# \#parallel-training

**URL:** https://discuss.ai.google.dev/tag/parallel-training/113.md

[Latest](https://discuss.ai.google.dev/latest.md) · [Categories](https://discuss.ai.google.dev/categories.md) · [Tags](https://discuss.ai.google.dev/tags.md)

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## [Support for parallel agents using git worktrees](https://discuss.ai.google.dev/t/support-for-parallel-agents-using-git-worktrees/116140)

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**Author:** [@Hasan\_Iqbal](https://discuss.ai.google.dev/u/Hasan_Iqbal)\
**Replies:** 0\
**Last updated:** [January 19, 2026, 10:41am UTC](https://discuss.ai.google.dev/t/support-for-parallel-agents-using-git-worktrees/116140 "2026-01-19T10:41:15Z")

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I tried this to keep my changes separate and have parallel agents working on the same repo. Is there any plan to support this / make it easier like in Cursor? Right now I have to create the worktree manually and then ope…

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## [Parallelizing TFF for faster Federated Learning](https://discuss.ai.google.dev/t/parallelizing-tff-for-faster-federated-learning/31411)

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**Author:** [@Amr\_Akmal](https://discuss.ai.google.dev/u/Amr_Akmal)\
**Replies:** 0\
**Last updated:** [July 3, 2024, 3:43am UTC](https://discuss.ai.google.dev/t/parallelizing-tff-for-faster-federated-learning/31411 "2024-07-03T03:43:26Z")

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Hello I’ve started learning about FL (Federated Learning) and started learning with the provided documentations. My question is, how can I parallelize the training process of each client so to be asynchronous? To my u…

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## [A library for deep learning and reinforcement learning](https://discuss.ai.google.dev/t/a-library-for-deep-learning-and-reinforcement-learning/31261)

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**Author:** [@NoteDancing](https://discuss.ai.google.dev/u/NoteDancing)\
**Replies:** 0\
**Last updated:** [January 13, 2024, 11:26am UTC](https://discuss.ai.google.dev/t/a-library-for-deep-learning-and-reinforcement-learning/31261 "2024-01-13T11:26:31Z")

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Hello everyone, I wrote a machine learning library that implements parallel training based on the multiprocessing module. I haven’t done enough testing yet. Is anyone interested in testing its parallel training performan…