prune ecosystem example

pl_examples/__init__.py

@@ -8,3 +8,40 @@
 
 TORCHVISION_AVAILABLE = _module_available("torchvision")
 DALI_AVAILABLE = _module_available("nvidia.dali")
+
+
+LIGHTNING_LOGO = """
+                    ####
+                ###########
+             ####################
+         ############################
+    #####################################
+##############################################
+#########################  ###################
+#######################    ###################
+####################      ####################
+##################       #####################
+################        ######################
+#####################        #################
+######################     ###################
+#####################    #####################
+####################   #######################
+###################  #########################
+##############################################
+    #####################################
+         ############################
+             ####################
+                  ##########
+                     ####
+"""
+
+
+def nice_print(msg, last=False):
+    print()
+    print("\033[0;35m" + msg + "\033[0m")
+    if last:
+        print()
+
+
+def cli_lightning_logo():
+    nice_print(LIGHTNING_LOGO)

pl_examples/basic_examples/autoencoder.py

@@ -21,7 +21,7 @@
 from torch.utils.data import random_split
 
 import pytorch_lightning as pl
-from pl_examples import TORCHVISION_AVAILABLE
+from pl_examples import TORCHVISION_AVAILABLE, cli_lightning_logo
 
 if TORCHVISION_AVAILABLE:
     from torchvision.datasets.mnist import MNIST
@@ -105,4 +105,5 @@ def cli_main():
 
 
 if __name__ == '__main__':
+    cli_lightning_logo()
     cli_main()

pl_examples/basic_examples/backbone_image_classifier.py

@@ -19,7 +19,7 @@
 from torch.utils.data import DataLoader, random_split
 
 import pytorch_lightning as pl
-from pl_examples import DATASETS_PATH, TORCHVISION_AVAILABLE
+from pl_examples import DATASETS_PATH, TORCHVISION_AVAILABLE, cli_lightning_logo
 
 if TORCHVISION_AVAILABLE:
     from torchvision.datasets.mnist import MNIST
@@ -125,4 +125,5 @@ def cli_main():
 
 
 if __name__ == '__main__':
+    cli_lightning_logo()
     cli_main()

pl_examples/basic_examples/conv_sequential_example.py

@@ -29,6 +29,7 @@
 import torchvision
 
 import pytorch_lightning as pl
+from pl_examples import cli_lightning_logo
 from pytorch_lightning import Trainer
 from pytorch_lightning.metrics.functional import accuracy
 from pytorch_lightning.plugins.ddp_sequential_plugin import DDPSequentialPlugin
@@ -190,6 +191,7 @@ def instantiate_datamodule(args):
 
 
 if __name__ == "__main__":
+    cli_lightning_logo()
     parser = ArgumentParser(description="Pipe Example")
     parser.add_argument("--use_ddp_sequential", action="store_true")
     parser = Trainer.add_argparse_args(parser)

pl_examples/basic_examples/dali_image_classifier.py

@@ -22,7 +22,7 @@
 from torch.utils.data import random_split
 
 import pytorch_lightning as pl
-from pl_examples import TORCHVISION_AVAILABLE, DALI_AVAILABLE
+from pl_examples import TORCHVISION_AVAILABLE, DALI_AVAILABLE, cli_lightning_logo
 
 if TORCHVISION_AVAILABLE:
     from torchvision.datasets.mnist import MNIST
@@ -204,4 +204,5 @@ def cli_main():
 
 
 if __name__ == "__main__":
+    cli_lightning_logo()
     cli_main()

pl_examples/basic_examples/simple_image_classifier.py

@@ -19,6 +19,7 @@
 from torch.nn import functional as F
 
 import pytorch_lightning as pl
+from pl_examples import cli_lightning_logo
 from pl_examples.basic_examples.mnist_datamodule import MNISTDataModule
 
 
@@ -103,4 +104,5 @@ def cli_main():
 
 
 if __name__ == '__main__':
+    cli_lightning_logo()
     cli_main()

pl_examples/bug_report_model.py

@@ -22,6 +22,8 @@
 import os
 import torch
 from torch.utils.data import Dataset
+
+from pl_examples import cli_lightning_logo
 from pytorch_lightning import Trainer, LightningModule
 
 
@@ -137,4 +139,5 @@ def on_train_epoch_start(self) -> None:
 
 
 if __name__ == '__main__':
+    cli_lightning_logo()
     run_test()

pl_examples/domain_templates/computer_vision_fine_tuning.py

@@ -1,3 +1,16 @@
+# Copyright The PyTorch Lightning team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# 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.
 """Computer vision example on Transfer Learning.
 
 This computer vision example illustrates how one could fine-tune a pre-trained
@@ -40,6 +53,7 @@
 from torchvision.datasets.utils import download_and_extract_archive
 
 import pytorch_lightning as pl
+from pl_examples import cli_lightning_logo
 from pytorch_lightning import _logger as log
 
 BN_TYPES = (torch.nn.BatchNorm1d, torch.nn.BatchNorm2d, torch.nn.BatchNorm3d)
@@ -451,4 +465,5 @@ def get_args() -> argparse.Namespace:
 
 
 if __name__ == '__main__':
+    cli_lightning_logo()
     main(get_args())

pl_examples/domain_templates/generative_adversarial_net.py

@@ -1,3 +1,16 @@
+# Copyright The PyTorch Lightning team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# 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.
 """
 To run this template just do:
 python generative_adversarial_net.py
@@ -18,6 +31,7 @@
 from torch.utils.data import DataLoader
 from torchvision.datasets import MNIST
 
+from pl_examples import cli_lightning_logo
 from pytorch_lightning.core import LightningModule, LightningDataModule
 from pytorch_lightning.trainer import Trainer
 
@@ -211,6 +225,7 @@ def main(args: Namespace) -> None:
 
 
 if __name__ == '__main__':
+    cli_lightning_logo()
     parser = ArgumentParser()
 
     # Add program level args, if any.

pl_examples/domain_templates/imagenet.py

@@ -1,3 +1,16 @@
+# Copyright The PyTorch Lightning team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# 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.
 """
 This example is largely adapted from https://github.com/pytorch/examples/blob/master/imagenet/main.py
 
@@ -32,6 +45,7 @@
 import torchvision.transforms as transforms
 
 import pytorch_lightning as pl
+from pl_examples import cli_lightning_logo
 from pytorch_lightning.core import LightningModule
 
 
@@ -246,4 +260,5 @@ def run_cli():
 
 
 if __name__ == '__main__':
+    cli_lightning_logo()
     run_cli()

pl_examples/domain_templates/reinforce_learn_Qnet.py

@@ -1,3 +1,16 @@
+# Copyright The PyTorch Lightning team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# 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.
 """
 Deep Reinforcement Learning: Deep Q-network (DQN)
 
@@ -33,6 +46,7 @@
 from torch.utils.data.dataset import IterableDataset
 
 import pytorch_lightning as pl
+from pl_examples import cli_lightning_logo
 
 
 class DQN(nn.Module):
@@ -349,6 +363,7 @@ def main(args) -> None:
 
 
 if __name__ == '__main__':
+    cli_lightning_logo()
     torch.manual_seed(0)
     np.random.seed(0)
 

pl_examples/domain_templates/semantic_segmentation.py

@@ -1,3 +1,17 @@
+# Copyright The PyTorch Lightning team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# 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.
+
 import os
 import random
 from argparse import ArgumentParser, Namespace
@@ -10,6 +24,7 @@
 from torch.utils.data import DataLoader, Dataset
 
 import pytorch_lightning as pl
+from pl_examples import cli_lightning_logo
 from pl_examples.domain_templates.unet import UNet
 from pytorch_lightning.loggers import WandbLogger
 
@@ -225,6 +240,7 @@ def main(hparams: Namespace):
 
 
 if __name__ == '__main__':
+    cli_lightning_logo()
     parser = ArgumentParser()
     parser.add_argument("--data_path", type=str, help="path where dataset is stored")
     parser.add_argument("--gpus", type=int, default=-1, help="number of available GPUs")

pl_examples/domain_templates/unet.py

@@ -1,3 +1,17 @@
+# Copyright The PyTorch Lightning team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# 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.
+
 import torch
 import torch.nn as nn
 import torch.nn.functional as F

pl_examples/pytorch_ecosystem/__init__.py

@@ -0,0 +1,13 @@
+# Copyright The PyTorch Lightning team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# 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.