update samples from Release-53 as a part of 1.19.0 SDK stable release

how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-setup-schedule-for-a-published-pipeline.ipynb

@@ -19,8 +19,8 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "# How to Setup a Schedule for a Published Pipeline\n",
-        "In this notebook, we will show you how you can run an already published pipeline on a schedule."
+        "# How to Setup a Schedule for a Published Pipeline or Pipeline Endpoint\n",
+        "In this notebook, we will show you how you can run an already published pipeline or a pipeline endpoint on a schedule."
       ]
     },
     {
@@ -159,6 +159,43 @@
         "print(\"Newly published pipeline id: {}\".format(published_pipeline1.id))"
       ]
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "nteract": {
+          "transient": {
+            "deleting": false
+          }
+        }
+      },
+      "source": [
+        "### Create a Pipeline Endpoint\n",
+        "Alternatively, you can create a schedule to run a pipeline endpoint instead of a published pipeline. You will need this to create a schedule against a pipeline endpoint in the last section of this notebook. "
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "jupyter": {
+          "outputs_hidden": false,
+          "source_hidden": false
+        },
+        "nteract": {
+          "transient": {
+            "deleting": false
+          }
+        }
+      },
+      "outputs": [],
+      "source": [
+        "from azureml.pipeline.core import PipelineEndpoint\n",
+        "\n",
+        "pipeline_endpoint = PipelineEndpoint.publish(workspace=ws, name=\"ScheduledPipelineEndpoint\",\n",
+        "                                            pipeline=pipeline1, description=\"Publish pipeline endpoint for schedule test\")\n",
+        "pipeline_endpoint"
+      ]
+    },
     {
       "cell_type": "markdown",
       "metadata": {},
@@ -196,14 +233,24 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "### Create a schedule for the pipeline using a recurrence\n",
+        "### Create a schedule for the published pipeline using a recurrence\n",
         "This schedule will run on a specified recurrence interval."
       ]
     },
     {
       "cell_type": "code",
       "execution_count": null,
-      "metadata": {},
+      "metadata": {
+        "jupyter": {
+          "outputs_hidden": false,
+          "source_hidden": false
+        },
+        "nteract": {
+          "transient": {
+            "deleting": false
+          }
+        }
+      },
       "outputs": [],
       "source": [
         "from azureml.pipeline.core.schedule import ScheduleRecurrence, Schedule\n",
@@ -308,7 +355,11 @@
     {
       "cell_type": "code",
       "execution_count": null,
-      "metadata": {},
+      "metadata": {
+        "gather": {
+          "logged": 1606157800044
+        }
+      },
       "outputs": [],
       "source": [
         "# Set the wait_for_provisioning flag to False if you do not want to wait  \n",
@@ -410,7 +461,11 @@
     {
       "cell_type": "code",
       "execution_count": null,
-      "metadata": {},
+      "metadata": {
+        "gather": {
+          "logged": 1606157862620
+        }
+      },
       "outputs": [],
       "source": [
         "# Set the wait_for_provisioning flag to False if you do not want to wait  \n",
@@ -419,14 +474,151 @@
         "schedule = Schedule.get(ws, schedule_id)\n",
         "print(\"Disabled schedule {}. New status is: {}\".format(schedule.id, schedule.status))"
       ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "nteract": {
+          "transient": {
+            "deleting": false
+          }
+        }
+      },
+      "source": [
+        "### Create a schedule for a pipeline endpoint\n",
+        "Alternative to creating schedules for a published pipeline, you can also create schedules to run pipeline endpoints.\n",
+        "Retrieve the pipeline endpoint id to create a schedule. "
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "gather": {
+          "logged": 1606157888851
+        },
+        "jupyter": {
+          "outputs_hidden": false,
+          "source_hidden": false
+        },
+        "nteract": {
+          "transient": {
+            "deleting": false
+          }
+        }
+      },
+      "outputs": [],
+      "source": [
+        "pipeline_endpoint_by_name = PipelineEndpoint.get(workspace=ws, name=\"ScheduledPipelineEndpoint\")\n",
+        "published_pipeline_endpoint_id = pipeline_endpoint_by_name.id\n",
+        "\n",
+        "recurrence = ScheduleRecurrence(frequency=\"Day\", interval=2, hours=[22], minutes=[30]) # Runs every other day at 10:30pm\n",
+        "\n",
+        "schedule = Schedule.create_for_pipeline_endpoint(workspace=ws, name=\"My_Endpoint_Schedule\",\n",
+        "                                                 pipeline_endpoint_id=published_pipeline_endpoint_id,\n",
+        "                                                 experiment_name='Schedule_Run',\n",
+        "                                                 recurrence=recurrence, description=\"Schedule_Run\",\n",
+        "                                                 wait_for_provisioning=True)\n",
+        "\n",
+        "# You may want to make sure that the schedule is provisioned properly\n",
+        "# before making any further changes to the schedule\n",
+        "\n",
+        "print(\"Created schedule with id: {}\".format(schedule.id))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "nteract": {
+          "transient": {
+            "deleting": false
+          }
+        }
+      },
+      "source": [
+        "### Get all schedules for a given pipeline endpoint\n",
+        "Once you have the pipeline endpoint ID, then you can get all schedules for that pipeline endopint."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "jupyter": {
+          "outputs_hidden": false,
+          "source_hidden": false
+        },
+        "nteract": {
+          "transient": {
+            "deleting": false
+          }
+        }
+      },
+      "outputs": [],
+      "source": [
+        "schedules_for_pipeline_endpoints = Schedule.\\\n",
+        "    get_schedules_for_pipeline_endpoint_id(ws,\n",
+        "                                           pipeline_endpoint_id=published_pipeline_endpoint_id)\n",
+        "print('Got all schedules for pipeline endpoint:', published_pipeline_endpoint_id, 'Count:',\n",
+        "      len(schedules_for_pipeline_endpoints))\n",
+        "\n",
+        "print('done')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "nteract": {
+          "transient": {
+            "deleting": false
+          }
+        }
+      },
+      "source": [
+        "### Disable the schedule created for running the pipeline endpont\n",
+        "Recall the best practice of disabling schedules when not in use.\n",
+        "The number of schedule triggers allowed per month per region per subscription is 100,000.\n",
+        "This is calculated using the project trigger counts for all active schedules."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "jupyter": {
+          "outputs_hidden": false,
+          "source_hidden": false
+        },
+        "nteract": {
+          "transient": {
+            "deleting": false
+          }
+        }
+      },
+      "outputs": [],
+      "source": [
+        "fetched_schedule = Schedule.get(ws, schedule_id)\n",
+        "print(\"Using schedule with id: {}\".format(fetched_schedule.id))\n",
+        "\n",
+        "# Set the wait_for_provisioning flag to False if you do not want to wait  \n",
+        "# for the call to provision the schedule in the backend.\n",
+        "fetched_schedule.disable(wait_for_provisioning=True)\n",
+        "fetched_schedule = Schedule.get(ws, schedule_id)\n",
+        "print(\"Disabled schedule {}. New status is: {}\".format(fetched_schedule.id, fetched_schedule.status))"
+      ]
     }
   ],
   "metadata": {
     "authors": [
       {
-        "name": "sanpil"
+        "name": "shbijlan"
       }
     ],
+    "categories": [
+      "how-to-use-azureml",
+      "machine-learning-pipelines",
+      "intro-to-pipelines"
+    ],
     "category": "tutorial",
     "compute": [
       "AML Compute"
@@ -441,7 +633,7 @@
     "framework": [
       "Azure ML"
     ],
-    "friendly_name": "How to Setup a Schedule for a Published Pipeline",
+    "friendly_name": "How to Setup a Schedule for a Published Pipeline or Pipeline Endpoint",
     "kernelspec": {
       "display_name": "Python 3.6",
       "language": "python",
@@ -459,6 +651,9 @@
       "pygments_lexer": "ipython3",
       "version": "3.6.7"
     },
+    "nteract": {
+      "version": "nteract-front-end@1.0.0"
+    },
     "order_index": 10,
     "star_tag": [
       "featured"
@@ -466,7 +661,7 @@
     "tags": [
       "None"
     ],
-    "task": "Demonstrates the use of Schedules for Published Pipelines"
+    "task": "Demonstrates the use of Schedules for Published Pipelines and Pipeline endpoints"
   },
   "nbformat": 4,
   "nbformat_minor": 2

how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-with-automated-machine-learning-step.ipynb

@@ -30,7 +30,7 @@
         "## Introduction\n",
         "In this example we showcase how you can use AzureML Dataset to load data for AutoML via AML Pipeline. \n",
         "\n",
-        "If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, make sure you have executed the [configuration](https://aka.ms/pl-config) before running this notebook.\n",
+        "If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, make sure you have executed the [configuration](https://aka.ms/pl-config) before running this notebook, please also take a look at the [Automated ML setup-using-a-local-conda-environment](https://github.com/Azure/MachineLearningNotebooks/tree/master/how-to-use-azureml/automated-machine-learning#setup-using-a-local-conda-environment) section to setup the environment.\n",
         "\n",
         "In this notebook you will learn how to:\n",
         "1. Create an `Experiment` in an existing `Workspace`.\n",

how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-with-automated-machine-learning-step.yml

@@ -2,7 +2,3 @@ name: aml-pipelines-with-automated-machine-learning-step
 dependencies:
 - pip:
   - azureml-sdk
-  - azureml-train-automl
-  - azureml-widgets
-  - matplotlib
-  - pandas_ml

how-to-use-azureml/machine-learning-pipelines/nyc-taxi-data-regression-model-building/nyc-taxi-data-regression-model-building.ipynb

@@ -284,7 +284,7 @@
         "# Specify CondaDependencies obj, add necessary packages\n",
         "aml_run_config.environment.python.conda_dependencies = CondaDependencies.create(\n",
         "    conda_packages=['pandas','scikit-learn'], \n",
-        "    pip_packages=['azureml-sdk[automl,explain]', 'pyarrow'])\n",
+        "    pip_packages=['azureml-sdk[automl]', 'pyarrow'])\n",
         "\n",
         "print (\"Run configuration created.\")"
       ]