Minor Fine-Tuning Scheduler Tutorial Updates for PTL 1.7

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/datamodules.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/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