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Name

Submitting a Cloud Machine Learning Engine training job as a pipeline step

Label

GCP, Cloud ML Engine, Machine Learning, pipeline, component, Kubeflow, Kubeflow Pipeline

Summary

A Kubeflow Pipeline component to submit a Cloud ML Engine training job as a step in a pipeline.

Details

Intended use

Use this component to submit a training job to Cloud ML Engine from a Kubeflow Pipeline.

Runtime arguments

Argument Description Optional Data type Accepted values Default
project_id The ID of the Google Cloud Platform (GCP) project of the job. No GCPProjectID
python_module The name of the Python module to run after installing the training program. Yes String None
package_uris The Cloud Storage location of the packages that contain the training program and any additional dependencies. The maximum number of package URIs is 100. Yes List None
region The Compute Engine region in which the training job is run. Yes GCPRegion us-central1
args The command line arguments to pass to the training program. Yes List None
job_dir A Cloud Storage path in which to store the training outputs and other data needed for training. This path is passed to your TensorFlow program as the job-dir command-line argument. The benefit of specifying this field is that Cloud ML validates the path for use in training. Yes GCSPath None
python_version The version of Python used in training. If it is not set, the default version is 2.7. Python 3.5 is available when the runtime version is set to 1.4 and above. Yes String None
runtime_version The runtime version of Cloud ML Engine to use for training. If it is not set, Cloud ML Engine uses the default. Yes String 1
master_image_uri The Docker image to run on the master replica. This image must be in Container Registry. Yes GCRPath None
worker_image_uri The Docker image to run on the worker replica. This image must be in Container Registry. Yes GCRPath None
training_input The input parameters to create a training job. Yes Dict TrainingInput None
job_id_prefix The prefix of the job ID that is generated. Yes String None
wait_interval The number of seconds to wait between API calls to get the status of the job. Yes Integer 30

Input data schema

The component accepts two types of inputs:

  • A list of Python packages from Cloud Storage.
    • You can manually build a Python package and upload it to Cloud Storage by following this guide.
  • A Docker container from Container Registry.
    • Follow this guide to publish and use a Docker container with this component.

Output

Name Description Type
job_id The ID of the created job. String
job_dir The Cloud Storage path that contains the trained model output files. GCSPath

Cautions & requirements

To use the component, you must:

  • Set up a cloud environment by following this guide.
  • The component can authenticate to GCP. Refer to Authenticating Pipelines to GCP for details.
  • Grant the following access to the Kubeflow user service account:
    • Read access to the Cloud Storage buckets which contain the input data, packages, or Docker images.
    • Write access to the Cloud Storage bucket of the output directory.

Detailed description

The component builds the TrainingInput payload and submits a job via the Cloud ML Engine REST API.

The steps to use the component in a pipeline are:

  1. Install the Kubeflow Pipeline SDK:




In [ ]:



    


%%capture --no-stderr

KFP_PACKAGE = 'https://storage.googleapis.com/ml-pipeline/release/0.1.14/kfp.tar.gz'
!pip3 install $KFP_PACKAGE --upgrade
  1. Load the component using KFP SDK




In [ ]:



    


import kfp.components as comp

mlengine_train_op = comp.load_component_from_url(
    'https://raw.githubusercontent.com/kubeflow/pipelines/01a23ae8672d3b18e88adf3036071496aca3552d/components/gcp/ml_engine/train/component.yaml')
help(mlengine_train_op)

Sample

Note: The following sample code works in an IPython notebook or directly in Python code.

In this sample, you use the code from the census estimator sample to train a model in Cloud ML Engine. To upload the code to Cloud ML Engine, package the Python code and upload it to a Cloud Storage bucket.

Note: You must have read and write permissions on the bucket that you use as the working directory.

Set sample parameters





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# Required Parameters
PROJECT_ID = '<Please put your project ID here>'
GCS_WORKING_DIR = 'gs://<Please put your GCS path here>' # No ending slash



    











In [ ]:



    


# Optional Parameters
EXPERIMENT_NAME = 'CLOUDML - Train'
TRAINER_GCS_PATH = GCS_WORKING_DIR + '/train/trainer.tar.gz'
OUTPUT_GCS_PATH = GCS_WORKING_DIR + '/train/output/'

Clean up the working directory





In [ ]:



    


%%capture --no-stderr
!gsutil rm -r $GCS_WORKING_DIR

Download the sample trainer code to local





In [ ]:



    


%%capture --no-stderr
!wget https://github.com/GoogleCloudPlatform/cloudml-samples/archive/master.zip
!unzip master.zip

Package code and upload the package to Cloud Storage





In [ ]:



    


%%capture --no-stderr
%%bash -s "$TRAINER_GCS_PATH"
pushd ./cloudml-samples-master/census/estimator/
python setup.py sdist
gsutil cp dist/preprocessing-1.0.tar.gz $1
popd
rm -fr ./cloudml-samples-master/ ./master.zip ./dist

Example pipeline that uses the component





In [ ]:



    


import kfp.dsl as dsl
import json
@dsl.pipeline(
    name='CloudML training pipeline',
    description='CloudML training pipeline'
)
def pipeline(
    project_id = PROJECT_ID,
    python_module = 'trainer.task',
    package_uris = json.dumps([TRAINER_GCS_PATH]),
    region = 'us-central1',
    args = json.dumps([
        '--train-files', 'gs://cloud-samples-data/ml-engine/census/data/adult.data.csv',
        '--eval-files', 'gs://cloud-samples-data/ml-engine/census/data/adult.test.csv',
        '--train-steps', '1000',
        '--eval-steps', '100',
        '--verbosity', 'DEBUG'
    ]),
    job_dir = OUTPUT_GCS_PATH,
    python_version = '',
    runtime_version = '1.10',
    master_image_uri = '',
    worker_image_uri = '',
    training_input = '',
    job_id_prefix = '',
    wait_interval = '30'):
    task = mlengine_train_op(
        project_id=project_id, 
        python_module=python_module, 
        package_uris=package_uris, 
        region=region, 
        args=args, 
        job_dir=job_dir, 
        python_version=python_version,
        runtime_version=runtime_version, 
        master_image_uri=master_image_uri, 
        worker_image_uri=worker_image_uri, 
        training_input=training_input, 
        job_id_prefix=job_id_prefix, 
        wait_interval=wait_interval)

Compile the pipeline





In [ ]:



    


pipeline_func = pipeline
pipeline_filename = pipeline_func.__name__ + '.zip'
import kfp.compiler as compiler
compiler.Compiler().compile(pipeline_func, pipeline_filename)

Submit the pipeline for execution





In [ ]:



    


#Specify pipeline argument values
arguments = {}

#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)

Inspect the results

Use the following command to inspect the contents in the output directory:





In [ ]:



    


!gsutil ls $OUTPUT_GCS_PATH

References

License

By deploying or using this software you agree to comply with the AI Hub Terms of Service and the Google APIs Terms of Service. To the extent of a direct conflict of terms, the AI Hub Terms of Service will control.

Content source: kubeflow/kfp-tekton-backend

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