A demo for integrating PrimeHub and Airflow

primehub_tutorial.py

import logging as log
import datetime
from datetime import timedelta
import requests
import numpy as np

# The DAG object; we'll need this to instantiate a DAG
from airflow import DAG
from airflow.models import Variable

# Operators; we need this to operate!
from airflow.operators.python import PythonOperator

# PrimeHub SDK
from primehub import PrimeHub, PrimeHubConfig


# These args will get passed on to each operator
# You can override them on a per-task basis during operator initialization
default_args = {
    "owner": "airflow",
    "depends_on_past": False,
    "email": ["airflow@example.com"],
    "email_on_failure": False,
    "email_on_retry": False,
    "retries": 0,
    "retry_delay": timedelta(minutes=5),
}
with DAG(
    "primehub_tutorial",
    default_args=default_args,
    description="A demo of Airflow and PrimeHub Python SDK",
    schedule_interval=None,
    start_date=datetime.datetime.today(),
    tags=["primehub"],
) as dag:
    def init_primehub_sdk():
        ph = PrimeHub(PrimeHubConfig())
        ph.config.set_endpoint(Variable.get("PRIMEHUB_API_ENDPOINT"))
        ph.config.set_token(Variable.get("PRIMEHUB_API_TOKEN"))
        ph.config.set_group(Variable.get("PRIMEHUB_GROUP"))

        if ph.is_ready():
            log.info("PrimeHub Python SDK setup successfully")
        else:
            log.info("PrimeHub Python SDK couldn't get the group information, follow the 00-getting-started.ipynb to complete it")

        return ph

    def submit_primehub_job(config):
        ph = init_primehub_sdk()
        log.info("[ Submit ]")
        short_job = ph.jobs.submit(config)
        log.info(short_job)

        log.info("[ Waiting ]")
        ph.jobs.wait(short_job["id"])
        log.info("[ Job Done ]")

    def update_script():
        config = {
            "instanceType": "cpu-1",
            "image": "base-notebook",
            "displayName": "update_script",
            "command": "mkdir -p /home/jovyan/phusers/airflow-demo && cd /home/jovyan/phusers/airflow-demo && wget https://raw.githubusercontent.com/InfuseAI/primehub/master/examples/notebook-examples/data/tensorflow2/0_quickstart/0_tensorflow2_training.ipynb"
        }
        submit_primehub_job(config)

    def train_model():
        config = {
            "instanceType": "cpu-1",
            "image": "tf-2",
            "displayName": "training",
            "command": "cd /home/jovyan/phusers/airflow-demo && papermill 0_tensorflow2_training.ipynb 0_tensorflow2_training_output.ipynb"
        }
        submit_primehub_job(config)

    deploy_id = "deploy-from-airflow-xyz"
    deploy_name = "deploy-from-airflow"

    def deploy_model():
        ph = init_primehub_sdk()

        latest_model = sorted([x['name'] for x in ph.files.list('/example-models')])[-1]
        log.info(f"[ Deploy ] {latest_model}")

        deployments = ph.deployments.list()
        exist = any([1 for d in deployments if d['name'] == deploy_name])
        if not exist:
            config = {
                "id": deploy_id,
                "name": deploy_name,
                "modelImage": "infuseai/tensorflow2-prepackaged:v0.2.0",
                "modelURI": f"phfs:///example-models/{latest_model}",
                "instanceType": "cpu-1",
                "replicas": 1
            }
            ph.deployments.create(config)
        else:
            ph.deployments.stop(deploy_id)
            config = {
                "modelURI": f"phfs:///example-models/{latest_model}",
            }
            ph.deployments.update(deploy_id, config)

        log.info("[ Wait Deployment ]")
        ph.deployments.wait(deploy_id)

        log.info("[ Deploy Done ]")

    def verify_deployment():
        ph = init_primehub_sdk()
        endpoint = ph.deployments.get(deploy_id)["endpoint"]
        headers = {'Content-Type': 'application/json'}
	data = '{ "data": {"ndarray": [[[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 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        response = requests.post(endpoint, headers=headers, data=data)
        result = np.argmax(response.json()["data"]["ndarray"][0])
        if result != 7:
            log.info("Wrong prediction")


    # [START main_flow]
    t_update = PythonOperator(
        task_id="update",
        python_callable=update_script,
    )
    t_train = PythonOperator(
        task_id="train",
        python_callable=train_model,
    )
    t_deploy = PythonOperator(
        task_id="deploy",
        python_callable=deploy_model,
    )
    t_verify = PythonOperator(
        task_id="verify",
        python_callable=verify_deployment,
    )
    t_update >> t_train
    t_train >> t_deploy
    t_deploy >> t_verify
    # [END main_flow]