Simplify progress bar args (Lightning-AI#1108)

* show progress bar dependent on refresh_rate * test progress_bar_refresh control show bar * remove show_progress_bar from other tests * borda fixes * flake8 fix * changelog update prog bar refresh rate * move show_progress_bar to deprecated 0.9 api * rm show_progress_bar references, test deprecated * Update pytorch_lightning/trainer/__init__.py * fix test * changelog * minor CHANGELOG.md format * Update pytorch_lightning/trainer/__init__.py * Update pytorch_lightning/trainer/trainer.py Co-authored-by: Gerard Bentley <gbkh2015@mymail.pomona.edu> Co-authored-by: William Falcon <waf2107@columbia.edu> Co-authored-by: Jirka Borovec <Borda@users.noreply.github.com> Co-authored-by: J. Borovec <jirka.borovec@seznam.cz>
alexeykarnachev · Apr 4, 2020 · 5ba1728 · 5ba1728
1 parent 3cffda3
commit 5ba1728
Show file tree

Hide file tree

Showing 15 changed files with 79 additions and 55 deletions.
diff --git a/CHANGELOG.md b/CHANGELOG.md
@@ -26,6 +26,7 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 
 ### Changed
 
+- Changed `progress_bar_refresh_rate` trainer flag to disable progress bar when set to 0. ([#1108](https://github.com/PyTorchLightning/pytorch-lightning/pull/1108))
 - Enhanced `load_from_checkpoint` to also forward params to the model ([#1307](https://github.com/PyTorchLightning/pytorch-lightning/pull/1307))
 - Updated references to self.forward() to instead use the `__call__` interface. ([#1211](https://github.com/PyTorchLightning/pytorch-lightning/pull/1211))
 - Added option to run without an optimizer by returning `None` from `configure_optimizers`. ([#1279](https://github.com/PyTorchLightning/pytorch-lightning/pull/1279))
@@ -44,6 +45,7 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 ### Deprecated
 
 - Deprecated Trainer argument `print_nan_grads` ([#1097](https://github.com/PyTorchLightning/pytorch-lightning/pull/1097))
+- Deprecated Trainer argument `show_progress_bar` ([#1108](https://github.com/PyTorchLightning/pytorch-lightning/pull/1108))
 
 ### Removed
 
@@ -72,9 +74,9 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 
 ### Added
 
-- Added automatic sampler setup. Depending on DDP or TPU, lightning configures the sampler correctly (user needs to do nothing)  ([#926](https://github.com/PyTorchLightning/pytorch-lightning/pull/926))
-- Added `reload_dataloaders_every_epoch=False` flag for trainer. Some users require reloading data every epoch  ([#926](https://github.com/PyTorchLightning/pytorch-lightning/pull/926))
-- Added `progress_bar_refresh_rate=50` flag for trainer. Throttle refresh rate on notebooks  ([#926](https://github.com/PyTorchLightning/pytorch-lightning/pull/926))
+- Added automatic sampler setup. Depending on DDP or TPU, lightning configures the sampler correctly (user needs to do nothing) ([#926](https://github.com/PyTorchLightning/pytorch-lightning/pull/926))
+- Added `reload_dataloaders_every_epoch=False` flag for trainer. Some users require reloading data every epoch ([#926](https://github.com/PyTorchLightning/pytorch-lightning/pull/926))
+- Added `progress_bar_refresh_rate=50` flag for trainer. Throttle refresh rate on notebooks ([#926](https://github.com/PyTorchLightning/pytorch-lightning/pull/926))
 - Updated governance docs
 - Added a check to ensure that the metric used for early stopping exists before training commences ([#542](https://github.com/PyTorchLightning/pytorch-lightning/pull/542))
 - Added `optimizer_idx` argument to `backward` hook ([#733](https://github.com/PyTorchLightning/pytorch-lightning/pull/733))
@@ -97,7 +99,6 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 - Added TPU gradient clipping ([#963](https://github.com/PyTorchLightning/pytorch-lightning/pull/963))
 - Added max/min number of steps in `Trainer` ([#728](https://github.com/PyTorchLightning/pytorch-lightning/pull/728))
 
-
 ### Changed
 
 - Improved `NeptuneLogger` by adding `close_after_fit` argument to allow logging after training([#908](https://github.com/PyTorchLightning/pytorch-lightning/pull/1084))
@@ -109,17 +110,17 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 - Freezed models `hparams` as `Namespace` property ([#1029](https://github.com/PyTorchLightning/pytorch-lightning/pull/1029))
 - Dropped `logging` config in package init ([#1015](https://github.com/PyTorchLightning/pytorch-lightning/pull/1015))
 - Renames model steps ([#1051](https://github.com/PyTorchLightning/pytorch-lightning/pull/1051))
-    * `training_end` >> `training_epoch_end`
-    * `validation_end` >> `validation_epoch_end`
-    * `test_end` >> `test_epoch_end`
+  - `training_end` >> `training_epoch_end`
+  - `validation_end` >> `validation_epoch_end`
+  - `test_end` >> `test_epoch_end`
 - Refactor dataloading, supports infinite dataloader ([#955](https://github.com/PyTorchLightning/pytorch-lightning/pull/955))
 - Create single file in `TensorBoardLogger` ([#777](https://github.com/PyTorchLightning/pytorch-lightning/pull/777))
 
 ### Deprecated
 
 - Deprecated `pytorch_lightning.logging` ([#767](https://github.com/PyTorchLightning/pytorch-lightning/pull/767))
 - Deprecated `LightningModule.load_from_metrics` in favour of `LightningModule.load_from_checkpoint` ([#995](https://github.com/PyTorchLightning/pytorch-lightning/pull/995), [#1079](https://github.com/PyTorchLightning/pytorch-lightning/pull/1079))
-- Deprecated `@data_loader` decorator  ([#926](https://github.com/PyTorchLightning/pytorch-lightning/pull/926))
+- Deprecated `@data_loader` decorator ([#926](https://github.com/PyTorchLightning/pytorch-lightning/pull/926))
 - Deprecated model steps `training_end`, `validation_end` and `test_end` ([#1051](https://github.com/PyTorchLightning/pytorch-lightning/pull/1051), [#1056](https://github.com/PyTorchLightning/pytorch-lightning/pull/1056))
 
 ### Removed
@@ -309,9 +310,7 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 
 ### Added
 
-- Added the flag `log_gpu_memory` to `Trainer` to deactivate logging of GPU
-memory utilization
-- Added SLURM resubmit functionality (port from test-tube)
+- Added the flag `log_gpu_memory` to `Trainer` to deactivate logging of GPU memory utilization
 - Added optional weight_save_path to trainer to remove the need for a checkpoint_callback when using cluster training
 - Added option to use single gpu per node with `DistributedDataParallel`
 

diff --git a/pytorch_lightning/trainer/__init__.py b/pytorch_lightning/trainer/__init__.py
@@ -646,6 +646,8 @@ def on_train_end(self):
     # default used by the Trainer
     trainer = Trainer(progress_bar_refresh_rate=1)
 
+    # disable progress bar
+    trainer = Trainer(progress_bar_refresh_rate=0)
 
 reload_dataloaders_every_epoch
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
@@ -702,12 +704,9 @@ def on_train_end(self):
 show_progress_bar
 ^^^^^^^^^^^^^^^^^
 
-If true shows tqdm progress bar
+.. warning:: .. deprecated:: 0.7.2
 
-Example::
-
-    # default used by the Trainer
-    trainer = Trainer(show_progress_bar=True)
+    Set `progress_bar_refresh_rate` to 0 instead. Will remove 0.9.0.
 
 test_percent_check
 ^^^^^^^^^^^^^^^^^^

diff --git a/pytorch_lightning/trainer/deprecated_api.py b/pytorch_lightning/trainer/deprecated_api.py
@@ -87,3 +87,22 @@ def nb_sanity_val_steps(self, nb):
                       "`num_sanity_val_steps` since v0.5.0"
                       " and this method will be removed in v0.8.0", DeprecationWarning)
         self.num_sanity_val_steps = nb
+
+
+class TrainerDeprecatedAPITillVer0_9(ABC):
+
+    def __init__(self):
+        super().__init__()  # mixin calls super too
+
+    @property
+    def show_progress_bar(self):
+        """Back compatibility, will be removed in v0.9.0"""
+        warnings.warn("Argument `show_progress_bar` is now set by `progress_bar_refresh_rate` since v0.7.2"
+                      " and this method will be removed in v0.9.0", DeprecationWarning)
+        return self.progress_bar_refresh_rate >= 1
+
+    @show_progress_bar.setter
+    def show_progress_bar(self, tf):
+        """Back compatibility, will be removed in v0.9.0"""
+        warnings.warn("Argument `show_progress_bar` is now set by `progress_bar_refresh_rate` since v0.7.2"
+                      " and this method will be removed in v0.9.0", DeprecationWarning)
diff --git a/pytorch_lightning/trainer/distrib_data_parallel.py b/pytorch_lightning/trainer/distrib_data_parallel.py
@@ -281,7 +281,7 @@ def ddp_train(self, gpu_idx, model):
             self.node_rank = 0
 
         # show progressbar only on progress_rank 0
-        self.show_progress_bar = self.show_progress_bar and self.node_rank == 0 and gpu_idx == 0
+        self.progress_bar_refresh_rate = self.progress_bar_refresh_rate if self.node_rank == 0 and gpu_idx == 0 else 0
 
         # determine which process we are and world size
         if self.use_ddp:

diff --git a/pytorch_lightning/trainer/distrib_parts.py b/pytorch_lightning/trainer/distrib_parts.py
@@ -480,7 +480,7 @@ def tpu_train(self, tpu_core_idx, model):
         self.tpu_global_core_rank = xm.get_ordinal()
 
         # avoid duplicating progress bar
-        self.show_progress_bar = self.show_progress_bar and self.tpu_global_core_rank == 0
+        self.progress_bar_refresh_rate = self.progress_bar_refresh_rate if self.tpu_global_core_rank == 0 else 0
 
         # track current tpu
         self.current_tpu_idx = tpu_core_idx

diff --git a/pytorch_lightning/trainer/evaluation_loop.py b/pytorch_lightning/trainer/evaluation_loop.py
@@ -163,7 +163,6 @@ class TrainerEvaluationLoopMixin(ABC):
     num_val_batches: int
     fast_dev_run: ...
     process_position: ...
-    show_progress_bar: ...
     process_output: ...
     training_tqdm_dict: ...
     proc_rank: int
@@ -278,7 +277,7 @@ def _evaluate(self, model: LightningModule, dataloaders, max_batches: int, test_
                 dl_outputs.append(output)
 
                 # batch done
-                if batch_idx % self.progress_bar_refresh_rate == 0:
+                if self.progress_bar_refresh_rate >= 1 and batch_idx % self.progress_bar_refresh_rate == 0:
                     if test_mode:
                         self.test_progress_bar.update(self.progress_bar_refresh_rate)
                     else:
@@ -361,7 +360,7 @@ def run_evaluation(self, test_mode: bool = False):
         desc = 'Testing' if test_mode else 'Validating'
         total = max_batches if max_batches != float('inf') else None
         pbar = tqdm(desc=desc, total=total, leave=test_mode, position=position,
-                    disable=not self.show_progress_bar, dynamic_ncols=True, file=sys.stdout)
+                    disable=not self.progress_bar_refresh_rate, dynamic_ncols=True, file=sys.stdout)
         setattr(self, f'{"test" if test_mode else "val"}_progress_bar', pbar)
 
         # run evaluation

diff --git a/pytorch_lightning/trainer/trainer.py b/pytorch_lightning/trainer/trainer.py
@@ -21,7 +21,8 @@
 from pytorch_lightning.trainer.callback_config import TrainerCallbackConfigMixin
 from pytorch_lightning.trainer.callback_hook import TrainerCallbackHookMixin
 from pytorch_lightning.trainer.data_loading import TrainerDataLoadingMixin
-from pytorch_lightning.trainer.deprecated_api import TrainerDeprecatedAPITillVer0_8
+from pytorch_lightning.trainer.deprecated_api import (TrainerDeprecatedAPITillVer0_8,
+                                                      TrainerDeprecatedAPITillVer0_9)
 from pytorch_lightning.trainer.distrib_data_parallel import TrainerDDPMixin
 from pytorch_lightning.trainer.distrib_parts import TrainerDPMixin, parse_gpu_ids, determine_root_gpu_device
 from pytorch_lightning.trainer.evaluation_loop import TrainerEvaluationLoopMixin
@@ -66,12 +67,13 @@ class Trainer(
     TrainerCallbackConfigMixin,
     TrainerCallbackHookMixin,
     TrainerDeprecatedAPITillVer0_8,
+    TrainerDeprecatedAPITillVer0_9,
 ):
     DEPRECATED_IN_0_8 = (
         'gradient_clip', 'nb_gpu_nodes', 'max_nb_epochs', 'min_nb_epochs',
         'add_row_log_interval', 'nb_sanity_val_steps'
     )
-    DEPRECATED_IN_0_9 = ('use_amp',)
+    DEPRECATED_IN_0_9 = ('use_amp', 'show_progress_bar')
 
     def __init__(
             self,
@@ -86,7 +88,7 @@ def __init__(
             gpus: Optional[Union[List[int], str, int]] = None,
             num_tpu_cores: Optional[int] = None,
             log_gpu_memory: Optional[str] = None,
-            show_progress_bar: bool = True,
+            show_progress_bar=None,  # backward compatible, todo: remove in v0.9.0
             progress_bar_refresh_rate: int = 1,
             overfit_pct: float = 0.0,
             track_grad_norm: int = -1,
@@ -161,9 +163,12 @@ def __init__(
 
             log_gpu_memory: None, 'min_max', 'all'. Might slow performance
 
-            show_progress_bar: If true shows tqdm progress bar
+            show_progress_bar:
+                .. warning:: .. deprecated:: 0.7.2
+
+                        Set `progress_bar_refresh_rate` to postive integer to enable. Will remove 0.9.0.
 
-            progress_bar_refresh_rate: How often to refresh progress bar (in steps)
+            progress_bar_refresh_rate: How often to refresh progress bar (in steps). Value ``0`` disables progress bar.
 
             overfit_pct: How much of training-, validation-, and test dataset to check.
 
@@ -414,7 +419,9 @@ def __init__(
 
         # can't init progress bar here because starting a new process
         # means the progress_bar won't survive pickling
-        self.show_progress_bar = show_progress_bar
+        # backward compatibility
+        if show_progress_bar is not None:
+            self.show_progress_bar = show_progress_bar
 
         # logging
         self.log_save_interval = log_save_interval
@@ -821,7 +828,7 @@ def run_pretrain_routine(self, model: LightningModule):
             pbar = tqdm(desc='Validation sanity check',
                         total=self.num_sanity_val_steps * len(self.val_dataloaders),
                         leave=False, position=2 * self.process_position,
-                        disable=not self.show_progress_bar, dynamic_ncols=True)
+                        disable=not self.progress_bar_refresh_rate, dynamic_ncols=True)
             self.main_progress_bar = pbar
             # dummy validation progress bar
             self.val_progress_bar = tqdm(disable=True)

diff --git a/pytorch_lightning/trainer/training_loop.py b/pytorch_lightning/trainer/training_loop.py
@@ -623,7 +623,7 @@ def optimizer_closure():
                 self.get_model().on_batch_end()
 
         # update progress bar
-        if batch_idx % self.progress_bar_refresh_rate == 0:
+        if self.progress_bar_refresh_rate >= 1 and batch_idx % self.progress_bar_refresh_rate == 0:
             self.main_progress_bar.update(self.progress_bar_refresh_rate)
             self.main_progress_bar.set_postfix(**self.training_tqdm_dict)
 

diff --git a/tests/models/test_amp.py b/tests/models/test_amp.py
@@ -21,7 +21,6 @@ def test_amp_single_gpu(tmpdir):
 
     trainer_options = dict(
         default_save_path=tmpdir,
-        show_progress_bar=True,
         max_epochs=1,
         gpus=1,
         distributed_backend='ddp',
@@ -42,7 +41,6 @@ def test_no_amp_single_gpu(tmpdir):
 
     trainer_options = dict(
         default_save_path=tmpdir,
-        show_progress_bar=True,
         max_epochs=1,
         gpus=1,
         distributed_backend='dp',
@@ -66,7 +64,6 @@ def test_amp_gpu_ddp(tmpdir):
 
     trainer_options = dict(
         default_save_path=tmpdir,
-        show_progress_bar=True,
         max_epochs=1,
         gpus=2,
         distributed_backend='ddp',
@@ -90,7 +87,6 @@ def test_amp_gpu_ddp_slurm_managed(tmpdir):
     model = LightningTestModel(hparams)
 
     trainer_options = dict(
-        show_progress_bar=True,
         max_epochs=1,
         gpus=[0],
         distributed_backend='ddp',
@@ -128,7 +124,7 @@ def test_cpu_model_with_amp(tmpdir):
 
     trainer_options = dict(
         default_save_path=tmpdir,
-        show_progress_bar=False,
+        progress_bar_refresh_rate=0,
         logger=tutils.get_default_testtube_logger(tmpdir),
         max_epochs=1,
         train_percent_check=0.4,

diff --git a/tests/models/test_cpu.py b/tests/models/test_cpu.py
@@ -27,7 +27,6 @@ def test_early_stopping_cpu_model(tmpdir):
         gradient_clip_val=1.0,
         overfit_pct=0.20,
         track_grad_norm=2,
-        show_progress_bar=True,
         logger=tutils.get_default_testtube_logger(tmpdir),
         train_percent_check=0.1,
         val_percent_check=0.1,
@@ -48,7 +47,7 @@ def test_lbfgs_cpu_model(tmpdir):
     trainer_options = dict(
         default_save_path=tmpdir,
         max_epochs=2,
-        show_progress_bar=False,
+        progress_bar_refresh_rate=0,
         weights_summary='top',
         train_percent_check=1.0,
         val_percent_check=0.2,
@@ -67,7 +66,7 @@ def test_default_logger_callbacks_cpu_model(tmpdir):
         max_epochs=1,
         gradient_clip_val=1.0,
         overfit_pct=0.20,
-        show_progress_bar=False,
+        progress_bar_refresh_rate=0,
         train_percent_check=0.01,
         val_percent_check=0.01,
     )
@@ -95,7 +94,7 @@ def test_running_test_after_fitting(tmpdir):
 
     trainer_options = dict(
         default_save_path=tmpdir,
-        show_progress_bar=False,
+        progress_bar_refresh_rate=0,
         max_epochs=8,
         train_percent_check=0.4,
         val_percent_check=0.2,
@@ -133,7 +132,7 @@ class CurrentTestModel(LightTrainDataloader, LightTestMixin, TestModelBase):
     checkpoint = tutils.init_checkpoint_callback(logger)
 
     trainer_options = dict(
-        show_progress_bar=False,
+        progress_bar_refresh_rate=0,
         max_epochs=1,
         train_percent_check=0.4,
         val_percent_check=0.2,
@@ -226,7 +225,7 @@ def test_cpu_model(tmpdir):
 
     trainer_options = dict(
         default_save_path=tmpdir,
-        show_progress_bar=False,
+        progress_bar_refresh_rate=0,
         logger=tutils.get_default_testtube_logger(tmpdir),
         max_epochs=1,
         train_percent_check=0.4,
@@ -247,7 +246,7 @@ def test_all_features_cpu_model(tmpdir):
         gradient_clip_val=1.0,
         overfit_pct=0.20,
         track_grad_norm=2,
-        show_progress_bar=False,
+        progress_bar_refresh_rate=0,
         logger=tutils.get_default_testtube_logger(tmpdir),
         accumulate_grad_batches=2,
         max_epochs=1,
@@ -344,7 +343,7 @@ def test_single_gpu_model(tmpdir):
 
     trainer_options = dict(
         default_save_path=tmpdir,
-        show_progress_bar=False,
+        progress_bar_refresh_rate=0,
         max_epochs=1,
         train_percent_check=0.1,
         val_percent_check=0.1,

diff --git a/tests/models/test_gpu.py b/tests/models/test_gpu.py
@@ -27,7 +27,6 @@ def test_multi_gpu_model_ddp2(tmpdir):
     model, hparams = tutils.get_default_model()
     trainer_options = dict(
         default_save_path=tmpdir,
-        show_progress_bar=True,
         max_epochs=1,
         train_percent_check=0.4,
         val_percent_check=0.2,
@@ -49,7 +48,7 @@ def test_multi_gpu_model_ddp(tmpdir):
     model, hparams = tutils.get_default_model()
     trainer_options = dict(
         default_save_path=tmpdir,
-        show_progress_bar=False,
+        progress_bar_refresh_rate=0,
         max_epochs=1,
         train_percent_check=0.4,
         val_percent_check=0.2,
@@ -69,7 +68,7 @@ def test_ddp_all_dataloaders_passed_to_fit(tmpdir):
 
     model, hparams = tutils.get_default_model()
     trainer_options = dict(default_save_path=tmpdir,
-                           show_progress_bar=False,
+                           progress_bar_refresh_rate=0,
                            max_epochs=1,
                            train_percent_check=0.4,
                            val_percent_check=0.2,
@@ -165,7 +164,7 @@ def test_multi_gpu_none_backend(tmpdir):
     model, hparams = tutils.get_default_model()
     trainer_options = dict(
         default_save_path=tmpdir,
-        show_progress_bar=False,
+        progress_bar_refresh_rate=0,
         max_epochs=1,
         train_percent_check=0.1,
         val_percent_check=0.1,
@@ -184,7 +183,7 @@ def test_multi_gpu_model_dp(tmpdir):
     model, hparams = tutils.get_default_model()
     trainer_options = dict(
         default_save_path=tmpdir,
-        show_progress_bar=False,
+        progress_bar_refresh_rate=0,
         distributed_backend='dp',
         max_epochs=1,
         train_percent_check=0.1,