Minor Fine-Tuning Scheduler Tutorial Updates for PTL 1.7

.azure/ipynb-publish.yml

@@ -64,6 +64,7 @@ jobs:
         set -e
         sudo apt-get update -q --fix-missing
         sudo apt install -y tree ffmpeg
+        pip install --upgrade pip
         pip --version
         pip install --requirement requirements.txt
         pip install --requirement requirements/data.txt

.azure/ipynb-tests.yml

@@ -44,6 +44,7 @@ jobs:
         set -e
         sudo apt-get update -q --fix-missing
         sudo apt install -y tree ffmpeg
+        pip install --upgrade pip
         pip --version
         pip install --requirement requirements.txt
         pip install --requirement requirements/data.txt

lightning_examples/basic-gan/gan.py

@@ -20,7 +20,7 @@
 # ### MNIST DataModule
 #
 # Below, we define a DataModule for the MNIST Dataset. To learn more about DataModules, check out our tutorial
-# on them or see the [latest docs](https://pytorch-lightning.readthedocs.io/en/stable/extensions/datamodules.html).
+# on them or see the [latest release docs](https://pytorch-lightning.readthedocs.io/en/stable/data/datamodule.html).
 
 
 # %%
@@ -43,9 +43,6 @@ def __init__(
             ]
         )
 
-        # self.dims is returned when you call dm.size()
-        # Setting default dims here because we know them.
-        # Could optionally be assigned dynamically in dm.setup()
         self.dims = (1, 28, 28)
         self.num_classes = 10
 
@@ -248,7 +245,7 @@ def on_validation_epoch_end(self):
 
 # %%
 dm = MNISTDataModule()
-model = GAN(*dm.size())
+model = GAN(*dm.dims)
 trainer = Trainer(
     accelerator="auto",
     devices=1 if torch.cuda.is_available() else None,  # limiting got iPython runs

lightning_examples/datamodules/.meta.yml

@@ -8,7 +8,7 @@ description: This notebook will walk you through how to start using Datamodules.
   the release of `pytorch-lightning` version 0.9.0, we have included a new class called
   `LightningDataModule` to help you decouple data related hooks from your `LightningModule`.
   The most up-to-date documentation on datamodules can be found
-  [here](https://pytorch-lightning.readthedocs.io/en/stable/extensions/datamodules.html).
+  [here](https://pytorch-lightning.readthedocs.io/en/stable/data/datamodule.html).
 requirements:
   - torchvision
 accelerator:

lightning_examples/datamodules/datamodules.py

@@ -146,7 +146,7 @@ def test_dataloader(self):
 # 1. ```__init__```
 #     - Takes in a `data_dir` arg that points to where you have downloaded/wish to download the MNIST dataset.
 #     - Defines a transform that will be applied across train, val, and test dataset splits.
-#     - Defines default `self.dims`, which is a tuple returned from `datamodule.size()` that can help you initialize models.
+#     - Defines default `self.dims`.
 #
 #
 # 2. ```prepare_data```
@@ -176,9 +176,6 @@ def __init__(self, data_dir: str = PATH_DATASETS):
             ]
         )
 
-        # self.dims is returned when you call dm.size()
-        # Setting default dims here because we know them.
-        # Could optionally be assigned dynamically in dm.setup()
         self.dims = (1, 28, 28)
         self.num_classes = 10
 
@@ -274,7 +271,7 @@ def configure_optimizers(self):
 # Init DataModule
 dm = MNISTDataModule()
 # Init model from datamodule's attributes
-model = LitModel(*dm.size(), dm.num_classes)
+model = LitModel(*dm.dims, dm.num_classes)
 # Init trainer
 trainer = Trainer(
     max_epochs=3,
@@ -341,7 +338,7 @@ def test_dataloader(self):
 
 # %%
 dm = CIFAR10DataModule()
-model = LitModel(*dm.size(), dm.num_classes, hidden_size=256)
+model = LitModel(*dm.dims, dm.num_classes, hidden_size=256)
 tqdm_progress_bar = TQDMProgressBar(refresh_rate=20)
 trainer = Trainer(
     max_epochs=5,

lightning_examples/finetuning-scheduler/.meta.yml

@@ -1,20 +1,19 @@
-title: Finetuning Scheduler
+title: Fine-Tuning Scheduler
 author: "[Dan Dale](https://github.com/speediedan)"
 created: 2021-11-29
-updated: 2022-06-10
+updated: 2022-08-06
 license: CC BY-SA
 build: 0
 tags:
-  - Finetuning
+  - Fine-Tuning
 description: |
-  This notebook introduces the [Finetuning Scheduler](https://finetuning-scheduler.readthedocs.io/en/stable/index.html) extension
-  and demonstrates the use of it to finetune a small foundational model on the
+  This notebook introduces the [Fine-Tuning Scheduler](https://finetuning-scheduler.readthedocs.io/en/stable/index.html) extension
+  and demonstrates the use of it to fine-tune a small foundational model on the
   [RTE](https://huggingface.co/datasets/viewer/?dataset=super_glue&config=rte) task of
   [SuperGLUE](https://super.gluebenchmark.com/) with iterative early-stopping defined according to a user-specified
   schedule. It uses Hugging Face's ``datasets`` and ``transformers`` libraries to retrieve the relevant benchmark data
   and foundational model weights. The required dependencies are installed via the finetuning-scheduler ``[examples]`` extra.
 requirements:
-  - finetuning-scheduler[examples]
-  - hydra-core>=1.1.0
+  - finetuning-scheduler[examples]>=0.2.0
 accelerator:
   - GPU

lightning_examples/mnist-hello-world/hello-world.py

@@ -94,7 +94,7 @@ def configure_optimizers(self):
 #     - If you don't mind loading all your datasets at once, you can set up a condition to allow for both 'fit' related setup and 'test' related setup to run whenever `None` is passed to `stage` (or ignore it altogether and exclude any conditionals).
 #     - **Note this runs across all GPUs and it *is* safe to make state assignments here**
 #
-# 3. [x_dataloader()](https://pytorch-lightning.readthedocs.io/en/stable/api_references.html#core-api) ♻️
+# 3. [x_dataloader()](https://pytorch-lightning.readthedocs.io/en/stable/api/pytorch_lightning.core.hooks.DataHooks.html#pytorch_lightning.core.hooks.DataHooks.train_dataloader) ♻️
 #     - `train_dataloader()`, `val_dataloader()`, and `test_dataloader()` all return PyTorch `DataLoader` instances that are created by wrapping their respective datasets that we prepared in `setup()`
 
 

lightning_examples/mnist-tpu-training/mnist-tpu.py

@@ -23,7 +23,7 @@
 # ### Defining The `MNISTDataModule`
 #
 # Below we define `MNISTDataModule`. You can learn more about datamodules
-# in [docs](https://pytorch-lightning.readthedocs.io/en/stable/extensions/datamodules.html).
+# in [docs](https://pytorch-lightning.readthedocs.io/en/stable/data/datamodule.html).
 
 
 # %%
@@ -33,9 +33,6 @@ def __init__(self, data_dir: str = "./"):
         self.data_dir = data_dir
         self.transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,))])
 
-        # self.dims is returned when you call dm.size()
-        # Setting default dims here because we know them.
-        # Could optionally be assigned dynamically in dm.setup()
         self.dims = (1, 28, 28)
         self.num_classes = 10
 
@@ -151,7 +148,7 @@ def configure_optimizers(self):
 # Init DataModule
 dm = MNISTDataModule()
 # Init model from datamodule's attributes
-model = LitModel(*dm.size(), dm.num_classes)
+model = LitModel(*dm.dims, dm.num_classes)
 # Init trainer
 trainer = Trainer(
     max_epochs=3,
@@ -170,7 +167,7 @@ def configure_optimizers(self):
 # Init DataModule
 dm = MNISTDataModule()
 # Init model from datamodule's attributes
-model = LitModel(*dm.size(), dm.num_classes)
+model = LitModel(*dm.dims, dm.num_classes)
 # Init trainer
 trainer = Trainer(
     max_epochs=3,