MLFlow Tracking Server Operator

Overview

In this repository we are providing our data scientists with tooling to perform hyper parameter tuning with Kubernetes/OpenShift. Our business requirement is to have the ability to track parameters and metrics from their machine learning job in Kubernetes/OpenShift. We researched many tools but at this time the one that fits our use case for experiment tracking is MLFlow. We will be using the experiment tracking feature provided by MLFlow. We have ported MLFlow to run on OpenShift and is now available for data scientists to utilize.

The goal is for experiment tracking is to allow our data scientists to test many parameters at scale using OpenShift or Kubernetes. You can then compare different runs of your machine learning job with different parameters and choose the best parameters that yield the best metrics. See below screenshot.

Preparing Images With S2I

To run your experiments you first need to get your code into docker images and install the python dependencies. We have provided a template and by exporting your jupyter notebook into a python file you can check it into your github. Make sure to set the APP_FILE to the name of the file you exported. We have provided a template (mlflow-job-bc.yaml) to make that process simple to set up.

Installing Tracking Server Operator

At the moment we have decided to go with a shared MLFlow tracking server and each data scientist will have their own experiment sandbox (more on this later on in this document). In case you need to upgrade or want to install your own instance of tracking then the following command should allow for you to install mlflow tracking server in your namespace:

Note: If your using minishift or minikube you must have cluster-admin priviledges to install ai_v1alpha1_trackingserver_crd.yaml

# Setup Service Account
$ kubectl create -f deploy/service_account.yaml
# Setup RBAC
$ kubectl create -f deploy/role.yaml
$ kubectl create -f deploy/role_binding.yaml
# Setup the CRD
$ kubectl create -f deploy/crds/ai_v1alpha1_trackingserver_crd.yaml
# Deploy the app-operator
$ kubectl create -f deploy/operator.yaml

Note: Once the tracking server operator is installed then you can deploy the custom resource:

# Create an TrackingServer CR
# The default controller will watch for TrackingServer objects and create a pod for each CR
$ kubectl create -f deploy/crds/ai_v1alpha1_trackingserver_cr.yaml

In the ai_v1alpha1_trackingserver_cr.yaml file you can select which version of MLFlow you would like to run by using setting the Image spec to the version you would like for example for mlflow 0.8.0 you can use:

apiVersion: ai.mlflow.org/v1alpha1
kind: TrackingServer
metadata:
 name: sample-tracking-server
spec:
 # Add fields here
 size: 1
 Image: "quay.io/zmhassan/mlflow:0.8.0"

Jupyterhub Integration

In the OpenDatahub we have our own jupyter environment where we've installed the mlflow dependencies into the jupyter notebook images in the datahub. If you hve your own jupyter then just install mlflow via pip. pip install mlflow==$version Caution: Make sure your mlflow version matches the version of the tracking server.

It is recommended to review the mlflow website to learn about which functions you would use to do experiment tracking. See following link:

https://www.mlflow.org/docs/latest/python_api/mlflow.tracking.html

See image below of our jupter environment. Once you have mlflow dependencies you can start writing your data science experiment.

There are some methods to know about such as the following:

mlflow.log_param($1,$2) Used to log parameter used to train model

---

mlflow.log_metric($1,$2) Used to log metrics as a result of utilizing parameters to train model.

See mlflow documentation for additional details on python api for different use cases:

https://www.mlflow.org/docs/latest/genindex.html

Experiment Isolation

Each data scientist may want to have some isolation when running experiments and to achieve that you can utilize experiment id’s which provide some isolation. We will work on a more robust solution but at this time this solves our current problem. On the left side you will see sandboxes.

Special Environment Variables

• Environment variables:

  • MLFLOW_EXPERIMENT_ID - If you want to share the same mlflow server but want your experiments isolated from other data scientists then you can create your own sandbox. In the job run you provide an experiment id (must be integer). Example MLFLOW_EXPERIMENT_ID=1

To utilize for example zhassan experiment_id you would need to do the following.

Set the environment variable in your notebook: os.environ["MLFLOW_EXPERIMENT_ID"]='4'

Note: If you would like to create additional experiment ids other then the default of 0 then login into openshift console and connect into the terminal for mlflow and run the following command:

# Note: '<name>'- the name you would like to set as your experiment sandbox.
mlflow experiments create <name>
 

---

Deleting Experiments

You can delete experiments by logging into the mlflow console and changing directories to your /opt/app-root/src/mlruns/<experiment_id>; which contains all subfolders. Look for the folder with the run_id you wish to delete and delete that folder. In the future we may prevent this and have automated backups of experiments in s3.

Connecting To Tracking Server:

When utilizing jupyter notebook you need to set the tracking server uri either through environment variable or through code.

Choose one of the following options:

1. Set the environment variable in your notebook:

os.environ["MLFLOW_TRACKING_URI"]='http://localhost:5000'

1. Point your jupyter notebook to mlflow tracking server:

mlflow.set_tracking_uri("http://localhost:5000")

Storing Models in S3 with MLFlow

When training your model in addition to the hyper parameters you can also version your model.

You can store your models in S3 aws or Ceph Storage with S3 endpoint if you have MLFLOW_S3_ENDPOINT_URL set

You will need to be running 0.8.2 of mlflow server with the following environment variables set:

Optional only if your running Ceph or Minio:

• MLFLOW_S3_ENDPOINT_URL='http://0.0.0.0:9090'

Required:

• MLFLOW_EXTRA_OPS='--default-artifact-root s3://BUCKET_NAME/'
• AWS_ACCESS_KEY_ID=?????????????
• AWS_SECRET_ACCESS_KEY=?????????????

For Example you can use the following custom resource sample file to create MLFlow Tracking Server that stores models to S3:

apiVersion: ai.mlflow.org/v1alpha1
kind: TrackingServer
metadata:
  name: zak-tracking-server
spec:
  # Add fields here
  size: 2
  Image: "quay.io/zmhassan/mlflow:0.8.2"
  AWS_SECRET_NAME: "ceph-s3-secret"
  S3_ENDPOINT_URL: "http://0.0.0.0:9090"