> ML_LITERATURE // MORITZ-2018-RAY-DISTRIBUTED-EXECUTION-FRAMEWORK-FOR-EMERGING-AI_v1.0
Ray: A Distributed Execution Framework for Emerging AI Applications
Philipp Moritz, Robert Nishihara, Stephanie Wang, Alexey Tumanov, Richard Liaw, Edward Liang, Melih Elibol, Zongheng Yang, William Paul, Michael I. Jordan, Ion Stoica · USENIX Symposium on Operating Systems Design and Implementation (OSDI) (2018)
systems2018industry-standardartifactsAvailable
Principal Contribution
Unified actor and task-parallel dynamic execution engine powered by a shared-memory object store (Plasma) for scaling distributed Python workloads.
Operational Relevance
Directly guides deployment choices and architecture selection for task-distributed-training, task-reinforcement-learning.
Assumptions
- Standard empirical regularity and statistical stability hold across evaluation domains
Limitations
- Performance characteristics depend on domain distribution and compute allocation parameters
Connected Algorithms, Architectures & Tools
Related Algorithms:
Related Architectures:
Implementing Libraries: