pyrelational is a python active learning library for rapidly implementing active learning pipelines from data management, model development (and Bayesian approximation), to creating novel active learning strategies.
Lightning AI :zap: is excited to announce the release of Lightning 2.4. This is mainly a compatibility upgrade for PyTorch 2.4 and Python 3.12, with a sprinkle of a few features and bug fixes.
Did you know? The Lightning philosophy extends beyond a boilerplate-free deep learning framework: We've been hard at work bringing you Lightning Studio. Code together, prototype, train, deploy, host AI web apps. All from your browser, with zero setup.
Changes
PyTorch Lightning
Made saving non-distributed checkpoints fully atomic (#20011)
Added dump_stats flag to AdvancedProfiler (#19703)
Added a flag verbose to the seed_everything() function (#20108)
The TQDMProgressBar now provides an option to retain prior training epoch bars (#19578)
Added the count of modules in train and eval mode to the printed ModelSummary table (#20159)
Triggering KeyboardInterrupt (Ctrl+C) during .fit(), .evaluate(), .test() or .predict() now terminates all processes launched by the Trainer and exits the program (#19976)
Changed the implementation of how seeds are chosen for dataloader workers when using seed_everything(..., workers=True) (#20055)
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Updates the requirements on lightning to permit the latest version.
Release notes
Sourced from lightning's releases.
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Commits
2129fdf
fix(ci): resolve input str -> num conversion (#20169)cf24a19
fix(docs): remove dead link from readme (#20170)a3e60ad
ci/docs: disable optional cache pkg (#20168)87ffd8c
ci: fix cleaning caches (#20167)b3ee85d
Prepare Lightning 2.4.0 release (#20154)631911c
Add special logic for 'step' in _optimizer_to_device (#20019)345450b
Fix parameter count in ModelSummary when parameters are DTensors (#20163)3de60f4
docs: fix typo inlinkcheck_ignore
(#20164)e9d4ef8
Add diffusion example to README (#20161)d4de8e2
Count number of modules in train/eval mode in ModelSummary (#20159)Dependabot will resolve any conflicts with this PR as long as you don't alter it yourself. You can also trigger a rebase manually by commenting
@dependabot rebase
.Dependabot commands and options
You can trigger Dependabot actions by commenting on this PR: - `@dependabot rebase` will rebase this PR - `@dependabot recreate` will recreate this PR, overwriting any edits that have been made to it - `@dependabot merge` will merge this PR after your CI passes on it - `@dependabot squash and merge` will squash and merge this PR after your CI passes on it - `@dependabot cancel merge` will cancel a previously requested merge and block automerging - `@dependabot reopen` will reopen this PR if it is closed - `@dependabot close` will close this PR and stop Dependabot recreating it. You can achieve the same result by closing it manually - `@dependabot show