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AI Research
arXiv CS.AI·Jun 10·5 min read
Piper: A Programmable Distributed Training System
TL;DR
Existing distributed training systems require manual design of parallelism strategies, making them inflexible. Piper is a programmable distributed training system that allows users to define training strategies using model annotations and scheduling directives.
✦ Why It Matters
Engineers can leverage Piper to easily implement and optimize diverse distributed training strategies without deep expertise in parallelism.
Key Takeaways
- Utilize Piper to define training strategies with minimal annotations.
- Leverage the intermediate representation for efficient execution planning.
- Combine multiple parallelism strategies for enhanced performance.
- Adapt quickly to new training strategies without extensive rework.
How It Works
Piper operates by allowing users to specify their training strategies through model annotations and scheduling directives. These directives modify Piper's intermediate representation (IR), which serves as a comprehensive model of the training process.
The IR enables the system to generate optimized execution plans for each device, ensuring that the training process is both efficient and adaptable to various parallelism strategies.