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LoicGrobol / zeldarose

Train transformer-based models.
https://zeldarose.readthedocs.io
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Update pytorch-lightning requirement from <1.10.0,>=1.8.0 to >=1.8.0,<2.1.0 #48

Closed dependabot[bot] closed 1 year ago

dependabot[bot] commented 1 year ago

⚠️ Dependabot is rebasing this PR ⚠️

Rebasing might not happen immediately, so don't worry if this takes some time.

Note: if you make any changes to this PR yourself, they will take precedence over the rebase.

Updates the requirements on pytorch-lightning to permit the latest version.

Release notes

Sourced from pytorch-lightning's releases.

Lightning 2.0: Fast, Flexible, Stable

Lightning AI is excited to announce the release of Lightning 2.0 :zap:

Over the last couple of years PyTorch Lightning has become the preferred deep learning framework for researchers and ML developers around the world, with close to 50 million downloads and 18k OSS projects, from top universities to leading labs.

With the help of over 800 contributors, we have added many features and functionalities to make it the most complete research toolkit possible, but some of these changes also introduced issues:

  • API changes to the trainer
  • Trainer code became harder to follow
  • Many integrations made Lightning appear bloated
  • The trainer became harder to customize / takes away what I instead need to tweak / have control over.

To make the research experience better, we are introducing 2.0:

  • No API changes - We commit to backward compatibility in the 2.0 series
  • Simplified abstraction layers, removed legacy functionality, integrations out of the main repo. This improves the project's readability and debugging experience.
  • Introducing Fabric. Scale any PyTorch model with just a few lines of code. Read-on!

Highlights

PyTorch 2.0 and torch.compile

Lightning 2.0 is best friends with PyTorch 2.0. You can torch.compile your LightningModules now!

import torch
import lightning as L

model = LitModel()


This will compile forward and {training,validation,test,predict}_step


compiled_model = torch.compile(model)


trainer = L.Trainer()
trainer.fit(compiled_model)

PyTorch reports that on average, "models runs 43% faster in training on an NVIDIA A100 GPU. At Float32 precision, it runs 21% faster on average and at AMP Precision it runs 51% faster on average" (source). If you want to learn more about torch.compile and how such speedups can be achieved, read the official PyTorch 2.0 blog post.

... (truncated)

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dependabot[bot] commented 1 year ago

OK, I won't notify you again about this release, but will get in touch when a new version is available. If you'd rather skip all updates until the next major or minor version, let me know by commenting @dependabot ignore this major version or @dependabot ignore this minor version. You can also ignore all major, minor, or patch releases for a dependency by adding an ignore condition with the desired update_types to your config file.

If you change your mind, just re-open this PR and I'll resolve any conflicts on it.