Skip to content
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

Jump to bottom

Add artifcact_location arg to MLFlow logger #6677

Merged
merged 3 commits into from
Mar 25, 2021
Merged

Add artifcact_location arg to MLFlow logger #6677

merged 3 commits into from
Mar 25, 2021

Conversation

ethanwharris
Copy link
Member

What does this PR do?

Fixes #6668

Before submitting

  • Was this discussed/approved via a GitHub issue? (not for typos and docs)
  • Did you read the contributor guideline, Pull Request section?
  • Did you make sure your PR does only one thing, instead of bundling different changes together?
  • [N/A] Did you make sure to update the documentation with your changes? (if necessary)
  • Did you write any new necessary tests? (not for typos and docs)
  • Did you verify new and existing tests pass locally with your changes?
  • Did you update the CHANGELOG? (not for typos, docs, test updates, or internal minor changes/refactorings)

PR review

Anyone in the community is free to review the PR once the tests have passed.
Before you start reviewing make sure you have read Review guidelines. In short, see the following bullet-list:

  • Is this pull request ready for review? (if not, please submit in draft mode)
  • Check that all items from Before submitting are resolved
  • Make sure the title is self-explanatory and the description concisely explains the PR
  • Add labels and milestones (and optionally projects) to the PR so it can be classified

Did you have fun?

Make sure you had fun coding 🙃

@ethanwharris ethanwharris added feature Is an improvement or enhancement logger Related to the Loggers priority: 2 Low priority task labels Mar 25, 2021
@codecov
Copy link

codecov bot commented Mar 25, 2021 •
edited
Loading

Codecov Report

Merging #6677 (164c8dc) into master (2cbdc01) will decrease coverage by 4%.
The diff coverage is 100%.

@@           Coverage Diff           @@
##           master   #6677    +/-   ##
=======================================
- Coverage      91%     87%    -4%     
=======================================
  Files         192     192            
  Lines       12230   12231     +1     
=======================================
- Hits        11143   10629   -514     
- Misses       1087    1602   +515

@ethanwharris ethanwharris mentioned this pull request Mar 25, 2021
Closed
@Borda Borda enabled auto-merge (squash) March 25, 2021 22:11
@Borda Borda added the ready PRs ready to be merged label Mar 25, 2021
awaelchli
awaelchli approved these changes Mar 25, 2021
View reviewed changes
Copy link
Contributor

@awaelchli awaelchli left a comment

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

great!

@Borda Borda merged commit 6b990f3 into Lightning-AI:master Mar 25, 2021
@carmocca carmocca added this to the 1.3 milestone Mar 29, 2021
facebook-github-bot pushed a commit to facebookresearch/d2go that referenced this pull request Apr 14, 2021
@facebook-github-bot
Synchronize PyTorchLightning/pytorch-lightning (revision 0b843848@mas…
44e4108 
…ter) to github/third-party/PyTorchLightning/pytorch-lightning

Summary:
### New commit log messages
## [UnReleased] - 2021-MM-DD

### Added

- Added more explicit exception message when trying to execute `trainer.test()` or `trainer.validate()` with `fast_dev_run=True` ([#6667](Lightning-AI/pytorch-lightning#6667))

- Added `LightningCLI` class to provide simple reproducibility with minimum boilerplate training cli. ([#4492](Lightning-AI/pytorch-lightning#4492))

- Trigger warning when non-metric logged value with multi processes hasn't been reduced ([#6417](Lightning-AI/pytorch-lightning#6417))

- Added `gradient_clip_algorithm` argument to Trainer for gradient clipping by value ([#6123](Lightning-AI/pytorch-lightning#6123)).

- Added a way to print to terminal without breaking up the progress bar ([#5470](Lightning-AI/pytorch-lightning#5470))

- Added support to checkpoint after training steps in `ModelCheckpoint` callback ([#6146](Lightning-AI/pytorch-lightning#6146))

- Added `checkpoint` parameter to callback's `on_save_checkpoint` hook ([#6072](Lightning-AI/pytorch-lightning#6072))

- Added `RunningStage.SANITY_CHECKING` ([#4945](Lightning-AI/pytorch-lightning#4945))

- Added `TrainerState.{FITTING,VALIDATING,TESTING,PREDICTING,TUNING}` ([#4945](Lightning-AI/pytorch-lightning#4945))

- Added `Trainer.validate()` method to perform one evaluation epoch over the validation set ([#4948](Lightning-AI/pytorch-lightning#4948))

- Added `LightningEnvironment` for Lightning-specific DDP ([#5915](Lightning-AI/pytorch-lightning#5915))

- Added `teardown()` hook to LightningDataModule ([#4673](Lightning-AI/pytorch-lightning#4673))

- Added `auto_insert_metric_name` parameter to `ModelCheckpoint` ([#6277](Lightning-AI/pytorch-lightning#6277))

- Added arg to `self.log` that enables users to give custom names when dealing with multiple dataloaders ([#6274](Lightning-AI/pytorch-lightning#6274))

- Added `teardown` method to `BaseProfiler` to enable subclasses defining post-profiling steps outside of `__del__` ([#6370](Lightning-AI/pytorch-lightning#6370))

- Added `setup` method to `BaseProfiler` to enable subclasses defining pre-profiling steps for every process ([#6633](Lightning-AI/pytorch-lightning#6633))

- Added no return warning to predict ([#6139](Lightning-AI/pytorch-lightning#6139))

- Added `Trainer.predict` config validation ([#6543](Lightning-AI/pytorch-lightning#6543))

- Added `AbstractProfiler` interface ([#6621](Lightning-AI/pytorch-lightning#6621))

- Added support for including module names for forward in the autograd trace of `PyTorchProfiler` ([#6349](Lightning-AI/pytorch-lightning#6349))

- Added support for the PyTorch 1.8.1 autograd profiler ([#6618](Lightning-AI/pytorch-lightning#6618))

- Added `outputs` parameter to callback's `on_validation_epoch_end` & `on_test_epoch_end` hooks ([#6120](Lightning-AI/pytorch-lightning#6120))

- Added `configure_sharded_model` hook ([#6679](Lightning-AI/pytorch-lightning#6679))

- Added support for `precision=64`, enabling training with double precision ([#6595](Lightning-AI/pytorch-lightning#6595))

- Added support for DDP communication hooks ([#6736](Lightning-AI/pytorch-lightning#6736))

- Added `artifact_location` argument to `MLFlowLogger` which will be passed to the `MlflowClient.create_experiment` call ([#6677](Lightning-AI/pytorch-lightning#6677))

- Added `model` parameter to precision plugins' `clip_gradients` signature ([#6764](Lightning-AI/pytorch-lightning#6764))

### Changed

- Renamed `pytorch_lightning.callbacks.swa` to `pytorch_lightning.callbacks.stochastic_weight_avg` ([#6259](Lightning-AI/pytorch-lightning#6259))

- Refactor `RunningStage` and `TrainerState` usage ([#4945](Lightning-AI/pytorch-lightning#4945))

- Changed `trainer.evaluating` to return `True` if validating or testing ([#4945](Lightning-AI/pytorch-lightning#4945))

- Changed `setup()` and `teardown()` stage argument to take any of `{fit,validate,test,predict}` ([#6386](Lightning-AI/pytorch-lightning#6386))

- Changed profilers to save separate report files per state and rank ([#6621](Lightning-AI/pytorch-lightning#6621))

- Changed `PyTorchProfiler` to use `torch.autograd.profiler.record_function` to record functions ([#6349](Lightning-AI/pytorch-lightning#6349))

### Deprecated

- `period` has been deprecated in favor of `every_n_val_epochs` in the `ModelCheckpoint` callback ([#6146](Lightning-AI/pytorch-lightning#6146))

- Deprecated `trainer.running_sanity_check` in favor of `trainer.sanity_checking` ([#4945](Lightning-AI/pytorch-lightning#4945))

- Deprecated `Profiler(output_filename)` in favor of `dirpath` and `filename` ([#6621](Lightning-AI/pytorch-lightning#6621))

- Deprecated `PytorchProfiler(profiled_functions)` in favor of `record_functions` ([#6349](Lightning-AI/pytorch-lightning#6349))

- Deprecated metrics in favor of `torchmetrics` ([#6505](Lightning-AI/pytorch-lightning#6505),
    [#6530](Lightning-AI/pytorch-lightning#6530),
    [#6540](Lightning-AI/pytorch-lightning#6540),
    [#6547](Lightning-AI/pytorch-lightning#6547),
    [#6515](Lightning-AI/pytorch-lightning#6515),
    [#6572](Lightning-AI/pytorch-lightning#6572),
    [#6573](Lightning-AI/pytorch-lightning#6573),
    [#6584](Lightning-AI/pytorch-lightning#6584),
    [#6636](Lightning-AI/pytorch-lightning#6636),
    [#6637](Lightning-AI/pytorch-lightning#6637),
    [#6649](Lightning-AI/pytorch-lightning#6649),
    [#6659](Lightning-AI/pytorch-lightning#6659),
)

### Removed

- Removed support for passing a bool value to `profiler` argument of Trainer ([#6164](Lightning-AI/pytorch-lightning#6164))

- Removed no return warning from val/test step ([#6139](Lightning-AI/pytorch-lightning#6139))

- Removed passing a `ModelCheckpoint` instance to `Trainer(checkpoint_callback)` ([#6166](Lightning-AI/pytorch-lightning#6166))

- Removed deprecated Trainer argument `enable_pl_optimizer` and `automatic_optimization` ([#6163](Lightning-AI/pytorch-lightning#6163))

- Removed deprecated metrics ([#6161](Lightning-AI/pytorch-lightning#6161))
    * from `pytorch_lightning.metrics.functional.classification` removed `to_onehot`, `to_categorical`, `get_num_classes`, `roc`, `multiclass_roc`, `average_precision`, `precision_recall_curve`, `multiclass_precision_recall_curve`
    * from `pytorch_lightning.metrics.functional.reduction` removed `reduce`, `class_reduce`

- Removed deprecated `ModelCheckpoint` arguments `prefix`, `mode="auto"` ([#6162](Lightning-AI/pytorch-lightning#6162))

- Removed `mode='auto'` from `EarlyStopping` ([#6167](Lightning-AI/pytorch-lightning#6167))

- Removed legacy references for magic keys in the `Result` object ([#6016](Lightning-AI/pytorch-lightning#6016))

- Removed deprecated `LightningModule` `hparams` setter ([#6207](Lightning-AI/pytorch-lightning#6207))

- Removed legacy code to log or include metrics in the progress bar by returning them in a dict with the `"log"/"progress_bar"` magic keys. Use `self.log` instead ([#6734](Lightning-AI/pytorch-lightning#6734))

- Removed `optimizer_idx` argument from `training_step` in manual optimization ([#6093](Lightning-AI/pytorch-lightning#6093))

### Fixed

- Set better defaults for `rank_zero_only.rank` when training is launched with SLURM and torchelastic ([#6802](Lightning-AI/pytorch-lightning#6802))

- Made the `Plugin.reduce` method more consistent across all Plugins to reflect a mean-reduction by default ([#6011](Lightning-AI/pytorch-lightning#6011))

- Move lightning module to correct device type when using LightningDistributedWrapper ([#6070](Lightning-AI/pytorch-lightning#6070))

- Do not print top-k verbose log with `ModelCheckpoint(monitor=None)` ([#6109](Lightning-AI/pytorch-lightning#6109))

- Fixed csv extension check ([#6436](Lightning-AI/pytorch-lightning#6436))

- Fixed `ModelCheckpoint(monitor=None, save_last=True)` not saving checkpoints ([#6136](Lightning-AI/pytorch-lightning#6136))

- Fixed `ModelCheckpoint(save_top_k=0, save_last=True)` not saving the `last` checkpoint ([#6136](Lightning-AI/pytorch-lightning#6136))

- Fixed `.teardown(stage='fit')` getting called during `trainer.test` ([#6386](Lightning-AI/pytorch-lightning#6386))

- Fixed `.on_fit_{start,end}()` getting called during `trainer.test` ([#6386](Lightning-AI/pytorch-lightning#6386))

- Fixed LightningModule `all_gather` on cpu tensors ([#6416](Lightning-AI/pytorch-lightning#6416))

- Fixed torch distributed not available in setup hook for DDP ([#6506](Lightning-AI/pytorch-lightning#6506))

- Fixed `EarlyStopping` logic when `min_epochs` or `min_steps` requirement is not met ([#6705](Lightning-AI/pytorch-lightning#6705))

## [1.2.7] - 2021-04-06

### Fixed

- Fixed resolve a bug with omegaconf and xm.save ([#6741](Lightning-AI/pytorch-lightning#6741))
- Fixed an issue with IterableDataset when __len__ is not defined ([#6828](Lightning-AI/pytorch-lightning#6828))
- Sanitize None params during pruning ([#6836](Lightning-AI/pytorch-lightning#6836))
- Enforce an epoch scheduler interval when using SWA ([#6588](Lightning-AI/pytorch-lightning#6588))
- Fixed TPU Colab hang issue, post training ([#6816](Lightning-AI/pytorch-lightning#6816))
- Fixed a bug where `TensorBoardLogger` would give a warning and not log correctly to a symbolic link `save_dir` ([#6730](Lightning-AI/pytorch-lightning#6730))

## [1.2.6] - 2021-03-30

### Changed

- Changed the behavior of `on_epoch_start` to run at the beginning of validation & test epoch ([#6498](Lightning-AI/pytorch-lightning#6498))

### Removed

- Removed legacy code to include `step` dictionary returns in `callback_metrics`. Use `self.log_dict` instead. ([#6682](Lightning-AI/pytorch-lightning#6682))

### Fixed

- Fixed `DummyLogger.log_hyperparams` raising a `TypeError` when running with `fast_dev_run=True` ([#6398](Lightning-AI/pytorch-lightning#6398))
- Fixed error on TPUs when there was no `ModelCheckpoint` ([#6654](Lightning-AI/pytorch-lightning#6654))
- Fixed `trainer.test` freeze on TPUs ([#6654](Lightning-AI/pytorch-lightning#6654))
- Fixed a bug where gradients were disabled after calling `Trainer.predict` ([#6657](Lightning-AI/pytorch-lightning#6657))
- Fixed bug where no TPUs were detected in a TPU pod env ([#6719](Lightning-AI/pytorch-lightning#6719))

## [1.2.5] - 2021-03-23

### Changed

- Update Gradient Clipping for the TPU Accelerator ([#6576](Lightning-AI/pytorch-lightning#6576))
- Refactored setup for typing friendly ([#6590](Lightning-AI/pytorch-lightning#6590))

### Fixed

- Fixed a bug where `all_gather` would not work correctly with `tpu_cores=8` ([#6587](Lightning-AI/pytorch-lightning#6587))
- Fixed comparing required versions ([#6434](Lightning-AI/pytorch-lightning#6434))
- Fixed duplicate logs appearing in console when using the python logging module ([#6275](Lightning-AI/pytorch-lightning#6275))
- Added Autocast in validation, test and predict modes for Native AMP ([#6565](Lightning-AI/pytorch-lightning#6565))

Reviewed By: shuyingsunshine21

Differential Revision: D27528929

fbshipit-source-id: 311c88f71461c2c79bbf185e28d7a6d683ccc26f
Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment
Reviewers

@justusschock justusschock justusschock approved these changes

@Borda Borda Borda approved these changes

@awaelchli awaelchli awaelchli approved these changes

@carmocca carmocca Awaiting requested review from carmocca

@SeanNaren SeanNaren Awaiting requested review from SeanNaren

@tchaton tchaton Awaiting requested review from tchaton tchaton is a code owner

@williamFalcon williamFalcon Awaiting requested review from williamFalcon

Assignees
No one assigned
Labels
feature Is an improvement or enhancement logger Related to the Loggers priority: 2 Low priority task ready PRs ready to be merged
Projects
None yet
Milestone
pl:1.3
Development

Successfully merging this pull request may close these issues.

Add ability to specify artifact_location when using MLFlowLogger
5 participants
@ethanwharris @awaelchli @Borda @justusschock @carmocca