PARTIAL #6 - Add Markdown links checker to avoid broken links in docu…

…mentation

.github/workflows/test.yml

@@ -5,12 +5,12 @@ name: test
 
 on:
   push:
-    branches: [ develop, master ]
+    branches: [develop, master]
   pull_request:
-    branches: [ develop, master ]
+    branches: [develop, master]
 
 jobs:
-  build:
+  lint_and_test:
     runs-on: ${{ matrix.os }}
     strategy:
       matrix:
@@ -34,7 +34,7 @@ jobs:
         # stop the build if there are Python syntax errors or undefined names
         flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics --exclude kedro_mlflow/template/project/run.py
         # exit-zero treats all errors as warnings. The GitHub editor is 127 chars wide
-        flake8 . --count --max-complexity=10 --max-line-length=127 --statistics --exclude kedro_mlflow/template/project/run.py
+        flake8 . --count --exit-zero --max-complexity=10 --max-line-length=127 --statistics --exclude kedro_mlflow/template/project/run.py
     - name: Test with pytest and generate coverage report
       run: |
         pytest --cov=./ --cov-report=xml
@@ -45,3 +45,10 @@ jobs:
         file: ./coverage.xml
         env_vars: OS,PYTHON
         fail_ci_if_error: true
+  markdown-link-check:
+    runs-on: ubuntu-latest
+    steps:
+    - uses: actions/checkout@v2
+    - uses: gaurav-nelson/github-action-markdown-link-check@v1
+      with:
+        config-file: 'mlc_config.json'

CHANGELOG.md

@@ -41,7 +41,7 @@
 -   `pipeline_ml_factory` now accepts that `inference` pipeline `inputs` may be in `training` pipeline `inputs` ([#71](https://github.com/Galileo-Galilei/kedro-mlflow/issues/71))
 -   `pipeline_ml_factory` now infer automatically the schema of the input dataset to validate data automatically at inference time. The output schema can be declared manually in `model_signature` argument ([#70](https://github.com/Galileo-Galilei/kedro-mlflow/issues/70))
 -   Add two DataSets for model logging and saving: `MlflowModelLoggerDataSet` and `MlflowModelSaverDataSet` ([#12](https://github.com/Galileo-Galilei/kedro-mlflow/issues/12))
--   `MlflowPipelineHook` and `MlflowNodeHook` are now [auto-registered](https://kedro.readthedocs.io/en/latest/07_extend_kedro/04_hooks.html#registering-your-hook-implementations-with-kedro) if you use `kedro>=0.16.4` ([#29](https://github.com/Galileo-Galilei/kedro-mlflow/issues/29))
+-   `MlflowPipelineHook` and `MlflowNodeHook` are now [auto-registered](https://kedro.readthedocs.io/en/latest/07_extend_kedro/02_hooks.html#registering-your-hook-implementations-with-kedro) if you use `kedro>=0.16.4` ([#29](https://github.com/Galileo-Galilei/kedro-mlflow/issues/29))
 
 ### Fixed
 
@@ -61,7 +61,7 @@
 
 ### Removed
 
--   `kedro mlflow init` command is no longer declaring hooks in `run.py`. You must now [register your hooks manually](docs/source/03_tutorial/02_setup.md#declaring-kedro-mlflow-hooks) in the `run.py` if you use `kedro>=0.16.0, <0.16.3` ([#62](https://github.com/Galileo-Galilei/kedro-mlflow/issues/62)).
+-   `kedro mlflow init` command is no longer declaring hooks in `run.py`. You must now [register your hooks manually](https://kedro-mlflow.readthedocs.io/en/stable/source/02_installation/02_setup.html#declaring-kedro-mlflow-hooks) in the `run.py` if you use `kedro>=0.16.0, <0.16.3` ([#62](https://github.com/Galileo-Galilei/kedro-mlflow/issues/62)).
 -   Remove `pipeline_ml` function which was deprecated in 0.3.0. It is now replaced by `pipeline_ml_factory` ([#105](https://github.com/Galileo-Galilei/kedro-mlflow/issues/105))
 -   Remove `MlflowDataSet` dataset which was deprecated in 0.3.0. It is now replaced by `MlflowArtifactDataSet` ([#105](https://github.com/Galileo-Galilei/kedro-mlflow/issues/105))
 

README.md

@@ -18,7 +18,7 @@
 
 # What is kedro-mlflow?
 
-``kedro-mlflow`` is a [kedro-plugin](https://kedro.readthedocs.io/en/stable/04_user_guide/10_developing_plugins.html) for lightweight and portable integration of [mlflow](https://mlflow.org/docs/latest/index.html) capabilities inside [kedro](https://kedro.readthedocs.io/en/stable/index.html) projects. It enforces [``Kedro`` principles](https://kedro.readthedocs.io/en/stable/12_faq/01_faq.html?highlight=principles#what-is-the-philosophy-behind-kedro) to make mlflow usage as production ready as possible. Its core functionalities are :
+``kedro-mlflow`` is a [kedro-plugin](https://kedro.readthedocs.io/en/stable/07_extend_kedro/04_plugins.html) for lightweight and portable integration of [mlflow](https://mlflow.org/docs/latest/index.html) capabilities inside [kedro](https://kedro.readthedocs.io/en/stable/index.html) projects. It enforces [``Kedro`` principles](https://kedro.readthedocs.io/en/stable/12_faq/01_faq.html?highlight=principles#what-is-the-philosophy-behind-kedro) to make mlflow usage as production ready as possible. Its core functionalities are :
 
 - **versioning**: `kedro-mlflow` intends to enhance reproducibility for machine learning experimentation. With `kedro-mlflow` installed, you can effortlessly register your parameters or your datasets with minimal configuration in a kedro run. Later, you will be able to browse your runs in the mlflow UI, and retrieve the runs you want. This is directly linked to [Mlflow Tracking](https://www.mlflow.org/docs/latest/tracking.html).
 - **model packaging**: ``kedro-mlflow`` intends to be be an agnostic machine learning framework for people who want to write portable, production ready machine learning pipelines. It offers a convenient API to convert a Kedro pipeline to a ``model`` in the mlflow sense. Consequently, you can *API-fy* or serve your Kedro pipeline with one line of code, or share a model with without worrying of the preprocessing to be made for further use. This is directly linked to [Mlflow Models](https://www.mlflow.org/docs/latest/models.html).
@@ -39,19 +39,19 @@ If you want to use the ``develop`` version of the package which is the most up t
 pip install --upgrade git+https://github.com/Galileo-Galilei/kedro-mlflow.git@develop
 ```
 
-I strongly recommend to use ``conda`` (a package manager) to create an environment and to read [``kedro`` installation guide](https://kedro.readthedocs.io/en/stable/02_getting_started/01_prerequisites.html).
+I strongly recommend to use ``conda`` (a package manager) to create an environment and to read [``kedro`` installation guide](https://kedro.readthedocs.io/en/latest/02_get_started/02_install.html).
 
 # Getting started
 
 The documentation contains:
 
-- [A "hello world" example](https://kedro-mlflow.readthedocs.io/en/latest/source/02_hello_world_example/index.html) which demonstrates how you to **setup your project**, **version parameters** and **datasets**, and browse your runs in the UI.
-- A more [detailed tutorial](https://kedro-mlflow.readthedocs.io/en/latest/source/03_tutorial/index.html) to show more advanced features (mlflow configuration through the plugin, package and serve a kedro ``Pipeline``...)
+- [A "hello world" example](https://kedro-mlflow.readthedocs.io/en/stable/source/03_getting_started/index.html) which demonstrates how you to **setup your project**, **version parameters** and **datasets**, and browse your runs in the UI.
+- A more [detailed tutorial](https://kedro-mlflow.readthedocs.io/en/stable/source/04_experimentation_tracking/index.html) to show more advanced features (mlflow configuration through the plugin, package and serve a kedro ``Pipeline``...)
 
 Some frequently asked questions on more advanced features:
 
-- You want to log additional metrics to the run? -> [Try ``MlflowMetricsDataSet``](https://kedro-mlflow.readthedocs.io/en/latest/source/03_tutorial/07_version_metrics.html) !
-- You want to log nice dataviz of your pipeline that you register with ``MatplotlibWriter``? -> [Try ``MlflowArtifactDataSet`` to log any local files (.png, .pkl, .csv...) *automagically*](https://kedro-mlflow.readthedocs.io/en/latest/source/02_hello_world_example/02_first_steps.html#artifacts)!
+- You want to log additional metrics to the run? -> [Try ``MlflowMetricsDataSet``](https://kedro-mlflow.readthedocs.io/en/stable/source/04_experimentation_tracking/05_version_metrics.html) !
+- You want to log nice dataviz of your pipeline that you register with ``MatplotlibWriter``? -> [Try ``MlflowArtifactDataSet`` to log any local files (.png, .pkl, .csv...) *automagically*](https://kedro-mlflow.readthedocs.io/en/stable/source/04_experimentation_tracking/03_version_datasets.html)!
 - You want to create easily an API to share your awesome model to anyone? -> [See if ``pipeline_ml_factory`` can fit your needs](https://github.com/Galileo-Galilei/kedro-mlflow/issues/16)
 - You want to do something that is not straigthforward with current implementation? Open an issue, and let's see what happens!
 
@@ -65,7 +65,7 @@ This package is still in active development. We use [SemVer](https://semver.org/
 
 The user must be aware that we will not reach `1.0.0` milestone before Kedro does (mlflow has already reached `1.0.0`).
 
-If you want to see how to migrate from one version of `kedro-mlflow` to another, see the [migration guide](docs/source/03_tutorial/00_migration_guide.md).
+If you want to see how to migrate from one version of `kedro-mlflow` to another, see the [migration guide](https://kedro-mlflow.readthedocs.io/en/stable/source/02_installation/03_migration_guide.html).
 
 # Can I contribute?
 

docs/source/01_introduction/01_introduction.md

@@ -47,7 +47,7 @@ We discuss hereafter how the two libraries compete on the different functionalit
 
 ### Versioning: Kedro 1 - 1 Mlflow
 
-The ``Kedro`` [``Journal`` aims at reproducibility](https://kedro.readthedocs.io/en/stable/04_user_guide/13_journal.html), but is not focused on machine learning. The `Journal` keeps track of two elements:
+The ``Kedro`` [``Journal`` aims at reproducibility](https://kedro.readthedocs.io/en/latest/kedro.versioning.Journal.html), but is not focused on machine learning. The `Journal` keeps track of two elements:
 
 - the CLI arguments, including *on the fly* parameters. This makes the command used to run the pipeline fully reproducible.
 - the ``AbstractVersionedDataSet`` for which versioning is activated. It consists in copying the data whom ``versioned`` argument is ``True`` when the ``save`` method of the ``AbstractVersionedDataSet`` is called.

docs/source/02_installation/01_installation.md

@@ -6,7 +6,7 @@
 
 I strongly recommend to use ``conda`` (a package manager) to create an environment in order to avoid version conflicts between packages.
 
-I also recommend to read [Kedro installation guide](https://kedro.readthedocs.io/en/stable/02_getting_started/01_prerequisites.html) to set up your Kedro project.
+I also recommend to read [Kedro installation guide](https://kedro.readthedocs.io/en/latest/02_get_started/02_install.html) to set up your Kedro project.
 
 ```console
 conda create -n <your-environment-name> python=<3.[6-8].X>

docs/source/02_installation/02_setup.md

@@ -4,7 +4,7 @@ This section assume that [you have installed `kedro-mlflow` in your virtual envi
 
 ## Create a kedro project
 
-This plugin must be used in an existing kedro project. If you do not have a kedro project yet, you can create it with ``kedro new`` command. [See the kedro docs for a tutorial](https://kedro.readthedocs.io/en/latest/02_getting_started/03_new_project.html).
+This plugin must be used in an existing kedro project. If you do not have a kedro project yet, you can create it with ``kedro new`` command. [See the kedro docs for a tutorial](https://kedro.readthedocs.io/en/latest/02_get_started/04_new_project.html).
 
 If you do not have a real-world project, you can use a kedro example and [follow the "Getting started" example](../03_getting_started/01_example_project.md) to make a demo of this plugin out of the box.
 
@@ -36,13 +36,13 @@ you should see the following message:
 
 ### Declaring kedro-mlflow hooks
 
-``kedro_mlflow`` hooks implementations must be registered with Kedro. There are three ways of registring [hooks](https://kedro.readthedocs.io/en/latest/07_extend_kedro/04_hooks.html?highlight=hooks).
+``kedro_mlflow`` hooks implementations must be registered with Kedro. There are three ways of registering [hooks](https://kedro.readthedocs.io/en/latest/07_extend_kedro/02_hooks.html).
 
 **Note that you must register the two hooks provided by kedro-mlflow** (``MlflowPipelineHook`` and ``MlflowNodeHook``) for the plugin to work.
 
 #### Declaring hooks through auto-discovery (for `kedro>=0.16.4`) [Default behaviour]
 
-If you use `kedro>=0.16.4`, `kedro-mlflow` hooks are auto-registered automatically by default without any action from your side. You can [disable this behaviour](https://kedro.readthedocs.io/en/latest/07_extend_kedro/04_hooks.html#disable-auto-registered-plugins-hooks) in your `.kedro.yml` or your `pyproject.toml` file.
+If you use `kedro>=0.16.4`, `kedro-mlflow` hooks are auto-registered automatically by default without any action from your side. You can [disable this behaviour](https://kedro.readthedocs.io/en/latest/07_extend_kedro/02_hooks.html#disable-auto-registered-plugins-hooks) in your `.kedro.yml` or your `pyproject.toml` file.
 
 #### Declaring hooks through code, in ``ProjectContext`` (for `kedro>=0.16.0, <=0.16.3`)
 

docs/source/02_installation/03_migration_guide.md

@@ -6,7 +6,7 @@ This page explains how to migrate an existing kedro project to a more up to date
 
 ### Catalog entries
 
-Replace the follwoing entries:
+Replace the following entries:
 
 |old                                    |new                                              |
 |:--------------------------------------|:------------------------------------------------|

docs/source/03_getting_started/01_example_project.md

@@ -12,7 +12,7 @@ pip install kedro-mlflow==0.4.1
 
 ## Install the toy project
 
-For this end to end example, we will use the [kedro starter](https://kedro.readthedocs.io/en/latest/02_getting_started/05_starters.html#creating-new-projects-with-kedro-starters) with the [iris dataset](https://github.com/quantumblacklabs/kedro-starter-pandas-iris).
+For this end to end example, we will use the [kedro starter](https://kedro.readthedocs.io/en/latest/02_get_started/06_starters.html) with the [iris dataset](https://github.com/quantumblacklabs/kedro-starter-pandas-iris).
 
 We use this project because:
 

docs/source/04_experimentation_tracking/02_version_parameters.md

@@ -26,7 +26,7 @@ The hook **detects parameters through their prefix ``params:`` or the value ``pa
 
 ### How can I register a parameter if I use a ``TemplatedConfigLoader``?
 
-If you [use a ``TemplatedConfigLoader``](https://kedro.readthedocs.io/en/stable/04_user_guide/03_configuration.html?highlight=TemplatedConfigLoader#templating-configuration) to enable dynamic parameters contruction at runtime or dependency between configuration files, and if we assume your ``src/<project-name>/run.py`` file looks like:
+If you [use a ``TemplatedConfigLoader``](https://kedro.readthedocs.io/en/latest/04_kedro_project_setup/02_configuration.html#templating-configuration) to enable dynamic parameters contruction at runtime or dependency between configuration files, and if we assume your ``src/<project-name>/run.py`` file looks like:
 
 ```python
 from kedro.config import TemplatedConfigLoader  # new import

docs/source/04_experimentation_tracking/03_version_datasets.md

@@ -1,4 +1,5 @@
 # Versioning Kedro DataSets
+
 ## What is artifact tracking?
 
 Mlflow defines artifacts as "any data a user may want to track during code execution". This includes, but is not limited to:
@@ -13,7 +14,7 @@ Artifacts are a very flexible and convenient way to "bind" any data type to your
 
 ## How to version data in a kedro project?
 
-kedro-mlflow introduces a new ``AbstractDataSet`` called ``MlflowArtifactDataSet``. It is a wrapper for any ``AbstractDataSet`` which decorates the underlying dataset ``save`` method and logs the file automatically in mlflow as an artifact each time the ``save`` method is called.
+``kedro-mlflow`` introduces a new ``AbstractDataSet`` called ``MlflowArtifactDataSet``. It is a wrapper for any ``AbstractDataSet`` which decorates the underlying dataset ``save`` method and logs the file automatically in mlflow as an artifact each time the ``save`` method is called.
 
 Since it is an ``AbstractDataSet``, it can be used with the YAML API. Assume that you have the following entry in the ``catalog.yml``:
 

docs/source/04_experimentation_tracking/04_version_models.md

@@ -21,7 +21,7 @@ my_sklearn_model:
     flavor: mlflow.sklearn
 ```
 
-More informations on available parameters are available in the [dedicated section](docs\source\05_python_objects\01_DataSets.md#mlflowmodelloggerdataset).
+More informations on available parameters are available in the [dedicated section](../07_python_objects/01_DataSets.md#mlflowmodelloggerdataset).
 
 You are now able to use ``my_sklearn_model`` in your nodes. Since this model is registered in mlflow, you can also leverage the [mlflow model serving abilities](https://www.mlflow.org/docs/latest/cli.html#mlflow-models-serve) or [predicting on batch abilities](https://www.mlflow.org/docs/latest/cli.html#mlflow-models-predict), as well as the [mlflow models registry](https://www.mlflow.org/docs/latest/model-registry.html) to manage the lifecycle of this model.
 

mlc_config.json

@@ -0,0 +1,6 @@
+{
+    "aliveStatusCodes": [
+        429,
+        200
+    ]
+}