docs/getting-started/iris_classification.md

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# Getting started / ***Iris Classification***

* [Setup your local environment](#Setup-your-local-environment)
* [Setup liminal](#setup-liminal)
    * [Liminal build](#Liminal-build)
    * [Liminal create](#Liminal-create)
    * [Liminal deploy](#Liminal-deploy)
    * [Liminal start](#Liminal-start)
* [Liminal YAML walkthrough](#Liminal-YAML-walkthrough)
* [Evaluate the Iris Classification model](#Evaluate-the-iris-classification-model)
* [Debugging Kubernetes Deployments](#Debugging-Kubernetes-Deployments)
* [Closing up](#Closing-up)

In this tutorial, we will guide you through setting up Apache Liminal on your local machine and run a simple machine-learning workflow, based on the classic Iris dataset classification example. \
More details in this [link](https://scikit-learn.org/stable/auto_examples/datasets/plot_iris_dataset.html).

#### Prerequisites

* [Python 3 (3.6 and up)](https://www.python.org/downloads)
* [Python Virtual Environments](https://pypi.org/project/virtualenv)
* [Docker Desktop](https://www.docker.com/products/docker-desktop)
* [Kubernetes CLI (kubectl)](https://kubernetes.io/docs/tasks/tools/install-kubectl-macos)

*Note: Make sure kubernetes cluster is running in docker desktop*

We will define the following steps and services to implement the Iris classification example: \
Train, Validate & Deploy - Training and validation execution is managed by Liminal Airflow extension. The training task trains a regression model using a public dataset. \
We then validate the model and deploy it to a model-store in mounted volume. \
Inference - online inference is done using a Python Flask service running on the local Kubernetes in docker desktop. The service exposes the `/predict` endpoint. It reads the model stored in the mounted drive and uses it to evaluate the request.
## Setup your local env environment

In the dev folder, clone the example code from liminal:


```BASH
git clone https://github.com/apache/incubator-liminal
```
***Note:*** *You just cloned the entire Liminal Project, you actually only need examples folder.*



Create a python virtual environment to isolate your runs:

```BASH
cd incubator-liminal/examples/aws-ml-app-demo
python3 -m venv env
```

Activate your virtual environment:

```BASH
source env/bin/activate
```

Now we are ready to install liminal:

```BASH
pip install apache-liminal
```

## Setup liminal
### Liminal build
The build will create docker images based on the liminal.yml file in the `images` section.
```BASH
liminal build
```

### Liminal create
All tasks use a mounted volume as defined in the pipeline YAML. \
In our case the mounted volume will point to the liminal Iris Classification example.
The training task trains a regression model using a public dataset. We then validate the model and deploy it to a model-store in the mounted volume.

Create a kubernetes local volume:
```BASH
liminal create
```

### Liminal deploy
The deploy command deploys a liminal server and deploys any liminal.yml files in your working directory or any of its subdirectories to your liminal home directory.
```BASH
liminal deploy --clean
```

*Note: liminal home directory is located in the path defined in LIMINAL_HOME env variable.
If the LIMINAL_HOME environemnet variable is not defined, home directory defaults to
~/liminal_home directory.*

### Liminal start
The start command spins up 3 containers that load the Apache Airflow stack. Liminal's Airflow extension is responsible to execute the workflows defined in the liminal.yml file as standard Airflow DAGs.
```BASH
liminal start
```

It runs the following three containers:
* liminal-postgress
* liminal-webserver
* liminal-scheduler

Once liminal server has completed starting up, you can navigate to admin UI in your browser:
[http://localhost:8080](http://localhost:8080)


![](../nstatic/iris-classification/airflow_main.png)

***Important:** Set off/on toggle to activate your pipeline (DAG), nothing will happen otherwise!*

You can go to graph view to see all the tasks configured in the liminal.yml file:
[http://localhost:8080/admin/airflow/graph?dag_id=my_datascience_pipeline](
http://localhost:8080/admin/airflow/graph?dag_id=my_datascience_pipeline
)

#### Now lets see what actually happened to our task:
![](../nstatic/iris-classification/airflow_view_dag.png)


#### Click on “train” and you will get this popup:
![](../nstatic/iris-classification/airflow_view_log.png)


#### Click on “view log” button and you can see the log of the current task run:
![](../nstatic/iris-classification/airflow_task_log.png)

## Liminal YAML walkthrough
* [Mounted volumes](#Mounted-volumes)
* [Pipeline flow](#Pipeline-flow)

### Mounted volumes
Declaration of the mounted volume in your liminal YAML:
```YAML
name: MyDataScienceApp
owner: Bosco Albert Baracus
volumes:
  - volume: gettingstartedvol
    claim_name: gettingstartedvol-pvc
    local:
      path: .
```

### Pipeline flow
Declaration of the pipeline tasks flow in your liminal YAML:
```YAML
pipelines:
  - pipeline: my_datascience_pipeline
    ...
    schedule: 0 * 1 * *
    tasks:
      - task: train
        type: python
        description: train model
        image: myorg/mydatascienceapp
        cmd: python -u training.py train
        ...
      - task: validate
        type: python
        description: validate model and deploy
        image: myorg/mydatascienceapp
        cmd: python -u training.py validate
        ...
```

##### Each task will internally mount the volume defined above to an internal representation, described under the task section in the yml:

```YAML
pipelines:
    ...
    tasks:
      - task: train
        ...
        env:
          MOUNT_PATH: /mnt/gettingstartedvol
        mounts:
          - mount: mymount
            volume: gettingstartedvol
            path: /mnt/gettingstartedvol
```
###### We specify the `MOUNT_PATH` in which we store the trained model.

## Evaluate the iris classification model

Once the Iris Classification model trainging is completed and model is deployed (to the mounted volume), you can launch a pod of the pre-built image which contains a flask server, by applying the following Kubernetes manifest configuration:
```BASH
kubectl apply -f manifests/aws-ml-app-demo.yaml
```

Alternatively, create a Kubernetes pod from stdin:
```YAML
cat <<EOF | kubectl apply -f -
---
apiVersion: v1
kind: Pod
metadata:
  name: aws-ml-app-demo
spec:
  volumes:
    - name: task-pv-storage
      persistentVolumeClaim:
        claimName: gettingstartedvol-pvc
  containers:
    - name: task-pv-container
      imagePullPolicy: Never
      image: myorg/mydatascienceapp
      lifecycle:
        postStart:
          exec:
            command: ["/bin/bash", "-c", "apt update && apt install curl -y"]
      ports:
        - containerPort: 80
          name: "http-server"
      volumeMounts:
        - mountPath: "/mnt/gettingstartedvol"
          name: task-pv-storage
EOF
```

Check that the service is running:
```BASH
kubectl get pods --namespace=default
```

Check that the service is up:
```BASH
kubectl exec -it --namespace=default aws-ml-app-demo -- /bin/bash -c "curl localhost/healthcheck"
```

Check the prediction:
```BASH
kubectl exec -it --namespace=default aws-ml-app-demo -- /bin/bash -c "curl -X POST -d '{\"petal_width\": \"2.1\"}' localhost/predict"
```

## Debugging Kubernetes Deployments
kubectl get pods will help you check your pod status:
```BASH
kubectl get pods --namespace=default
```
kubectl logs will help you check your pods log:
```BASH
kubectl logs --namespace=default aws-ml-app-demo
```
kubectl exec to get a shell to a running container:
```BASH
kubectl exec --namespace=default aws-ml-app-demo -- bash
```
Then you can check the mounted volume `df -h` and to verify the result of the model.


## Here are the entire list of commands, if you want to start from scratch:

```
git clone https://github.com/apache/incubator-liminal
cd examples/aws-ml-app-demo
python3 -m venv env
source env/bin/activate
rm -rf ~/liminal_home
pip uninstall apache-liminal
pip install apache-liminal
Liminal build
Liminal create
liminal deploy --clean
liminal start
```

## Closing up

To make sure liminal containers are stopped use:
```
liminal stop
```

To deactivate the python virtual env use:
```
deactivate
```

To terminate the kubernetes pod:
```
kubectl delete pod --namespace=default aws-ml-app-demo
```