Update Lightning Examples (#487)

@ash0ts i'm going to merge this now, didn't get a chance to review, but some people needs some working PL examples 


* Update Image_Classification_using_PyTorch_Lightning.ipynb

Updates links and pip installs

* Update fine tuning ptl notebook

* update lightning imports

* Update Optimize_Pytorch_Lightning_models_with_Weights_&_Biases.ipynb

remove broken arg dataloader_idx

* fix broken flags and deprecated functions

* Update Wandb_End_to_End_with_PyTorch_Lightning.ipynb

* Update Supercharge_your_Training_with_Pytorch_Lightning_and_Weights_and_Biases.ipynb

colabs/pytorch-lightning/Fine_tuning_a_Transformer_with_Pytorch_Lightning.ipynb

@@ -64,7 +64,7 @@
    "outputs": [],
    "source": [
     "# Install some dependencies\n",
-    "!pip install pandas torch pytorch-lightning transformers==4.1.1 -q\n",
+    "!pip install pandas torch lightning transformers\n",
     "!pip install -Uq wandb"
    ]
   },
@@ -81,7 +81,7 @@
     "import transformers\n",
     "import numpy as np\n",
     "import pandas as pd\n",
-    "import pytorch_lightning as pl"
+    "import lightning.pytorch as pl"
    ]
   },
   {
@@ -245,7 +245,7 @@
     "  \n",
     "  # Download the raw cola data from the 'zipfile' reference we added to the cola-raw artifact.\n",
     "  raw_data_artifact = run.use_artifact(\"cola-raw:latest\")\n",
-    "  zip_path = raw_data_artifact.get_path(\"zipfile\").download()\n",
+    "  zip_path = raw_data_artifact.get_entry(\"zipfile\").download()\n",
     "  !unzip -o $zip_path  # jupyter hack to unzip data :P\n",
     "  \n",
     "  # Read in the raw data, log it to W&B as a wandb.Table\n",
@@ -298,7 +298,7 @@
     "\n",
     "  # Download the preprocessed data\n",
     "  pp_data_artifact = run.use_artifact(\"preprocessed-data:latest\")\n",
-    "  data_path = pp_data_artifact.get_path(\"dataset\").download()\n",
+    "  data_path = pp_data_artifact.get_entry(\"dataset\").download()\n",
     "  dataset = torch.load(data_path)\n",
     "\n",
     "  # Calculate the number of samples to include in each set.\n",
@@ -410,8 +410,8 @@
     "\n",
     "    # Load the datasets from the split-dataset artifact\n",
     "    data = run.use_artifact(\"split-dataset:latest\")\n",
-    "    train_dataset = torch.load(data.get_path(\"train-data\").download())\n",
-    "    val_dataset = torch.load(data.get_path(\"validation-data\").download())\n",
+    "    train_dataset = torch.load(data.get_entry(\"train-data\").download())\n",
+    "    val_dataset = torch.load(data.get_entry(\"validation-data\").download())\n",
     "\n",
     "    # Extract the config object associated with the run\n",
     "    config = run.config\n",
@@ -426,7 +426,7 @@
     "    gpus = -1 if torch.cuda.is_available() else 0\n",
     "    \n",
     "    # Construct a Trainer object with the W&B logger we created and epoch set by the config object\n",
-    "    trainer = pl.Trainer(max_epochs=config.epochs, gpus=gpus, logger=logger)\n",
+    "    trainer = pl.Trainer(max_epochs=config.epochs, logger=logger)\n",
     "    \n",
     "    # Build data loaders for our datasets, using the batch_size from our config object\n",
     "    train_data_loader = torch.utils.data.DataLoader(train_dataset, batch_size=config.batch_size)\n",
@@ -536,6 +536,18 @@
   "kernelspec": {
    "display_name": "Python 3",
    "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.2"
   }
  },
  "nbformat": 4,

colabs/pytorch-lightning/Image_Classification_using_PyTorch_Lightning.ipynb

@@ -27,7 +27,7 @@
     "\n",
     "# Image Classification using PyTorch Lightning ⚡️\n",
     "\n",
-    "We will build an image classification pipeline using PyTorch Lightning. We will follow this [style guide](https://pytorch-lightning.readthedocs.io/en/stable/starter/style_guide.html) to increase the readability and reproducibility of our code. A cool explanation of this available [here](https://wandb.ai/wandb/wandb-lightning/reports/Image-Classification-using-PyTorch-Lightning--VmlldzoyODk1NzY)."
+    "We will build an image classification pipeline using PyTorch Lightning. We will follow this [style guide](https://lightning.ai/docs/pytorch/stable/starter/style_guide.html) to increase the readability and reproducibility of our code. A cool explanation of this available [here](https://wandb.ai/wandb/wandb-lightning/reports/Image-Classification-using-PyTorch-Lightning--VmlldzoyODk1NzY)."
    ]
   },
   {
@@ -46,7 +46,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "!pip install pytorch-lightning -q\n",
+    "!pip install lightning torchvision -q\n",
     "# install weights and biases\n",
     "!pip install wandb -qU"
    ]
@@ -65,9 +65,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import pytorch_lightning as pl\n",
+    "import lightning.pytorch as pl\n",
     "# your favorite machine learning tracking tool\n",
-    "from pytorch_lightning.loggers import WandbLogger\n",
+    "from lightning.pytorch.loggers import WandbLogger\n",
     "\n",
     "import torch\n",
     "from torch import nn\n",
@@ -115,7 +115,7 @@
     "- Apply transforms (rotate, tokenize, etc…).\n",
     "- Wrap inside a DataLoader.\n",
     "\n",
-    "Learn more about datamodules [here](https://pytorch-lightning.readthedocs.io/en/stable/extensions/datamodules.html). Let's build a datamodule for the Cifar-10 dataset. "
+    "Learn more about datamodules [here](https://lightning.ai/docs/pytorch/stable/data/datamodule.html). Let's build a datamodule for the Cifar-10 dataset. "
    ]
   },
   {
@@ -168,8 +168,8 @@
    "source": [
     "## 📱 Callbacks\n",
     "\n",
-    "A callback is a self-contained program that can be reused across projects. PyTorch Lightning comes with few [built-in callbacks](https://pytorch-lightning.readthedocs.io/en/latest/extensions/callbacks.html#built-in-callbacks) which are regularly used. \n",
-    "Learn more about callbacks in PyTorch Lightning [here](https://pytorch-lightning.readthedocs.io/en/latest/extensions/callbacks.html)."
+    "A callback is a self-contained program that can be reused across projects. PyTorch Lightning comes with few [built-in callbacks](https://lightning.ai/docs/pytorch/latest/extensions/callbacks.html#built-in-callbacks) which are regularly used. \n",
+    "Learn more about callbacks in PyTorch Lightning [here](https://lightning.ai/docs/pytorch/latest/extensions/callbacks.html)."
    ]
   },
   {
@@ -179,7 +179,7 @@
    "source": [
     "### Built-in Callbacks\n",
     "\n",
-    "In this tutorial, we will use [Early Stopping](https://pytorch-lightning.readthedocs.io/en/latest/api/pytorch_lightning.callbacks.EarlyStopping.html#pytorch_lightning.callbacks.EarlyStopping) and [Model Checkpoint](https://pytorch-lightning.readthedocs.io/en/latest/api/pytorch_lightning.callbacks.ModelCheckpoint.html#pytorch_lightning.callbacks.ModelCheckpoint) built-in callbacks. They can be passed to the `Trainer`.\n"
+    "In this tutorial, we will use [Early Stopping](https://lightning.ai/docs/pytorch/latest/api/lightning.pytorch.callbacks.EarlyStopping.html#lightning.callbacks.EarlyStopping) and [Model Checkpoint](https://lightning.ai/docs/pytorch/latest/api/lightning.pytorch.callbacks.ModelCheckpoint.html#pytorch_lightning.callbacks.ModelCheckpoint) built-in callbacks. They can be passed to the `Trainer`.\n"
    ]
   },
   {
@@ -437,7 +437,7 @@
     "I hope you find this report helpful. I will encourage to play with the code and train an image classifier with a dataset of your choice. \n",
     "\n",
     "Here are some resources to learn more about PyTorch Lightning:\n",
-    "- [Step-by-step walk-through](https://pytorch-lightning.readthedocs.io/en/latest/starter/introduction.html) - This is one of the official tutorials. Their documentation is really well written and I highly encourage it as a good learning resource.\n",
+    "- [Step-by-step walk-through](https://lightning.ai/docs/pytorch/latest/starter/introduction.html) - This is one of the official tutorials. Their documentation is really well written and I highly encourage it as a good learning resource.\n",
     "- [Use Pytorch Lightning with Weights & Biases](https://wandb.me/lightning) - This is a quick colab that you can run through to learn more about how to use W&B with PyTorch Lightning."
    ]
   }
@@ -452,6 +452,18 @@
   "kernelspec": {
    "display_name": "Python 3",
    "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.2"
   }
  },
  "nbformat": 4,

colabs/pytorch-lightning/Optimize_Pytorch_Lightning_models_with_Weights_&_Biases.ipynb

@@ -28,8 +28,8 @@
     "Coupled with the [Weights & Biases integration](https://docs.wandb.com/library/integrations/lightning), you can quickly train and monitor models for full traceability and reproducibility with only 2 extra lines of code:\n",
     "\n",
     "```python\n",
-    "from pytorch_lightning.loggers import WandbLogger\n",
-    "from pytorch_lightning import Trainer\n",
+    "from lightning.pytorch.loggers import WandbLogger\n",
+    "from lightning.pytorch import Trainer\n",
     "\n",
     "wandb_logger = WandbLogger()\n",
     "trainer = Trainer(logger=wandb_logger)\n",
@@ -64,7 +64,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "!pip install -q pytorch-lightning wandb"
+    "!pip install -q lightning wandb torchvision"
    ]
   },
   {
@@ -150,6 +150,15 @@
     "* Call self.log in `training_step` and `validation_step` to log the metrics"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import lightning.pytorch as pl"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -160,9 +169,8 @@
     "from torch.nn import Linear, CrossEntropyLoss, functional as F\n",
     "from torch.optim import Adam\n",
     "from torchmetrics.functional import accuracy\n",
-    "from pytorch_lightning import LightningModule\n",
     "\n",
-    "class MNIST_LitModule(LightningModule):\n",
+    "class MNIST_LitModule(pl.LightningModule):\n",
     "\n",
     "    def __init__(self, n_classes=10, n_layer_1=128, n_layer_2=256, lr=1e-3):\n",
     "        '''method used to define our model parameters'''\n",
@@ -273,7 +281,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from pytorch_lightning.callbacks import ModelCheckpoint\n",
+    "from lightning.pytorch.callbacks import ModelCheckpoint\n",
     "\n",
     "checkpoint_callback = ModelCheckpoint(monitor='val_accuracy', mode='max')"
    ]
@@ -284,9 +292,9 @@
    "source": [
     "## 💡 Tracking Experiments with WandbLogger\n",
     "\n",
-    "PyTorch Lightning has a `WandbLogger` to easily log your experiments with Wights & Biases. Just pass it to your `Trainer` to log to W&B. See the [WandbLogger docs](https://pytorch-lightning.readthedocs.io/en/stable/extensions/generated/pytorch_lightning.loggers.WandbLogger.html#pytorch_lightning.loggers.WandbLogger) for all parameters. Note, to log the metrics to a specific W&B Team, pass your Team name to the `entity` argument in `WandbLogger`\n",
+    "PyTorch Lightning has a `WandbLogger` to easily log your experiments with Wights & Biases. Just pass it to your `Trainer` to log to W&B. See the [WandbLogger docs](https://lightning.ai/docs/pytorch/stable/extensions/generated/pytorch_lightning.loggers.WandbLogger.html#pytorch_lightning.loggers.WandbLogger) for all parameters. Note, to log the metrics to a specific W&B Team, pass your Team name to the `entity` argument in `WandbLogger`\n",
     "\n",
-    "#### `pytorch_lightning.loggers.WandbLogger()`\n",
+    "#### `lightning.pytorch.loggers.WandbLogger()`\n",
     "\n",
     "| Functionality | Argument/Function | PS |\n",
     "| ------ | ------ | ------ |\n",
@@ -295,9 +303,9 @@
     "| Organize runs by project | `WandbLogger(... ,project='my_project')` | |\n",
     "| Log histograms of gradients and parameters | `WandbLogger.watch(model)`  | `WandbLogger.watch(model, log='all')` to log parameter histograms  |\n",
     "| Log hyperparameters | Call `self.save_hyperparameters()` within `LightningModule.__init__()` |\n",
-    "| Log custom objects (images, audio, video, molecules…) | Use `WandbLogger.log_text`, `WandbLogger.log_image` and `WandbLogger.log_table` |\n",
+    "| Log custom objects (images, audio, video, molecules…) | Use `WandbLogger.log_text`, `WandbLogger.log_image` and `WandbLogger.log_table`, etc. |\n",
     "\n",
-    "See the [WandbLogger docs](https://pytorch-lightning.readthedocs.io/en/stable/extensions/generated/pytorch_lightning.loggers.WandbLogger.html#pytorch_lightning.loggers.WandbLogger) here for all parameters. "
+    "See the [WandbLogger docs](https://lightning.ai/docs/pytorch/stable/extensions/generated/pytorch_lightning.loggers.WandbLogger.html#pytorch_lightning.loggers.WandbLogger) here for all parameters. "
    ]
   },
   {
@@ -306,8 +314,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from pytorch_lightning.loggers import WandbLogger\n",
-    "from pytorch_lightning import Trainer\n",
+    "from lightning.pytorch.loggers import WandbLogger\n",
+    "from lightning.pytorch import Trainer\n",
     "\n",
     "wandb_logger = WandbLogger(project='MNIST', # group runs in \"MNIST\" project\n",
     "                           log_model='all') # log all new checkpoints during training"
@@ -334,12 +342,12 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from pytorch_lightning.callbacks import Callback\n",
+    "from lightning.pytorch.callbacks import Callback\n",
     " \n",
     "class LogPredictionsCallback(Callback):\n",
     "    \n",
     "    def on_validation_batch_end(\n",
-    "        self, trainer, pl_module, outputs, batch, batch_idx, dataloader_idx):\n",
+    "        self, trainer, pl_module, outputs, batch, batch_idx):\n",
     "        \"\"\"Called when the validation batch ends.\"\"\"\n",
     " \n",
     "        # `outputs` comes from `LightningModule.validation_step`\n",

colabs/pytorch-lightning/Profile_PyTorch_Code.ipynb

@@ -88,7 +88,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "!pip install -q wandb pytorch_lightning torch_tb_profiler"
+    "!pip install -q wandb lightning torch_tb_profiler torchvision"
    ]
   },
   {
@@ -99,7 +99,7 @@
    "source": [
     "import glob\n",
     "\n",
-    "import pytorch_lightning as pl\n",
+    "import lightning.pytorch as pl\n",
     "import torch\n",
     "import torch.nn as nn\n",
     "import torch.nn.functional as F\n",
@@ -357,7 +357,7 @@
     "    with profiler:\n",
     "        profiler_callback = TorchTensorboardProfilerCallback(profiler)\n",
     "\n",
-    "        trainer = pl.Trainer(gpus=1, max_epochs=1, max_steps=total_steps,\n",
+    "        trainer = pl.Trainer(max_epochs=1, max_steps=total_steps,\n",
     "                            logger=pl.loggers.WandbLogger(log_model=True, save_code=True),\n",
     "                            callbacks=[profiler_callback], precision=wandb.config.precision)\n",
     "\n",