Skip to content

Added Tip for better rendering #3781

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Merged
S1ro1 merged 1 commit into from
Sep 15, 2025
Merged

Added Tip for better rendering #3781

Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
9 changes: 4 additions & 5 deletions examples/torch_native_parallelism/README.md
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -19,13 +19,12 @@ For example, with 8 nodes, you can run the script as such:
accelerate launch --num-processes 8 nd_parallel.py \
--dp-replicate-size 2 \
--dp-shard-size 2 \
--tp-size 2 \
--tp-size 2
```

<Tip>
Only use TP intra-node - therefore max TP size you should need is 8. You can also use a lower size, as FSDP (`--dp-shard-size`) can be faster on smaller models with
shorter sequence lengths. If you cannot fit your model into memory, utilize `--dp-shard-size` as much as you can. Afterwards, to scale up and utilize all your resources, use `--dp-replicate-size`. This is only a general guideline, you can (and should) experiment with different parallelism configurations to find the best one for your model and hardware. You can learn more about the general strategies for parallelism in our [blog](https://huggingface.co/blog/accelerate-nd-parallel), or if you really want to dive deep, read the [Ultra-Scale Playbook](https://huggingface.co/spaces/nanotron/ultrascale-playbook).
</Tip>
> [!Tip]
> Only use TP intra-node - therefore max TP size you should need is 8. You can also use a lower size, as FSDP (`--dp-shard-size`) can be faster on smaller models with shorter sequence lengths. If you cannot fit your model into memory, utilize `--dp-shard-size` as much as you can. Afterwards, to scale up and utilize all your resources, use `--dp-replicate-size`. This is only a general guideline, you can (and should) experiment with different parallelism configurations to find the best one for your model and hardware. You can learn more about the general strategies for parallelism in our [blog](https://huggingface.co/blog/accelerate-nd-parallel), or if you really want to dive deep, read the [Ultra-Scale Playbook](https://huggingface.co/spaces/nanotron/ultrascale-playbook).


This feature is also fully integrated into 🤗 transformers `Trainer`. To use it, simply launch your script with path to your accelerate configuration file. You can see a minimal example of such script in `nd_parallel_trainer.py`.
We provide 2 pre-configured configuration files:
Expand Down