airflow.providers.google.cloud.triggers.mlengine¶

Classes¶

MLEngineStartTrainingJobTrigger

MLEngineStartTrainingJobTrigger run on the trigger worker to perform starting training job operation.

Module Contents¶

class airflow.providers.google.cloud.triggers.mlengine.MLEngineStartTrainingJobTrigger(conn_id, job_id, region, poll_interval=4.0, package_uris=None, training_python_module=None, training_args=None, runtime_version=None, python_version=None, job_dir=None, project_id=PROVIDE_PROJECT_ID, labels=None, gcp_conn_id='google_cloud_default', impersonation_chain=None)[source]¶

Bases: airflow.triggers.base.BaseTrigger

MLEngineStartTrainingJobTrigger run on the trigger worker to perform starting training job operation.

Parameters:

conn_id (str) – Reference to google cloud connection id
job_id (str) – The ID of the job. It will be suffixed with hash of job configuration
project_id (str) – Google Cloud Project where the job is running
poll_interval (float) – polling period in seconds to check for the status

conn_id[source]¶

job_id[source]¶

project_id = None[source]¶

region[source]¶

poll_interval = 4.0[source]¶

runtime_version = None[source]¶

python_version = None[source]¶

job_dir = None[source]¶

package_uris = None[source]¶

training_python_module = None[source]¶

training_args = None[source]¶

labels = None[source]¶

gcp_conn_id = 'google_cloud_default'[source]¶

impersonation_chain = None[source]¶

serialize()[source]¶

Serialize MLEngineStartTrainingJobTrigger arguments and classpath.

async run()[source]¶

Get current job execution status and yields a TriggerEvent.