Lite Docs and Example Improvements (#10303)

Co-authored-by: Rohit Gupta <[email protected]>
Co-authored-by: Adrian Wälchli <[email protected]>

Author: 3 people

.azure-pipelines/gpu-tests.yml

@@ -108,8 +108,6 @@ jobs:
         bash pl_examples/run_examples.sh --trainer.gpus=1
         bash pl_examples/run_examples.sh --trainer.gpus=2 --trainer.strategy=ddp
         bash pl_examples/run_examples.sh --trainer.gpus=2 --trainer.strategy=ddp --trainer.precision=16
-        bash pl_examples/run_examples.sh --trainer.gpus=2 --trainer.strategy=dp
-        bash pl_examples/run_examples.sh --trainer.gpus=2 --trainer.strategy=dp --trainer.precision=16
       env:
         PL_USE_MOCKED_MNIST: "1"
       displayName: 'Testing: examples'

.gitignore

@@ -24,6 +24,9 @@ __pycache__/
 *.py[cod]
 *$py.class
 timit_data/
+grid_generated*
+grid_ori*
+
 
 
 # C extensions

docs/source/starter/lightning_lite.rst

@@ -3,15 +3,14 @@ LightningLite - Stepping Stone to Lightning
 ###########################################
 
 
+:class:`~pytorch_lightning.lite.LightningLite` enables pure PyTorch users to scale their existing code
+on any kind of device while retaining full control over their own loops and optimization logic.
+
 .. image:: https://pl-public-data.s3.amazonaws.com/docs/static/images/lite/lightning_lite.gif
-    :alt: Animation showing how to convert a standard training loop to a Lightning loop
-    :width: 600px
+    :alt: Animation showing how to convert your PyTorch code to LightningLite.
+    :width: 500
     :align: center
 
-|
-
-:class:`~pytorch_lightning.lite.LightningLite` enables pure PyTorch users to scale their existing code
-on any kind of device while retaining full control over their own loops and optimization logic.
 
 :class:`~pytorch_lightning.lite.LightningLite` is the right tool for you if you match one of the two following descriptions:
 
@@ -246,6 +245,9 @@ from its hundreds of features.
 
 You can see our :class:`~pytorch_lightning.lite.LightningLite` as a
 future :class:`~pytorch_lightning.core.lightning.LightningModule` and slowly refactor your code into its API.
+Below, the :meth:`~pytorch_lightning.core.lightning.LightningModule.training_step`, :meth:`~pytorch_lightning.core.lightning.LightningModule.forward`,
+:meth:`~pytorch_lightning.core.lightning.LightningModule.configure_optimizers`, :meth:`~pytorch_lightning.core.lightning.LightningModule.train_dataloader`
+are being implemented.
 
 
 .. code-block:: python
@@ -300,7 +302,7 @@ future :class:`~pytorch_lightning.core.lightning.LightningModule` and slowly ref
 
 
 Finally, change the :meth:`~pytorch_lightning.lite.LightningLite.run` into a
-:meth:`~pytorch_lightning.core.lightning.LightningModule.__init__` and drop the inner code for setting up the components.
+:meth:`~pytorch_lightning.core.lightning.LightningModule.__init__` and drop the fit method.
 
 .. code-block:: python
 

pl_examples/README.md

@@ -25,7 +25,7 @@ In this folder, we have 2 simple examples:
 
 - [Image Classifier](./basic_examples/backbone_image_classifier.py) (trains arbitrary datasets with arbitrary backbones).
 - [Image Classifier + DALI](./basic_examples/mnist_examples/image_classifier_4_dali.py) (defines the model inside the `LightningModule`).
-- [Autoencoder](./basic_examples/autoencoder.py) (shows how the `LightningModule` can be used as a system)
+- [Autoencoder](./basic_examples/autoencoder.py)
 
 ______________________________________________________________________
 
@@ -37,6 +37,14 @@ for advanced use cases.
 
 ______________________________________________________________________
 
+## Basic Examples
+
+In this folder, we have 1 simple example:
+
+- [Image Classifier + DALI](./integration_examples/dali_image_classifier.py) (defines the model inside the `LightningModule`).
+
+______________________________________________________________________
+
 ## Loop examples
 
 Contains implementations leveraging [loop customization](https://pytorch-lightning.readthedocs.io/en/latest/extensions/loops.html) to enhance the Trainer with new optimization routines.

pl_examples/basic_examples/README.md

@@ -14,7 +14,7 @@ Trains a simple CNN over MNIST using vanilla PyTorch.
 
 ```bash
 # CPU
-python image_classifier_1_pytorch.py
+python mnist_examples/image_classifier_1_pytorch.py
 ```
 
 ______________________________________________________________________
@@ -25,7 +25,7 @@ This script shows you how to scale the previous script to enable GPU and multi-G
 
 ```bash
 # CPU / multiple GPUs if available
-python image_classifier_2_lite.py
+python mnist_examples/image_classifier_2_lite.py
 ```
 
 ______________________________________________________________________
@@ -36,7 +36,7 @@ This script shows you how to prepare your conversion from [LightningLite](https:
 
 ```bash
 # CPU / multiple GPUs if available
-python image_classifier_3_lite_to_lightning_module.py
+python mnist_examples/image_classifier_3_lite_to_lightning_module.py
 ```
 
 ______________________________________________________________________
@@ -47,10 +47,10 @@ This script shows you the result of the conversion to the `LightningModule` and
 
 ```bash
 # CPU
-python image_classifier_4_lightning_module.py
+python mnist_examples/image_classifier_4_lightning_module.py
 
 # GPUs (any number)
-python image_classifier_4_lightning_module.py --trainer.gpus 2
+python mnist_examples/image_classifier_4_lightning_module.py --trainer.gpus 2
 ```
 
 ______________________________________________________________________
@@ -61,11 +61,56 @@ This script shows you how to extract the data related components into a `Lightni
 
 ```bash
 # CPU
-python image_classifier_5_lightning_datamodule.py
+python mnist_examples/image_classifier_5_lightning_datamodule.py
 
 # GPUs (any number)
-python image_classifier_5_lightning_datamodule.py --trainer.gpus 2
+python mnist_examples/image_classifier_5_lightning_datamodule.py --trainer.gpus 2
 
 # Distributed Data Parallel (DDP)
-python image_classifier_5_lightning_datamodule.py --trainer.gpus 2 --trainer.strategy 'ddp'
+python mnist_examples/image_classifier_5_lightning_datamodule.py --trainer.gpus 2 --trainer.strategy 'ddp'
+```
+
+______________________________________________________________________
+
+#### AutoEncoder
+
+This script shows you how to implement a CNN auto-encoder.
+
+```bash
+# CPU
+python autoencoder.py
+
+# GPUs (any number)
+python autoencoder.py --trainer.gpus 2
+
+# Distributed Data Parallel (DDP)
+python autoencoder.py --trainer.gpus 2 --trainer.strategy 'ddp'
+```
+
+______________________________________________________________________
+
+#### Backbone Image Classifier
+
+This script shows you how to implement a `LightningModule` as a system.
+A system describes a `LightningModule` which takes a single `torch.nn.Module` which makes exporting to producion simpler.
+
+```bash
+# CPU
+python backbone_image_classifier.py
+
+# GPUs (any number)
+python backbone_image_classifier.py --trainer.gpus 2
+
+# Distributed Data Parallel (DDP)
+python backbone_image_classifier.py --trainer.gpus 2 --trainer.strategy 'ddp'
+```
+
+______________________________________________________________________
+
+#### PyTorch Profiler
+
+This script shows you how to activate the [PyTorch Profiler](https://github.com/pytorch/kineto) with Lightning.
+
+```bash
+python profiler_example.py
 ```

pl_examples/basic_examples/mnist_examples/image_classifier_4_lightning_module.py

@@ -18,6 +18,7 @@
 import torch
 import torchvision.transforms as T
 from torch.nn import functional as F
+from torchmetrics import Accuracy
 
 from pl_examples import cli_lightning_logo
 from pl_examples.basic_examples.mnist_datamodule import MNIST
@@ -31,6 +32,7 @@ def __init__(self, model=None, lr=1.0, gamma=0.7, batch_size=32):
         super().__init__()
         self.save_hyperparameters()
         self.model = model or Net()
+        self.test_acc = Accuracy()
 
     def forward(self, x):
         return self.model(x)
@@ -45,6 +47,7 @@ def test_step(self, batch, batch_idx):
         x, y = batch
         logits = self.forward(x)
         loss = F.nll_loss(logits, y.long())
+        self.log("test_acc", self.test_acc(logits, y))
         return loss
 
     def configure_optimizers(self):

pl_examples/basic_examples/mnist_examples/image_classifier_5_lightning_datamodule.py

@@ -18,6 +18,7 @@
 import torch
 import torchvision.transforms as T
 from torch.nn import functional as F
+from torchmetrics import Accuracy
 
 from pl_examples import cli_lightning_logo
 from pl_examples.basic_examples.mnist_datamodule import MNIST
@@ -31,6 +32,7 @@ def __init__(self, model, lr=1.0, gamma=0.7, batch_size=32):
         super().__init__()
         self.save_hyperparameters()
         self.model = model or Net()
+        self.test_acc = Accuracy()
 
     def forward(self, x):
         return self.model(x)
@@ -45,6 +47,7 @@ def test_step(self, batch, batch_idx):
         x, y = batch
         logits = self.forward(x)
         loss = F.nll_loss(logits, y.long())
+        self.log("test_acc", self.test_acc(logits, y))
         return loss
 
     def configure_optimizers(self):