Merge branch 'main' into dependabot-pip-gradio-lte-4.0.2

README.md

@@ -3,7 +3,7 @@
 ### Install Lightning
 
 ```bash
-pip install lightning
+pip install lightning[app]
 ```
 
 ### Locally

app.py

@@ -1,17 +1,17 @@
 import os.path as ops
 
-import lightning as L
+from lightning.app import CloudCompute, LightningFlow, LightningApp
 
 from quick_start.components import ImageServeGradio, PyTorchLightningScript
 
 
-class TrainDeploy(L.LightningFlow):
+class TrainDeploy(LightningFlow):
     def __init__(self):
         super().__init__()
         self.train_work = PyTorchLightningScript(
             script_path=ops.join(ops.dirname(__file__), "./train_script.py"),
             script_args=["--trainer.max_epochs=10"],
-            cloud_compute=L.CloudCompute("cpu-medium", idle_timeout=60),
+            cloud_compute=CloudCompute("cpu-medium", idle_timeout=60),
         )
 
         self.serve_work = ImageServeGradio()
@@ -32,4 +32,4 @@ def configure_layout(self):
         return tabs
 
 
-app = L.LightningApp(TrainDeploy())
+app = LightningApp(TrainDeploy())

app_hpo.py

@@ -1,7 +1,7 @@
 import os.path as ops
 
-import lightning as L
 import optuna
+from lightning.app import LightningFlow, CloudCompute, LightningApp
 from lightning_hpo import BaseObjective, Optimizer
 
 from quick_start.components import ImageServeGradio, PyTorchLightningScript
@@ -13,7 +13,7 @@ def distributions():
         return {"model.lr": optuna.distributions.LogUniformDistribution(0.0001, 0.1)}
 
 
-class TrainDeploy(L.LightningFlow):
+class TrainDeploy(LightningFlow):
     def __init__(self):
         super().__init__()
         self.train_work = Optimizer(
@@ -23,7 +23,7 @@ def __init__(self):
             n_trials=4,
         )
 
-        self.serve_work = ImageServeGradio(L.CloudCompute("cpu"))
+        self.serve_work = ImageServeGradio(CloudCompute("cpu"))
 
     def run(self):
         # 1. Run the python script that trains the model
@@ -39,4 +39,4 @@ def configure_layout(self):
         return [tab_1, tab_2]
 
 
-app = L.LightningApp(TrainDeploy())
+app = LightningApp(TrainDeploy())

requirements.txt

@@ -5,5 +5,5 @@ gradio <4.1.0
 pyyaml <=6.0.1
 protobuf <4.25.0  # 4.21 breaks with wandb, tensorboard, or pytorch-lightning: https://github.com/protocolbuffers/protobuf/issues/10048
 websockets
-lightning
+lightning[app] >=2.1.0
 tensorboard

tests/integration_app/app.py

@@ -1,6 +1,6 @@
 import logging
 
-from lightning import LightningApp, LightningFlow
+from lightning.app import LightningApp, LightningFlow
 
 from quick_start import PyTorchLightningScript, ImageServeGradio
 

train_script.py

@@ -1,8 +1,8 @@
 import os
 
-import lightning as L
 import torch
 import torchvision.transforms as T
+from lightning import LightningModule, LightningDataModule
 from lightning.pytorch.cli import LightningCLI
 from torch import nn
 from torch.nn import functional as F
@@ -35,7 +35,7 @@ def forward(self, x):
         return output
 
 
-class ImageClassifier(L.LightningModule):
+class ImageClassifier(LightningModule):
     def __init__(self, model=None, lr=1.0, gamma=0.7, batch_size=32):
         super().__init__()
         self.save_hyperparameters(ignore="model")
@@ -69,7 +69,7 @@ def configure_optimizers(self):
         return torch.optim.Adadelta(self.model.parameters(), lr=self.hparams.lr)
 
 
-class MNISTDataModule(L.LightningDataModule):
+class MNISTDataModule(LightningDataModule):
     def __init__(self, batch_size=32):
         super().__init__()
         self.save_hyperparameters()