MLflow is an open source platform to manage the Machine Learning (ML) lifecycle, including experimentation, reproducibility, deployment, and a central model registry. To learn more about MLflow, please refer to its documentation.


In order to use MLflow on Summit, load the module as shown below:

$ module load workflows
$ module load mlflow/1.22.0

Run the following command to verify that MLflow is available:

$ mlflow --version
mlflow, version 1.22.0

Hello world!

To run this MLflow demo on Summit, you will create a directory with two files and then submit a batch job to LSF from a Summit login node.

First, create a directory mlflow-example to contain two files. The first will be named MLproject:

name: demo

    command: "python3"

The second will be named

import mlflow

print("MLflow Version:", mlflow.version.VERSION)
print("Tracking URI:", mlflow.tracking.get_tracking_uri())

with mlflow.start_run() as run:
    print("Run ID:",
    print("Artifact URI:", mlflow.get_artifact_uri())
    with open("hello.txt", "w") as f:
        f.write("Hello world!")

Finally, create an LSF batch script called mlflow_demo.lsf, and change abc123 to match your own project identifier:

#BSUB -P abc123
#BSUB -W 10
#BSUB -nnodes 1

#BSUB -J mlflow_demo
#BSUB -o mlflow_demo.o%J
#BSUB -e mlflow_demo.e%J

module load git
module load workflows
module load mlflow/1.22.0

jsrun -n 1 mlflow run ./mlflow-example --no-conda

Finally, submit the batch job to LSF by executing the following command from a Summit login node:

$ bsub mlflow_demo.lsf

Congratulations! Once the job completes, you will be able to check the standard output files to find the tracking and artifact directories.