Clean up Argparse interface with trainer (#1606)

* fixed distutil parsing

* fixed distutil parsing

* Apply suggestions from code review

* log

* fixed distutil parsing

* fixed distutil parsing

* fixed distutil parsing

* fixed distutil parsing

* doctest

* fixed hparams section

* fixed hparams section

* fixed hparams section

* formatting

Co-authored-by: Jirka Borovec <Borda@users.noreply.github.com>
Co-authored-by: J. Borovec <jirka.borovec@seznam.cz>

CHANGELOG.md

@@ -87,6 +87,8 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 
 - Fixes automatic parser bug ([#1585](https://github.com/PyTorchLightning/pytorch-lightning/issues/1585))
 
+- Fixed bool conversion from string ([#1606](https://github.com/PyTorchLightning/pytorch-lightning/issues/1606))
+
 ## [0.7.3] - 2020-04-09
 
 ### Added

docs/source/hyperparameters.rst

@@ -3,36 +3,111 @@ Hyperparameters
 Lightning has utilities to interact seamlessly with the command line ArgumentParser
 and plays well with the hyperparameter optimization framework of your choice.
 
-LightiningModule hparams
+ArgumentParser
+^^^^^^^^^^^^^^
+Lightning is designed to augment a lot of the functionality of the built-in Python ArgumentParser
+
+.. code-block:: python
+
+    from argparse import ArgumentParser
+
+    parser = ArgumentParser()
+    parser.add_argument('--layer_1_dim', type=int, default=128)
+
+    args = parser.parse_args()
+
+This allows you to call your program like so:
+
+.. code-block:: bash
+
+    python trainer.py --layer_1_dim 64
+
+
+Argparser Best Practices
 ^^^^^^^^^^^^^^^^^^^^^^^^
+It is best practice to layer your arguments in three sections.
 
-Normally, we don't hard-code the values to a model. We usually use the command line to
-modify the network. The `Trainer` can add all the available options to an ArgumentParser.
+    1. Trainer args (gpus, num_nodes, etc...)
+    2. Model specific arguments (layer_dim, num_layers, learning_rate, etc...)
+    3. Program arguments (data_path, cluster_email, etc...)
+
+We can do this as follows. First, in your LightningModule, define the arguments
+specific to that module. Remember that data splits or data paths may also be specific to
+a module (ie: if your project has a model that trains on Imagenet and another on CIFAR-10).
 
 .. code-block:: python
 
+        class LitModel(LightningModule):
+
+        @staticmethod
+        def add_model_specific_args(parent_parser):
+            parser = ArgumentParser(parents=[parent_parser], add_help=False)
+            parser.add_argument('--encoder_layers', type=int, default=12)
+            parser.add_argument('--data_path', type=str, default='/some/path')
+            return parser
+
+Now in your main trainer file, add the Trainer args, the program args, and add the model args
+
+.. code-block:: python
+
+    # ----------------
+    # trainer_main.py
+    # ----------------
     from argparse import ArgumentParser
 
     parser = ArgumentParser()
 
-    # parametrize the network
-    parser.add_argument('--layer_1_dim', type=int, default=128)
-    parser.add_argument('--layer_2_dim', type=int, default=256)
-    parser.add_argument('--batch_size', type=int, default=64)
+    # add PROGRAM level args
+    parser.add_argument('--conda_env', type=str, default='some_name')
+    parser.add_argument('--notification_email', type=str, default='will@email.com')
+
+    # add model specific args
+    parser = LitModel.add_model_specific_args(parser)
 
-    # add all the available options to the trainer
+    # add all the available trainer options to argparse
+    # ie: now --gpus --num_nodes ... --fast_dev_run all work in the cli
     parser = pl.Trainer.add_argparse_args(parser)
 
-    args = parser.parse_args()
+    hparams = parser.parse_args()
 
-Now we can parametrize the LightningModule.
+Now you can call run your program like so
+
+.. code-block:: bash
+
+    python trainer_main.py --gpus 2 --num_nodes 2 --conda_env 'my_env' --encoder_layers 12
+
+Finally, make sure to start the training like so:
+
+.. code-block:: bash
+
+    hparams = parser.parse_args()
+
+    # YES
+    model = LitModel(hparams)
+
+    # NO
+    # model = LitModel(learning_rate=hparams.learning_rate, ...)
+
+    # YES
+    trainer = Trainer.from_argparse_args(hparams, early_stopping_callback=...)
+
+    # NO
+    trainer = Trainer(gpus=hparams.gpus, ...)
+
+
+LightiningModule hparams
+^^^^^^^^^^^^^^^^^^^^^^^^
+
+Normally, we don't hard-code the values to a model. We usually use the command line to
+modify the network and read those values in the LightningModule
 
 .. code-block:: python
-    :emphasize-lines: 5,6,7,12,14
 
     class LitMNIST(pl.LightningModule):
       def __init__(self, hparams):
         super().__init__()
+
+        # do this to save all arguments in any logger (tensorboard)
         self.hparams = hparams
 
         self.layer_1 = torch.nn.Linear(28 * 28, hparams.layer_1_dim)
@@ -49,86 +124,44 @@ Now we can parametrize the LightningModule.
       def configure_optimizers(self):
         return Adam(self.parameters(), lr=self.hparams.learning_rate)
 
-    hparams = parse_args()
-    model = LitMNIST(hparams)
+      @staticmethod
+      def add_model_specific_args(parent_parser):
+        parser = ArgumentParser(parents=[parent_parser], add_help=False)
 
-.. note:: Bonus! if (hparams) is in your module, Lightning will save it into the checkpoint and restore your
-    model using those hparams exactly.
+        parser.add_argument('--layer_1_dim', type=int, default=128)
+        parser.add_argument('--layer_2_dim', type=int, default=256)
+        parser.add_argument('--batch_size', type=int, default=64)
+        parser.add_argument('--learning_rate', type=float, default=0.002)
+        return parser
 
-And we can also add all the flags available in the Trainer to the Argparser.
+Now pass in the params when you init your model
 
 .. code-block:: python
 
-    # add all the available Trainer options to the ArgParser
-    parser = pl.Trainer.add_argparse_args(parser)
-    args = parser.parse_args()
-
-And now you can start your program with
+    hparams = parse_args()
+    model = LitMNIST(hparams)
 
-.. code-block:: bash
+The line `self.hparams = hparams` is very special. This line assigns your hparams to the LightningModule.
+This does two things:
 
-    # now you can use any trainer flag
-    $ python main.py --num_nodes 2 --gpus 8
+    1. It adds them automatically to tensorboard logs under the hparams tab.
+    2. Lightning will save those hparams to the checkpoint and use them to restore the module correctly.
 
 Trainer args
 ^^^^^^^^^^^^
-
-It also gets annoying to map each argument into the Argparser. Luckily we have
-a default parser
+To recap, add ALL possible trainer flags to the argparser and init the Trainer this way
 
 .. code-block:: python
 
     parser = ArgumentParser()
-
-    # add all options available in the trainer such as (max_epochs, etc...)
     parser = Trainer.add_argparse_args(parser)
+    hparams = parser.parse_args()
 
-We set up the main training entry point file like this:
-
-.. code-block:: python
-
-    def main(args):
-        model = LitMNIST(hparams=args)
-        trainer = Trainer(max_epochs=args.max_epochs)
-        trainer.fit(model)
+    trainer = Trainer.from_argparse_args(hparams)
 
-    if __name__ == '__main__':
-        parser = ArgumentParser()
+    # or if you need to pass in callbacks
+    trainer = Trainer.from_argparse_args(hparams, checkpoint_callback=..., callbacks=[...])
 
-        # adds all the trainer options as default arguments (like max_epochs)
-        parser = Trainer.add_argparse_args(parser)
-
-        # parametrize the network
-        parser.add_argument('--layer_1_dim', type=int, default=128)
-        parser.add_argument('--layer_1_dim', type=int, default=256)
-        parser.add_argument('--batch_size', type=int, default=64)
-        args = parser.parse_args()
-
-        # train
-        main(args)
-
-And now we can train like this:
-
-.. code-block:: bash
-
-    $ python main.py --layer_1_dim 128 --layer_2_dim 256 --batch_size 64 --max_epochs 64
-
-But it would also be nice to pass in any arbitrary argument to the trainer.
-We can do it by changing how we init the trainer.
-
-.. code-block:: python
-
-    def main(args):
-        model = LitMNIST(hparams=args)
-
-        # makes all trainer options available from the command line
-        trainer = Trainer.from_argparse_args(args)
-
-and now we can do this:
-
-.. code-block:: bash
-
-    $ python main.py --gpus 1 --min_epochs 12 --max_epochs 64 --arbitrary_trainer_arg some_value
 
 Multiple Lightning Modules
 ^^^^^^^^^^^^^^^^^^^^^^^^^^
@@ -173,7 +206,7 @@ Now we can allow each model to inject the arguments it needs in the main.py
             model = LitMNIST(hparams=args)
 
         model = LitMNIST(hparams=args)
-        trainer = Trainer(max_epochs=args.max_epochs)
+        trainer = Trainer.from_argparse_args(args)
         trainer.fit(model)
 
     if __name__ == '__main__':
@@ -182,6 +215,8 @@ Now we can allow each model to inject the arguments it needs in the main.py
 
         # figure out which model to use
         parser.add_argument('--model_name', type=str, default='gan', help='gan or mnist')
+
+        # THIS LINE IS KEY TO PULL THE MODEL NAME
         temp_args = parser.parse_known_args()
 
         # let the model add what it wants

pytorch_lightning/trainer/logging.py

@@ -5,7 +5,7 @@
 
 from pytorch_lightning.core import memory
 from pytorch_lightning.loggers import TensorBoardLogger, LightningLoggerBase, LoggerCollection
-from pytorch_lightning.utilities import memory_utils
+from pytorch_lightning.utilities.memory import recursive_detach
 
 
 class TrainerLoggingMixin(ABC):
@@ -174,7 +174,7 @@ def process_output(self, output, train=False):
 
         # detach all metrics for callbacks to prevent memory leaks
         # no .item() because it will slow things down
-        callback_metrics = memory_utils.recursive_detach(callback_metrics)
+        callback_metrics = recursive_detach(callback_metrics)
 
         return loss, progress_bar_metrics, log_metrics, callback_metrics, hiddens
 

pytorch_lightning/trainer/trainer.py

@@ -1,4 +1,3 @@
-import distutils
 import inspect
 import os
 from argparse import ArgumentParser
@@ -33,6 +32,7 @@
 from pytorch_lightning.trainer.lr_finder import TrainerLRFinderMixin
 from pytorch_lightning.utilities.exceptions import MisconfigurationException
 from pytorch_lightning.utilities import rank_zero_warn
+from pytorch_lightning.utilities import parsing
 
 
 try:
@@ -599,17 +599,19 @@ def add_argparse_args(cls, parent_parser: ArgumentParser) -> ArgumentParser:
         """
         parser = ArgumentParser(parents=[parent_parser], add_help=False, )
 
-        depr_arg_names = cls.get_deprecated_arg_names()
+        blacklist = ['kwargs']
+        depr_arg_names = cls.get_deprecated_arg_names() + blacklist
 
         allowed_types = (str, float, int, bool)
+
         # TODO: get "help" from docstring :)
         for arg, arg_types, arg_default in (at for at in cls.get_init_arguments_and_types()
                                             if at[0] not in depr_arg_names):
 
             for allowed_type in (at for at in allowed_types if at in arg_types):
                 if allowed_type is bool:
                     def allowed_type(x):
-                        return bool(distutils.util.strtobool(x))
+                        return bool(parsing.strtobool(x))
 
                 if arg == 'gpus':
                     def allowed_type(x):
@@ -636,9 +638,11 @@ def arg_default(x):
         return parser
 
     @classmethod
-    def from_argparse_args(cls, args):
+    def from_argparse_args(cls, args, **kwargs):
 
         params = vars(args)
+        params.update(**kwargs)
+
         return cls(**params)
 
     @property

pytorch_lightning/trainer/training_loop.py

@@ -141,7 +141,6 @@ def training_step(self, batch, batch_idx):
 
 """
 
-import copy
 from abc import ABC, abstractmethod
 from typing import Callable
 from typing import Union, List
@@ -154,11 +153,9 @@ def training_step(self, batch, batch_idx):
 from pytorch_lightning.callbacks.base import Callback
 from pytorch_lightning.core.lightning import LightningModule
 from pytorch_lightning.loggers import LightningLoggerBase
-from pytorch_lightning.overrides.data_parallel import LightningDistributedDataParallel, LightningDataParallel
 from pytorch_lightning.utilities.exceptions import MisconfigurationException
 from pytorch_lightning.trainer.supporters import TensorRunningAccum
 from pytorch_lightning.utilities import rank_zero_warn
-from pytorch_lightning.utilities import memory_utils
 
 try:
     from apex import amp

pytorch_lightning/utilities/parsing.py

@@ -0,0 +1,20 @@
+def strtobool(val):
+    """Convert a string representation of truth to true (1) or false (0).
+    Copied from the python implementation distutils.utils.strtobool
+
+    True values are 'y', 'yes', 't', 'true', 'on', and '1'; false values
+    are 'n', 'no', 'f', 'false', 'off', and '0'.  Raises ValueError if
+    'val' is anything else.
+
+    >>> strtobool('YES')
+    1
+    >>> strtobool('FALSE')
+    0
+    """
+    val = val.lower()
+    if val in ('y', 'yes', 't', 'true', 'on', '1'):
+        return 1
+    elif val in ('n', 'no', 'f', 'false', 'off', '0'):
+        return 0
+    else:
+        raise ValueError(f'invalid truth value {val}')