Zach Mueller

Yes, just wrap them all in the accumulate function as shown in the earlier PR linked

@Afera672 what version of Accelerate are you using? And can you do `echo ~/.cache/huggingface/accelerate/default_config.yml` and tell me what it outputs?

And also please look at the examples in the fastai docs that showcase how to use this functionality: https://docs.fast.ai/tutorial.distributed.html There is an important note there: > It is important to...

Your fastai code looks wrong to me. Also it would be helpful if you could wrap the code in code ticks (`) so that the code gets preformatted properly. Try...

Hi @Afera672, would it be possible to upload the notebook you're using as a github gist so I can follow along exactly and clearly how things are going? Thanks!

Thanks @Afera672, your issue is this line, you shouldn't be re-making dataloaders: ```python learn = unet_learner(resnet34,dls, dls=TfmdDL(after_item=ToTensor(4,80,80), after_batch=[IntToFloatTensor(), *aug_transforms()], bs=8)) ``` To fix, (or at least get further) change that...

I'd recommend opening a thread on the fastai forums for more help, since the issue is with the framework more than Accelerate specifically :) https://forums.fast.ai

When PyTorch adds this feature, we'll make sure it's possible in Accelerate: https://github.com/pytorch/pytorch/issues/88415

@Sreyashi-Bhattacharjee this is currently unsupported yet for multi-CPU, changing to a FR and adding it to our timetable

Awesome! That's fantastic @yuvalkirstain! Would you be willing to put in a PR describing what all is in there for others to know about quickly on our example zoo documentation?...