Add MLflow typo checker (mlflow#10224)

Signed-off-by: harupy <[email protected]>
Signed-off-by: mlflow-automation <[email protected]>
Co-authored-by: mlflow-automation <[email protected]>

Author: harupy

.gitignore

@@ -1,4 +1,4 @@
-# Mlflow
+# MLflow
 mlruns/
 mlartifacts/
 outputs/

.pre-commit-config.yaml

@@ -55,3 +55,8 @@ repos:
         entry: 'grep "Signed-off-by:"'
         language: system
         stages: [prepare-commit-msg]
+
+      - id: mlflow-typo
+        name: mlflow-typo
+        entry: dev/mlflow-typo.sh
+        language: system

CHANGELOG.md

@@ -31,7 +31,7 @@ Features:
 - [Tracking] Introduce support for Spark 3.5's SparkConnect mode within MLflow to allow logging models created using this operation mode of Spark (#9534, @WeichenXu123)
 - [Tracking] Add support for logging system metrics to the MLflow fluent API (#9557, #9712, #9714, @chenmoneygithub)
 - [Tracking] Add callbacks within MLflow for Keras and Tensorflow (#9454, #9637, #9579, @chenmoneygithub)
-- [Tracking] Introduce a fluent login API for Databricks within Mlflow (#9665, #10180, @chenmoneygithub)
+- [Tracking] Introduce a fluent login API for Databricks within MLflow (#9665, #10180, @chenmoneygithub)
 - [Tracking] Add support for customizing auth for http requests from the MLflow client via a plugin extension (#10049, @lu-ohai)
 - [Tracking] Introduce experimental asynchronous logging support for metrics, params, and tags (#9705, @sagarsumant)
 - [Auth] Modify the behavior of user creation in MLflow Authentication so that only admins can create new users (#9700, @gabrielfu)
@@ -1341,7 +1341,7 @@ Bug fixes and documentation updates:
 - Fixed bug where MLflow model schema enforcement raised exceptions when validating string columns using pandas >= 1.0 (#3130, @harupy)
 - Fixed bug where `mlflow.spark.log_model` did not save model signature and input examples (#3151, @harupy)
 - Fixed bug in runs UI where tags table did not reflect deletion of tags. (#3135, @ParseDark)
-- Added example illustrating the use of RAPIDS with MLFlow (#3028, @drobison00)
+- Added example illustrating the use of RAPIDS with MLflow (#3028, @drobison00)
 
 Small bug fixes and doc updates (#3326, #3344, #3314, #3289, #3225, #3288, #3279, #3265, #3263, #3260, #3255, #3267, #3266, #3264, #3256, #3253, #3231, #3245, #3191, #3238, #3192, #3188, #3189, #3180, #3178, #3166, #3181, #3142, #3165, #2960, #3129, #3244, #3359 @harupy; #3236, #3141, @AveshCSingh; #3295, #3163, @arjundc-db; #3241, #3200, @zhidongqu-db; #3338, #3275, @sueann; #3020, @magnus-m; #3322, #3219, @dmatrix; #3341, #3179, #3355, #3360, #3363 @smurching; #3124, @jdlesage; #3232, #3146, @ankitmathur-db; #3140, @andreakress; #3174, #3133, @mlflow-automation; #3062, @cafeal; #3193, @tomasatdatabricks; 3115, @fhoering; #3328, @apurva-koti; #3046, @OlivierBondu; #3194, #3158, @dmatrix; #3250, @shivp950; #3259, @simonhessner; #3357 @dbczumar)
 

SECURITY.md

@@ -12,9 +12,9 @@ of MLflow, we may opt to backport patches to previous versions.
 
 ## Reporting a Vulnerability
 
-When finding a security vulnerability in Mlflow, please perform the following actions:
+When finding a security vulnerability in MLflow, please perform the following actions:
 
-- [Open an issue](https://github.com/mlflow/mlflow/issues/new?assignees=&labels=bug&template=bug_report_template.md&title=%5BBUG%5D%20Security%20Vulnerability) on the Mlflow repository. Ensure that you use `[BUG] Security Vulnerability` as the title and _do not_ mention any vulnerability details in the issue post.
+- [Open an issue](https://github.com/mlflow/mlflow/issues/new?assignees=&labels=bug&template=bug_report_template.md&title=%5BBUG%5D%20Security%20Vulnerability) on the MLflow repository. Ensure that you use `[BUG] Security Vulnerability` as the title and _do not_ mention any vulnerability details in the issue post.
 - Send a notification [email](mailto:[email protected]) to `[email protected]` that contains, at a minimum:
   - The link to the filed issue stub.
   - Your GitHub handle.

dev/mlflow-typo.sh

@@ -0,0 +1,7 @@
+#!/usr/bin/env bash
+
+if grep -nP '\bM(lf|LF|lF)low\b' "$@"; then
+    exit 1
+else
+    exit 0
+fi

docs/source/R-api.rst

@@ -45,7 +45,7 @@ Get information from a Databricks job execution context
 
 Parses the data from a job execution context when running on Databricks
 in a non-interactive mode. This function extracts relevant data that
-Mlflow needs in order to properly utilize the MLflow APIs from this
+MLflow needs in order to properly utilize the MLflow APIs from this
 context.
 
 .. code:: r

docs/source/getting-started/logging-first-model/step2-mlflow-client.rst

@@ -53,7 +53,7 @@ We now have a client interface to the tracking server that can both send data to
 The Default Experiment
 ----------------------
 
-Before we get to logging anything to the Tracking Server, let's take a look at a key feature that exists at the outset of starting any Mlflow
+Before we get to logging anything to the Tracking Server, let's take a look at a key feature that exists at the outset of starting any MLflow
 Tracking Server: the Default Experiment.
 
 The Default Experiment is a placeholder that is used to encapsulate all run information if an explicit Experiment is not declared.
@@ -114,7 +114,7 @@ Running it
    :align: center
    :alt: Exploring the Default Experiment
 
-   Using the Mlflow Client's search_experiments() API to view the Default Experiment
+   Using the MLflow Client's search_experiments() API to view the Default Experiment
 
 In the next step, we'll create our first experiment and dive into the options that are available for
 providing metadata information that helps to keep track of related experiments and organize our

docs/source/llms/index.rst

@@ -87,7 +87,7 @@ configuration and management of your LLM serving needs, select the provider that
             </a>
             <a href="gateway/index.html#providers">
                 <div class="logo-card">
-                    <img src="../_static/images/logos/mlflow-logo.svg" alt="Mlflow Logo">
+                    <img src="../_static/images/logos/mlflow-logo.svg" alt="MLflow Logo">
                 </div>
             </a>
             <a href="gateway/index.html#providers">

docs/source/llms/llm-evaluate/index.rst

@@ -177,7 +177,7 @@ Use a Custom List of Metrics
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
 Using the pre-defined metrics associated with a given model type is not the only way to generate scoring metrics 
-for LLM evaluation in MLFlow. You can specify a custom list of metrics in the `extra_metrics` argument in `mlflow.evaluate`:
+for LLM evaluation in MLflow. You can specify a custom list of metrics in the `extra_metrics` argument in `mlflow.evaluate`:
 
 * To add additional metrics to the default metrics list of pre-defined model type, keep the `model_type` and add your metrics to ``extra_metrics``:
 

docs/source/llms/llm-evaluate/notebooks/rag-evaluation.ipynb

@@ -250,7 +250,7 @@
     "    {\n",
     "        \"questions\": [\n",
     "            \"What is MLflow?\",\n",
-    "            \"How to run Mlflow.evalaute()?\",\n",
+    "            \"How to run MLflow.evalaute()?\",\n",
     "            \"How to log_table()?\",\n",
     "            \"How to load_table()?\",\n",
     "        ],\n",
@@ -626,9 +626,9 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
-       "      <td>How to run Mlflow.evalaute()?</td>\n",
+       "      <td>How to run MLflow.evalaute()?</td>\n",
        "      <td>You can use the MLflow Python API to run Mlfl...</td>\n",
-       "      <td>How to run Mlflow.evalaute()?</td>\n",
+       "      <td>How to run MLflow.evalaute()?</td>\n",
        "      <td>[{'lc_attributes': {}, 'lc_secrets': {}, 'meta...</td>\n",
        "      <td>2.885425</td>\n",
        "      <td>30</td>\n",
@@ -679,7 +679,7 @@
       "text/plain": [
        "                       questions  \\\n",
        "0                What is MLflow?   \n",
-       "1  How to run Mlflow.evalaute()?   \n",
+       "1  How to run MLflow.evalaute()?   \n",
        "2            How to log_table()?   \n",
        "3           How to load_table()?   \n",
        "\n",
@@ -691,7 +691,7 @@
        "\n",
        "                           query  \\\n",
        "0                What is MLflow?   \n",
-       "1  How to run Mlflow.evalaute()?   \n",
+       "1  How to run MLflow.evalaute()?   \n",
        "2            How to log_table()?   \n",
        "3           How to load_table()?   \n",
        "\n",

docs/source/llms/rag/notebooks/question-generation-retrieval-evaluation.ipynb

@@ -281,7 +281,7 @@
    },
    "source": [
     "## Step 4: Get Document Data\n",
-    "We scrape the documents from the Mlflow website to use to generate questions."
+    "We scrape the documents from the MLflow website to use to generate questions."
    ]
   },
   {
@@ -1069,7 +1069,7 @@
     }
    },
    "source": [
-    "We append 5 benchmark queries to help visualize how diverse the questions are. The first four of these questions are semantically similar and all asking about Mlflow, while the last is different and refers to spark and model registry."
+    "We append 5 benchmark queries to help visualize how diverse the questions are. The first four of these questions are semantically similar and all asking about MLflow, while the last is different and refers to spark and model registry."
    ]
   },
   {
@@ -1125,9 +1125,9 @@
    "source": [
     "benchmark_questions = [\n",
     "    \"What is MLflow?\",\n",
-    "    \"What is Mlflow about?\",\n",
-    "    \"Tell me about Mlflow Tracking\",\n",
-    "    \"How does Mlflow work?\",\n",
+    "    \"What is MLflow about?\",\n",
+    "    \"Tell me about MLflow Tracking\",\n",
+    "    \"How does MLflow work?\",\n",
     "    \"How is spark used in model registry?\",\n",
     "]\n",
     "questions_to_embed = questions_to_embed + benchmark_questions"

docs/source/models.rst

@@ -4114,7 +4114,7 @@ Let's examine the custom flavor module in more detail. The first step is to impo
 inluding ``sktime`` library, various MLflow utilities as well as the MLflow ``pyfunc`` module which
 is required to add the ``pyfunc`` specification to the MLflow model configuration. Note also the
 import of the ``flavor`` module itself. This will be passed to the
-:py:func:`mlflow.models.Model.log()` method to log the model as an artifact to the current Mlflow
+:py:func:`mlflow.models.Model.log()` method to log the model as an artifact to the current MLflow
 run.
 
 .. code-block:: python

docs/source/new-features/index.rst

@@ -209,7 +209,7 @@ Find out about the details of major features, changes, and deprecations below.
                     <div class="header">
                         Introducing the MLflow AI Gateway
                     </div>
-                    <img class="card-image" src="../_static/images/logos/mlflow-logo.svg" alt="Mlflow"></img>
+                    <img class="card-image" src="../_static/images/logos/mlflow-logo.svg" alt="MLflow"></img>
                     <div class="body">
                         We're excited to announce the newest top-level component in the MLflow ecosystem: <strong>The AI Gateway</strong>. 
                     </div>

docs/source/plugins.rst

@@ -25,8 +25,8 @@ The MLflow Python API supports several types of plugins:
 * **Run context providers**: specify context tags to be set on runs created via the
   :py:func:`mlflow.start_run` fluent API.
 * **Model Registry Store**: override model registry backend logic, e.g. to log to a third-party storage solution
-* **MLFlow Project backend**: override the local execution backend to execute a project on your own cluster (Databricks, kubernetes, etc.)
-* **MLFlow ModelEvaluator**: Define custom model evaluator, which can be used in :py:func:`mlflow.evaluate` API.
+* **MLflow Project backend**: override the local execution backend to execute a project on your own cluster (Databricks, kubernetes, etc.)
+* **MLflow ModelEvaluator**: Define custom model evaluator, which can be used in :py:func:`mlflow.evaluate` API.
 
 .. contents:: Table of Contents
   :local:
@@ -156,7 +156,7 @@ The example package contains a ``setup.py`` that declares a number of
             "mlflow.project_backend": "dummy-backend=mlflow_test_plugin.dummy_backend:PluginDummyProjectBackend",
             # Define a MLflow model deployment plugin for target 'faketarget'
             "mlflow.deployments": "faketarget=mlflow_test_plugin.fake_deployment_plugin",
-            # Define a Mlflow model evaluator with name "dummy_evaluator"
+            # Define a MLflow model evaluator with name "dummy_evaluator"
             "mlflow.model_evaluator": "dummy_evaluator=mlflow_test_plugin.dummy_evaluator:DummyEvaluator",
         },
     )
@@ -286,8 +286,8 @@ reference implementations as an example:
 * `Example ArtifactRepository tests <https://github.com/mlflow/mlflow/blob/branch-1.5/tests/store/artifact/test_local_artifact_repo.py>`_
 * `Example RunContextProvider tests <https://github.com/mlflow/mlflow/blob/branch-1.5/tests/tracking/context/test_git_context.py>`_
 * `Example Model Registry Store tests <https://github.com/mlflow/mlflow/blob/branch-1.5/tests/store/model_registry/test_sqlalchemy_store.py>`_
-* `Example Custom Mlflow Evaluator tests <https://github.com/mlflow/mlflow/blob/branch-1.23/tests/resources/mlflow-test-plugin/mlflow_test_plugin/dummy_evaluator.py>`_
-* `Example Custom Mlflow server tests <https://github.com/mlflow/mlflow/blob/branch-2.2.0/tests/server/test_handlers.py>`_
+* `Example Custom MLflow Evaluator tests <https://github.com/mlflow/mlflow/blob/branch-1.23/tests/resources/mlflow-test-plugin/mlflow_test_plugin/dummy_evaluator.py>`_
+* `Example Custom MLflow server tests <https://github.com/mlflow/mlflow/blob/branch-2.2.0/tests/server/test_handlers.py>`_
 
 
 Distributing Your Plugin

docs/source/tracking.rst

@@ -280,7 +280,7 @@ MLflow's Tracking Server can be used in an exclusive artifact proxied artifact h
 
     mlflow server --artifacts-destination s3://bucket_name --artifacts-only --host remote_host
 
-Running an MLFlow server in ``--artifacts-only`` mode:
+Running an MLflow server in ``--artifacts-only`` mode:
 
  * **Part 1a and b**:
 
@@ -909,7 +909,7 @@ This simplifies access requirements for users of the MLflow client, eliminating
 configure access tokens or username and password environment variables for the underlying object store when writing or retrieving artifacts.
 To disable proxied access for artifacts, specify ``--no-serve-artifacts``.
 
-Provided an Mlflow server configuration where the ``--default-artifact-root`` is ``s3://my-root-bucket``,
+Provided an MLflow server configuration where the ``--default-artifact-root`` is ``s3://my-root-bucket``,
 the following patterns will all resolve to the configured proxied object store location of ``s3://my-root-bucket/mlartifacts``:
 
  * ``https://<host>:<port>/mlartifacts``

examples/evaluation/rag-evaluation.ipynb

@@ -250,7 +250,7 @@
     "    {\n",
     "        \"questions\": [\n",
     "            \"What is MLflow?\",\n",
-    "            \"How to run Mlflow.evaluate()?\",\n",
+    "            \"How to run MLflow.evaluate()?\",\n",
     "            \"How to log_table()?\",\n",
     "            \"How to load_table()?\",\n",
     "        ],\n",
@@ -483,9 +483,9 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
-       "      <td>How to run Mlflow.evaluate()?</td>\n",
-       "      <td>\\n\\nYou can run Mlflow.evaluate() by using the...</td>\n",
-       "      <td>How to run Mlflow.evaluate()?</td>\n",
+       "      <td>How to run MLflow.evaluate()?</td>\n",
+       "      <td>\\n\\nYou can run MLflow.evaluate() by using the...</td>\n",
+       "      <td>How to run MLflow.evaluate()?</td>\n",
        "      <td>[{'lc_attributes': {}, 'lc_namespace': ['langc...</td>\n",
        "      <td>1.083653</td>\n",
        "      <td>39</td>\n",
@@ -494,7 +494,7 @@
        "      <td>4.7</td>\n",
        "      <td>4.5</td>\n",
        "      <td>5</td>\n",
-       "      <td>The output states that \"You can run Mlflow.eva...</td>\n",
+       "      <td>The output states that \"You can run MLflow.eva...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
@@ -533,19 +533,19 @@
       "text/plain": [
        "                       questions  \\\n",
        "0                What is MLflow?   \n",
-       "1  How to run Mlflow.evaluate()?   \n",
+       "1  How to run MLflow.evaluate()?   \n",
        "2            How to log_table()?   \n",
        "3           How to load_table()?   \n",
        "\n",
        "                                             outputs  \\\n",
        "0   MLflow is an open source platform for managin...   \n",
-       "1  \\n\\nYou can run Mlflow.evaluate() by using the...   \n",
+       "1  \\n\\nYou can run MLflow.evaluate() by using the...   \n",
        "2  \\n\\nYou can use the log_table() function in ML...   \n",
        "3          load_table() is not a function in MLflow.   \n",
        "\n",
        "                           query  \\\n",
        "0                What is MLflow?   \n",
-       "1  How to run Mlflow.evaluate()?   \n",
+       "1  How to run MLflow.evaluate()?   \n",
        "2            How to log_table()?   \n",
        "3           How to load_table()?   \n",
        "\n",
@@ -569,7 +569,7 @@
        "\n",
        "   faithfulness/v1/score                      faithfulness/v1/justification  \n",
        "0                      5  The output provided by the model is a detailed...  \n",
-       "1                      5  The output states that \"You can run Mlflow.eva...  \n",
+       "1                      5  The output states that \"You can run MLflow.eva...  \n",
        "2                      1  The output provides a detailed explanation of ...  \n",
        "3                      5  The output states that \"load_table() is not a ...  "
       ]

examples/flower_classifier/README.rst

@@ -78,7 +78,7 @@ To train the model, run the example as a standard MLflow project:
     mlflow run examples/flower_classifier
 
 This will download the training dataset from ``tensorflow.org``, train a classifier using Keras and
-log results with Mlflow.
+log results with MLflow.
 
 To test your model, run the included scoring scripts. For example, say your model was trained with
 run_id ``101``.

examples/gateway/huggingface/readme.md

@@ -97,4 +97,4 @@ See the [example script](example.py) within this directory to see how to query t
 
 ## Setting the parameters of TGI
 
-When you make a request to MLFlow gateway, the information you provide in the request body will be sent to TGI. This gives you more control over the output you receive from TGI. However, it's important to note that you cannot turn off `details` and `decoder_input_details`, as they are necessary for MLFlow Gateway to work correctly.
+When you make a request to MLflow gateway, the information you provide in the request body will be sent to TGI. This gives you more control over the output you receive from TGI. However, it's important to note that you cannot turn off `details` and `decoder_input_details`, as they are necessary for MLflow Gateway to work correctly.

examples/gateway/mlflow_serving/README.md

@@ -2,7 +2,7 @@
 
 In order to utilize the MLflow AI Gateway with MLflow model serving, a few steps must be taken
 in addition to those for configuring access to SaaS models (such as Anthropic and OpenAI). The first and most obvious
-step that must be taken prior to interfacing with an Mlflow served model is that a model needs to be logged to the
+step that must be taken prior to interfacing with an MLflow served model is that a model needs to be logged to the
 MLflow tracking server.
 
 An important consideration for deciding whether to interface the MLflow AI Gateway with a specific model is to evaluate the PyFunc interface that the model will
@@ -156,7 +156,7 @@ mlflow models serve -m file:///Users/me/demos/mlruns/0/bc8bdb7fb90c406eb95603a97
 
 ## Update the config.yaml to add a new completions route
 
-Ensure that the Mlflow serving endpoint starts and is ready for traffic.
+Ensure that the MLflow serving endpoint starts and is ready for traffic.
 
 ```commandline
 2023/08/08 17:39:14 INFO mlflow.models.flavor_backend_registry: Selected backend for flavor 'python_function'
@@ -362,7 +362,7 @@ routes:
         model_server_url: http://127.0.0.1:9030
 ```
 
-## Start the Mlflow AI Gateway server
+## Start the MLflow AI Gateway server
 
 Now that both routes (or all 3, if adding in the optional MPT-7B-instruct model route) are defined within the configuration YAML file and the Model Serving servers are ready to receive queries, we can start the MLflow AI Gateway server.