Release 0.5.0

[0.5.0] - 2021-02-21

Added

• A new long_parameters_strategy key is added in the mlflow.yml (under in the hook/node section). You can specify different strategies (fail, truncate or tag) to handle parameters over 250 characters which cause crashes for some mlflow backend. (#69)
• Add an env parameter to kedro mlflow init command to specify under which conf/ subfolder the mlflow.yml should be created. (#159)
• The input parameters of the inference pipeline of a PipelineML object are now automatically pickle-ised and converted as artifacts. (#158)
• Detailed documentation on how to use pipeline_ml_factory function, and more generally on how to use kedro-mlflow as mlops framework. This comes from an example repo kedro-mlflow-tutorial. (#16)

Fixed

• Pin the kedro version to force it to be strictly inferior to 0.17 which is not compatible with current kedro-mlflow version (#143)
• It is no longer assumed for the project to run that the mlflow.yml is located under conf/base. The project will run as soon as the configuration file is discovered by the registered ConfigLoader (#159)

Changed

• The KedroPipelineModel.load_context() method now loads all the DataSets in memory in the DataCatalog. It is also now possible to specify the runner to execute the model as well as the copy_mode when executing the inference pipeline (instead of deepcopying the datasets between each nodes which is kedro's default). This makes the API serving with mlflow serve command considerably faster (~20 times faster) for models which need compiling (e.g. keras, tensorflow ...) (#133)
• The CLI projects commands are now always accessible even if you have not called kedro mlflow init yet to create a mlflow.yml configuration file (#159)

�