deepspeedai / deepspeedai/DeepSpeedExamples
Dynamic batch support
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Description
Machine Translation usually takes dynamically sized batch composed of X tokens instead of X sentences as training input. I'm wondering why deepspeed requires specifying train_batch_size and train_micro_batch_size_per_gpu, both of which refer to the number of samples. Is this a concern for implementation details? Or is it possible to support dynamic size as in the case of machine translation without extra cost of efficiency and memory usage?
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Research direction
No files, tests, or entry points are named. Start with DeepSpeed's batch-size configuration documentation and existing examples, then establish whether dynamically sized token batches can be supported without extra efficiency or memory cost. Done requires a tested implementation or a clear maintainer decision on feasibility.
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Assessment
- Tech stack
- python
- Domain
- machine-learning
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100