Galileo-Galilei/kedro-mlflow 0.8.0 on GitHub

[0.8.0] - 2022-01-05

✨ Add a kedro mlflow modelify command to export a pipeline as a mlflow model (#261)
📝 Format code blocks in documentation with blacken-docs
👷 Enforce the use of black and isort in the CI to enforce style guidelines for developers

✨ 💥 The pipeline_ml_factory accepts 2 new arguments log_model_kwargs (which will be passed as is to mlflow.pyfunc.log_model) and kpm_kwargs (which will be passed as is to KedroPipelineModel). This ensures perfect consistency with mlflow API and offers new possibility like saving the project source code alongside the model (#67). Note that model_signature, conda_env and model_name arguments are removed, and replace respectively by log_model_kwargs["signature"], log_model_kwargs["conda_env"] and log_model_kwargs["artifact_path"].
✨ 💥 The KedroPipelineModel custom mlflow model now accepts any kedro Pipeline as input (provided they have a single DataFrame input and a single output because this is an mlflow limitation) instead of only PipelineML objects. This simplifies the API for user who want to customise the model logging (#171). KedroPipelineModel.__init__ argument pipeline_ml is renamed pipeline to reflect this change.
🗑️ kedro_mlflow.io.metrics.MlflowMetricsDataSet is no longer deprecated because there is no alternative for now to log many metrics at the same time.
💥 Refactor mlflow.yml to match mlflow's API (#77). To migrate projects with kedro<0.8.0, please update their mlflow.yml with kedro mlflow init --force command.

🐛 KedroMlflowConfig.setup() methods now sets the experiment globally to ensure all runs are launched under the experiment specified in the configuration even in interactive mode (#256).

🔥 💥 KedroMlflowConfig and get_mlflow_config were deprecated since 0.7.3 and are now removed from kedro_mlflow.framework.context. Direct import must now use kedro_mlflow.config.

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