[Docs] Offline batch inference guide (static assignment) #4144
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…to batch-inference
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Would be nice to show the first few lines of metadata file.
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| NUM_PARALLEL_GROUPS=16 |
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"groups" is surprising/unfamiliar; do you mean "workers"?
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| Large-Scale Offline Batch Inference | ||
| =================================== |
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Overall may need a figure to illustrate all concepts and how they relate: chunks / work items (is one item = one jsonl in this case?) / groups / workers, ..
At least a bullet list at the top to explain these. I'm currently confused by the above.
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| llm = LLM(model='meta-llama/Meta-Llama-3.1-7B-Instruct', tensor_parallel_size=1) | ||
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| def batch_inference(llm: LLM, data_path: str): |
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Rename? It seems to be working on 1 work item / 1 batch only, and there's no 'batching'?
| Scale Out to Multiple Nodes | ||
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| To scale out the inference to multiple machines, we can group the data chunks into multiple pieces so that each machine can process one piece. |
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Reduce concept: yet another one here 'piece'!
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An initial guide for the offline batch inference with static assignment.
Future TODOs:
Tested (run the relevant ones):
bash format.shpytest tests/test_smoke.pypytest tests/test_smoke.py::test_fill_in_the_nameconda deactivate; bash -i tests/backward_compatibility_tests.sh