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Kubeflow + MLflow on Juju with Microk8s

Get Started

Deploy Standalone MLflow Server

You can follow these always up-to-date docs for a step-by-step procedure.

Deploy MLflow Server with Kubeflow

You can follow these always up-to-date docs for a step-by-step procedure.

Run an Example Model with Kubeflow

Our docs also provide instructions on how to run MLflow examples.

Access Artifacts

Based on the setup in the Get Started section, artifacts will be stored in MinIO. You can access the artifacts using the MinIO client or Boto3 with Python.

Get MinIO Access Key and Secret Access Key

Run the MLflow action to retrieve MinIO credentials:

juju run-action mlflow-server/0 get-minio-credentials --wait

# Expected result
unit-mlflow-server-0:
  UnitId: mlflow-server/0
  id: "2"
  results:
    access-key: minio
    secret-access-key: P7B9CT4YX39QDF22LOG83EU9PA2UOA
  status: completed
  timing:
    completed: 2023-10-05 06:15:16 +0000 UTC
    enqueued: 2023-10-05 06:15:15 +0000 UTC
    started: 2023-10-05 06:15:15 +0000 UTC

MinIO client

Install the MinIO client following the official guide.

Set alias for the minio

mc alias set <alias> http://`juju status --format yaml | yq .applications.minio.units.minio/*.address`:9000 $AWS_ACCESS_KEY_ID $AWS_SECRET_ACCESS_KEY

To list the content in the default MLflow bucket:

mc ls <alias>/mlflow

To read the content of a specific file:

mc cat <alias>/<path to file>

Boto3

As an alternative, you can use Boto3 to interact with MinIO in Python:

import boto3
minio = boto3.client(
        "s3",
        endpoint_url=os.getenv("MLFLOW_S3_ENDPOINT_URL"),
        config=boto3.session.Config(signature_version="s3v4"),
    )

Note: If you are accessing the bucket outside of a Kubeflow notebook server, replace the OS environment variable with the MinIO unit's IP with :9000 at the end. Run this in the terminal to get the IP:

echo http://`juju status --format yaml | yq .applications.minio.units.minio/*.address`:9000

To list files in the default bucket mlflow:

response = minio.list_objects_v2(Bucket="mlflow")
files = response.get("Contents")
for file in files:
    print(f"file_name: {file['Key']}, size: {file['Size']}")

To download a specific file:

minio.download_file(default_bucket_name,'<minio file path>', '<notebook server file path>')