[Doc] Fix broken references in Ray Tune documentation

doc/source/tune/api/execution.rst

@@ -54,3 +54,4 @@ tune.run_experiments
 
     run_experiments
     Experiment
+    TuneError

doc/source/tune/api/logging.rst

@@ -59,8 +59,10 @@ See the :doc:`tutorial here </tune/examples/tune-mlflow>`.
 
 .. autosummary::
     :nosignatures:
+    :toctree: doc/
 
-    tune.logger.mlflow.MLflowLoggerCallback
+    ~air.integrations.mlflow.MLflowLoggerCallback
+    ~air.integrations.mlflow.setup_mlflow
 
 Wandb Integration
 -----------------
@@ -71,8 +73,10 @@ See the :doc:`tutorial here </tune/examples/tune-wandb>`.
 
 .. autosummary::
     :nosignatures:
+    :toctree: doc/
 
-    tune.logger.wandb.WandbLoggerCallback
+    ~air.integrations.wandb.WandbLoggerCallback
+    ~air.integrations.wandb.setup_wandb
 
 
 Comet Integration
@@ -84,8 +88,9 @@ See the :doc:`tutorial here </tune/examples/tune-comet>`.
 
 .. autosummary::
     :nosignatures:
+    :toctree: doc/
 
-    tune.logger.comet.CometLoggerCallback
+    ~air.integrations.comet.CometLoggerCallback
 
 Aim Integration
 ---------------
@@ -119,4 +124,3 @@ The non-relevant metrics (like timing stats) can be disabled on the left to show
 relevant ones (like accuracy, loss, etc.).
 
 .. image:: ../images/ray-tune-viskit.png
-

doc/source/tune/examples/tune_analyze_results.ipynb

@@ -479,7 +479,7 @@
             "id": "184bd3ee",
             "metadata": {},
             "source": [
-                "The last reported metrics might not contain the best accuracy each trial achieved. If we want to get maximum accuracy that each trial reported throughout its training, we can do so by using {meth}`ResultGrid.get_dataframe <ray.tune.result_grid.ResultGrid.get_dataframe>` specifying a metric and mode used to filter each trial's training history."
+                "The last reported metrics might not contain the best accuracy each trial achieved. If we want to get maximum accuracy that each trial reported throughout its training, we can do so by using {meth}`~ray.tune.ResultGrid.get_dataframe` specifying a metric and mode used to filter each trial's training history."
             ]
         },
         {

doc/source/tune/faq.rst

@@ -634,7 +634,7 @@ You can configure this by setting the `RAY_CHDIR_TO_TRIAL_DIR=0` environment var
 This explicitly tells Tune to not change the working directory
 to the trial directory, giving access to paths relative to the original working directory.
 One caveat is that the working directory is now shared between workers, so the
-:meth:`train.get_context().get_trial_dir() <ray.train.context.TrainContext.get_.get_trial_dir>`
+:meth:`train.get_context().get_trial_dir() <ray.train.context.TrainContext.get_trial_dir>`
 API should be used to get the path for saving trial-specific outputs.
 
 .. literalinclude:: doc_code/faq.py

doc/source/tune/tutorials/tune-distributed.rst

@@ -237,7 +237,7 @@ even after failure.
 Recovering From Failures
 ~~~~~~~~~~~~~~~~~~~~~~~~
 
-Tune automatically persists the progress of your entire experiment (a ``Tuner.fit()`` session), so if an experiment crashes or is otherwise cancelled, it can be resumed through :meth:`Tuner.restore() <ray.tune.tuner.Tuner.restore>`.
+Tune automatically persists the progress of your entire experiment (a ``Tuner.fit()`` session), so if an experiment crashes or is otherwise cancelled, it can be resumed through :meth:`~ray.tune.Tuner.restore`.
 
 .. _tune-distributed-common:
 

doc/source/tune/tutorials/tune-storage.rst

@@ -212,11 +212,11 @@ you can resume it any time starting from the experiment state saved in the cloud
 There are a few options for restoring an experiment:
 ``resume_unfinished``, ``resume_errored`` and ``restart_errored``.
 Please see the documentation of
-:meth:`Tuner.restore() <ray.tune.tuner.Tuner.restore>` for more details.
+:meth:`~ray.tune.Tuner.restore` for more details.
 
 
 Advanced configuration
 ----------------------
 
 See :ref:`Ray Train's section on advanced storage configuration <train-storage-advanced>`.
-All of the configurations also apply to Ray Tune.
+All of the configurations also apply to Ray Tune.

python/ray/tune/experiment/trial.py

@@ -97,7 +97,7 @@ def validate(formats):
         """Validates formats.
 
         Raises:
-            ValueError if the format is unknown.
+            ValueError: if the format is unknown.
         """
         for i in range(len(formats)):
             formats[i] = formats[i].strip().lower()
@@ -659,7 +659,7 @@ def update_resources(self, resources: Union[dict, PlacementGroupFactory]):
         Should only be called when the trial is not running.
 
         Raises:
-            ValueError if trial status is running.
+            ValueError: if trial status is running.
         """
         if self.status is Trial.RUNNING:
             raise ValueError("Cannot update resources while Trial is running.")

python/ray/tune/tuner.py

@@ -391,7 +391,7 @@ def fit(self) -> ResultGrid:
     def get_results(self) -> ResultGrid:
         """Get results of a hyperparameter tuning run.
 
-        This method returns the same results as :meth:`fit() <ray.tune.tuner.Tuner.fit>`
+        This method returns the same results as :meth:`~ray.tune.Tuner.fit`
         and can be used to retrieve the results after restoring a tuner without
         calling ``fit()`` again.