From 3471fb5aef2be27b94dd646ba69eafed9c1b21c2 Mon Sep 17 00:00:00 2001
From: Mauricio Villegas <mauricio_ville@yahoo.com>
Date: Wed, 7 Apr 2021 08:31:28 +0200
Subject: [PATCH 1/9] Changed simple_image_classifier.py to use LightningCLI

---
 .../basic_examples/simple_image_classifier.py | 66 +++++++------------
 1 file changed, 22 insertions(+), 44 deletions(-)

diff --git a/pl_examples/basic_examples/simple_image_classifier.py b/pl_examples/basic_examples/simple_image_classifier.py
index 3f7079d665ea8..08d549527a917 100644
--- a/pl_examples/basic_examples/simple_image_classifier.py
+++ b/pl_examples/basic_examples/simple_image_classifier.py
@@ -11,8 +11,13 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
+"""
+MNIST simple image classifier example.
+
+To run:
+python simple_image_classifier.py --trainer.max_epochs=50
+"""
 
-from argparse import ArgumentParser
 from pprint import pprint
 
 import torch
@@ -21,6 +26,7 @@
 import pytorch_lightning as pl
 from pl_examples import cli_lightning_logo
 from pl_examples.basic_examples.mnist_datamodule import MNISTDataModule
+from pytorch_lightning.utilities.cli import LightningCLI
 
 
 class LitClassifier(pl.LightningModule):
@@ -32,7 +38,11 @@ class LitClassifier(pl.LightningModule):
     )
     """
 
-    def __init__(self, hidden_dim=128, learning_rate=1e-3):
+    def __init__(
+        self,
+        hidden_dim: int = 128,
+        learning_rate: float = 0.0001,
+    ):
         super().__init__()
         self.save_hyperparameters()
 
@@ -66,49 +76,17 @@ def test_step(self, batch, batch_idx):
     def configure_optimizers(self):
         return torch.optim.Adam(self.parameters(), lr=self.hparams.learning_rate)
 
-    @staticmethod
-    def add_model_specific_args(parent_parser):
-        parser = parent_parser.add_argument_group("LitClassifier")
-        parser.add_argument('--hidden_dim', type=int, default=128)
-        parser.add_argument('--learning_rate', type=float, default=0.0001)
-        return parent_parser
-
-
-def cli_main():
-    pl.seed_everything(1234)
-
-    # ------------
-    # args
-    # ------------
-    parser = ArgumentParser()
-    parser = pl.Trainer.add_argparse_args(parser)
-    parser = LitClassifier.add_model_specific_args(parser)
-    parser = MNISTDataModule.add_argparse_args(parser)
-    args = parser.parse_args()
-
-    # ------------
-    # data
-    # ------------
-    dm = MNISTDataModule.from_argparse_args(args)
-
-    # ------------
-    # model
-    # ------------
-    model = LitClassifier(args.hidden_dim, args.learning_rate)
-
-    # ------------
-    # training
-    # ------------
-    trainer = pl.Trainer.from_argparse_args(args)
-    trainer.fit(model, datamodule=dm)
-
-    # ------------
-    # testing
-    # ------------
-    result = trainer.test(model, datamodule=dm)
-    pprint(result)
+
+class MyLightningCLI(LightningCLI):
+
+    def before_instantiate_classes(self):
+        pl.seed_everything(1234)
+
+    def after_fit(self):
+        result = self.trainer.test(self.model, datamodule=self.datamodule)
+        pprint(result)
 
 
 if __name__ == '__main__':
     cli_lightning_logo()
-    cli_main()
+    MyLightningCLI(LitClassifier, MNISTDataModule)

From 4462f6bb4b30aefd0a5c3cf0836611ce162c524b Mon Sep 17 00:00:00 2001
From: Mauricio Villegas <mauricio_ville@yahoo.com>
Date: Fri, 9 Apr 2021 15:39:59 +0200
Subject: [PATCH 2/9] - Added fail_untyped, seed_everything, subclass required
 needed for the examples. - Increased minimum required jsonargparse version to
 3.9.0. - Improvements to simple_image_classifier.py example. - Changed
 autoencoder.py and backbone_image_classifier.py examples to use LightningCLI.
 - Updated pl_examples/test_examples.py so that tests succeed.

---
 pl_examples/basic_examples/autoencoder.py     | 78 +++++++--------
 .../backbone_image_classifier.py              | 96 ++++++++++---------
 .../basic_examples/simple_image_classifier.py | 10 +-
 pl_examples/test_examples.py                  | 18 ++--
 pytorch_lightning/utilities/cli.py            | 19 ++--
 requirements/extra.txt                        |  2 +-
 tests/utilities/test_cli.py                   |  2 +
 7 files changed, 119 insertions(+), 106 deletions(-)

diff --git a/pl_examples/basic_examples/autoencoder.py b/pl_examples/basic_examples/autoencoder.py
index 6841b8555ef1f..58ec3cdfe2bb8 100644
--- a/pl_examples/basic_examples/autoencoder.py
+++ b/pl_examples/basic_examples/autoencoder.py
@@ -11,8 +11,12 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
+"""
+MNIST autoencoder example.
 
-from argparse import ArgumentParser
+To run:
+python autoencoder.py --trainer.max_epochs=50
+"""
 
 import torch
 import torch.nn.functional as F
@@ -21,6 +25,7 @@
 
 import pytorch_lightning as pl
 from pl_examples import _DATASETS_PATH, _TORCHVISION_AVAILABLE, _TORCHVISION_MNIST_AVAILABLE, cli_lightning_logo
+from pytorch_lightning.utilities.cli import LightningCLI
 
 if _TORCHVISION_AVAILABLE:
     from torchvision import transforms
@@ -86,45 +91,40 @@ def configure_optimizers(self):
         return optimizer
 
 
+class MyDataModule(pl.LightningDataModule):
+
+    def __init__(
+        self,
+        batch_size: int = 32,
+    ):
+        super().__init__()
+        dataset = MNIST(_DATASETS_PATH, train=True, download=True, transform=transforms.ToTensor())
+        self.mnist_test = MNIST(_DATASETS_PATH, train=False, download=True, transform=transforms.ToTensor())
+        self.mnist_train, self.mnist_val = random_split(dataset, [55000, 5000])
+        self.batch_size = batch_size
+
+    def train_dataloader(self):
+        return DataLoader(self.mnist_train, batch_size=self.batch_size)
+
+    def val_dataloader(self):
+        return DataLoader(self.mnist_val, batch_size=self.batch_size)
+
+    def test_dataloader(self):
+        return DataLoader(self.mnist_test, batch_size=self.batch_size)
+
+
+class MyLightningCLI(LightningCLI):
+
+    def before_parse_arguments(self, parser):
+        parser.set_defaults(seed_everything=1234)
+
+    def after_fit(self):
+        result = self.trainer.test(test_dataloaders=self.datamodule.test_dataloader())
+        print(result)
+
+
 def cli_main():
-    pl.seed_everything(1234)
-
-    # ------------
-    # args
-    # ------------
-    parser = ArgumentParser()
-    parser.add_argument('--batch_size', default=32, type=int)
-    parser.add_argument('--hidden_dim', type=int, default=64)
-    parser = pl.Trainer.add_argparse_args(parser)
-    args = parser.parse_args()
-
-    # ------------
-    # data
-    # ------------
-    dataset = MNIST(_DATASETS_PATH, train=True, download=True, transform=transforms.ToTensor())
-    mnist_test = MNIST(_DATASETS_PATH, train=False, download=True, transform=transforms.ToTensor())
-    mnist_train, mnist_val = random_split(dataset, [55000, 5000])
-
-    train_loader = DataLoader(mnist_train, batch_size=args.batch_size)
-    val_loader = DataLoader(mnist_val, batch_size=args.batch_size)
-    test_loader = DataLoader(mnist_test, batch_size=args.batch_size)
-
-    # ------------
-    # model
-    # ------------
-    model = LitAutoEncoder(args.hidden_dim)
-
-    # ------------
-    # training
-    # ------------
-    trainer = pl.Trainer.from_argparse_args(args)
-    trainer.fit(model, train_loader, val_loader)
-
-    # ------------
-    # testing
-    # ------------
-    result = trainer.test(test_dataloaders=test_loader)
-    print(result)
+    MyLightningCLI(LitAutoEncoder, MyDataModule)
 
 
 if __name__ == '__main__':
diff --git a/pl_examples/basic_examples/backbone_image_classifier.py b/pl_examples/basic_examples/backbone_image_classifier.py
index 1c78d264a8681..969ee6ab6e5ad 100644
--- a/pl_examples/basic_examples/backbone_image_classifier.py
+++ b/pl_examples/basic_examples/backbone_image_classifier.py
@@ -11,8 +11,12 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
+"""
+MNIST backbone image classifier example.
 
-from argparse import ArgumentParser
+To run:
+python backbone_image_classifier.py --trainer.max_epochs=50
+"""
 
 import torch
 from torch.nn import functional as F
@@ -20,6 +24,7 @@
 
 import pytorch_lightning as pl
 from pl_examples import _DATASETS_PATH, _TORCHVISION_AVAILABLE, _TORCHVISION_MNIST_AVAILABLE, cli_lightning_logo
+from pytorch_lightning.utilities.cli import LightningCLI
 
 if _TORCHVISION_AVAILABLE:
     from torchvision import transforms
@@ -58,7 +63,11 @@ class LitClassifier(pl.LightningModule):
     )
     """
 
-    def __init__(self, backbone, learning_rate=1e-3):
+    def __init__(
+        self,
+        backbone,
+        learning_rate: float = 0.0001,
+    ):
         super().__init__()
         self.save_hyperparameters()
         self.backbone = backbone
@@ -91,53 +100,48 @@ def configure_optimizers(self):
         # self.hparams available because we called self.save_hyperparameters()
         return torch.optim.Adam(self.parameters(), lr=self.hparams.learning_rate)
 
-    @staticmethod
-    def add_model_specific_args(parent_parser):
-        parser = parent_parser.add_argument_group("LitClassifier")
-        parser.add_argument('--learning_rate', type=float, default=0.0001)
-        return parent_parser
+
+class MyDataModule(pl.LightningDataModule):
+
+    def __init__(
+        self,
+        batch_size: int = 32,
+    ):
+        super().__init__()
+        dataset = MNIST(_DATASETS_PATH, train=True, download=True, transform=transforms.ToTensor())
+        self.mnist_test = MNIST(_DATASETS_PATH, train=False, download=True, transform=transforms.ToTensor())
+        self.mnist_train, self.mnist_val = random_split(dataset, [55000, 5000])
+        self.batch_size = batch_size
+
+    def train_dataloader(self):
+        return DataLoader(self.mnist_train, batch_size=self.batch_size)
+
+    def val_dataloader(self):
+        return DataLoader(self.mnist_val, batch_size=self.batch_size)
+
+    def test_dataloader(self):
+        return DataLoader(self.mnist_test, batch_size=self.batch_size)
+
+
+class MyLightningCLI(LightningCLI):
+
+    def add_arguments_to_parser(self, parser):
+        parser.add_class_arguments(Backbone, 'model.backbone')
+
+    def before_parse_arguments(self, parser):
+        parser.set_defaults(seed_everything=1234)
+
+    def instantiate_model(self):
+        self.config_init['model']['backbone'] = Backbone(**self.config['model']['backbone'])
+        super().instantiate_model()
+
+    def after_fit(self):
+        result = self.trainer.test(test_dataloaders=self.datamodule.test_dataloader())
+        print(result)
 
 
 def cli_main():
-    pl.seed_everything(1234)
-
-    # ------------
-    # args
-    # ------------
-    parser = ArgumentParser()
-    parser.add_argument('--batch_size', default=32, type=int)
-    parser.add_argument('--hidden_dim', type=int, default=128)
-    parser = pl.Trainer.add_argparse_args(parser)
-    parser = LitClassifier.add_model_specific_args(parser)
-    args = parser.parse_args()
-
-    # ------------
-    # data
-    # ------------
-    dataset = MNIST(_DATASETS_PATH, train=True, download=True, transform=transforms.ToTensor())
-    mnist_test = MNIST(_DATASETS_PATH, train=False, download=True, transform=transforms.ToTensor())
-    mnist_train, mnist_val = random_split(dataset, [55000, 5000])
-
-    train_loader = DataLoader(mnist_train, batch_size=args.batch_size)
-    val_loader = DataLoader(mnist_val, batch_size=args.batch_size)
-    test_loader = DataLoader(mnist_test, batch_size=args.batch_size)
-
-    # ------------
-    # model
-    # ------------
-    model = LitClassifier(Backbone(hidden_dim=args.hidden_dim), args.learning_rate)
-
-    # ------------
-    # training
-    # ------------
-    trainer = pl.Trainer.from_argparse_args(args)
-    trainer.fit(model, train_loader, val_loader)
-
-    # ------------
-    # testing
-    # ------------
-    result = trainer.test(test_dataloaders=test_loader)
-    print(result)
+    MyLightningCLI(LitClassifier, MyDataModule)
 
 
 if __name__ == '__main__':
diff --git a/pl_examples/basic_examples/simple_image_classifier.py b/pl_examples/basic_examples/simple_image_classifier.py
index 08d549527a917..ffb0b83d66f6d 100644
--- a/pl_examples/basic_examples/simple_image_classifier.py
+++ b/pl_examples/basic_examples/simple_image_classifier.py
@@ -79,14 +79,18 @@ def configure_optimizers(self):
 
 class MyLightningCLI(LightningCLI):
 
-    def before_instantiate_classes(self):
-        pl.seed_everything(1234)
+    def before_parse_arguments(self, parser):
+        parser.set_defaults(seed_everything=1234)
 
     def after_fit(self):
         result = self.trainer.test(self.model, datamodule=self.datamodule)
         pprint(result)
 
 
+def cli_main():
+    MyLightningCLI(LitClassifier, MNISTDataModule)
+
+
 if __name__ == '__main__':
     cli_lightning_logo()
-    MyLightningCLI(LitClassifier, MNISTDataModule)
+    cli_main()
diff --git a/pl_examples/test_examples.py b/pl_examples/test_examples.py
index b930957a26346..1d9cd5d9f6d8d 100644
--- a/pl_examples/test_examples.py
+++ b/pl_examples/test_examples.py
@@ -22,24 +22,24 @@
 from pl_examples import _DALI_AVAILABLE
 
 ARGS_DEFAULT = """
---default_root_dir %(tmpdir)s \
---max_epochs 1 \
---batch_size 32 \
---limit_train_batches 2 \
---limit_val_batches 2 \
+--trainer.default_root_dir %(tmpdir)s \
+--trainer.max_epochs 1 \
+--trainer.limit_train_batches 2 \
+--trainer.limit_val_batches 2 \
+--data.batch_size 32 \
 """
 
 ARGS_GPU = ARGS_DEFAULT + """
---gpus 1 \
+--trainer.gpus 1 \
 """
 
 ARGS_DP = ARGS_DEFAULT + """
---gpus 2 \
---accelerator dp \
+--trainer.gpus 2 \
+--trainer.accelerator dp \
 """
 
 ARGS_AMP = """
---precision 16 \
+--trainer.precision 16 \
 """
 
 
diff --git a/pytorch_lightning/utilities/cli.py b/pytorch_lightning/utilities/cli.py
index 33c424829606a..97fd75ccbed08 100644
--- a/pytorch_lightning/utilities/cli.py
+++ b/pytorch_lightning/utilities/cli.py
@@ -13,13 +13,14 @@
 # limitations under the License.
 import os
 from argparse import Namespace
-from typing import Any, Dict, Type, Union
+from typing import Any, Dict, Optional, Type, Union
 
 from pytorch_lightning.callbacks import Callback
 from pytorch_lightning.core.datamodule import LightningDataModule
 from pytorch_lightning.core.lightning import LightningModule
 from pytorch_lightning.trainer.trainer import Trainer
 from pytorch_lightning.utilities import _module_available
+from pytorch_lightning.utilities.seed import seed_everything
 
 _JSONARGPARSE_AVAILABLE = _module_available("jsonargparse")
 if _JSONARGPARSE_AVAILABLE:
@@ -63,8 +64,8 @@ def add_lightning_class_args(
         """
         assert issubclass(lightning_class, (Trainer, LightningModule, LightningDataModule))
         if subclass_mode:
-            return self.add_subclass_arguments(lightning_class, nested_key)
-        return self.add_class_arguments(lightning_class, nested_key)
+            return self.add_subclass_arguments(lightning_class, nested_key, required=True)
+        return self.add_class_arguments(lightning_class, nested_key, fail_untyped=False)
 
 
 class SaveConfigCallback(Callback):
@@ -161,6 +162,8 @@ def __init__(
         self.add_core_arguments_to_parser()
         self.before_parse_arguments(self.parser)
         self.parse_arguments()
+        if self.config['seed_everything'] is not None:
+            seed_everything(self.config['seed_everything'])
         self.before_instantiate_classes()
         self.instantiate_classes()
         self.prepare_fit_kwargs()
@@ -178,10 +181,14 @@ def add_arguments_to_parser(self, parser: LightningArgumentParser) -> None:
         Args:
             parser: The argument parser object to which arguments should be added
         """
-        pass
 
     def add_core_arguments_to_parser(self) -> None:
         """Adds arguments from the core classes to the parser"""
+        self.parser.add_argument(
+            '--seed_everything',
+            type=Optional[int],
+            help='Set to an int to run seed_everything with this value before classes instantiation',
+        )
         self.parser.add_lightning_class_args(self.trainer_class, 'trainer')
         trainer_defaults = {'trainer.' + k: v for k, v in self.trainer_defaults.items() if k != 'callbacks'}
         self.parser.set_defaults(trainer_defaults)
@@ -195,7 +202,6 @@ def before_parse_arguments(self, parser: LightningArgumentParser) -> None:
         Args:
             parser: The argument parser object that will be used to parse
         """
-        pass
 
     def parse_arguments(self) -> None:
         """Parses command line arguments and stores it in self.config"""
@@ -203,7 +209,6 @@ def parse_arguments(self) -> None:
 
     def before_instantiate_classes(self) -> None:
         """Implement to run some code before instantiating the classes"""
-        pass
 
     def instantiate_classes(self) -> None:
         """Instantiates the classes using settings from self.config"""
@@ -249,7 +254,6 @@ def prepare_fit_kwargs(self) -> None:
 
     def before_fit(self) -> None:
         """Implement to run some code before fit is started"""
-        pass
 
     def fit(self) -> None:
         """Runs fit of the instantiated trainer class and prepared fit keyword arguments"""
@@ -257,4 +261,3 @@ def fit(self) -> None:
 
     def after_fit(self) -> None:
         """Implement to run some code after fit has finished"""
-        pass
diff --git a/requirements/extra.txt b/requirements/extra.txt
index 46a726fe05c43..c1dcc1cd4032a 100644
--- a/requirements/extra.txt
+++ b/requirements/extra.txt
@@ -9,5 +9,5 @@ onnxruntime>=1.3.0
 hydra-core>=1.0
 # todo: when switch to standard package stream, drop `fairscale` from hard mocked docs libs
 https://github.com/PyTorchLightning/fairscale/archive/pl_1.2.0.zip
-jsonargparse[signatures]>=3.3.1
+jsonargparse[signatures]>=3.9.0
 deepspeed>=0.3.13
diff --git a/tests/utilities/test_cli.py b/tests/utilities/test_cli.py
index 9b2c825c7aeb8..e6c067266cc8b 100644
--- a/tests/utilities/test_cli.py
+++ b/tests/utilities/test_cli.py
@@ -284,12 +284,14 @@ def test_lightning_cli_args(tmpdir):
         f'--trainer.default_root_dir={tmpdir}',
         '--trainer.max_epochs=1',
         '--trainer.weights_summary=null',
+        '--seed_everything=1234',
     ]
 
     with mock.patch('sys.argv', ['any.py'] + cli_args):
         cli = LightningCLI(BoringModel, BoringDataModule, trainer_defaults={'callbacks': [LearningRateMonitor()]})
 
     assert cli.fit_result == 1
+    assert cli.config['seed_everything'] == 1234
     config_path = tmpdir / 'lightning_logs' / 'version_0' / 'config.yaml'
     assert os.path.isfile(config_path)
     with open(config_path) as f:

From 51483fc1f0deb2ea87b901cb95c6d744f76b6747 Mon Sep 17 00:00:00 2001
From: Mauricio Villegas <mauricio_ville@yahoo.com>
Date: Fri, 9 Apr 2021 16:19:13 +0200
Subject: [PATCH 3/9] Disabled testcode for LightningCLI subclasses since this
 requires a config

---
 docs/source/common/lightning_cli.rst | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/source/common/lightning_cli.rst b/docs/source/common/lightning_cli.rst
index 6d8ae6701fd40..a5cdf9f93e14f 100644
--- a/docs/source/common/lightning_cli.rst
+++ b/docs/source/common/lightning_cli.rst
@@ -224,7 +224,7 @@ datamodule class. However, there are many cases in which the objective is to eas
 multiple models and datasets. For these cases the tool can be configured such that a model and/or a datamodule is
 specified by an import path and init arguments. For example, with a tool implemented as:
 
-.. testcode::
+.. code-block:: python
 
     from pytorch_lightning.utilities.cli import LightningCLI
 

From 5873a2fa32a3f17aae9b65e4fe3ab50844e644b4 Mon Sep 17 00:00:00 2001
From: Mauricio Villegas <mauricio_ville@yahoo.com>
Date: Tue, 13 Apr 2021 08:17:35 +0200
Subject: [PATCH 4/9] - Added seed_everything_default to LightningCLI. -
 Changed dali_image_classifier.py example to use LightningCLI. - Simplified
 use of LightningCLI in autoencoder.py, backbone_image_classifier.py and
 simple_image_classifier.py. - Adapted other files related to the changed
 examples.

---
 pl_examples/basic_examples/README.md          | 12 +--
 pl_examples/basic_examples/autoencoder.py     | 14 +--
 .../backbone_image_classifier.py              | 11 +--
 .../basic_examples/dali_image_classifier.py   | 91 ++++++++-----------
 .../basic_examples/simple_image_classifier.py | 16 +---
 pl_examples/basic_examples/submit_ddp2_job.sh |  6 +-
 pl_examples/basic_examples/submit_ddp_job.sh  |  6 +-
 pl_examples/run_ddp-examples.sh               |  4 +-
 pytorch_lightning/utilities/cli.py            |  4 +
 9 files changed, 68 insertions(+), 96 deletions(-)

diff --git a/pl_examples/basic_examples/README.md b/pl_examples/basic_examples/README.md
index b02ea21c7940d..9c37c98a315c7 100644
--- a/pl_examples/basic_examples/README.md
+++ b/pl_examples/basic_examples/README.md
@@ -8,10 +8,10 @@ Trains MNIST where the model is defined inside the `LightningModule`.
 python simple_image_classifier.py
 
 # gpus (any number)
-python simple_image_classifier.py --gpus 2
+python simple_image_classifier.py --trainer.gpus 2
 
 # dataparallel
-python simple_image_classifier.py --gpus 2 --distributed_backend 'dp'
+python simple_image_classifier.py --trainer.gpus 2 --trainer.distributed_backend 'dp'
 ```
 
 ---
@@ -30,10 +30,10 @@ Generic image classifier with an arbitrary backbone (ie: a simple system)
 python backbone_image_classifier.py
 
 # gpus (any number)
-python backbone_image_classifier.py --gpus 2
+python backbone_image_classifier.py --trainer.gpus 2
 
 # dataparallel
-python backbone_image_classifier.py --gpus 2 --distributed_backend 'dp'
+python backbone_image_classifier.py --trainer.gpus 2 --trainer.distributed_backend 'dp'
 ```
 
 ---
@@ -44,10 +44,10 @@ Showing the power of a system... arbitrarily complex training loops
 python autoencoder.py
 
 # gpus (any number)
-python autoencoder.py --gpus 2
+python autoencoder.py --trainer.gpus 2
 
 # dataparallel
-python autoencoder.py --gpus 2 --distributed_backend 'dp'
+python autoencoder.py --trainer.gpus 2 --trainer.distributed_backend 'dp'
 ```
 ---
 # Multi-node example
diff --git a/pl_examples/basic_examples/autoencoder.py b/pl_examples/basic_examples/autoencoder.py
index 58ec3cdfe2bb8..3a016eb93269c 100644
--- a/pl_examples/basic_examples/autoencoder.py
+++ b/pl_examples/basic_examples/autoencoder.py
@@ -113,18 +113,10 @@ def test_dataloader(self):
         return DataLoader(self.mnist_test, batch_size=self.batch_size)
 
 
-class MyLightningCLI(LightningCLI):
-
-    def before_parse_arguments(self, parser):
-        parser.set_defaults(seed_everything=1234)
-
-    def after_fit(self):
-        result = self.trainer.test(test_dataloaders=self.datamodule.test_dataloader())
-        print(result)
-
-
 def cli_main():
-    MyLightningCLI(LitAutoEncoder, MyDataModule)
+    cli = LightningCLI(LitAutoEncoder, MyDataModule, seed_everything_default=1234)
+    result = cli.trainer.test(cli.model, datamodule=cli.datamodule)
+    print(result)
 
 
 if __name__ == '__main__':
diff --git a/pl_examples/basic_examples/backbone_image_classifier.py b/pl_examples/basic_examples/backbone_image_classifier.py
index 969ee6ab6e5ad..68b3145e32fb4 100644
--- a/pl_examples/basic_examples/backbone_image_classifier.py
+++ b/pl_examples/basic_examples/backbone_image_classifier.py
@@ -128,20 +128,15 @@ class MyLightningCLI(LightningCLI):
     def add_arguments_to_parser(self, parser):
         parser.add_class_arguments(Backbone, 'model.backbone')
 
-    def before_parse_arguments(self, parser):
-        parser.set_defaults(seed_everything=1234)
-
     def instantiate_model(self):
         self.config_init['model']['backbone'] = Backbone(**self.config['model']['backbone'])
         super().instantiate_model()
 
-    def after_fit(self):
-        result = self.trainer.test(test_dataloaders=self.datamodule.test_dataloader())
-        print(result)
-
 
 def cli_main():
-    MyLightningCLI(LitClassifier, MyDataModule)
+    cli = MyLightningCLI(LitClassifier, MyDataModule, seed_everything_default=1234)
+    result = cli.trainer.test(cli.model, datamodule=cli.datamodule)
+    print(result)
 
 
 if __name__ == '__main__':
diff --git a/pl_examples/basic_examples/dali_image_classifier.py b/pl_examples/basic_examples/dali_image_classifier.py
index 08bf64da252bf..e3eb3026db97a 100644
--- a/pl_examples/basic_examples/dali_image_classifier.py
+++ b/pl_examples/basic_examples/dali_image_classifier.py
@@ -12,7 +12,6 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 from abc import ABC
-from argparse import ArgumentParser
 from distutils.version import LooseVersion
 from random import shuffle
 from warnings import warn
@@ -30,6 +29,7 @@
     _TORCHVISION_MNIST_AVAILABLE,
     cli_lightning_logo,
 )
+from pytorch_lightning.utilities.cli import LightningCLI
 
 if _TORCHVISION_AVAILABLE:
     from torchvision import transforms
@@ -136,7 +136,11 @@ def __len__(self):
 
 class LitClassifier(pl.LightningModule):
 
-    def __init__(self, hidden_dim=128, learning_rate=1e-3):
+    def __init__(
+        self,
+        hidden_dim: int = 128,
+        learning_rate: float = 0.0001,
+    ):
         super().__init__()
         self.save_hyperparameters()
 
@@ -173,64 +177,43 @@ def test_step(self, batch, batch_idx):
     def configure_optimizers(self):
         return torch.optim.Adam(self.parameters(), lr=self.hparams.learning_rate)
 
-    @staticmethod
-    def add_model_specific_args(parent_parser):
-        parser = parent_parser.add_argument_group("LitClassifier")
-        parser.add_argument('--hidden_dim', type=int, default=128)
-        parser.add_argument('--learning_rate', type=float, default=0.0001)
-        return parent_parser
+
+class MyDataModule(pl.LightningDataModule):
+
+    def __init__(
+        self,
+        batch_size: int = 32,
+    ):
+        super().__init__()
+        dataset = MNIST(_DATASETS_PATH, train=True, download=True, transform=transforms.ToTensor())
+        self.mnist_test = MNIST(_DATASETS_PATH, train=False, download=True, transform=transforms.ToTensor())
+        self.mnist_train, self.mnist_val = random_split(dataset, [55000, 5000])
+
+        eii_train = ExternalMNISTInputIterator(self.mnist_train, batch_size)
+        eii_val = ExternalMNISTInputIterator(self.mnist_val, batch_size)
+        eii_test = ExternalMNISTInputIterator(self.mnist_test, batch_size)
+
+        self.pipe_train = ExternalSourcePipeline(batch_size=batch_size, eii=eii_train, num_threads=2, device_id=0)
+        self.pipe_val = ExternalSourcePipeline(batch_size=batch_size, eii=eii_val, num_threads=2, device_id=0)
+        self.pipe_test = ExternalSourcePipeline(batch_size=batch_size, eii=eii_test, num_threads=2, device_id=0)
+
+    def train_dataloader(self):
+        return DALIClassificationLoader(self.pipe_train, size=len(self.mnist_train), auto_reset=True, fill_last_batch=True)
+
+    def val_dataloader(self):
+        return DALIClassificationLoader(self.pipe_val, size=len(self.mnist_val), auto_reset=True, fill_last_batch=False)
+
+    def test_dataloader(self):
+        return DALIClassificationLoader(self.pipe_test, size=len(self.mnist_test), auto_reset=True, fill_last_batch=False)
 
 
 def cli_main():
     if not _DALI_AVAILABLE:
         return
 
-    pl.seed_everything(1234)
-
-    # ------------
-    # args
-    # ------------
-    parser = ArgumentParser()
-    parser.add_argument('--batch_size', default=32, type=int)
-    parser = pl.Trainer.add_argparse_args(parser)
-    parser = LitClassifier.add_model_specific_args(parser)
-    args = parser.parse_args()
-
-    # ------------
-    # data
-    # ------------
-    dataset = MNIST(_DATASETS_PATH, train=True, download=True, transform=transforms.ToTensor())
-    mnist_test = MNIST(_DATASETS_PATH, train=False, download=True, transform=transforms.ToTensor())
-    mnist_train, mnist_val = random_split(dataset, [55000, 5000])
-
-    eii_train = ExternalMNISTInputIterator(mnist_train, args.batch_size)
-    eii_val = ExternalMNISTInputIterator(mnist_val, args.batch_size)
-    eii_test = ExternalMNISTInputIterator(mnist_test, args.batch_size)
-
-    pipe_train = ExternalSourcePipeline(batch_size=args.batch_size, eii=eii_train, num_threads=2, device_id=0)
-    train_loader = DALIClassificationLoader(pipe_train, size=len(mnist_train), auto_reset=True, fill_last_batch=True)
-
-    pipe_val = ExternalSourcePipeline(batch_size=args.batch_size, eii=eii_val, num_threads=2, device_id=0)
-    val_loader = DALIClassificationLoader(pipe_val, size=len(mnist_val), auto_reset=True, fill_last_batch=False)
-
-    pipe_test = ExternalSourcePipeline(batch_size=args.batch_size, eii=eii_test, num_threads=2, device_id=0)
-    test_loader = DALIClassificationLoader(pipe_test, size=len(mnist_test), auto_reset=True, fill_last_batch=False)
-
-    # ------------
-    # model
-    # ------------
-    model = LitClassifier(args.hidden_dim, args.learning_rate)
-
-    # ------------
-    # training
-    # ------------
-    trainer = pl.Trainer.from_argparse_args(args)
-    trainer.fit(model, train_loader, val_loader)
-
-    # ------------
-    # testing
-    # ------------
-    trainer.test(test_dataloaders=test_loader)
+    cli = LightningCLI(LitClassifier, MyDataModule, seed_everything_default=1234)
+    result = cli.trainer.test(cli.model, datamodule=cli.datamodule)
+    print(result)
 
 
 if __name__ == "__main__":
diff --git a/pl_examples/basic_examples/simple_image_classifier.py b/pl_examples/basic_examples/simple_image_classifier.py
index ffb0b83d66f6d..d401e884a2f18 100644
--- a/pl_examples/basic_examples/simple_image_classifier.py
+++ b/pl_examples/basic_examples/simple_image_classifier.py
@@ -18,8 +18,6 @@
 python simple_image_classifier.py --trainer.max_epochs=50
 """
 
-from pprint import pprint
-
 import torch
 from torch.nn import functional as F
 
@@ -77,18 +75,10 @@ def configure_optimizers(self):
         return torch.optim.Adam(self.parameters(), lr=self.hparams.learning_rate)
 
 
-class MyLightningCLI(LightningCLI):
-
-    def before_parse_arguments(self, parser):
-        parser.set_defaults(seed_everything=1234)
-
-    def after_fit(self):
-        result = self.trainer.test(self.model, datamodule=self.datamodule)
-        pprint(result)
-
-
 def cli_main():
-    MyLightningCLI(LitClassifier, MNISTDataModule)
+    cli = LightningCLI(LitClassifier, MNISTDataModule, seed_everything_default=1234)
+    result = cli.trainer.test(cli.model, datamodule=cli.datamodule)
+    print(result)
 
 
 if __name__ == '__main__':
diff --git a/pl_examples/basic_examples/submit_ddp2_job.sh b/pl_examples/basic_examples/submit_ddp2_job.sh
index 026589a604c36..e31c7a185b73c 100755
--- a/pl_examples/basic_examples/submit_ddp2_job.sh
+++ b/pl_examples/basic_examples/submit_ddp2_job.sh
@@ -24,4 +24,8 @@ source activate $1
 # -------------------------
 
 # run script from above
-srun python3 simple_image_classifier.py --accelerator 'ddp2' --gpus 2 --num_nodes 2 --max_epochs 5
+srun python3 simple_image_classifier.py \
+  --trainer.accelerator 'ddp2' \
+  --trainer.gpus 2 \
+  --trainer.num_nodes 2 \
+  --trainer.max_epochs 5
diff --git a/pl_examples/basic_examples/submit_ddp_job.sh b/pl_examples/basic_examples/submit_ddp_job.sh
index b4f5ff0a64d92..177c19b3fdd72 100755
--- a/pl_examples/basic_examples/submit_ddp_job.sh
+++ b/pl_examples/basic_examples/submit_ddp_job.sh
@@ -24,4 +24,8 @@ source activate $1
 # -------------------------
 
 # run script from above
-srun python3 simple_image_classifier.py --accelerator 'ddp' --gpus 2 --num_nodes 2 --max_epochs 5
+srun python3 simple_image_classifier.py \
+  --trainer.accelerator 'ddp' \
+  --trainer.gpus 2 \
+  --trainer.num_nodes 2 \
+  --trainer.max_epochs 5
diff --git a/pl_examples/run_ddp-examples.sh b/pl_examples/run_ddp-examples.sh
index 6cc36364e397d..6a1a63b5205e7 100644
--- a/pl_examples/run_ddp-examples.sh
+++ b/pl_examples/run_ddp-examples.sh
@@ -1,7 +1,7 @@
 #!/bin/bash
 
-ARGS_EXTRA_DDP=" --gpus 2 --accelerator ddp"
-ARGS_EXTRA_AMP=" --precision 16"
+ARGS_EXTRA_DDP=" --trainer.gpus 2 --trainer.accelerator ddp"
+ARGS_EXTRA_AMP=" --trainer.precision 16"
 
 python pl_examples/basic_examples/simple_image_classifier.py $@ ${ARGS_EXTRA_DDP}
 python pl_examples/basic_examples/simple_image_classifier.py $@ ${ARGS_EXTRA_DDP} ${ARGS_EXTRA_AMP}
diff --git a/pytorch_lightning/utilities/cli.py b/pytorch_lightning/utilities/cli.py
index 97fd75ccbed08..93729a53db25d 100644
--- a/pytorch_lightning/utilities/cli.py
+++ b/pytorch_lightning/utilities/cli.py
@@ -97,6 +97,7 @@ def __init__(
         save_config_callback: Type[SaveConfigCallback] = SaveConfigCallback,
         trainer_class: Type[Trainer] = Trainer,
         trainer_defaults: Dict[str, Any] = None,
+        seed_everything_default: int = None,
         description: str = 'pytorch-lightning trainer command line tool',
         env_prefix: str = 'PL',
         env_parse: bool = False,
@@ -132,6 +133,7 @@ def __init__(
             save_config_callback: A callback class to save the training config.
             trainer_class: An optional extension of the Trainer class.
             trainer_defaults: Set to override Trainer defaults or add persistent callbacks.
+            seed_everything_default: Default value for seed_everything argument.
             description: Description of the tool shown when running --help.
             env_prefix: Prefix for environment variables.
             env_parse: Whether environment variable parsing is enabled.
@@ -152,6 +154,7 @@ def __init__(
         self.save_config_callback = save_config_callback
         self.trainer_class = trainer_class
         self.trainer_defaults = {} if trainer_defaults is None else trainer_defaults
+        self.seed_everything_default = seed_everything_default
         self.subclass_mode_model = subclass_mode_model
         self.subclass_mode_data = subclass_mode_data
         self.parser_kwargs = {} if parser_kwargs is None else parser_kwargs
@@ -187,6 +190,7 @@ def add_core_arguments_to_parser(self) -> None:
         self.parser.add_argument(
             '--seed_everything',
             type=Optional[int],
+            default=self.seed_everything_default,
             help='Set to an int to run seed_everything with this value before classes instantiation',
         )
         self.parser.add_lightning_class_args(self.trainer_class, 'trainer')

From 79e8b27293224499692e748442fd4860f973f39b Mon Sep 17 00:00:00 2001
From: Mauricio Villegas <mauricio_ville@yahoo.com>
Date: Tue, 13 Apr 2021 08:30:45 +0200
Subject: [PATCH 5/9] Fix pep8 issues in dali_image_classifier.py

---
 .../basic_examples/dali_image_classifier.py   | 21 ++++++++++++++++---
 1 file changed, 18 insertions(+), 3 deletions(-)

diff --git a/pl_examples/basic_examples/dali_image_classifier.py b/pl_examples/basic_examples/dali_image_classifier.py
index e3eb3026db97a..a4db77115f647 100644
--- a/pl_examples/basic_examples/dali_image_classifier.py
+++ b/pl_examples/basic_examples/dali_image_classifier.py
@@ -198,13 +198,28 @@ def __init__(
         self.pipe_test = ExternalSourcePipeline(batch_size=batch_size, eii=eii_test, num_threads=2, device_id=0)
 
     def train_dataloader(self):
-        return DALIClassificationLoader(self.pipe_train, size=len(self.mnist_train), auto_reset=True, fill_last_batch=True)
+        return DALIClassificationLoader(
+            self.pipe_train,
+            size=len(self.mnist_train),
+            auto_reset=True,
+            fill_last_batch=True
+        )
 
     def val_dataloader(self):
-        return DALIClassificationLoader(self.pipe_val, size=len(self.mnist_val), auto_reset=True, fill_last_batch=False)
+        return DALIClassificationLoader(
+            self.pipe_val,
+            size=len(self.mnist_val),
+            auto_reset=True,
+            fill_last_batch=False
+        )
 
     def test_dataloader(self):
-        return DALIClassificationLoader(self.pipe_test, size=len(self.mnist_test), auto_reset=True, fill_last_batch=False)
+        return DALIClassificationLoader(
+            self.pipe_test,
+            size=len(self.mnist_test),
+            auto_reset=True,
+            fill_last_batch=False
+        )
 
 
 def cli_main():

From 1ed66b60f45474ad6eb9933cdf72c14cbf9e600b Mon Sep 17 00:00:00 2001
From: Mauricio Villegas <mauricio_ville@yahoo.com>
Date: Tue, 13 Apr 2021 10:03:28 +0200
Subject: [PATCH 6/9] Updated azure-pipelines.yml

---
 azure-pipelines.yml | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/azure-pipelines.yml b/azure-pipelines.yml
index 85664bac74b67..574ef265fbfb4 100644
--- a/azure-pipelines.yml
+++ b/azure-pipelines.yml
@@ -117,8 +117,8 @@ jobs:
         set -e
         python -m pytest pl_examples -v --maxfail=2 --durations=0
         pip install . --user --quiet
-        bash pl_examples/run_examples-args.sh --gpus 1 --max_epochs 1 --batch_size 64 --limit_train_batches 5 --limit_val_batches 3
-        bash pl_examples/run_ddp-examples.sh --max_epochs 1 --batch_size 32 --limit_train_batches 2 --limit_val_batches 2
+        bash pl_examples/run_examples-args.sh --trainer.gpus 1 --trainer.max_epochs 1 --data.batch_size 64 --trainer.limit_train_batches 5 --limit_val_batches 3
+        bash pl_examples/run_ddp-examples.sh --trainer.max_epochs 1 --data.batch_size 32 --trainer.limit_train_batches 2 --trainer.limit_val_batches 2
         # cd pl_examples/basic_examples
         # bash submit_ddp_job.sh
         # bash submit_ddp2_job.sh

From 34acebaafebd5a23ae7657aaf19a654c41342725 Mon Sep 17 00:00:00 2001
From: Mauricio Villegas <mauricio_ville@yahoo.com>
Date: Tue, 13 Apr 2021 10:53:02 +0200
Subject: [PATCH 7/9] Updated azure-pipelines.yml

---
 azure-pipelines.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/azure-pipelines.yml b/azure-pipelines.yml
index 574ef265fbfb4..bd11b05812a4f 100644
--- a/azure-pipelines.yml
+++ b/azure-pipelines.yml
@@ -117,7 +117,7 @@ jobs:
         set -e
         python -m pytest pl_examples -v --maxfail=2 --durations=0
         pip install . --user --quiet
-        bash pl_examples/run_examples-args.sh --trainer.gpus 1 --trainer.max_epochs 1 --data.batch_size 64 --trainer.limit_train_batches 5 --limit_val_batches 3
+        bash pl_examples/run_examples-args.sh --trainer.gpus 1 --trainer.max_epochs 1 --data.batch_size 64 --trainer.limit_train_batches 5 --trainer.limit_val_batches 3
         bash pl_examples/run_ddp-examples.sh --trainer.max_epochs 1 --data.batch_size 32 --trainer.limit_train_batches 2 --trainer.limit_val_batches 2
         # cd pl_examples/basic_examples
         # bash submit_ddp_job.sh

From f2fcafbc7a217f5aae6ac023108390bc395aa71e Mon Sep 17 00:00:00 2001
From: Carlos Mocholi <carlossmocholi@gmail.com>
Date: Tue, 13 Apr 2021 13:43:14 +0200
Subject: [PATCH 8/9] distributed_backend -> accelerator

---
 pl_examples/basic_examples/README.md | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/pl_examples/basic_examples/README.md b/pl_examples/basic_examples/README.md
index 9c37c98a315c7..ffacf6895e3c8 100644
--- a/pl_examples/basic_examples/README.md
+++ b/pl_examples/basic_examples/README.md
@@ -11,7 +11,7 @@ python simple_image_classifier.py
 python simple_image_classifier.py --trainer.gpus 2
 
 # dataparallel
-python simple_image_classifier.py --trainer.gpus 2 --trainer.distributed_backend 'dp'
+python simple_image_classifier.py --trainer.gpus 2 --trainer.accelerator 'dp'
 ```
 
 ---
@@ -33,7 +33,7 @@ python backbone_image_classifier.py
 python backbone_image_classifier.py --trainer.gpus 2
 
 # dataparallel
-python backbone_image_classifier.py --trainer.gpus 2 --trainer.distributed_backend 'dp'
+python backbone_image_classifier.py --trainer.gpus 2 --trainer.accelerator 'dp'
 ```
 
 ---
@@ -47,7 +47,7 @@ python autoencoder.py
 python autoencoder.py --trainer.gpus 2
 
 # dataparallel
-python autoencoder.py --trainer.gpus 2 --trainer.distributed_backend 'dp'
+python autoencoder.py --trainer.gpus 2 --trainer.accelerator 'dp'
 ```
 ---
 # Multi-node example

From c2bfea33711e343a2889be54e12359b462a24515 Mon Sep 17 00:00:00 2001
From: Mauricio Villegas <mauricio_ville@yahoo.com>
Date: Tue, 13 Apr 2021 19:42:56 +0200
Subject: [PATCH 9/9] Changed profiler_example.py to use LightningCLI.

---
 .../basic_examples/profiler_example.py        | 38 ++++++++-----------
 1 file changed, 15 insertions(+), 23 deletions(-)

diff --git a/pl_examples/basic_examples/profiler_example.py b/pl_examples/basic_examples/profiler_example.py
index ca640a96f9588..0a6f855b2f109 100644
--- a/pl_examples/basic_examples/profiler_example.py
+++ b/pl_examples/basic_examples/profiler_example.py
@@ -23,7 +23,6 @@
 """
 
 import sys
-from argparse import ArgumentParser
 
 import torch
 import torchvision
@@ -31,27 +30,26 @@
 import torchvision.transforms as T
 
 from pl_examples import cli_lightning_logo
-from pytorch_lightning import LightningDataModule, LightningModule, Trainer
+from pytorch_lightning import LightningDataModule, LightningModule
+from pytorch_lightning.utilities.cli import LightningCLI
 
 DEFAULT_CMD_LINE = (
-    "--max_epochs",
-    "1",
-    "--limit_train_batches",
-    "15",
-    "--limit_val_batches",
-    "15",
-    "--profiler",
-    "pytorch",
-    "--gpus",
-    f"{int(torch.cuda.is_available())}",
+    "--trainer.max_epochs=1",
+    "--trainer.limit_train_batches=15",
+    "--trainer.limit_val_batches=15",
+    "--trainer.profiler=pytorch",
+    f"--trainer.gpus={int(torch.cuda.is_available())}",
 )
 
 
 class ModelToProfile(LightningModule):
 
-    def __init__(self, model):
+    def __init__(
+        self,
+        name: str = "resnet50"
+    ):
         super().__init__()
-        self.model = model
+        self.model = getattr(models, name)(pretrained=True)
         self.criterion = torch.nn.CrossEntropyLoss()
 
     def training_step(self, batch, batch_idx):
@@ -85,16 +83,10 @@ def val_dataloader(self, *args, **kwargs):
 
 
 def cli_main():
+    if len(sys.argv) == 1:
+        sys.argv += DEFAULT_CMD_LINE
 
-    parser = ArgumentParser()
-    parser = Trainer.add_argparse_args(parser)
-    cmd_line = None if len(sys.argv) != 1 else DEFAULT_CMD_LINE
-    args = parser.parse_args(args=cmd_line)
-
-    model = ModelToProfile(models.resnet50(pretrained=True))
-    datamodule = CIFAR10DataModule()
-    trainer = Trainer(**vars(args))
-    trainer.fit(model, datamodule=datamodule)
+    LightningCLI(ModelToProfile, CIFAR10DataModule)
 
 
 if __name__ == '__main__':