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Added Schedule endpoint
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‎schedule-pipeline-endpoint/Readme.md

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# Schedule a pipeline endpoint
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**Problem statement**: You can schedule a published pipeline following (the docs instructions](https://docs.microsoft.com/en-us/azure/machine-learning/how-to-trigger-published-pipeline#create-a-schedule).
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The problem with this approach is that if the data scientist changes a component and publishes a new pipeline, they have to stop previous schedules and recreate them.
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In this demo you will schedule an endpoint that was published for the [R forecasting pipeline created by custom components](../cli-v2-r-forecasting). Scheduling the endpoint and not the published pipeline allows the data scientist to replace the default pipeline ID of the endpoint when they feel that their code is stable enough to replace the existing scheduled execution.
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Limitation: If the input parameters for the pipeline change, the schedule needs to be recreated to pass the additional parameters.

‎schedule-pipeline-endpoint/ScheduleEndpoint.ipynb

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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Schedule an endpoint"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Get the endpoint name from the AzureML studio in the Pipelines -> Pipeline endpoints\n",
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"pipeline_endpoint_name = \"forecast-with-r\""
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Azure ML SDK Version: 1.35.0\n"
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]
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}
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],
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"source": [
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"import azureml\n",
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"\n",
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"from azureml.core import Workspace\n",
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"\n",
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"print(\"Azure ML SDK Version: \", azureml.core.VERSION)\n",
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"# Connect to Workspace\n",
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"ws = Workspace.from_config()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"metadata": {},
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"outputs": [],
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"source": [
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"from azureml.pipeline.core import PipelineEndpoint, Schedule \n",
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"\n",
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"pipeline_endpoint = PipelineEndpoint.get(workspace=ws, name=pipeline_endpoint_name)\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Provisioning status: Completed\n",
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"Created schedule with id: d3fa9aa6-cf31-4d80-9152-becf4360ff20\n"
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]
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}
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],
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"source": [
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"from azureml.pipeline.core.schedule import ScheduleRecurrence, Schedule, TimeZone\n",
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"from datetime import datetime\n",
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"\n",
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"recurrence = ScheduleRecurrence(frequency=\"Day\", \n",
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" interval=1, \n",
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" hours=[22], \n",
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" minutes=[30],\n",
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" time_zone=TimeZone.EEuropeStandardTime,\n",
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" start_time = datetime.now() # This will prevent starting the pipeline immediately after scheduling it\n",
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" ) # Runs every other day at 10:30pm Eastern European Standard Time (aka Greece)\n",
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"\n",
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"schedule = Schedule.create_for_pipeline_endpoint(workspace=ws, name=f\"{pipeline_endpoint_name}-schedule\",\n",
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" pipeline_endpoint_id=pipeline_endpoint.id, \n",
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" pipeline_parameters = { # These are the parameters that will be passed to the pipeline\n",
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" \"start_date\": \"2020-01-01\",\n",
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" \"key_vault\": \"my-key-vault\",\n",
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" },\n",
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" experiment_name=f\"{pipeline_endpoint_name}-schedule-run\",\n",
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" recurrence=recurrence,\n",
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" wait_for_provisioning=True,\n",
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" description=\"Scheduled pipeline run\")\n",
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"\n",
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"# You may want to make sure that the schedule is provisioned properly\n",
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"# before making any further changes to the schedule\n",
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"\n",
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"print(\"Created schedule with id: {}\".format(schedule.id))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 13,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Your workspace has the following schedules set up:\n",
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"Schedule Id: 79e8a585-4e9d-4060-849e-342553f108fd (Published pipeline: 3855df31-cff6-4341-9707-6841abc862fb, Published endpoint: None)\n"
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]
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}
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],
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"source": [
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"from azureml.pipeline.core.schedule import Schedule\n",
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"# Use active_only=False to get all schedules including disabled schedules\n",
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"schedules = Schedule.list(ws, active_only=True) \n",
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"print(\"Your workspace has the following schedules set up:\")\n",
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"for schedule in schedules:\n",
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" print(\"Schedule Id: {} (Published pipeline: {}, Published endpoint: {})\".format(schedule.id, schedule.pipeline_id, schedule.pipeline_endpoint_id))\n",
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" if schedule.id == \"a specific schedule id you want to disable\":\n",
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" schedule.disable(wait_for_provisioning=True)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"interpreter": {
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"hash": "aaf752ee7e264d07f2aa436f1fcdea415bc1d1a9b82629a37d86a1397f966a41"
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},
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"kernelspec": {
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"display_name": "Python 3.6.9 ('azureml_py36_automl')",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.6.9"
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},
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"orig_nbformat": 4
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}

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