[Docs] Remove unnecessary testoutputs (ray-project#39141) (ray-projec…

…t#39320)

ray-project/pytest-sphinx#5 changed our tooling to only check outputs if you provide a testoutput (previously, if you didn't provide a testoutput, then our tooling would expect your testcode to produce no output). As a follow up, this PR removes unnecessary testoutput blocks.

Signed-off-by: Balaji Veeramani <balaji@anyscale.com>
Co-authored-by: Balaji Veeramani <balaji@anyscale.com>

doc/source/data/batch_inference.rst

@@ -454,10 +454,6 @@ Models that have been trained with :ref:`Ray Train <train-docs>` can then be use
     )
     result = trainer.fit()
 
-.. testoutput::
-    :hide:
-
-    ...
 
 **Step 2:** Extract the :class:`Checkpoint <ray.train.Checkpoint>` from the training :class:`Result <ray.train.Result>`.
 

doc/source/data/working-with-pytorch.rst

@@ -77,10 +77,6 @@ Ray Data integrates with :ref:`Ray Train <train-docs>` for easy data ingest for
     )
     trainer.fit()
 
-.. testoutput::
-    :hide:
-
-    ...
 
 For more details, see the :ref:`Ray Train user guide <data-ingest-torch>`.
 

doc/source/ray-contribute/writing-code-snippets.rst

@@ -269,17 +269,13 @@ If your output is nondeterministic and you want to display a sample output, add
 
         0.969461416250246
 
-If your output is hard to test and you don't want to display a sample output, use
-ellipses and `:hide:`. ::
+If your output is hard to test and you don't want to display a sample output, exclude
+the ``testoutput``. ::
 
     .. testcode::
 
         print("This output is hidden and untested")
 
-    .. testoutput::
-        :hide:
-
-        ...
 
 ------------------------------
 How to test examples with GPUs

doc/source/ray-observability/user-guides/debug-apps/general-debugging.rst

@@ -68,10 +68,6 @@ And I have this code:
 
   print(ray.get(futures))
 
-.. testoutput::
-    :hide:
-
-    ...
 
 then you will get a mix of True and False. If
 ``check_file()`` runs on the Head Node, or we're running

doc/source/train/user-guides/data-loading-preprocessing.rst

@@ -342,10 +342,6 @@ For example, to split only the training dataset, do the following:
     )
     my_trainer.fit()
 
-.. testoutput::
-    :hide:
-
-    ...
 
 Full customization (advanced)
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
@@ -408,10 +404,6 @@ For use cases not covered by the default config class, you can also fully custom
     )
     my_trainer.fit()
 
-.. testoutput::
-    :hide:
-
-    ... 
 
 The subclass must be serializable, since Ray Train copies it from the driver script to the driving actor of the Trainer. Ray Train calls its :meth:`configure <ray.train.DataConfig.configure>` method on the main actor of the Trainer group to create the data iterators for each worker.
 
@@ -460,11 +452,6 @@ First, randomize each :ref:`block <dataset_concept>` of your dataset via :meth:`
     )
     my_trainer.fit()
 
-.. testoutput::
-    :hide:
-
-    ...
-
 
 If your model is sensitive to shuffle quality, call :meth:`Dataset.random_shuffle <ray.data.Dataset.random_shuffle>` to perform a global shuffle.
 
@@ -576,11 +563,6 @@ You can use this with Ray Train Trainers by applying them on the dataset before
     print(StandardScaler.deserialize(metadata["preprocessor_pkl"]))
 
 
-.. testoutput::
-    :hide:
-
-    ...
-
 In this example, we persist the fitted preprocessor using the ``Trainer(metadata={...})`` constructor argument. This arg specifies a dict that will available from ``TrainContext.get_metadata()`` and ``checkpoint.get_metadata()`` for checkpoints saved from the Trainer. This enables recreation of the fitted preprocessor for use for inference.
 
 Performance tips
@@ -620,10 +602,6 @@ For example, the following code prefetches 10 batches at a time for each trainin
     )
     my_trainer.fit()
 
-.. testoutput::
-    :hide:
-
-    ...
 
 .. _dataset_cache_performance:
 
@@ -669,10 +647,6 @@ Transformations that you want run per-epoch, such as randomization, should go af
 
     # Pass train_ds to the Trainer
 
-.. testoutput::
-    :hide:
-
-    ...
 
 Adding CPU-only nodes to your cluster
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~