Anyscale has significantly enhanced its Ray framework to better support large-scale AI workloads. Recent improvements address bottlenecks in driver performance and actor scheduling, leading to substantial speedups for batch inference and data shuffling tasks. These optimizations enable Ray to handle much larger training clusters, scaling to 10,000 nodes and supporting up to 40,000 actors, a considerable increase from previous capabilities. AI
IMPACT Enables more efficient and larger-scale training and inference for AI workloads using the Ray framework.
RANK_REASON Blog post detailing performance improvements and scaling capabilities of an existing software framework.
- Andrew Sy Kim
- Anyscale, Inc.
- ByteDance
- Dhyey Shah
- Edward Oakes
- Joshua B. Lee
- Kartica Modi
- Mao Yancan
- Mengjin Yang
- Ray
- Ray Core
- Ray Data
- Steve Alexander
- Zac Policzer
AI-generated summary · Google Gemini · from 1 sources. How we write summaries →
