deploying-ml-apps.md

title	sub_link	meta_title	meta_description	visibility	status	tags	sidebar
Deploying ML apps with Streamlit	serving/deploying-ml-apps	Deploying ML apps - Polyaxon quick start tutorial - Core Concepts	Deploying ML apps - Become familiar with the ecosystem of Polyaxon tools with a top-level overview and useful links to get you started.	public	published	tutorials concepts notebook quick-start	intro

Overview

In the previous guide we trained several models. In this section we will learn how to deploy a model as an interactive dashboard. This is an example of creating a parametrized app that you can host using Polyaxon. The same principles in this guides can be used to deploy similar apps using other projects like Dash, Voila, custom front-end service, ...

All code and manifests used in this tutorial can be found in this github repo.

Note: On Polyaxon EE or Polyaxon Cloud, the app will be protected and can only be accessed by users who have access to your organization following the permissions defined for each member.

Logged model

In our training script we used Polyaxon to log a model every time we run an experiment:

# Logging the model
tracking.log_model(model_path, name="iris-model", framework="scikit-learn", versioned=False)

Note: the versioned was removed in version >v1.17 and is the default behavior.

Deploying the model

We will deploy a simple streamlit service that will load the best model based on accuracy as an Iris classification App. The app will make predictions based on the features and will display an image corresponding to the flower class.

import streamlit as st
import pandas as pd
import joblib
import argparse

from PIL import Image


def load_model(model_path: str):
    model = open(model_path, "rb")
    return joblib.load(model)


if __name__ == '__main__':
    parser = argparse.ArgumentParser()
    parser.add_argument(
        '--model-path',
        type=str,
    )
    args = parser.parse_args()

    setosa = Image.open("images/iris-setosa.png")
    versicolor = Image.open("images/iris-versicolor.png")
    virginica = Image.open("images/iris-virginica.png")
    classifier = load_model(args.model_path)

    st.title("Iris flower species Classification")
    st.sidebar.title("Features")
    parameter_list = [
        "Sepal length (cm)",
        "Sepal Width (cm)",
        "Petal length (cm)",
        "Petal Width (cm)"
    ]
    sliders = []
    for parameter, parameter_df in zip(parameter_list, ['5.2', '3.2', '4.2', '1.2']):
        values = st.sidebar.slider(
            label=parameter,
            key=parameter,
            value=float(parameter_df),
            min_value=0.0,
            max_value=8.0,
            step=0.1
        )
        sliders.append(values)

    input_variables = pd.DataFrame([sliders], columns=parameter_list)

    prediction = classifier.predict(input_variables)
    if prediction == 0:
        st.image(setosa)
    elif prediction == 1:
        st.image(versicolor)
    else:
        st.image(virginica)

The custom component has a single input, it expects a run uuid, it then loads the model and copy it under the path polyaxon-ml-serving/streamlit-app/model.joblib.

version: 1.1
kind: component
name: iris-classification
tags: ["streamlit", "app"]

inputs:
- name: uuid
  type: str

run:
  kind: service
  ports: [8501]
  rewritePath: true
  init:
  - git: {"url": "https://github.com/polyaxon/polyaxon-ml-serving"}
  - artifacts: {"files": [["{{ uuid }}/outputs/model/model.joblib", "{{ globals.artifacts_path }}/polyaxon-ml-serving/streamlit-app/model.joblib"]]}
  container:
    image: polyaxon/polyaxon-examples:ml-serving
    workingDir: "{{ globals.artifacts_path }}/polyaxon-ml-serving/streamlit-app"
    command: [streamlit, run, app.py]
    args: ["--", "--model-path=./model.joblib"]

To schedule the app with Polyaxon:

polyaxon run -f streamlit-app/polyaxonfile.yaml -P uuid=f8176c9463a345908ce6865c9c7894a9

Note that the uuid f8176c9463a345908ce6865c9c7894a9 will be different in your use case.

Saving the custom app in the component hub

By adding more parameters to this app, users can save this component to their private hub and allow users to easily schedule the custom dashboard using the CLI/UI.