liorsav commented on a change in pull request #57: URL: https://github.com/apache/incubator-liminal/pull/57#discussion_r676143721
########## File path: docs/getting-started/iris_classification.md ########## @@ -0,0 +1,291 @@ +<!-- +Licensed to the Apache Software Foundation (ASF) under one +or more contributor license agreements. See the NOTICE file +distributed with this work for additional information +regarding copyright ownership. The ASF licenses this file +to you under the Apache License, Version 2.0 (the +"License"); you may not use this file except in compliance +with the License. You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, +software distributed under the License is distributed on an +"AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +KIND, either express or implied. See the License for the +specific language governing permissions and limitations +under the License. +--> + +# Getting started / ***Iris Classification*** + +In this tutorial, we will guide you through setting up Apache Liminal on your local machine and run the simple machine-learning workflow example. + +* [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) + + +#### 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) + +*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) + + + + +***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: + + + +#### Click on “train” and you will get this popup: + + + +#### Click on “view log” button and you can see the log of the current task run: + + +## Liminal YAML walkthrough +* [Mounted volumes](#Mounted-volumes) +* [Pipeline flow](#Pipeline-flow) + +### Mounted volumes +Describe 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 +Setting up the pipeline flow with the following tasks: +```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 is complete, you can launch a pod of the pre-built image which contains a flask server: Review comment: Once the iris classification model is complete, you can launch a pod of the pre-built image which contains a flask server: replace with Once the iris classification model training 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 configuration: -- This is an automated message from the Apache Git Service. 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