ALLUXIO DAY III 2021
April 27, 2021
Increasingly powerful compute accelerators and large training dataset have made the storage layer a potential bottleneck in deep learning training/inference.
Offline inference job usually consumes and produces tens of tera-bytes data while running more than 10 hours.
For a large-scale job, it usually causes high IO pressure, increase job failure rate, and bring many challenges for system stability.
We adopt alluxio which acts as an intermediate storage tier between the compute tier and cloud storage to optimize IO throughput of deep learning inference job.
For the production workload, the performance improves 18% and we seldom see job failure because of storage issue.
ALLUXIO DAY III 2021
April 27, 2021
Increasingly powerful compute accelerators and large training dataset have made the storage layer a potential bottleneck in deep learning training/inference.
Offline inference job usually consumes and produces tens of tera-bytes data while running more than 10 hours.
For a large-scale job, it usually causes high IO pressure, increase job failure rate, and bring many challenges for system stability.
We adopt alluxio which acts as an intermediate storage tier between the compute tier and cloud storage to optimize IO throughput of deep learning inference job.
For the production workload, the performance improves 18% and we seldom see job failure because of storage issue.
Video:
Presentation Slides:
Videos:
Presentation Slides:
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Videos
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.
In this talk, Pritish Udgata from Adobe provides a comprehensive overview of implementation challenges and solutions for LLM agents.
Topic include:
- CoT vs RAG vs Agentic AI
- Anatomy of an agent
- Single Agent with MCP
- Multi Agents with A2A
- Implementation Challenges and Solutions