Add sample notebook and readme markdown for GCP components.

components/gcp/bigquery/query/README.md

@@ -0,0 +1,112 @@
+
+# Bigquery - Query
+
+## Intended Use
+A Kubeflow Pipeline component to submit a query to Google Cloud Bigquery service and dump outputs to a Google Cloud Storage blob. 
+
+## Run-Time Parameters:
+Name | Description
+:--- | :----------
+query | The query used by Bigquery service to fetch the results.
+project_id | The project to execute the query job.
+dataset_id | The ID of the persistent dataset to keep the results of the query. If the dataset does not exist, the operation will create a new one.
+table_id | The ID of the table to keep the results of the query. If absent, the operation will generate a random id for the table.
+output_gcs_path | The GCS blob path to dump the query results to.
+dataset_location | The location to create the dataset. Defaults to `US`.
+job_config | The full config spec for the query job. See [QueryJobConfig](https://googleapis.github.io/google-cloud-python/latest/bigquery/generated/google.cloud.bigquery.job.QueryJobConfig.html#google.cloud.bigquery.job.QueryJobConfig) for details.
+
+## Output:
+Name | Description
+:--- | :----------
+output_gcs_path | The GCS blob path to dump the query results to.
+
+## Sample
+
+Note: the sample code below works in both IPython notebook or python code directly.
+
+### Set sample parameters
+
+
+```python
+# Required Parameters
+PROJECT_ID = '<Please put your project ID here>'
+GCS_WORKING_DIR = 'gs://<Please put your GCS path here>' # No ending slash
+
+# Optional Parameters
+EXPERIMENT_NAME = 'Bigquery -Query'
+COMPONENT_SPEC_URI = 'https://raw.githubusercontent.com/kubeflow/pipelines/master/components/gcp/bigquery/query/component.yaml'
+```
+
+### Install KFP SDK
+
+
+```python
+# Install the SDK (Uncomment the code if the SDK is not installed before)
+# KFP_PACKAGE = 'https://storage.googleapis.com/ml-pipeline/release/0.1.11/kfp.tar.gz'
+# !pip3 install $KFP_PACKAGE --upgrade
+```
+
+### Load component definitions
+
+
+```python
+import kfp.components as comp
+
+bigquery_query_op = comp.load_component_from_url(COMPONENT_SPEC_URI)
+display(bigquery_query_op)
+```
+
+### Here is an illustrative pipeline that uses the component
+
+
+```python
+import kfp.dsl as dsl
+import kfp.gcp as gcp
+import json
+@dsl.pipeline(
+    name='Bigquery query pipeline',
+    description='Bigquery query pipeline'
+)
+def pipeline(
+    query, 
+    project_id, 
+    dataset_id='', 
+    table_id='', 
+    output_gcs_path='', 
+    dataset_location='US', 
+    job_config=''
+):
+    bigquery_query_op(query, project_id, dataset_id, table_id, output_gcs_path, dataset_location, 
+        job_config).apply(gcp.use_gcp_secret('user-gcp-sa'))
+```
+
+### Compile the pipeline
+
+
+```python
+pipeline_func = pipeline
+pipeline_filename = pipeline_func.__name__ + '.pipeline.tar.gz'
+import kfp.compiler as compiler
+compiler.Compiler().compile(pipeline_func, pipeline_filename)
+```
+
+### Submit the pipeline for execution
+
+
+```python
+#Specify pipeline argument values
+arguments = {
+    'query': 'SELECT * FROM `bigquery-public-data.stackoverflow.posts_questions` LIMIT 10',
+    'project_id': PROJECT_ID,
+    'output_gcs_path': '{}/bigquery/query/questions.csv'.format(GCS_WORKING_DIR)
+}
+
+#Get or create an experiment and submit a pipeline run
+import kfp
+client = kfp.Client()
+experiment = client.create_experiment(EXPERIMENT_NAME)
+
+#Submit a pipeline run
+run_name = pipeline_func.__name__ + ' run'
+run_result = client.run_pipeline(experiment.id, run_name, pipeline_filename, arguments)
+```

components/gcp/bigquery/query/component.yaml

@@ -26,7 +26,7 @@ outputs:
   - {name: output_gcs_path, description: 'The GCS blob path to dump the query results to.'}
 implementation:
   container:
-    image: gcr.io/ml-pipeline-dogfood/ml-pipeline-gcp:latest
+    image: gcr.io/ml-pipeline/ml-pipeline-gcp:latest
     args: [
       kfp_component.google.bigquery, query,
       --query, {inputValue: query},

components/gcp/bigquery/query/sample.ipynb

@@ -0,0 +1,207 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Bigquery - Query\n",
+    "\n",
+    "## Intended Use\n",
+    "A Kubeflow Pipeline component to submit a query to Google Cloud Bigquery service and dump outputs to a Google Cloud Storage blob. \n",
+    "\n",
+    "## Input:\n",
+    "Name | Description\n",
+    ":--- | :----------\n",
+    "query | The query used by Bigquery service to fetch the results.\n",
+    "project_id | The project to execute the query job.\n",
+    "dataset_id | The ID of the persistent dataset to keep the results of the query. If the dataset does not exist, the operation will create a new one.\n",
+    "table_id | The ID of the table to keep the results of the query. If absent, the operation will generate a random id for the table.\n",
+    "output_gcs_path | The GCS blob path to dump the query results to.\n",
+    "dataset_location | The location to create the dataset. Defaults to `US`.\n",
+    "job_config | The full config spec for the query job. See [QueryJobConfig](https://googleapis.github.io/google-cloud-python/latest/bigquery/generated/google.cloud.bigquery.job.QueryJobConfig.html#google.cloud.bigquery.job.QueryJobConfig) for details.\n",
+    "\n",
+    "## Output:\n",
+    "Name | Description\n",
+    ":--- | :----------\n",
+    "output_gcs_path | The GCS blob path to dump the query results to."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Sample\n",
+    "\n",
+    "Note: the sample code below works in both IPython notebook or python code directly."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Set sample parameters"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "parameters"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# Required Parameters\n",
+    "PROJECT_ID = '<Please put your project ID here>'\n",
+    "GCS_WORKING_DIR = 'gs://<Please put your GCS path here>' # No ending slash\n",
+    "\n",
+    "# Optional Parameters\n",
+    "EXPERIMENT_NAME = 'Bigquery -Query'\n",
+    "COMPONENT_SPEC_URI = 'https://raw.githubusercontent.com/kubeflow/pipelines/master/components/gcp/bigquery/query/component.yaml'"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Install KFP SDK"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "# Install the SDK (Uncomment the code if the SDK is not installed before)\n",
+    "# KFP_PACKAGE = 'https://storage.googleapis.com/ml-pipeline/release/0.1.11/kfp.tar.gz'\n",
+    "# !pip3 install $KFP_PACKAGE --upgrade"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Load component definitions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import kfp.components as comp\n",
+    "\n",
+    "bigquery_query_op = comp.load_component_from_url(COMPONENT_SPEC_URI)\n",
+    "display(bigquery_query_op)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Run the component as a single pipeline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import kfp.dsl as dsl\n",
+    "import kfp.gcp as gcp\n",
+    "import json\n",
+    "@dsl.pipeline(\n",
+    "    name='Bigquery query pipeline',\n",
+    "    description='Bigquery query pipeline'\n",
+    ")\n",
+    "def pipeline(\n",
+    "    query, \n",
+    "    project_id, \n",
+    "    dataset_id='', \n",
+    "    table_id='', \n",
+    "    output_gcs_path='', \n",
+    "    dataset_location='US', \n",
+    "    job_config=''\n",
+    "):\n",
+    "    bigquery_query_op(query, project_id, dataset_id, table_id, output_gcs_path, dataset_location, \n",
+    "        job_config).apply(gcp.use_gcp_secret('user-gcp-sa'))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Compile the pipeline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pipeline_func = pipeline\n",
+    "pipeline_filename = pipeline_func.__name__ + '.pipeline.tar.gz'\n",
+    "import kfp.compiler as compiler\n",
+    "compiler.Compiler().compile(pipeline_func, pipeline_filename)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Submit the pipeline for execution"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Specify pipeline argument values\n",
+    "arguments = {\n",
+    "    'query': 'SELECT * FROM `bigquery-public-data.stackoverflow.posts_questions` LIMIT 10',\n",
+    "    'project_id': PROJECT_ID,\n",
+    "    'output_gcs_path': '{}/bigquery/query/questions.csv'.format(GCS_WORKING_DIR)\n",
+    "}\n",
+    "\n",
+    "#Get or create an experiment and submit a pipeline run\n",
+    "import kfp\n",
+    "client = kfp.Client()\n",
+    "experiment = client.create_experiment(EXPERIMENT_NAME)\n",
+    "\n",
+    "#Submit a pipeline run\n",
+    "run_name = pipeline_func.__name__ + ' run'\n",
+    "run_result = client.run_pipeline(experiment.id, run_name, pipeline_filename, arguments)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}