PARTIAL #6 - Fix documentation typo with wrong reference to version

.bumpversion.cfg

@@ -4,3 +4,9 @@ current_version = 0.3.0
 [bumpversion:file:setup.py]
 
 [bumpversion:file:kedro_mlflow/__init__.py]
+[bumpversion:file:kedro-mlflow/docs/source/01_introduction/02_motivation.md]
+[bumpversion:file:kedro-mlflow/docs/source/01_introduction/03_installation.md]
+[bumpversion:file:kedro-mlflow/docs/source/02_hello_world_example/01_example_project.md]
+[bumpversion:file:kedro-mlflow/docs/source/02_hello_world_example/02_first_steps.md]
+[bumpversion:file:kedro-mlflow/docs/source/03_tutorial/04_version_parameters.md]
+[bumpversion:file:kedro-mlflow/docs/source/03_tutorial/05_version_datasets.md]

CHANGELOG.md

@@ -2,21 +2,18 @@
 
 ## [Unreleased]
 
-### Added
-
--
-
 ### Fixed
 
-- `get_mlflow_config` now uses the kedro context config_loader to get configs (#66). This indirectly solves the following issues:
-    -  `get_mlflow_config` now works in interactive mode if `load_context` is called  with a path different from the working directory (#30)
+- `get_mlflow_config` now uses the Kedro `ProjectContext` `ConfigLoader` to get configs (#66). This indirectly solves the following issues:
+    - `get_mlflow_config` now works in interactive mode if `load_context` is called  with a path different from the working directory (#30)
     - kedro_mlflow now works fine with kedro jupyter notebook independently of the working directory (#64)
-    - You can use global variables in `mlflow.yml` which  is now properly parsed if you use a `TemplatedConfigLoader`  (#72)
+    - You can use global variables in `mlflow.yml` which is now properly parsed if you use a `TemplatedConfigLoader`  (#72)
 - `mlflow init` is now getting conf path from context.CONF_ROOT instead of hardcoded conf folder. This makes the package robust to Kedro changes.
 
 ### Changed
 
-- `MlflowNodeHook` have now a before_pipeline_run hook which stores the ProjectContext and enable to retrieve configuration.
+- `MlflowNodeHook` now has a `before_pipeline_run` hook which stores the `ProjectContext` and enable to retrieve configuration (#66).
+- Documentation reference to the plugin is now dynamic when necessary (#6).
 
 ## [0.3.0] - 2020-10-11
 

docs/source/01_introduction/02_motivation.md

@@ -4,7 +4,7 @@
 
 Basically, you should use ``kedro-mlflow`` in **any ``Kedro`` project which involves machine learning** / deep learning. As stated in the [introduction](./01_introduction.md), ``Kedro``'s current versioning (as of version ``0.16.1``) is not sufficient for machine learning projects: it lacks a UI and a ``run`` management system. Besides, the ``KedroPipelineModel`` ability to serve a kedro pipeline as an API or a batch in one line of code is a great addition for collaboration and transition to production.
 
-If you do not use ``Kedro`` or if you do pure data manipulation which do not involve machine learning, this plugin is not what you are seeking for ;)
+If you do not use ``Kedro`` or if you do pure data manipulation which does not involve *machine learning*, this plugin is not what you are seeking for ;)
 
 ## Why should I use kedro-mlflow ?
 
@@ -32,15 +32,15 @@ Above implementations have the advantage of being very straightforward and *mlfl
 
 ``kedro-mlflow`` enforces these best practices while implementing a clear interface for each mlflow action in Kedro template. Below chart maps the mlflow action to perform with the Python API provided by kedro-mlflow and the location in Kedro template where the action should be performed.
 
-|Mlflow action                |Template file           |Python API              |
-|:----------------------------|:-----------------------|:-----------------------|
-|Set up configuration         |``mlflow.yml``          |``MlflowPipelineHook``  |
-|Logging parameters           |``run.py``              |``MlflowNodeHook``      |
-|Logging artifacts            |``catalog.yml``         |``MlflowArtifactDataSet``       |
-|Logging models               |NA                      |NA                      |
-|Logging metrics              |``catalog.yml``         |``MlflowMetricsDataSet``|
+|Mlflow action                |Template file           |Python API                                        |
+|:----------------------------|:-----------------------|:-------------------------------------------------|
+|Set up configuration         |``mlflow.yml``          |``MlflowPipelineHook``                            |
+|Logging parameters           |``run.py``              |``MlflowNodeHook``                                |
+|Logging artifacts            |``catalog.yml``         |``MlflowArtifactDataSet``                         |
+|Logging models               |NA                      |NA                                                |
+|Logging metrics              |``catalog.yml``         |``MlflowMetricsDataSet``                          |
 |Logging Pipeline as model    |``pipeline.py``         |``KedroPipelineModel`` and ``pipeline_ml_factory``|
 
-In the current version (``kedro_mlflow=0.2.0``), kedro-mlflow does not provide interface to log metrics, set tags or log models outside a Kedro ``Pipeline``. These decisions are subject to debate and design decisions (for instance, metrics are often updated in a loop during each epoch / training iteration and it does not always make sense to register the metric between computation steps, e.g. as a an I/O operation after a node run).
+In the current version (``kedro_mlflow=0.3.0``), `kedro-mlflow` does not provide interface to set tags or log models outside a Kedro ``Pipeline``. These decisions are subject to debate and design decisions (for instance, metrics are often updated in a loop during each epoch / training iteration and it does not always make sense to register the metric between computation steps, e.g. as a an I/O operation after a node run).
 
-_**Note:** the version ``0.2.0`` does not need any ``MLProject`` file to use mlflow inside your Kedro project. As seen in the [introduction](./01_introduction.md), this file overlaps with Kedro configuration files._
+_**Note:** the version ``0.3.0`` does not need any ``MLProject`` file to use mlflow inside your Kedro project. As seen in the [introduction](./01_introduction.md), this file overlaps with Kedro configuration files._

docs/source/01_introduction/03_installation.md

@@ -36,9 +36,9 @@ projects. It is developed as part of
 the Kedro initiative at QuantumBlack.
 
 Installed plugins:
-kedro_mlflow: 0.2.0 (hooks:global,project)
+kedro_mlflow: 0.3.0 (hooks:global,project)
 ```
-The version ``0.2.0`` of the plugin is installed and has both global and project commands.
+The version ``0.3.0`` of the plugin is installed and has both global and project commands.
 
 That's it! You are now ready to go!
 

docs/source/02_hello_world_example/01_example_project.md

@@ -91,6 +91,6 @@ Install the project dependencies:
 
 ```console
 pip install -r src/requirements.txt
-pip install --upgrade kedro-mlflow==0.2.0
+pip install --upgrade kedro-mlflow==0.3.0
 ```
 **Warning: Do not use ``kedro install`` commands does not seem to install the packages in your activated environment.**

docs/source/02_hello_world_example/02_first_steps.md

@@ -122,4 +122,4 @@ Reopen the ui, select the last run and see that the file was uploaded:
 
 This works for any type of file (including images with ``MatplotlibWriter``) and the UI even offers a preview for ``png`` and ``csv``, which is really convenient to compare runs.
 
-*Note: Mlflow offers specific logging for machine learning models that should be better suited for your use case, but is not supported yet in ``kedro-mlflow==0.2.0``*
+*Note: Mlflow offers specific logging for machine learning models that may be better suited for your use case, but is not supported yet in ``kedro-mlflow==0.3.0``*

docs/source/03_tutorial/04_version_parameters.md

@@ -1,7 +1,7 @@
 # Parameters versioning
 ## Automatic parameters versioning
 
-Parameters versioning is automatic when the ``MlflowNodeHook`` is added to [the hook list of the ``ProjectContext``](./02_setup.md). In ``kedro-mlflow==0.2.0``, this hook has a parameter called ``flatten_dict_params`` which enables to [log as distinct parameters the (key, value) pairs of a ```Dict`` parameter](../05_python_objects/02_Hooks.md).
+Parameters versioning is automatic when the ``MlflowNodeHook`` is added to [the hook list of the ``ProjectContext``](./02_setup.md). In ``kedro-mlflow==0.3.0``, the `mlflow.yml` configuration file has a parameter called ``flatten_dict_params`` which enables to [log as distinct parameters the (key, value) pairs of a ```Dict`` parameter](../05_python_objects/02_Hooks.md).
 
 You **do not need any additional configuration** to benefit from parameters versioning.
 

docs/source/03_tutorial/05_version_datasets.md

@@ -67,7 +67,7 @@ The location where artifact will be stored does not depends of the logging funct
 
 You can also refer to [this issue](https://github.com/Galileo-Galilei/kedro-mlflow/issues/15) for further details.
 
-In ``kedro-mlflow==0.2.0`` you must configure these elements by yourself. Further releases will introduce helpers for configuration.
+In ``kedro-mlflow==0.3.0`` you must configure these elements by yourself. Further releases will introduce helpers for configuration.
 
 ### Can I log an artifact in a specific run?
 The ``MlflowArtifactDataSet`` has an extra argument ``run_id`` which specifies the run in which the artifact will be logged. **Be cautious, because this argument will take precedence over the current run** when you call ``kedro run``, causing the artifact to be logged in another run that all the other data of the run.