build(deps): update pytorch-lightning requirement from <2.0.0,>=1.5 to >=1.5,<3.0.0 in /requirements

[dependabot]

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 ⚡

• Highlights
• Backward Incompatible Changes
  • PyTorch
  • Fabric
• Full Changelog
  • PyTorch
  • Fabric
  • App
• Contributors

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)

Commits

• 01834c8 Exclude some examples from docs navigation (#17081)
• f25dfbe update links to Discord (#17087)
• 2f39bb6 docs: migration guide to the latest [1/n] (#17034)
• 077106d Prepare 2.0.0 release (#17056)
• 888db1a Update README (#17082)
• a22242b Improve the error message for installing tensorboardx (#17053)
• 550fa5f Organize app examples (#17045)
• 5d58736 Rename PL installation to pip install lightning (#17074)
• 281efc6 Update data docs (#16839)
• 67b94ef Avoid inference_mode with FSDP (#17064)
• Additional commits viewable in compare view

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[codecov]

Codecov Report

Merging #1636 (e239f8b) into master (f2b85bb) will not change coverage.
The diff coverage is n/a.

Additional details and impacted files

@@          Coverage Diff           @@
##           master   #1636   +/-   ##
======================================
  Coverage      88%     88%           
======================================
  Files         236     236           
  Lines       13230   13230           
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  Hits        11648   11648           
  Misses       1582    1582           

[Borda]

pending on #1633 (comment)

[Borda]

some devices mishmash for running tests with lighting 2.0, could you pls check @SkafteNicki

RuntimeError: Expected all tensors to be on the same device, but found at least two devices, cuda:1 and CPU!