Alluxio’s capabilities as a Data Orchestration framework have encouraged users to onboard more of their data-driven applications to an Alluxio powered data access layer. Driven by strong interests from our open-source community, the core team of Alluxio started to re-design an efficient and transparent way for users to leverage data orchestration through the POSIX interface. This effort has a lot of progress with the collaboration with engineers from Microsoft, Alibaba and Tencent. Particularly, we have introduced a new JNI-based FUSE implementation to support POSIX data access, created a more efficient way to integrate Alluxio with FUSE service, as well as many improvements in relevant data operations like more efficient distributedLoad, optimizations on listing or calculating directories with a massive amount of files, which are common in model training. We will also share our engineering lessons and roadmap in future releases to support Machine Learning applications.
Alluxio’s capabilities as a Data Orchestration framework have encouraged users to onboard more of their data-driven applications to an Alluxio powered data access layer. Driven by strong interests from our open-source community, the core team of Alluxio started to re-design an efficient and transparent way for users to leverage data orchestration through the POSIX interface. This effort has a lot of progress with the collaboration with engineers from Microsoft, Alibaba and Tencent. Particularly, we have introduced a new JNI-based FUSE implementation to support POSIX data access, created a more efficient way to integrate Alluxio with FUSE service, as well as many improvements in relevant data operations like more efficient distributedLoad, optimizations on listing or calculating directories with a massive amount of files, which are common in model training. We will also share our engineering lessons and roadmap in future releases to support Machine Learning applications.
APACHECON 2021
Alluxio’s capabilities as a Data Orchestration framework have encouraged users to onboard more of their data-driven applications to an Alluxio powered data access layer. Driven by strong interests from our open-source community, the core team of Alluxio started to re-design an efficient and transparent way for users to leverage data orchestration through the POSIX interface. This effort has a lot of progress with the collaboration with engineers from Microsoft, Alibaba and Tencent. Particularly, we have introduced a new JNI-based FUSE implementation to support POSIX data access, created a more efficient way to integrate Alluxio with FUSE service, as well as many improvements in relevant data operations like more efficient distributedLoad, optimizations on listing or calculating directories with a massive amount of files, which are common in model training. We will also share our engineering lessons and roadmap in future releases to support Machine Learning applications.
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Videos
In this talk, Ojus Save walks you through a demo of how to build AI applications on Zoom. This demo shows you an AI agent that receives transcript data from RTMS and then decides if it has to create action items based on the transcripts that are received.
In this talk, Sandeep Joshi, , Senior Manager at NVIDIA, shares how to accelerate the data access between GPU and storage for AI. Sandeep will dive into two options: CPU- initiated GPUDirect Storage and GPU-initiated SCADA.
Bin Fan, VP of Technology at Alluxio, introduces how Alluxio, a software layer transparently sits between application and S3 (or other object stores), provides sub-ms time to first byte (TTFB) solution, with up to 45x lower latency.