129 lines
4.4 KiB
Python
129 lines
4.4 KiB
Python
#!/usr/bin/env python3
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# Uncomment the apply(use_aws_secret()) below if you are not using OIDC
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# more info : https://github.com/kubeflow/pipelines/tree/master/samples/contrib/aws-samples/README.md
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import kfp
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import os
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from kfp import components
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from kfp import dsl
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import random
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import string
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from kfp.aws import use_aws_secret
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cur_file_dir = os.path.dirname(__file__)
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components_dir = os.path.join(cur_file_dir, "../../../../components/aws/sagemaker/")
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robomaker_create_sim_app_op = components.load_component_from_file(
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components_dir + "/create_simulation_app/component.yaml"
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)
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robomaker_sim_job_op = components.load_component_from_file(
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components_dir + "/simulation_job/component.yaml"
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)
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robomaker_delete_sim_app_op = components.load_component_from_file(
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components_dir + "/delete_simulation_app/component.yaml"
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)
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launch_config = {
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"packageName": "object_tracker_simulation",
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"launchFile": "evaluation.launch",
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"environmentVariables": {
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"MODEL_S3_BUCKET": "your_sagemaker_bucket_name",
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"MODEL_S3_PREFIX": "rl-object-tracker-sagemaker-201116-051751",
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"ROS_AWS_REGION": "us-east-1",
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"MARKOV_PRESET_FILE": "object_tracker.py",
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"NUMBER_OF_ROLLOUT_WORKERS": "1",
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},
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"streamUI": True,
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}
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simulation_app_name = "robomaker-pipeline-simulation-application"
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sources_bucket = "your_sagemaker_bucket_name"
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sources_key = "object-tracker/simulation_ws.tar.gz"
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sources_architecture = "X86_64"
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simulation_software_name = "Gazebo"
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simulation_software_version = "7"
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robot_software_name = "ROS"
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robot_software_version = "Kinetic"
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rendering_engine_name = "OGRE"
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rendering_engine_version = "1.x"
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role = "your_sagemaker_role_name"
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output_bucket = "kf-pipelines-robomaker-output"
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output_key = "test-output-key"
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security_groups = ["sg-0490601e83f220e82"]
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subnets = [
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"subnet-0efc73526db16a4a4",
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"subnet-0b8af626f39e7d462",
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]
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@dsl.pipeline(
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name="RoboMaker Simulation Job Pipeline",
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description="RoboMaker simulation job and simulation application created via pipeline components",
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)
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def robomaker_simulation_job_app_pipeline(
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region="us-east-1",
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role=role,
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name=simulation_app_name
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+ "".join(random.choice(string.ascii_lowercase) for i in range(10)),
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sources=[
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{
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"s3Bucket": sources_bucket,
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"s3Key": sources_key,
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"architecture": sources_architecture,
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}
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],
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simulation_software_name=simulation_software_name,
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simulation_software_version=simulation_software_version,
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robot_software_name=robot_software_name,
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robot_software_version=robot_software_version,
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rendering_engine_name=rendering_engine_name,
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rendering_engine_version=rendering_engine_version,
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output_bucket=output_bucket,
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output_path=output_key,
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sim_app_launch_config=launch_config,
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vpc_security_group_ids=security_groups,
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vpc_subnets=subnets,
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):
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robomaker_create_sim_app = robomaker_create_sim_app_op(
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region=region,
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app_name=name,
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sources=sources,
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simulation_software_name=simulation_software_name,
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simulation_software_version=simulation_software_version,
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robot_software_name=robot_software_name,
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robot_software_version=robot_software_version,
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rendering_engine_name=rendering_engine_name,
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rendering_engine_version=rendering_engine_version,
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)
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# .apply(use_aws_secret('aws-secret', 'AWS_ACCESS_KEY_ID', 'AWS_SECRET_ACCESS_KEY'))
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robomaker_simulation_job = robomaker_sim_job_op(
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region=region,
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role=role,
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output_bucket=output_bucket,
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output_path=output_path,
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max_run=300,
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failure_behavior="Fail",
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sim_app_arn=robomaker_create_sim_app.outputs["arn"],
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sim_app_launch_config=sim_app_launch_config,
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vpc_security_group_ids=vpc_security_group_ids,
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vpc_subnets=vpc_subnets,
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use_public_ip="True",
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).after(robomaker_create_sim_app)
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# .apply(use_aws_secret('aws-secret', 'AWS_ACCESS_KEY_ID', 'AWS_SECRET_ACCESS_KEY'))
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robomaker_delete_sim_app = robomaker_delete_sim_app_op(
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region=region, arn=robomaker_create_sim_app.outputs["arn"],
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).after(robomaker_simulation_job, robomaker_create_sim_app)
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# .apply(use_aws_secret('aws-secret', 'AWS_ACCESS_KEY_ID', 'AWS_SECRET_ACCESS_KEY'))
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if __name__ == "__main__":
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kfp.compiler.Compiler().compile(
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robomaker_simulation_job_app_pipeline, __file__ + ".zip"
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)
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