CLI
init
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]
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?
ui
Opens the mlflow user interface with the project-specific settings of mlflow.yml. This interface enables to browse and compares runs.
ui [OPTIONS]
Options
- -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: 127.0.0.1). Use 0.0.0.0 to bind to all addresses if you want to access the tracking server from other machines.
modelify
Export a kedro pipeline as a mlflow model for serving
modelify [OPTIONS]
Options
- -p, --pipeline <pipeline_name>
Required A valid kedro pipeline name registered in pipeline_registry.py. 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
- --infer-signature
Should the signature of the input data be inferred for mlflow?
- --infer-input-example
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 https://www.mlflow.org/docs/latest/python_api/mlflow.pyfunc.html#mlflow.pyfunc.log_model
- --code-path <code_path>
The code path of mlflow.pyfunc.log_model, see https://www.mlflow.org/docs/latest/python_api/mlflow.pyfunc.html#mlflow.pyfunc.log_model
- --conda-env <conda_env>
The conda environment of mlflow.pyfunc.log_model, see https://www.mlflow.org/docs/latest/python_api/mlflow.pyfunc.html#mlflow.pyfunc.log_model
- --registered-model-name <registered_model_name>
The registered_model_name of mlflow.pyfunc.log_model, see https://www.mlflow.org/docs/latest/python_api/mlflow.pyfunc.html#mlflow.pyfunc.log_model
- --await-registration-for <await_registration_for>
The await_registration_for of mlflow.pyfunc.log_model, see https://www.mlflow.org/docs/latest/python_api/mlflow.pyfunc.html#mlflow.pyfunc.log_model
- --pip-requirements <pip_requirements>
The pip_requirements of mlflow.pyfunc.log_model, see https://www.mlflow.org/docs/latest/python_api/mlflow.pyfunc.html#mlflow.pyfunc.log_model
- --extra-pip-requirements <extra_pip_requirements>
The extra_pip_requirements of mlflow.pyfunc.log_model, see https://www.mlflow.org/docs/latest/python_api/mlflow.pyfunc.html#mlflow.pyfunc.log_model