FUSE implementation supports more efficient POSIX data access
- How improvements to multiple data operations, including distributedLoad, optimizations on listing or calculating directories with a massive amounts of files, etc., improve performance. In model training
- How these latest enhancements improve performance on TensorFlow and PyTorch training workloads, even with GPU-based training and compute
Videos:
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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.