MLFlow Reporting

UV supports reporting to an MLFlow backend via the uv.mlflow.reporter.MLFlowReporter class. Using this reporter, you can easily log your parameters, tags, and metrics to a local directory or to your existing MLFlow backend:

import uv
from uv.mlflow.reporter import MLFlowReporter

def train(steps: int, slope: float):
  for i in range(steps):
    uv.report(step=i, k='my_metric', v=i*slope)

def main(**kwargs):
  with uv.start_run(), uv.active_reporter(MLFlowReporter()):
    uv.report_params(kwargs)
    train(**kwargs)

main(steps=8, slope=1.0)

Here we can see the steps needed to set up and use an MLFlowReporter. We start an MLFlow run using with uv.start_run() so that our run will be properly closed when our code execution is finished. Then we create an MLFlowReporter and set it as active using uv.active_reporter(). Once we have done this, we are ready to store parameters and log metrics to our MLFlow backend.

By default, this will create a directory ./mlruns in your current folder and store your metrics data there. To view the results in your browser, you can run an mlflow ui instance in your current folder:

mlflow ui

By default, this serves results at http://localhost:5000. Open this page in your browser and you should be able to view the results of your run.

You can change the MLFlow experiment and run names by setting the environment variables MLFLOW_EXPERIMENT_NAME and MLFLOW_RUN_NAME, respectively, or you can set the experiment_name and run_name parameters in the uv.start_run() method. You can also use an existing MLFlow tracking server by setting the MLFLOW_TRACKING_URI environment variable.

Please see our mlflow tutorial code for a complete example.

For a more involved example that uses Caliban to build Docker images and integrate with an existing MLFlow SQL-based backend, see this tutorial.