261 lines
9.2 KiB
Plaintext
261 lines
9.2 KiB
Plaintext
{
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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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"# Name\n",
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"Data preparation using Apache Pig on YARN with Cloud Dataproc\n",
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"\n",
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"# Label\n",
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"Cloud Dataproc, GCP, Cloud Storage, YARN, Pig, Apache, Kubeflow, pipelines, components\n",
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"\n",
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"\n",
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"# Summary\n",
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"A Kubeflow Pipeline component to prepare data by submitting an Apache Pig job on YARN to Cloud Dataproc.\n",
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"\n",
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"\n",
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"# Details\n",
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"## Intended use\n",
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"Use the component to run an Apache Pig job as one preprocessing step in a Kubeflow Pipeline.\n",
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"\n",
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"## Runtime arguments\n",
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"| Argument | Description | Optional | Data type | Accepted values | Default |\n",
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"|----------|-------------|----------|-----------|-----------------|---------|\n",
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"| project_id | The ID of the Google Cloud Platform (GCP) project that the cluster belongs to. | No | GCPProjectID | | |\n",
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"| region | The Cloud Dataproc region to handle the request. | No | GCPRegion | | |\n",
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"| cluster_name | The name of the cluster to run the job. | No | String | | |\n",
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"| queries | The queries to execute the Pig job. Specify multiple queries in one string by separating them with semicolons. You do not need to terminate queries with semicolons. | Yes | List | | None |\n",
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"| query_file_uri | The HCFS URI of the script that contains the Pig queries. | Yes | GCSPath | | None |\n",
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"| script_variables | Mapping of the query’s variable names to their values (equivalent to the Pig command: SET name=\"value\";). | Yes | Dict | | None |\n",
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"| pig_job | The payload of a [PigJob](https://cloud.google.com/dataproc/docs/reference/rest/v1/PigJob). | Yes | Dict | | None |\n",
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"| job | The payload of a [Dataproc job](https://cloud.google.com/dataproc/docs/reference/rest/v1/projects.regions.jobs). | Yes | Dict | | None |\n",
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"| wait_interval | The number of seconds to pause between polling the operation. | Yes | Integer | | 30 |\n",
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"\n",
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"## Output\n",
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"Name | Description | Type\n",
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":--- | :---------- | :---\n",
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"job_id | The ID of the created job. | String\n",
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"\n",
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"## Cautions & requirements\n",
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"\n",
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"To use the component, you must:\n",
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"* Set up a GCP project by following this [guide](https://cloud.google.com/dataproc/docs/guides/setup-project).\n",
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"* [Create a new cluster](https://cloud.google.com/dataproc/docs/guides/create-cluster).\n",
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"* Run the component under a secret [Kubeflow user service account](https://www.kubeflow.org/docs/started/getting-started-gke/#gcp-service-accounts) in a Kubeflow cluster. For example:\n",
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"\n",
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" ```\n",
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" component_op(...).apply(gcp.use_gcp_secret('user-gcp-sa'))\n",
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" ```\n",
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"* Grant the Kubeflow user service account the role `roles/dataproc.editor` on the project.\n",
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"\n",
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"## Detailed description\n",
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"This component creates a Pig job from [Dataproc submit job REST API](https://cloud.google.com/dataproc/docs/reference/rest/v1/projects.regions.jobs/submit).\n",
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"\n",
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"Follow these steps to use the component in a pipeline:\n",
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"1. Install the Kubeflow Pipeline SDK:\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": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"%%capture --no-stderr\n",
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"\n",
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"KFP_PACKAGE = 'https://storage.googleapis.com/ml-pipeline/release/0.1.14/kfp.tar.gz'\n",
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"!pip3 install $KFP_PACKAGE --upgrade"
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]
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},
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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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"2. Load the component using KFP SDK"
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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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"import kfp.components as comp\n",
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"\n",
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"dataproc_submit_pig_job_op = comp.load_component_from_url(\n",
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" 'https://raw.githubusercontent.com/kubeflow/pipelines/e598176c02f45371336ccaa819409e8ec83743df/components/gcp/dataproc/submit_pig_job/component.yaml')\n",
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"help(dataproc_submit_pig_job_op)"
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]
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},
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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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"### Sample\n",
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"\n",
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"Note: The following sample code works in an IPython notebook or directly in Python code. See the sample code below to learn how to execute the template.\n",
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"\n",
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"\n",
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"#### Setup a Dataproc cluster\n",
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"\n",
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"[Create a new Dataproc cluster](https://cloud.google.com/dataproc/docs/guides/create-cluster) (or reuse an existing one) before running the sample code.\n",
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"\n",
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"\n",
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"#### Prepare a Pig query\n",
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"\n",
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"Either put your Pig queries in the `queries` list, or upload your Pig queries into a file to a Cloud Storage bucket and then enter the Cloud Storage bucket’s path in `query_file_uri`. In this sample, we will use a hard coded query in the `queries` list to select data from a local `passwd` file.\n",
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"\n",
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"For more details on Apache Pig, see the [Pig documentation.](http://pig.apache.org/docs/latest/)\n",
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"\n",
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"#### Set sample parameters"
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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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"tags": [
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"parameters"
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]
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},
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"outputs": [],
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"source": [
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"PROJECT_ID = '<Please put your project ID here>'\n",
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"CLUSTER_NAME = '<Please put your existing cluster name here>'\n",
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"\n",
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"REGION = 'us-central1'\n",
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"QUERY = '''\n",
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"natality_csv = load 'gs://public-datasets/natality/csv' using PigStorage(':');\n",
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"top_natality_csv = LIMIT natality_csv 10; \n",
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"dump natality_csv;'''\n",
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"EXPERIMENT_NAME = 'Dataproc - Submit Pig Job'"
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]
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},
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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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"#### Example pipeline that uses the component"
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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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"import kfp.dsl as dsl\n",
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"import kfp.gcp as gcp\n",
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"import json\n",
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"@dsl.pipeline(\n",
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" name='Dataproc submit Pig job pipeline',\n",
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" description='Dataproc submit Pig job pipeline'\n",
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")\n",
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"def dataproc_submit_pig_job_pipeline(\n",
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" project_id = PROJECT_ID, \n",
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" region = REGION,\n",
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" cluster_name = CLUSTER_NAME,\n",
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" queries = json.dumps([QUERY]),\n",
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" query_file_uri = '',\n",
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" script_variables = '', \n",
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" pig_job='', \n",
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" job='', \n",
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" wait_interval='30'\n",
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"):\n",
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" dataproc_submit_pig_job_op(\n",
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" project_id=project_id, \n",
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" region=region, \n",
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" cluster_name=cluster_name, \n",
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" queries=queries, \n",
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" query_file_uri=query_file_uri,\n",
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" script_variables=script_variables, \n",
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" pig_job=pig_job, \n",
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" job=job, \n",
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" wait_interval=wait_interval).apply(gcp.use_gcp_secret('user-gcp-sa'))\n",
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" "
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]
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},
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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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"#### Compile the pipeline"
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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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"pipeline_func = dataproc_submit_pig_job_pipeline\n",
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"pipeline_filename = pipeline_func.__name__ + '.zip'\n",
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"import kfp.compiler as compiler\n",
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"compiler.Compiler().compile(pipeline_func, pipeline_filename)"
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]
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},
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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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"#### Submit the pipeline for execution"
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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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"#Specify pipeline argument values\n",
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"arguments = {}\n",
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"\n",
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"#Get or create an experiment and submit a pipeline run\n",
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"import kfp\n",
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"client = kfp.Client()\n",
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"experiment = client.create_experiment(EXPERIMENT_NAME)\n",
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"\n",
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"#Submit a pipeline run\n",
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"run_name = pipeline_func.__name__ + ' run'\n",
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"run_result = client.run_pipeline(experiment.id, run_name, pipeline_filename, arguments)"
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]
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},
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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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"## References\n",
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"* [Create a new Dataproc cluster](https://cloud.google.com/dataproc/docs/guides/create-cluster) \n",
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"* [Pig documentation](http://pig.apache.org/docs/latest/)\n",
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"* [Dataproc job](https://cloud.google.com/dataproc/docs/reference/rest/v1/projects.regions.jobs)\n",
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"* [PigJob](https://cloud.google.com/dataproc/docs/reference/rest/v1/PigJob)\n",
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"\n",
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"## License\n",
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"By deploying or using this software you agree to comply with the [AI Hub Terms of Service](https://aihub.cloud.google.com/u/0/aihub-tos) and the [Google APIs Terms of Service](https://developers.google.com/terms/). To the extent of a direct conflict of terms, the AI Hub Terms of Service will control."
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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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": "3.6.4"
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}
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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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