Run an MLflow Project
Wrapper for the 'mlflow run' CLI command. See https://www.mlflow.org/docs/latest/cli.html#mlflow-run for more info.
mlflow_run( uri = ".", entry_point = NULL, version = NULL, parameters = NULL, experiment_id = NULL, experiment_name = NULL, backend = NULL, backend_config = NULL, no_conda = FALSE, storage_dir = NULL )
uri |
A directory containing modeling scripts, defaults to the current directory. |
entry_point |
Entry point within project, defaults to 'main' if not specified. |
version |
Version of the project to run, as a Git commit reference for Git projects. |
parameters |
A list of parameters. |
experiment_id |
ID of the experiment under which to launch the run. |
experiment_name |
Name of the experiment under which to launch the run. |
backend |
Execution backend to use for run. |
backend_config |
Path to JSON file which will be passed to the backend. For the Databricks backend, it should describe the cluster to use when launching a run on Databricks. |
no_conda |
If specified, assume that MLflow is running within a Conda environment with the necessary dependencies for the current project instead of attempting to create a new Conda environment. Only valid if running locally. |
storage_dir |
Valid only when 'backend' is local. MLflow downloads artifacts from distributed URIs passed to parameters of type 'path' to subdirectories of 'storage_dir'. |
The run associated with this run.
## Not run:
# This parametrized script trains a GBM model on the Iris dataset and can be run as an MLflow
# project. You can run this script (assuming it's saved at /some/directory/params_example.R)
# with custom parameters via:
# mlflow_run(entry_point = "params_example.R", uri = "/some/directory",
# parameters = list(num_trees = 200, learning_rate = 0.1))
install.packages("gbm")
library(mlflow)
library(gbm)
# define and read input parameters
num_trees <- mlflow_param(name = "num_trees", default = 200, type = "integer")
lr <- mlflow_param(name = "learning_rate", default = 0.1, type = "numeric")
# use params to fit a model
ir.adaboost <- gbm(Species ~., data=iris, n.trees=num_trees, shrinkage=lr)
## End(Not run)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.