[docs] change MLFlow to MLflow in docs (#13739)

doc/source/tune/_tutorials/overview.rst

@@ -71,9 +71,9 @@ Take a look at any of the below tutorials to get started with Tune.
    :description: :doc:`Track your experiment process with the Weights & Biases tools <tune-wandb>`
 
 .. customgalleryitem::
-    :tooltip: Use MLFlow with Ray Tune.
+    :tooltip: Use MLflow with Ray Tune.
     :figure: /images/mlflow.png
-    :description: :doc:`Log and track your hyperparameter sweep with MLFlow Tracking & AutoLogging <tune-mlflow>`
+    :description: :doc:`Log and track your hyperparameter sweep with MLflow Tracking & AutoLogging <tune-mlflow>`
 
 
 .. raw:: html

doc/source/tune/api_docs/logging.rst

@@ -162,7 +162,7 @@ CSVLogger
 MLFlowLogger
 ------------
 
-Tune also provides a default logger for `MLFlow <https://mlflow.org>`_. You can install MLFlow via ``pip install mlflow``.
+Tune also provides a default logger for `MLflow <https://mlflow.org>`_. You can install MLflow via ``pip install mlflow``.
 You can see the :doc:`tutorial here </tune/tutorials/tune-mlflow>`.
 
 WandbLogger

doc/source/tune/examples/index.rst

@@ -82,13 +82,13 @@ Pytorch Lightning
 - :doc:`/tune/examples/mnist_pytorch_lightning`: A comprehensive example using `Pytorch Lightning <https://github.com/PyTorchLightning/pytorch-lightning>`_ to train a MNIST model. This example showcases how to use various search optimization techniques. It utilizes the Ray Tune-provided :ref:`PyTorch Lightning callbacks <tune-integration-pytorch-lightning>`.
 - :ref:`A walkthrough tutorial for using Ray Tune with Pytorch-Lightning <tune-pytorch-lightning>`.
 
-Wandb, MLFlow
+Wandb, MLflow
 ~~~~~~~~~~~~~
 
 - :ref:`Tutorial <tune-wandb>` for using `wandb <https://www.wandb.ai/>`__ with Ray Tune
 - :doc:`/tune/examples/wandb_example`: Example for using `Weights and Biases <https://www.wandb.ai/>`__ with Ray Tune.
-- :doc:`/tune/examples/mlflow_example`: Example for using `MLFlow <https://github.com/mlflow/mlflow/>`__ with Ray Tune.
-- :doc:`/tune/examples/mlflow_ptl_example`: Example for using `MLFlow <https://github.com/mlflow/mlflow/>`__ and `Pytorch Lightning <https://github.com/PyTorchLightning/pytorch-lightning>`_ with Ray Tune.
+- :doc:`/tune/examples/mlflow_example`: Example for using `MLflow <https://github.com/mlflow/mlflow/>`__ with Ray Tune.
+- :doc:`/tune/examples/mlflow_ptl_example`: Example for using `MLflow <https://github.com/mlflow/mlflow/>`__ and `Pytorch Lightning <https://github.com/PyTorchLightning/pytorch-lightning>`_ with Ray Tune.
 
 Tensorflow/Keras
 ~~~~~~~~~~~~~~~~

doc/source/tune/index.rst

@@ -73,7 +73,7 @@ A key problem with machine learning frameworks is the need to restructure all of
 
 With Tune, you can optimize your model just by :ref:`adding a few code snippets <tune-tutorial>`.
 
-Further, Tune actually removes boilerplate from your code training workflow, automatically :ref:`managing checkpoints <tune-checkpoint>` and :ref:`logging results to tools <tune-logging>` such as MLFlow and TensorBoard.
+Further, Tune actually removes boilerplate from your code training workflow, automatically :ref:`managing checkpoints <tune-checkpoint>` and :ref:`logging results to tools <tune-logging>` such as MLflow and TensorBoard.
 
 
 Multi-GPU & distributed training out of the box