docs: switch todo to comment (#18893)

docs/source-app/levels/basic/build_a_dag.rst

@@ -3,7 +3,6 @@
 ###########################
 Example: Deploy a model API
 ###########################
-**Audience:** TODO:
 
 **Prereqs:** You have an app already running locally.
 

docs/source-app/levels/basic/create_a_model_demo.rst

@@ -3,7 +3,6 @@
 ############################
 Example: Create a model demo
 ############################
-**Audience:** TODO:
 
 **Prereqs:** You have an app already running locally.
 

docs/source-app/levels/basic/deploy_ai_model_api.rst

@@ -3,7 +3,6 @@
 ###########################
 Example: Deploy a model API
 ###########################
-**Audience:** TODO:
 
 **Prereqs:** You have an app already running locally.
 

docs/source-app/levels/basic/run_jupyter_notebook_on_the_cloud.rst

@@ -3,7 +3,6 @@
 #############################################
 Example: Develop a Jupyter Notebook component
 #############################################
-**Audience:** TODO:
 
 **Prereqs:** You have an app already running locally.
 

docs/source-app/levels/basic/train_pytorch_on_the_cloud.rst

@@ -3,7 +3,6 @@
 ###################################
 Example: Train PyTorch on the cloud
 ###################################
-**Audience:** TODO:
 
 **Prereqs:** You have an app already running locally.
 

docs/source-app/levels/intermediate/share_files_between_components.rst

@@ -7,13 +7,6 @@ Level 8: Share files between components
 
 ----
 
-****
-TODO
-****
-TODO
-
-----
-
 *************************************************
 Next steps: Render a web UI with other components
 *************************************************

docs/source-app/testing.rst

@@ -6,7 +6,7 @@
 Productionize your Apps
 #######################
 
-TODO: Cleanup
+.. TODO: Cleanup
 
 At the core of our system is an integration testing framework that will allow for a first-class experience creating integration tests for Lightning Apps. This document will explain how we can create a lightning app test, how we can execute it, and where to find more information.
 

docs/source-app/workflows/add_server/any_server.rst

@@ -134,7 +134,8 @@ To run the app on the cloud, use the ``--cloud`` argument.
 *****************************************
 Interact with a component from the server
 *****************************************
-TODO: how do we do this?
+
+.. TODO:: how do we do this?
 
 
 ----
@@ -142,7 +143,8 @@ TODO: how do we do this?
 *****************************************
 Interact with the server from a component
 *****************************************
-TODO: how do we do this?
+
+.. TODO:: how do we do this?
 
 ----
 

docs/source-app/workflows/arrange_tabs/arrange_app_intermediate.rst

@@ -1,7 +1,8 @@
 ###############################
 Arrange app tabs (intermediate)
 ###############################
-TODO:
+
+.. TODO:: fill-in
 
 ----
 

docs/source-app/workflows/build_lightning_app/from_pytorch_lightning_script.rst

@@ -71,7 +71,7 @@ Modify the template
 
 The command above generates an App file like this:
 
-.. note:: TODO: list the file and show how to extend it
+.. TODO:: list the file and show how to extend it
 
 .. code:: python
 

docs/source-fabric/guide/trainer_template.rst

@@ -4,4 +4,4 @@
 Template Trainer
 ################
 
-TODO: Write a guide explaining how to build a template like the one in https://github.com/Lightning-AI/lightning/tree/master/examples/fabric/build_your_own_trainer
+.. TODO:: Write a guide explaining how to build a template like the one in https://github.com/Lightning-AI/lightning/tree/master/examples/fabric/build_your_own_trainer

docs/source-pytorch/common/checkpointing_expert.rst

@@ -25,7 +25,7 @@ and :meth:`~lightning.pytorch.core.hooks.CheckpointHooks.on_load_checkpoint` met
 what's saved in the checkpoint.
 
 
-TODO: I don't understand this...
+.. TODO:: I don't understand this...
 
 ******************************
 Built-in Checkpoint IO Plugins

docs/source-pytorch/visualize/logging_advanced.rst

@@ -334,7 +334,7 @@ To track the timeseries over steps (*on_step*) as well as the accumulated epoch
 
 Setting both to True will generate two graphs with *_step* for the timeseries over steps and *_epoch* for the epoch metric.
 
-# TODO: show images of both
+.. TODO:: show images of both
 
 ----
 

docs/source-pytorch/visualize/logging_basic.rst

@@ -39,7 +39,7 @@ To log multiple metrics at once, use *self.log_dict*
     values = {"loss": loss, "acc": acc, "metric_n": metric_n}  # add more items if needed
     self.log_dict(values)
 
-TODO: show plot of metric changing over time
+.. TODO:: show plot of metric changing over time
 
 ----
 
@@ -52,7 +52,7 @@ To view metrics in the commandline progress bar, set the *prog_bar* argument to
 
     self.log(..., prog_bar=True)
 
-TODO: need progress bar here
+.. TODO:: need progress bar here
 
 ----
 
@@ -86,7 +86,7 @@ Accumulate a metric
 ===================
 When *self.log* is called inside the *training_step*, it generates a timeseries showing how the metric behaves over time.
 
-TODO: show chart
+.. TODO:: show chart
 
 However, For the validation and test sets we are not generally interested in plotting the metric values per batch of data. Instead, we want to compute a summary statistic (such as average, min or max) across the full split of data.
 
@@ -98,7 +98,7 @@ When you call self.log inside the *validation_step* and *test_step*, Lightning a
         value = batch_idx + 1
         self.log("average_value", value)
 
-TODO: show single point plotted
+.. TODO:: show single point plotted
 
 If you don't want to average you can also choose from ``{min,max,sum}`` by passing the *reduce_fx* argument.
 

docs/source-pytorch/visualize/logging_intermediate.rst

@@ -49,7 +49,7 @@ To track hyperparameters, first call *save_hyperparameters* from the LightningMo
 
 If your logger supports tracked hyperparameters, the hyperparameters will automatically show up on the logger dashboard.
 
-TODO: show tracked hyperparameters.
+.. TODO:: show tracked hyperparameters.
 
 ----
 
@@ -66,4 +66,4 @@ Multiple loggers support visualizing the model topology. Here's an example that
         prototype_array = torch.Tensor(32, 1, 28, 27)
         tensorboard_logger.log_graph(model=self, input_array=prototype_array)
 
-TODO: show tensorboard topology.
+.. TODO:: show tensorboard topology.