Merge pull request #80 from NealAnalyticsLLC/mlflow-on-azure-stack

Adding Sample notebook for MLflow

Research/mlflow-on-azure-stack/Dockerfile.tmpl

@@ -0,0 +1,6 @@
+FROM debian:stretch
+ARG BUNDLE_DIR
+RUN apt-get update && apt-get install -y ca-certificates
+RUN apt-get install -y wget
+# Use the BUNDLE_DIR build argument to copy files into the bundle
+COPY . $BUNDLE_DIR

Research/mlflow-on-azure-stack/Readme.md

@@ -2,10 +2,14 @@
 
 This module demonstrates how to create an MLflow environment in Kubernetes on Azure Stack
 
+## In Progress
+- packaging the steps in this Readme into a CNAB package that can be installed using Porter
+
 ## Prerequisites 
 - Access to an existing Kubernetes cluster
 - A kubeconfig file to connect to cluster
 - A private ACR repository in Azure
+- Anaconda
 - Docker desktop
 - Kubectl 
 
@@ -68,4 +72,9 @@ kubectl get service mlflow-service --namespace <your-namespace> --output jsonpat
 1. Open your browser window
 1. Enter the the IP from the previous step in the below format
 1. http://<ip from above>:5000
-1. You should see something like the image below
+1. You should see something like the image below
+
+## Step 9: Starting the Jupyter Notebook Tutorial
+Open Jupyter Notebook, it should have been installed along side Anaconda. Navigate to the "notebooks" directory and select the MLflow_Tutorial notebook.
+
+Follow along in the tutorial. Any cell can be run by pushing shift + enter.

Research/mlflow-on-azure-stack/cnab_install.md

@@ -0,0 +1,31 @@
+
+## Step 1: Install Porter
+Make sure you have Porter installed. You can find the installation instructions for your OS at the link provided below.
+
+[Porter Installation Instructions](https://porter.sh/install/)
+
+
+## Step 2: Build Porter CNAB
+First you will need to navigate to this directory in the repository
+Next, you will build the porter CNAB
+
+```sh
+porter build
+```
+
+## Step 3: Generate Credentials 
+This step is needed to connect to your Kubernetes cluster
+
+```sh
+porter credentials generate 
+```
+Enter path to your kubeconfig file when prompted
+
+## Step 4: Use Porter CNAB
+Run one of the below commands to interact with the CNAB
+
+```sh
+porter install --cred <name of your credential>
+porter upgrade --cred <name of your credential>
+porter uninstall --cred <name of your credential>
+```

Research/mlflow-on-azure-stack/helper.sh

@@ -0,0 +1,21 @@
+#!/usr/bin/env bash
+set -euo pipefail
+
+install() {
+  echo Installing MLflow
+  kubectl create namespace mlflow
+  kubectl apply -f ./tracking_server.yaml --namespace mlflow
+  kubectl get service mlflow-service --namespace mlflow --output jsonpath={.status.loadBalancer.ingress[0].ip}
+}
+
+upgrade() {
+  echo Upgrade functionality has not been implemented
+}
+
+uninstall() {
+  echo Uninstalling MLflow server
+  kubectl delete namespace mlflow --now=true --wait=true
+}
+
+# Call the requested function and pass the arguments as-is
+"$@"

Research/mlflow-on-azure-stack/mlflow.Dockerfile

@@ -0,0 +1,11 @@
+FROM continuumio/miniconda3
+
+WORKDIR /app
+
+ADD . /app
+
+RUN pip install mlflow
+
+EXPOSE 5000
+
+ENTRYPOINT mlflow server --host 0.0.0.0 --port 5000

Research/mlflow-on-azure-stack/notebooks/.gitignore

@@ -0,0 +1,6 @@
+# ignore files generated through the tutorial
+mlruns
+conda.yaml
+MLproject
+tutorial.py
+ElasticNet-paths.png