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Kubeflow + MLflow on Juju with Microk8s

Get Started

Deploy Standalone mlflow-server

juju deploy mlflow-server
juju deploy charmed-osm-mariadb-k8s mlflow-db
juju deploy minio
juju relate minio mlflow-server
juju relate mlflow-db mlflow-server

You could then use MLFlow Tracking API in your script to record your runs.

Deploy mlflow-server with kubeflow

Follow the quick start guide to deploy kubeflow on microk8s.

juju deploy mlflow-server
juju deploy charmed-osm-mariadb-k8s mlflow-db
juju relate minio mlflow-server
juju relate istio-pilot mlflow-server
juju relate mlflow-db mlflow-server
juju relate mlflow-server admission-webhook

MLFlow Dashboard

If you deployed MLFlow with Kubeflow, you could access the dashboard by going to http://10.64.140.43.nip.io/mlflow/#/ Otherwise, run microk8s kubectl get services -A | grep "mlflow-server", and open the mlflow ClusterIP in the browser with :5000 on the end.

MLFlow Dashboard Screenshot

Run an Example Model with Kubeflow

First, deploy MLFlow server with kubeflow.

Temporary workaround for missing pod-defaults: Run the following command to make a copy of pod defaults to user's namespace, which is admin following the guide. microk8s kubectl get poddefaults mlflow-server-minio -o yaml -n kubeflow | sed 's/namespace: kubeflow/namespace: admin/' | microk8s kubectl create -f -

Open http://10.64.140.43.nip.io/ and log in with the username and password set in the quick start guide.

Create a new notebook server, taking care to specify the mlflow-server-minio configuration. This will ensure that the correct environment variables are set so that the MLflow SDK can connect to the MLflow server.

config

Upload or paste in the example model code to the notebook server.

Run the first two cells and observe that your model metrics are recorded in MLflow! Run the other two cells to view list of files in the object storage and download a file to the notebook server.

screenshot

Access Artifacts

Based on the setup in the Get Started section, artifacts would be stored in minio. You could access the artifacts using the minio client or boto3 with python.

Get minio key and secret

Find admission webhook unit's name juju status | grep admission-webhook/

Run the command below to get minio envs

juju show-unit <admission webhook unit name> | yq .admission-webhook/*.relation-info[0].application-data

# expect result
pod-defaults: '{"minio": {"env": {"AWS_ACCESS_KEY_ID": "some id", "AWS_SECRET_ACCESS_KEY": "some secret key", "MLFLOW_S3_ENDPOINT_URL": "http://minio.kubeflow:9000", "MLFLOW_TRACKING_URI": "http://mlflow-server.kubeflow.svc.cluster.local:5000"}}}'

minio client

Install 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 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

These code are also included in the example notebook.

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 env with 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 of 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>')