life_sciences

OrchestrationBig Data & AnalyticsData Science


Providers:

Run this DAG

1. Install Astronomer CLISkip if you already have the CLI

2. Initate the project:

3. Copy and paste the code below into a file in the

dags
directory.

4. Add the following to your requirements.txt file:

5. Run the DAG:

#
# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not use this file except in compliance
# with the License. You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
# KIND, either express or implied. See the License for the
# specific language governing permissions and limitations
# under the License.
import os
from airflow import models
from airflow.providers.google.cloud.operators.life_sciences import LifeSciencesRunPipelineOperator
from airflow.utils import dates
PROJECT_ID = os.environ.get("GCP_PROJECT_ID", "example-project-id")
BUCKET = os.environ.get("GCP_GCS_LIFE_SCIENCES_BUCKET", "INVALID BUCKET NAME")
FILENAME = os.environ.get("GCP_GCS_LIFE_SCIENCES_FILENAME", 'input.in')
LOCATION = os.environ.get("GCP_LIFE_SCIENCES_LOCATION", 'us-central1')
# [START howto_configure_simple_action_pipeline]
SIMPLE_ACTION_PIPELINE = {
"pipeline": {
"actions": [
{"imageUri": "bash", "commands": ["-c", "echo Hello, world"]},
],
"resources": {
"regions": [f"{LOCATION}"],
"virtualMachine": {
"machineType": "n1-standard-1",
},
},
},
}
# [END howto_configure_simple_action_pipeline]
# [START howto_configure_multiple_action_pipeline]
MULTI_ACTION_PIPELINE = {
"pipeline": {
"actions": [
{
"imageUri": "google/cloud-sdk",
"commands": ["gsutil", "cp", f"gs://{BUCKET}/{FILENAME}", "/tmp"],
},
{"imageUri": "bash", "commands": ["-c", "echo Hello, world"]},
{
"imageUri": "google/cloud-sdk",
"commands": [
"gsutil",
"cp",
f"gs://{BUCKET}/{FILENAME}",
f"gs://{BUCKET}/output.in",
],
},
],
"resources": {
"regions": [f"{LOCATION}"],
"virtualMachine": {
"machineType": "n1-standard-1",
},
},
}
}
# [END howto_configure_multiple_action_pipeline]
with models.DAG(
"example_gcp_life_sciences",
default_args=dict(start_date=dates.days_ago(1)),
schedule_interval=None,
tags=['example'],
) as dag:
# [START howto_run_pipeline]
simple_life_science_action_pipeline = LifeSciencesRunPipelineOperator(
task_id='simple-action-pipeline',
body=SIMPLE_ACTION_PIPELINE,
project_id=PROJECT_ID,
location=LOCATION,
)
# [END howto_run_pipeline]
multiple_life_science_action_pipeline = LifeSciencesRunPipelineOperator(
task_id='multi-action-pipeline', body=MULTI_ACTION_PIPELINE, project_id=PROJECT_ID, location=LOCATION
)
simple_life_science_action_pipeline >> multiple_life_science_action_pipeline