Updates the template of a kedro project. Running this command is mandatory to use kedro-mlflow. This adds “conf/base/mlflow.yml”: This is a configuration file used for run parametrization when calling “kedro run” command.

init [OPTIONS]


-e, --env <env>

The name of the kedro environment where the ‘mlflow.yml’ should be created. Default to ‘local’

-f, --force

Update the template without any checks.

-s, --silent

Should message be logged when files are modified?


Opens the mlflow user interface with the project-specific settings of mlflow.yml. This interface enables to browse and compares runs.



-e, --env <env>

The environment within conf folder we want to retrieve.

-p, --port <port>

The port to listen on

-h, --host <host>

The network address to listen on (default: Use to bind to all addresses if you want to access the tracking server from other machines.


Export a kedro pipeline as a mlflow model for serving

modelify [OPTIONS]


-p, --pipeline <pipeline_name>

Required A valid kedro pipeline name registered in Available pipelines can be listed with in ‘kedro registry list’

-i, --input-name <input_name>

Required The name of kedro dataset which contains the data to predict on


Should the signature of the input data be inferred for mlflow?


Should the input_example of the input data be inferred for mlflow?

-r, --run-id <run_id>

The id of the mlflow run where the model will be logged. If unspecified, the command creates a new run.

--run-name <run_name>

The name of the mlflow run where the model will be logged. Defaults to ‘modelify’.

--copy-mode <copy_mode>

The copy mode to use when replacing each dataset by a MemoryDataset. Either a string (applied all datasets) or a dict mapping each dataset to a copy_mode.

--artifact-path <artifact_path>

The artifact path of mlflow.pyfunc.log_model, see

--code-path <code_path>

The code path of mlflow.pyfunc.log_model, see

--conda-env <conda_env>

The conda environment of mlflow.pyfunc.log_model, see

--registered-model-name <registered_model_name>

The registered_model_name of mlflow.pyfunc.log_model, see

--await-registration-for <await_registration_for>

The await_registration_for of mlflow.pyfunc.log_model, see

--pip-requirements <pip_requirements>

The pip_requirements of mlflow.pyfunc.log_model, see

--extra-pip-requirements <extra_pip_requirements>

The extra_pip_requirements of mlflow.pyfunc.log_model, see