Cli commands
init
kedro mlflow init
: this command is needed to initalize your project. You cannot run any other commands before you run this one once. It performs 2 actions:
- creates a mlflow.yml
configuration file in your conf/local
folder
- replace the src/PYTHON_PACKAGE/run.py
file by an updated version of the template. If your template has been modified since project creation, a warning will be raised. You can either run kedro mlflow init --force
to ignore this warning (but this will erase your run.py
) or set hooks manually.
init
has two arguments:
--env
which enable to specifiy another environment where the mlflow.yml should be created (e.g,base
)--force
which overrides themlflow.yml
if it already exists and replaces it with the default one. Use it with caution!
ui
kedro mlflow ui
: this command opens the mlflow UI (basically launches the mlflow ui
command )
ui
accepts the port and host arguments of mlflow ui
command. The default values used will be the ones defined in the mlflow.yml
configuration file under the ui
.
If you provide the arguments at runtime, they wil take priority over the mlflow.yml
, e.g. if you have:
# mlflow.yml
ui:
localhost: "0.0.0.0"
port: "5001"
then
kedro mlflow ui --port=5002
will open the ui on port 5002.
modelify
kedro mlflow modelify
: this command converts a kedro pipeline to a mlflow model and logs it in mlflow. It enables distributing the kedro pipeline as a standalone model and leverages all mlflow serving capabilities (as an API).
modelify
accepts the following arguments :
--pipeline
,-p
: The name of the kedro pipeline name registered inpipeline_registry.py
that you want to convert to a mlflow model.--input-name
,-i
: The name of the kedro dataset (incatalog.yml
) which is the input of your pipeline. It contains the data to predict on.--infer-signature
: A boolean which indicates if the signature of the input data should be inferred for mlflow or not.--infer-input-example
: A boolean which indicates if the input_example of the input data should be inferred for mlflow or not--run-id
,-r
: The id of the mlflow run where the model will be logged. If unspecified, the command creates a new run.--run-name
: The name of the mlflow run where the model will be logged. Defaults to"modelify"
.--copy-mode
: The copy mode to use when replacing each dataset by aMemoryDataset
. Either a string (applied all datasets) or a dict mapping each dataset to acopy_mode
.--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
: 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
: “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
: 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
: 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
: 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
: 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