* Get experiment_id from MLFlow only once instead of each training loop.
* Apply suggestions from code review
Co-authored-by: Rohit Gupta <rohitgr1998@gmail.com>
* add test that asserts mlflow client is called to retrieve experiment id only once
* make pep8 happy
* logs
Co-authored-by: Patrick Orlando <patrick.orlando@rea-group.com>
Co-authored-by: Jirka Borovec <Borda@users.noreply.github.com>
Co-authored-by: Rohit Gupta <rohitgr1998@gmail.com>
Co-authored-by: Jirka Borovec <jirka@pytorchlightning.ai>
* script
* docs
* simple test
* move test
* fix doctest
* no grad context
* extend tests
test
test
* datamodule test
* clean up test
* docs
* name
* fix import
* update changelog
* fix import
* skip pytorch 1.3 in test
* update codeblock
* skip bugged 1.4
* typehints
* doctest not working on all pytorch versions
* rename TestGAN to prevent pytest interference
* add note about pytorch version
* fix torchscript version inconsistency in tests
* reset training state + tests
* update docstring
* Apply suggestions from code review
Co-authored-by: Justus Schock <12886177+justusschock@users.noreply.github.com>
* update docstring, dict return
* add docs to index
* add link
* doc eval mode
* forward
* optional save to file path
* optional
* test torchscript device
* test save load with file path
* pep
* str
* Commit typing suggestion
Co-authored-by: ananthsub <ananth.subramaniam@gmail.com>
* skip test if cuda not available
Co-authored-by: Jirka Borovec <Borda@users.noreply.github.com>
Co-authored-by: Justus Schock <12886177+justusschock@users.noreply.github.com>
Co-authored-by: ananthsub <ananth.subramaniam@gmail.com>
* change t() to transpose() as xla devices do not support .t() on 1-dim tensor
* detach tensor before copying
* Revert "detach tensor before copying"
This reverts commit 37cc7bbe
* changed dims
* added test_result_obj_on_tpu
* detach before copying
* detach before copying
* detach before copying
* replace torch.cat with sum
* tests to ensure correct dataloading interval and sequence
* tests to ensure correct dataloading interval and sequence
* tests to ensure correct dataloading interval and sequence
* tests to ensure correct dataloading interval and sequence
* tests to ensure correct dataloading interval and sequence
* fix rmsle
* Updated test to match rmsle fix
* Updated RMSLE example result to match functional
* chlog
* add randomized test
* fix pep8
Co-authored-by: Jirka Borovec <jirka@pytorchlightning.ai>
Co-authored-by: Nicki Skafte <skaftenicki@gmail.com>
* Fix num_sanity_val_steps according to limit_val_steps
* fix test
* add num_sanity_batches
* pep
* update docstring in test
* add more test
* chlog
* update comments and docstring in test
Co-authored-by: Adrian Wälchli <aedu.waelchli@gmail.com>
Co-authored-by: Adrian Wälchli <adrian.waelchli@inf.unibe.ch>
Co-authored-by: Ananya Harsh Jha <ananya@pytorchlightning.ai>
When a LightningModule inherits from a class that implements `__new__()` such as `typing.Generic`, `inspect.signature(cls)` short-circuits and returns the signature of `__new__()` instead of `__init__()`. So, we need to be more specific and call inspection directly on the init function.
* re-enabled naming metrics in ckpt name
* re-enabled naming metrics in ckpt name
* re-enabled naming metrics in ckpt name
* re-enabled naming metrics in ckpt name
* re-enabled naming metrics in ckpt name
* re-enabled naming metrics in ckpt name
* fix result for dp
* fix result for dp
* fix result for dp
* fix result for dp
* fix result for dp
* fix result for dp
* fix result for dp
* fix result for dp
* fix result for dp
* fix result for dp
* fix result for dp
* fix result for dp
* added warning when changing monitor and using results obj
* added warning when changing monitor and using results obj
* added warning when changing monitor and using results obj
* added warning when changing monitor and using results obj
* add ddp script variations
* add ddp test
* rename
* shell
* test
* test
* try call
* try without subprocess
* test
* display the error
* list all variations
* try string
* try copy env
* debug
* pythonpath
* path
* update test
* change
* simple ddp test
* replace
* remove random port
* random port
* str
* clean up
* check run spawn
* clean up
* docs
* docs
* update test
* docs
* changelog
* changelog
* add val step arg to metrics
* add val step arg to metrics
* add val step arg to metrics
* add val step arg to metrics
* add val step arg to metrics
* add val step arg to metrics
* add val step arg to metrics
* add val step arg to metrics
* add val step arg to metrics
* add step metrics
* add step metrics
* override dist backend when using tpus
* added test
* updated doc string
* drop redundant info...
* more redundant info
Co-authored-by: Jirka Borovec <Borda@users.noreply.github.com>
Co-authored-by: rohitgr7 <rohitgr1998@gmail.com>
* Update lr_logger.py
when logging learning_rate, we should provide different choices to log including 'step' and 'epoch'
* Update lr_logger.py
add some type annotations and docstrings
* Update lr_logger.py
fixed a bug where `on_train_batch_start()` can't be triggered, instead, we should use on_batch_start(); add `interval` args so that we can record learning_rates with respect to `global_step` or `current_epoch`.
* Update lr_logger.py
restore _extract_lr()
* suggestion
* Update lr_logger.py
modify _extract_lr(), it no more need to pass `interval` parameter.
* Update test_lr_logger.py
SkafteNicki 's suggetion
* log_interval now supports `None`, `step`, `epoch`
* change `log_interval` to `logging_interval`
* Update test_lr_logger.py
* Update lr_logger.py
* put types check into `on_train_start()`
* cleanup
* docstring typos
* minor changes from suggestions
Co-authored-by: Jirka Borovec <jirka@pytorchlightning.ai>
Co-authored-by: rohitgr7 <rohitgr1998@gmail.com>
Co-authored-by: Adrian Wälchli <aedu.waelchli@gmail.com>
Co-authored-by: mergify[bot] <37929162+mergify[bot]@users.noreply.github.com>
* Add initial tracking of states in Trainer.
* Add INTERRUPTED state, improve tests, move state switching from callback to a trainer.
* Move part of a trainer state switching to a decorator.
* Add documentation.
* Fix docs, rename state enum, restore state to previous on exit if None, add tests for decorator only.
* Fix callback typing.
Co-authored-by: William Falcon <waf2107@columbia.edu>
* Add support to Tensorboard logger for OmegaConf hparams
Address https://github.com/PyTorchLightning/pytorch-lightning/issues/2844
We check if we can import omegaconf, and if the hparams are omegaconf instances. if so, we use OmegaConf.merge to preserve the typing, such that saving hparams to yaml actually triggers the OmegaConf branch
* avalaible
* chlog
* test
Co-authored-by: Jirka Borovec <jirka@pytorchlightning.ai>
* Override the default gather method to support scalars
* add computing average of a list
* bug: change if to elif
* add some tests
* change style
* change documentation
* use apply_to_collection in DP gather
* use apply_to_collection in DP gather
* fix warning msg
* override gather method in DP
* add tests for python scalars
* add python scalars to docstring
* Update message
* override gather method in DP
* formatting
* chlog
Co-authored-by: Jirka Borovec <Borda@users.noreply.github.com>
Co-authored-by: Jirka Borovec <jirka@pytorchlightning.ai>
* add test for none checkpoint in ddp_spawn
* fix code style
* make sure checkpoint_callback is none
* Fix tests
Co-authored-by: Justus Schock <12886177+justusschock@users.noreply.github.com>
* add ddp sync for logging in result step
* pep8
* pep8
* make ddp tests run also on cpu (except windowws)
* create class instance in ddp test
* revert automated formatting
* pep8
* Fix shuffle for distributed sampler
* add test
* test
* chlog
* update test
* update test
* update test
* assertions via callback
* define callback outside for pickling
* skip ddp test on windows
Co-authored-by: Adrian Wälchli <aedu.waelchli@gmail.com>
* Test using torchtext.data.Field with include_lengths=True/False
* Fix issue that Tensors in a Batch generated by torchtext with torchtext.data.Field configured as include_lengths=True
* Add description for fix of issue #2688
* changes to accomodate CodeFactor issues
* Another attemt to make last CodeFactor issue pass (it's a false alarm)
* temporarly disable test of test_grad_tracking to check if testing will pass
* reenable test in test_grad_norm
* Update CHANGELOG.md
Co-authored-by: Jirka Borovec <Borda@users.noreply.github.com>
* Renamed get_torchtext_data_iterator to _get_torchtext_data_iterator as suggested by @borda
* Update pytorch_lightning/utilities/apply_func.py
Co-authored-by: Adrian Wälchli <aedu.waelchli@gmail.com>
* adding tests more specific to batch_move_data_to_device with tochtext Batch
* added check that Tensors were moved to target device
* removed tests using RNN models to be moved into a separate PR
* fixing FLAKE8 errors that showed up after merge from master branch
modified: tests/base/datamodules.py
modified: tests/callbacks/test_model_checkpoint.py
* parameterized test to reduce code duplication
* Added check only if length tensor exist. Removed left over comments.
* rearranged device parameterization and added pytest.param
* Try to figure out why only one device is tested on Linux machines
* Testing on CPU and GPU devices (GPU test is skip if no cuda device is available.
* added test for TPU device (experimental)
* Adding test parameterization for TPU test (experimental)
* change import statement to limit what is imported for a TPU environment
* made test work with TPU
* Change to trigger CI
* Change to trigger CI
* uncommented TPU test to check CI
* reenabling TPU test
* small change to trigger CI build
* small change to trigger CI build
* small change to trigger CI build
* adding tests/utilities/test_apply_func_torchtext.py to CI TPU test
* try to make test not skipped on CI with TPU
* remove testing on TPU
* undo an accidental change to test_tpu.py (file should not have been touched)
* small change to trigger CI build
* small change to trigger CI build
* Update tests/utilities/test_apply_func_torchtext.py
* Revert to previous version
* Apply suggestions from code review
* Change to trigger CI
Co-authored-by: Thomas Schaaf <tschaaf@mmm.com>
Co-authored-by: Jirka Borovec <Borda@users.noreply.github.com>
Co-authored-by: Adrian Wälchli <aedu.waelchli@gmail.com>
Co-authored-by: Thomas Schaaf <tschaaf@cs.cmu.edu>
* export model to onnx
* prepare data before exporting
* support for dataloaders and tensors
* added tests
* use example_input_array
add to changelog
* updated docstring
* added onnx inference tests
* temp commit
* removed schema valid test
* add onnxruntime to environment.yml
* moved onnxruntime to environment.yml pip
* add example in doc
* add lines between code block
* added PR to changelog
* is file check
Co-authored-by: Jirka Borovec <Borda@users.noreply.github.com>
* remove *
Co-authored-by: Jirka Borovec <Borda@users.noreply.github.com>
* infer example outputs
* added doctest for onnx
* fix windows tests
* moved eval within condition block
* self.forward to self
* added docs
* fixed docs error
* added to toctree
* Update CHANGELOG.md
Co-authored-by: Jirka Borovec <Borda@users.noreply.github.com>
* pt 1.6
* don't use the new zipfile serialization for now
* quick flake8 fixes
* remove unnecessary f
* coalesce strings
* remove comma
* remove extra commas
* Apply suggestions from code review
Co-authored-by: Peter Yu <2057325+yukw777@users.noreply.github.com>
* set _use_new_zipfile_serialization to False only for pytorch 1.6.0
* remove unnecessary comments
* flake8 fixes
* use pkg_resources instead of packaging
* readme
* format
* version
* chlog
Co-authored-by: Peter Yu <peter@asapp.com>
Co-authored-by: Peter Yu <2057325+yukw777@users.noreply.github.com>
The speed up is achieved by:
- Moving the "where" out of the loop (and replacing with min for simplicity).
- Replacing manual sum and pow with torch.norm. Even though this results
in unnessecary computation (computing pow(root)) this is still a lot
faster.
- Preallocating the output gives a slight speed up.
Note that calling .to for all parameters results in a small speed
penalty (~4 ms in my case) but allows parameters on different devices.
Overall this reduces the time used for gradient clipping from 206ms to
74 ms for my model (Resnet50 + few additional vars, all vars on GPU).
* Fix fast_dev_run to run for all val_dataloaders
* fast_dev_run check
* changelog
* explicit
* limit_batches with fast_dev_run in init
* add test
* whitespace and comment fix
* comment and assertion
* added tests
* Fix fast_dev_run to run for all val_dataloaders
* fast_dev_run check
* changelog
* explicit
* limit_batches with fast_dev_run in init
* add test
* whitespace and comment fix
* comment and assertion
* added tests
* added tests
* added tests
* added tests
* update rtol
* Revert "update rtol"
This reverts commit 4320329540.
* added tests
Co-authored-by: William Falcon <waf2107@columbia.edu>
* fix weights_save path and drop ckpt_path
* add tests
* unused import
* update docs
* changelog
* pep8
* fix horovod test
* make backward compatible
* perform same test for all loggers
* fix for when logger=False and weights_save_path is set
* update changelog
* update docs
* update tests
* do not set save dir dynamically
* remove duplicate test
* remove duplicated tests
* update tests
* update tests
* remove remaining ckpt_path references
* move defaults to init as suggested by @Borda
* test deprecation
* reactor into gpu accelerator
* reactor into gpu accelerator
* reactor into gpu accelerator
* reactor into gpu accelerator
* reactor into gpu accelerator
* reactor into gpu accelerator
* reactor into gpu accelerator
* reactor into gpu accelerator
* reactor into gpu accelerator
* reactor into gpu accelerator
* reactor into gpu accelerator
* reactor into gpu accelerator
* reactor into gpu accelerator
* reactor into gpu accelerator
* reactor into gpu accelerator
* reactor into gpu accelerator
* reactor into gpu accelerator
* reactor into gpu accelerator
* reactor into gpu accelerator
* reactor into gpu accelerator
* reactor into gpu accelerator
* 🎨 warn instead of error out on loaders
* 🐛 test misconfiguration should still fail
* 🚧 .
* updated docs with new result obj
* updated docs with new result obj
* updated docs with new result obj
* updated docs with new result obj
* updated docs with new result obj
* updated docs with new result obj
* updated docs with new result obj
* updated docs with new result obj
* updated docs with new result obj
* updated docs with new result obj
* updated docs with new result obj
* updated docs with new result obj
* updated docs with new result obj
* updated docs with new result obj
* updated docs with new result obj
* updated docs with new result obj
* updated docs with new result obj
* updated docs with new result obj
* updated docs with new result obj
* updated docs with new result obj
* updated docs with new result obj
* updated docs with new result obj
* updated docs with new result obj
* updated docs with new result obj
* updated docs with new result obj
* updated docs with new result obj
* updated docs with new result obj
* updated docs with new result obj
* updated docs with new result obj
* updated docs with new result obj
* updated docs with new result obj
* updated docs with new result obj
* updated docs with new result obj
* updated docs with new result obj
* updated docs with new result obj
Co-authored-by: William Falcon <waf2107@columbia.edu>
* fix setup call while testing
* changelog
* drop if condition
* add test to check setup call
* flake8
* update test to check model stage
Co-authored-by: William Falcon <waf2107@columbia.edu>
* Horovod: Adjust base LR used by schedulers to match that of the optimizer after scaling by number of workers
* Added unit test
* Removed debug statements
* Updated changelog
* Apply suggestions from code review
Co-authored-by: William Falcon <waf2107@columbia.edu>
Co-authored-by: Jirka Borovec <Borda@users.noreply.github.com>
* metrics: added bleu score and test bleu
* metrics: fixed type hints in bleu
* bleu score moved to metrics/functional/nlp.py
* refactor with torch.Tensor
* Update test_sequence.py
* refactor as Borda requests and nltk==3.2
* locked nltk==3.3
* nltk>=3.3, parametrized smooth argument for test
* fix bleu_score example
* added class BLEUScore metrics and test
* added class BLEUScore metrics and test
* update CHANGELOG
* refactor with torchtext
* torchtext changed to optional import
* fix E501 line too long
* add else: in optional import
* remove pragma: no-cover
* constants changed to CAPITALS
* remove class in tests
* List -> Sequence, conda -> pip, cast with tensor
* add torchtext in test.txt
* remove torchtext from test.txt
* bump torchtext to 0.5.0
* bump torchtext to 0.5.0
* Apply suggestions from code review
* ignore bleu score in doctest, renamed to nlp.py
* back to implementation with torch
* remove --ignore in CI test, proper reference format
* apply justus comment
Co-authored-by: Jirka Borovec <Borda@users.noreply.github.com>
* recursive dtype device apply
* simplify
* simple test
* submodule test
* rename
* explicit
* type hints
* test for dp backend
* fix test skip
* rename
* add ddp_spawn test
* fix None index in test
* try fix ddp_spawn test
* changelog
* move _dtype and _device to mixin
* additional doctest
* r
* r
* r
* patched optimizer closure with sr
* patched optimizer closure with sr
* patched optimizer closure with sr
* added train step structured result
* added train step structured result
* added train step structured result
* added train step structured result
* added train step structured result
* added train step structured result
* added train step structured result
* added train step structured result
* added train step structured result
* added train step structured result
* added train step structured result
* added train step structured result
* added train step structured result
* added train step structured result
* added train step structured result
* added train step structured result
* added train step structured result
* added train step structured result
* added train step structured result
* added train step structured result
* added autoreduce for train step
* added auto reduce on train
* added auto reduce on train
* added auto reduce on train
* added auto reduce on train
* added auto reduce on train
* added auto reduce on train
* added hooks
* added hooks
* added hooks
* added hooks
* added hooks
* finished tests for structured results on train epoch
* finished tests for structured results on train epoch
* finished tests for structured results on train epoch
* finished tests for structured results on train epoch
* finished tests for structured results on train epoch
* finished tests for structured results on train epoch
* finished tests for structured results on train epoch
* finished tests for structured results on train epoch
* finished tests for structured results on train epoch
* finished tests for structured results on train epoch
* finished tests for structured results on train epoch
* finished tests for structured results on train epoch
* finished tests for structured results on train epoch
* finished tests for structured results on train epoch
* finished tests for structured results on train epoch
* finished tests for structured results on train epoch
* finished tests for structured results on train epoch
* finished tests for structured results on train epoch
* finished tests for structured results on train epoch
* finished tests for structured results on train epoch
* finished tests for structured results on train epoch
* finished tests for structured results on train epoch
* finished tests for structured results on train epoch
* finished tests for structured results on train epoch
* finished tests for structured results on train epoch
* finished tests for structured results on train epoch
* finished tests for structured results on train epoch
* finished tests for structured results on train epoch
* finished tests for structured results on train epoch
* finished tests for structured results on train epoch
* finished tests for structured results on train epoch
* finished tests for structured results on train epoch
* finished tests for structured results on train epoch
* finished tests for structured results on train epoch
* finished tests for structured results on train epoch
* finished tests for structured results on train epoch
* finished tests for structured results on train epoch
* finished tests for structured results on train epoch
* finished tests for structured results on train epoch
* finished tests for structured results on train epoch
* finished tests for structured results on train epoch
* finished tests for structured results on train epoch
* finished tests for structured results on train epoch
* finished tests for structured results on train epoch
* finished tests for structured results on train epoch
* finished tests for structured results on train epoch
* finished tests for structured results on train epoch
* finished tests for structured results on train epoch
* finished tests for structured results on train epoch
* finished tests for structured results on train epoch
* finished tests for structured results on train epoch
* finished tests for structured results on train epoch
* finished tests for structured results on train epoch
* finished tests for structured results on train epoch
* finished tests for structured results on train epoch
* finished tests for structured results on train epoch
* finished tests for structured results on train epoch
* finished tests for structured results on train epoch
* finished tests for structured results on train epoch
* finished tests for structured results on train epoch
* finished tests for structured results on train epoch
* finished tests for structured results on train epoch
* finished tests for structured results on train epoch
* finished tests for structured results on train epoch
* finished tests for structured results on train epoch
* finished tests for structured results on train epoch
* finished tests for structured results on train epoch
* finished tests for structured results on train epoch
* finished tests for structured results on train epoch
* finished tests for structured results on train epoch
* finished tests for structured results on train epoch
* finished tests for structured results on train epoch
* finished tests for structured results on train epoch
* finished tests for structured results on train epoch
* finished tests for structured results on train epoch
* finished tests for structured results on train epoch
* finished tests for structured results on train epoch
* finished tests for structured results on train epoch
* finished tests for structured results on train epoch
* finished tests for structured results on train epoch
* finished tests for structured results on train epoch
* finished tests for structured results on train epoch
* finished tests for structured results on train epoch
* finished tests for structured results on train epoch
* finished tests for structured results on train epoch
* cache
* finished tests for structured results on train epoch
* finished tests for structured results on train epoch
* finished tests for structured results on train epoch
* finished tests for structured results on train epoch
* finished tests for structured results on train epoch
* Update pytorch_lightning/callbacks/early_stopping.py
Co-authored-by: Jirka Borovec <Borda@users.noreply.github.com>
* Update pytorch_lightning/callbacks/early_stopping.py
Co-authored-by: Jirka Borovec <Borda@users.noreply.github.com>
* Update pytorch_lightning/callbacks/early_stopping.py
Co-authored-by: Jirka Borovec <Borda@users.noreply.github.com>
* Update pytorch_lightning/callbacks/model_checkpoint.py
* Update pytorch_lightning/core/step_result.py
* finished tests for structured results on train epoch
* finished tests for structured results on train epoch
* Apply suggestions from code review
Co-authored-by: Adrian Wälchli <aedu.waelchli@gmail.com>
Co-authored-by: Justus Schock <12886177+justusschock@users.noreply.github.com>
* Apply suggestions from code review
Co-authored-by: Justus Schock <12886177+justusschock@users.noreply.github.com>
* Apply suggestions from code review
Co-authored-by: Justus Schock <12886177+justusschock@users.noreply.github.com>
* simple
* finished tests for structured results on train epoch
* simple
* simple
* revert
* finished tests for structured results on train epoch
* finished tests for structured results on train epoch
* Update tests/base/deterministic_model.py
Co-authored-by: Jirka Borovec <Borda@users.noreply.github.com>
* finished tests for structured results on train epoch
* docstring typos
* finished tests for structured results on train epoch
* finished tests for structured results on train epoch
* finished tests for structured results on train epoch
* finished tests for structured results on train epoch
* finished tests for structured results on train epoch
* finished tests for structured results on train epoch
* finished tests for structured results on train epoch
* finished tests for structured results on train epoch
* finished tests for structured results on train epoch
* finished tests for structured results on train epoch
* finished tests for structured results on train epoch
* finished tests for structured results on train epoch
* finished tests for structured results on train epoch
* finished tests for structured results on train epoch
* finished tests for structured results on train epoch
* finished tests for structured results on train epoch
* finished tests for structured results on train epoch
* Update pytorch_lightning/core/step_result.py
Co-authored-by: Adrian Wälchli <aedu.waelchli@gmail.com>
* Update pytorch_lightning/overrides/data_parallel.py
Co-authored-by: Adrian Wälchli <aedu.waelchli@gmail.com>
Co-authored-by: Jirka <jirka@pytorchlightning.ai>
Co-authored-by: Jirka Borovec <Borda@users.noreply.github.com>
Co-authored-by: Adrian Wälchli <aedu.waelchli@gmail.com>
Co-authored-by: Justus Schock <12886177+justusschock@users.noreply.github.com>
* add tests for single scalar return from training
* add tests for single scalar return from training
* add tests for single scalar return from training
* fixing val step only
* fixing val step only
* fixing val step only
* fixing val step only
* fixing val step only
* fixing val step only
* fixing val step only
* fixing val step only
* fixing val step only
* fixing val step only
* fixing val step only
* fixing val step only
* fixing val step only
* fixing val step only
* fixing val step only
* fixing val step only
* fixing val step only
* fixing val step only
* fixing val step only
* fixing val step only
* fixing val step only
* fixing val step only
* add tests for single scalar return from training
* add tests for single scalar return from training
* add tests for single scalar return from training
* add tests for single scalar return from training
* add tests for single scalar return from training
* mlflow rework
* logger save_dir
* folder
* mlflow
* simplify
* fix test
* add a test for file dir contents
* new line
* changelog
* docs
* Update CHANGELOG.md
Co-authored-by: Jirka Borovec <Borda@users.noreply.github.com>
* test for comet logger
* improve mlflow checkpoint test
* prevent commet logger error on pytest exit
* test tensorboard save dir structure
* wandb save dir test
* skip test on windows
* add mlflow to pickle tests
* wandb
* code factor
* remove unused imports
* remove unused setter
* wandb mock
* wip mock
* wip mock
* wandb tests with mocking
* clean up
* clean up
* comments
* include wandblogger in test
* clean up
* missing argument
Co-authored-by: Jirka Borovec <Borda@users.noreply.github.com>
* skip ckpt test on rank > 0
* fx test
* add extra assert
* code factor
* add back removed
* add old loading code
* add back old
* unused import
* add same skip to run_model_without_loggers
* test if horovod now works with python 3.8
* test remove all 3.8 skips
* remove spawn
* fix
* fix test
* move load check up
* fix test multigpu
* rename
* fix gpu mode
* on gpu fix when on cpu
* move
* added base tests for tpu
* added base tests for tpu
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* fix deprecation warnings
* added base tests for tpu
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* Update pytorch_lightning/trainer/trainer.py
Co-authored-by: Jeremy Jordan <13970565+jeremyjordan@users.noreply.github.com>
* added base tests for tpu
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* added base tests for tpu
Co-authored-by: Jirka <jirka@pytorchlightning.ai>
Co-authored-by: Jeremy Jordan <13970565+jeremyjordan@users.noreply.github.com>
* fix tpu hang
* fix tpu hang
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* fix tpu hang
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* fix tpu hang
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* fix tpu hang
* fix tpu hang
* fix tpu hang
* fix tpu hang
* fix tpu hang
* fix tpu hang
* Fixes#2455
* Fixes#2455
* Fixes#2455
* Fixes#2455
* Fixes#2455
* Fixes#2455
* Fixes#2455
* Fixes#2455
* Fixes#2455
* Fixes#2455
* Fixes#2455
* Fixes#2455
* Fixes#2455
* Fixes#2455
* Fixes#2455
* Fixes#2455
* Fixes#2455
* Fixes#2455
* Fixes#2455
* Fixes#2455
* Fixes#2455
* Fixes#2455
* Fixes#2455
* Fixes#2455
* Fixes#2455
* Fixes#2455
* Fixes#2455
* added early stop tpu test
* added early stop tpu test
* added early stop tpu test
* added early stop tpu test
* added early stop tpu test
* added early stop tpu test
* added early stop tpu test
* added early stop tpu test
* added early stop tpu test
* added early stop tpu test
* added early stop tpu test
* added early stop tpu test
* added early stop tpu test
* added early stop tpu test
* added early stop tpu test
* added early stop tpu test
* added early stop tpu test
* added early stop tpu test
* added early stop tpu test
* added early stop tpu test
* added early stop tpu test
* added early stop tpu test
* fix and test for ddp block logging rank > 0
* rename
* use the dummy logger
* dummy logger test
* set the logger in model
* decorator for rank zero experiment
* simplify check
* simplify
* fix problem with None in checkpoint path
* revert configure logger
* unused import
* offline
* try rank 0 decorator in checkpoint
* try fix test
* imgs
* add asserts to make sure log zero only saves checkpoints
* add asserts to make sure log zero only saves checkpoints
* add asserts to make sure log zero only saves checkpoints
* add asserts to make sure log zero only saves checkpoints
* add asserts to make sure log zero only saves checkpoints
* fix tpu tests
* fix tpu tests
Co-authored-by: William Falcon <waf2107@columbia.edu>
William Falcon