sphinx conf warning fix

.azure/ipynb-publish.yml

@@ -62,6 +62,7 @@ jobs:
 
     - bash: |
         set -e
+        sudo apt-get update -q --fix-missing
         sudo apt install -y tree ffmpeg
         pip --version
         pip install --requirement requirements.txt

.azure/ipynb-tests.yml

@@ -42,6 +42,7 @@ jobs:
 
     - bash: |
         set -e
+        sudo apt-get update -q --fix-missing
         sudo apt install -y tree ffmpeg
         pip --version
         pip install --requirement requirements.txt

.github/workflows/ci_testing.yml

@@ -39,6 +39,7 @@ jobs:
 
       - name: Install dependencies
         run: |
+          sudo apt-get update -q --fix-missing
           sudo apt install -y ffmpeg
           pip --version
           pip install --requirement requirements.txt --find-links https://download.pytorch.org/whl/cpu/torch_stable.html

docs/source/conf.py

@@ -99,7 +99,7 @@
 #
 # This is also used if you do content translation via gettext catalogs.
 # Usually you set "language" from the command line for these cases.
-language = None
+language = "en"
 
 # List of patterns, relative to source directory, that match files and
 # directories to ignore when looking for source files.

lightning_examples/finetuning-scheduler/.meta.yml

@@ -15,5 +15,6 @@ description: |
   and foundational model weights. The required dependencies are installed via the finetuning-scheduler ``[examples]`` extra.
 requirements:
   - finetuning-scheduler[examples]
+  - hydra-core>=1.1.0
 accelerator:
   - GPU

lightning_examples/finetuning-scheduler/finetuning-scheduler.py

@@ -59,7 +59,7 @@
 # %% [markdown]
 # ## The Default Finetuning Schedule
 #
-# Schedule definition is facilitated via the [gen_ft_schedule](https://finetuning-scheduler.readthedocs.io/en/stable/api/finetuning_scheduler.fts_supporters.html#finetuning_scheduler.fts_supporters.SchedulingMixin.gen_ft_schedule) method which dumps a default finetuning schedule (by default using a naive, 2-parameters per level heuristic) which can be adjusted as
+# Schedule definition is facilitated via the [gen_ft_schedule](https://finetuning-scheduler.readthedocs.io/en/stable/api/finetuning_scheduler.fts_supporters.html#finetuning_scheduler.fts_supporters.ScheduleImplMixin.gen_ft_schedule) method which dumps a default finetuning schedule (by default using a naive, 2-parameters per level heuristic) which can be adjusted as
 # desired by the user and/or subsequently passed to the callback. Using the default/implicitly generated schedule will likely be less computationally efficient than a user-defined finetuning schedule but is useful for exploring a model's finetuning behavior and can serve as a good baseline for subsequent explicit schedule refinement.
 # While the current version of [FinetuningScheduler](https://finetuning-scheduler.readthedocs.io/en/stable/api/finetuning_scheduler.fts.html#finetuning_scheduler.fts.FinetuningScheduler) only supports single optimizer and (optional) lr_scheduler configurations, per-phase maximum learning rates can be set as demonstrated in the next section.