update lightning imports

wandb · Dec 4, 2023 · d57448e · d57448e
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diff --git a/colabs/pytorch-lightning/Optimize_Pytorch_Lightning_models_with_Weights_&_Biases.ipynb b/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"
@@ -334,7 +342,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from pytorch_lightning.callbacks import Callback\n",
+    "from lightning.pytorch.callbacks import Callback\n",
     " \n",
     "class LogPredictionsCallback(Callback):\n",
     "    \n",

diff --git a/colabs/pytorch-lightning/Profile_PyTorch_Code.ipynb b/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",

diff --git a/...h-lightning/Supercharge_your_Training_with_Pytorch_Lightning_and_Weights_and_Biases.ipynb b/...h-lightning/Supercharge_your_Training_with_Pytorch_Lightning_and_Weights_and_Biases.ipynb
@@ -81,7 +81,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "!pip install -qqq wandb pytorch-lightning torchmetrics"
+    "!pip install -qqq wandb lightning torchmetrics"
    ]
   },
   {
@@ -142,15 +142,15 @@
    "outputs": [],
    "source": [
     "# ⚡ PyTorch Lightning\n",
-    "import pytorch_lightning as pl\n",
+    "import lightning.pytorch as pl\n",
     "import torchmetrics\n",
     "pl.seed_everything(hash(\"setting random seeds\") % 2**32 - 1)\n",
     "\n",
     "# 🏋️‍♀️ Weights & Biases\n",
     "import wandb\n",
     "\n",
     "# ⚡ 🤝 🏋️‍♀️\n",
-    "from pytorch_lightning.loggers import WandbLogger\n"
+    "from lightning.pytorch.loggers import WandbLogger\n"
    ]
   },
   {

diff --git a/colabs/pytorch-lightning/Transfer_Learning_Using_PyTorch_Lightning.ipynb b/colabs/pytorch-lightning/Transfer_Learning_Using_PyTorch_Lightning.ipynb
@@ -38,7 +38,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "!pip install wandb pytorch-lightning -qqq"
+    "!pip install wandb lightning torchvision -qqq"
    ]
   },
   {
@@ -56,9 +56,9 @@
    "source": [
     "import os\n",
     "\n",
-    "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",

diff --git a/colabs/pytorch-lightning/Wandb_End_to_End_with_PyTorch_Lightning.ipynb b/colabs/pytorch-lightning/Wandb_End_to_End_with_PyTorch_Lightning.ipynb
@@ -32,7 +32,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "!pip install -q pytorch-lightning wandb"
+    "!pip install -q lightning wandb torchvision"
    ]
   },
   {
@@ -184,7 +184,7 @@
    "outputs": [],
    "source": [
     "from torchvision import transforms\n",
-    "import pytorch_lightning as pl\n",
+    "import lightning.pytorch as pl\n",
     "import torch\n",
     "from torch.utils.data import Dataset, DataLoader, random_split\n",
     "from skimage import io, transform\n",
@@ -368,7 +368,7 @@
     "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",
+    "from lightning.pytorch import LightningModule\n",
     "from torchvision import models\n",
     "\n",
     "class NatureLitModule(LightningModule):\n",
@@ -468,7 +468,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from pytorch_lightning.callbacks import Callback\n",
+    "from lightning.pytorch.callbacks import Callback\n",
     "\n",
     "class LogPredictionsCallback(Callback):\n",
     "\n",
@@ -525,9 +525,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from pytorch_lightning.callbacks import ModelCheckpoint\n",
-    "from pytorch_lightning.loggers import WandbLogger\n",
-    "from pytorch_lightning import Trainer\n",
+    "from lightning.pytorch.callbacks import ModelCheckpoint\n",
+    "from lightning.pytorch.loggers import WandbLogger\n",
+    "from lightning.pytorch import Trainer\n",
     "\n",
     "wandb.init(project=PROJECT_NAME,\n",
     "          entity=ENTITY,\n",