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Resume training by trained samples to avoid elastic job loss or over-reading of data.#40640

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zhengchenyu:resume.trained.samples
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Resume training by trained samples to avoid elastic job loss or over-reading of data.#40640
zhengchenyu wants to merge 1 commit into
huggingface:mainfrom
zhengchenyu:resume.trained.samples

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@zhengchenyu

@zhengchenyu zhengchenyu commented Sep 3, 2025

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What does this PR do?

Resume training by trained samples. Then we can avoid reading more or less data due to changes in the number of workers.

Fixes: #40245

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@zhengchenyu zhengchenyu changed the title Resume training by trained samples to avoid elastic job loss or over-reading of data Resume training by trained samples to avoid elastic job loss or over-reading of data. Sep 3, 2025
@zhengchenyu zhengchenyu marked this pull request as draft September 3, 2025 07:18
@zhengchenyu

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This PR depends on huggingface/accelerate#3757.

@Rocketknight1

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cc @SunMarc

@zhengchenyu

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The experiment used Qwen3 SFT and DeepSpeed ​​for training. After apply #41159, #41144, huggingface/accelerate#3757., and this pr. The following experimental results were obtained. The base training is performed on a single machine with 8 GPUs. And the switch training switches between single-machine 8 GPUs and dual-machine 8 GPUs every 100 steps.

截屏2025-10-24 16 12 13

This result proves that there is nothing wrong with this PR.

cc: @SunMarc @Rocketknight1

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When elastic training is enabled, data may be lost or over-read.

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