Docs on decision-making process for choosing batch size, learning rate, etc?
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- Python
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Description
At a first approximation, it's not obvious how to think about choosing a batch size and learning rate. Small batches reduce inference overhead on the GPUs and generally reduce iteration time, but can also lead to unstable training runs.
Reducing learning rate and using small batches seems like a reasonable approach to achieve good performance with low feedback latency, but still not sure how to calculate either number. Docs would be super helpful!
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Research direction
No file, test, or entry point is named. First clarify where training guidance belongs and review the current material on batch size and learning rate. Done should explain how to choose or calculate both values, including the stated trade-offs around iteration time, inference overhead, and training stability.
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Assessment
- Tech stack
- python
- Domain
- documentation, machine-learning
- Issue type
- Documentation
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 30/100