A distributed compute framework for scaling Python applications and machine learning workloads across clusters. It provides a unified runtime for data processing, model training, and serving with auto-scaling capabilities.
Tech Stack
PythonAI, ML & Data
Why ray?
• Highly flexible distributed runtime
• Massive ecosystem of AI libraries
• Efficient resource utilization
Limitations
• Complex cluster management
• Difficult to debug distributed state
• High memory overhead
3/6/2026
Last Update
7,292
Forks
3,438
Issues
Apache-2.0
License
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