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mlflow-on-kubernetes

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deploying MLflow on Minikube and Openshift 4

mlflow-on-kubernetes 최신버젼 다운로드

최종 버전 다운로드 (.zip)

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# Deploying MLflow on Minikube, Openshift 4, and Azure Kubernetes Service

Adapted from https://towardsdatascience.com/mlflow-part-2-deploying-a-tracking-server-to-minikube-a2d6671e6455

## Folder structure & pre-requisites

Folder structure:

.
    ├── Dockerfile
    ├── example
    │   ├── test.txt
    │   ├── train.csv
    │   └── train.py
    ├── k8s
    │   ├── mlflow_deployment.yaml
    │   ├── mlflow_minio.yaml
    │   └── mlflow_postgres.yaml
    └── README.md

Needed images:

- **MLflow**: As we will need a MLflow docker image in the mlflow_deployment.yaml manifest, look at  https://github.com/pdemeulenaer/mlflow-image to produce such, or simply use the DockerHub image pdemeulenaer/mlflow-server:537

- **Postgres**: postgres:11

- **Minio**: minio/minio:RELEASE.2020-07-27T18-37-02Z . Although 1-year old, but fits well so far. For more recent images, need to change logic.

# How to deploy MLflow on Openshift?

Needed commands:

First: login into your openshift cluster. I did use the RedHat Openshift Sandbox (https://developers.redhat.com/developer-sandbox, free, 2 weeks availability, renewable), like this:

$ oc login --token=sha256~some_token --server=some-os4-server:port

Then, in the k8s folder, execute the Postgres manifest:

$ kubectl apply -f mlflow_postgres.yaml

configmap/mlflow-postgres-config created
    statefulset.apps/mlflow-postgres created
    service/mlflow-postgres-service created

Then execute the Minio manifest:

$ kubectl apply -f mlflow_minio.yaml

deployment.apps/mlflow-minio created
    service/mlflow-minio-service created
    Warning: networking.k8s.io/v1beta1 Ingress is deprecated in v1.19+, unavailable in v1.22+; use networking.k8s.io/v1 Ingress
    ingress.networking.k8s.io/mlflow-minio-ingress created
    persistentvolumeclaim/mlflow-pvc created

Extract the (internal) ip addresses of both Postgres and Minio services, from here:

$ kubectl get pods,services

NAME                                READY   STATUS              RESTARTS   AGE
    pod/mlflow-minio-64447c6687-bs9rm   0/1     ContainerCreating   0          12s
    pod/mlflow-postgres-0               0/1     ContainerCreating   0          44s

NAME                              TYPE       CLUSTER-IP      EXTERNAL-IP   PORT(S)          AGE
    service/mlflow-minio-service      NodePort   **172.30.133.80**   <none>        9000:31586/TCP   12s
    service/mlflow-postgres-service   NodePort   **172.30.173.63**   <none>        5432:30221/TCP   44s

Here I need to use these ip addresses to include them in the manifest mlflow_deployment.yaml for minio and postgres adresses. Manual step so far... (still trying to figure out how to automate that)

$ kubectl apply -f mlflow_deployment.yaml

deployment.apps/mlflow-deployment created
    service/mlflow-service created
    Warning: networking.k8s.io/v1beta1 Ingress is deprecated in v1.19+, unavailable in v1.22+; use networking.k8s.io/v1 Ingress
    ingress.networking.k8s.io/mlflow-ingress created

Then for openshift we need to create a route:

$ oc expose service/mlflow-service

route.route.openshift.io/mlflow-service exposed

As we would also need to access the Minio storage from python code when logging models to MLflow, we create a route to the Minio:

$ oc expose service/mlflow-minio-service

route.route.openshift.io/mlflow-minio-service exposed

Note for Openshift: while using the Openshift free sandbox, I could see that when deploying yaml manifests including images from DockerHub, sometimes I get errors "too many requests", so as if there would be a limit of pulling images. Usually waiting a bit between 2 deployments allowed to clear the issue. But when the issue persists (sometimes it does), a way around would be to import the images within Openshift container registry, for example like this, for the minio image:

$ oc import-image docker.io/minio/minio –confirm

In the log message following such command is the full address of the image within Openshift container registry.

# How to deploy MLflow on Minikube?

Almost same, just the route creation is not needed within minikube, instead ingress solution working. To enable the addon in minikube:

$ minikube addons list

$ minikube addons enable ingress

Then get the minikube cluster ip address:

$ minikube ip

Then we modify the /etc/hosts file (following https://towardsdatascience.com/mlflow-part-2-deploying-a-tracking-server-to-minikube-a2d6671e6455) so that the browser translates “localhost” to your local IP address. Paste the minikube IP address (which is 192.168.99.104 in my case) followed by BOTH host names for the MLflow server and Minio artifact store (mlflow-server.local and mlflow-minio.local)

192.168.99.104 mlflow-server.local mlflow-minio.local

# How to deploy MLflow on AKS?

An easy way of deploying in AKS is to use the built-in Azure LoadBalancer in the services of Minio and MLflow deployment manifests (and comment out the Ingress parts). This is not very production friendly, as there is no DNS name per se, but already good enough for testing the functionality. The Minio and MLflow deployments are then available at:

http://20.76.152.86:9000/minio/mlflow (provided that 20.76.152.86 is the external ip of the deployement)

http://20.76.192.95:5000/ (provided that 20.76.192.95 is the external ip of the deployement)

The external IP's are found by

$ kubectl get svc

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