Release 0.11.9

[0.11.9] - 2023-07-23

Fixed

• 🐛 Make kedro-mlflow hook log parameters when the project is configured with the OmegaConfigLoader instead of raising an error (#430)

Removed

• ➖ Drop support for python=3.7 which has reached end-of-life status to prepare 0.19 (#391)

�

Release 0.11.8

[0.11.8] - 2023-02-13

Added

• ✨ Added support for Mlflow 2.0 (#390)

• ✨ The modelify command now accepts a --run-name to specifiy the run name where the model is logged (#408)

Fixed

• 📝 Update incorrect documentation about model registry with local relative filepath (#400)

• 🐛 The modelify command now creates a conda environment based on your environment python and kedro versions instead of hardcoded python=3.7 and kedro=0.16.5 (#405)

• 🐛 The modelify command now uses correctly the --pip-requirements argument instead of raising an error (#405)

• 🐛 The modelify command now uses modelify as a default run name (#408)

�

Release 0.11.7

[0.11.7] - 2023-01-28

Added

• ✨ Added a MlflowModelRegistryDataSet in kedro_mlflow.io.models to enable fetching a mlflow model from the mlflow model registry by its name(#260)

Fixed

• 🐛 Use __default__ as a run name if the pipeline is not specified in the kedro run commmand to avoid empty names (#392)

�

Release 0.11.6

[0.11.6] - 2023-01-09

Changed

• ✨ kedro-mlflow now uses the default configuration (ignoring mlflow.yml) if an active run already exists in the process where the pipeline is started, and uses this active run for logging. This enables using kedro-mlflow with an orchestrator which starts mlflow itself before running kedro (e.g. airflow, the mlflow run command, AzureML...) (#358)

�

Release 0.11.5

[0.11.5] - 2022-12-12

Added

• ✨ Added an extra server.mlflow_registry_uri key in mlflow.yml to set the mlflow registry uri. (#260)
• ✨ Add support for authorization with expiring tokens by adding an extra server.request_header_provider entry in mlflow.yml (#357)

Fixed

• 🐛 MlflowArtifactDataSet.load() now correctly loads the artifact when both artifact_path and run_id arguments are specified. Previous fix in 0.11.4 did not work because when the file already exist locally, mlflow did not download it again so tests were incorrectly passing (#362)

Removed

• 🔥 💥 Remove reload_kedro_mlflow line magic for notebook because kedro will deprecate the entrypoint in 0.18.3. It is still possible to access the mlflow client associated to the configuration in a notebook with context.mlflow.server._mlflow_client (#349). This is not considered as a breaking change since apparently no one uses it according to a discussion with kedro's team.

�

Release 0.11.4

[0.11.4] - 2022-10-04

Fixed

• 🐛 MlflowArtifactDataSet.load() now correctly loads the artifact when both artifact_path and run_id arguments are specified instead of raising an error (#362)

�

Release 0.11.3

[0.11.3] - 2022-09-06

Changed

• 🔊 kedro-mlflow has its default logging level set to INFO. This was the default for kedro<=0.18.1. For kedro>=0.18.2, you can change the level in logging.yml (#348)

Fixed

• 🐛 kedro-mlflow now use the package_name as experiment name by default if it is not specified. This is done to ensure consistency with the behaviour with no mlflow.yml file (#328)
• 📝 Update broken links to the most recent kedro and mlflow documentation

�

Release 0.11.2

[0.11.2] - 2022-08-28

Changed

• ✨ kedro-mlflow now runs even without a mlflow.yml file in your conf/<env> folder. As a consequence, running kedro mlflow init is now optional and should be only used for advanced configuration. (#328)

�

Release 0.11.1

[0.11.1] - 2022-07-06

Fixed

• 🐛 Make pipeline_ml_factory now correctly uses kpm_kwargs and log_model_kwargs instead of always using the default values. (#329)
• 🐛 kedro mlflow init command no longer raises both a success and an error message when the command is failing. (#336)

Changed

• ♻️ Refactor KedroMlflowConfig which no longer needs the project_path at instantiation. The uri validaiton is done at setup() time to be able to use the configuration not at a root of a kedro project. This is not considered as a breaking change, because the recommended way to retrieve the config is to use session.load_context().mlflow which automatically calls setup() and hence behaviour inside a kedro project is unmodified. (#314)

�

Release 0.11.0

[0.11.0] - 2022-06-18

Added

• ✨ 💥 The MLFLOW_TRACKING_URI environment variable is now used as the default tracking uri if the server.mlflow_tracking_uri config key is None. The mlflow.yml is changed to server: mlflow_tracking_uri: null to enforce this new behaviour as the default value. If the environment variable does not exists, it will behave like before. (#321).

Changed

• ♻️ 💥 Unify the MlflowPipelineHook and MlflowNodeHook in a single MlflowHook to ensure consistency in registration order (#315)
• ♻️ 🧑‍💻 💥 The get_mlflow_config public function is removed. If you need to access the mlflow configuration, you can do it automatically in the context mlflow attribute, e.g. session.load_context().mlflow (#310)

Removed

• ⚰️ 💥 Remove unused stores_environment_variables configuration option. This key must be removed from mlflow.yml.
• ⬆️ 🐛 Upgrade requirements to make support for kedro>=0.18.1, kedro<0.19.0 explicit. This is the only valid compatibility range since kedro-mlflow==0.10.0, but requirements had not been updated yet (#309).

�