Config Populator

The Config Populator takes a “template” config and generates a “frozen” config to be used by all subsequent components.

Input

• job_name (AppliedTaskIdentifier): which uniquely identifies an end-to-end task.

• task_config_uri (Uri): Path which points to a “template” GbmlConfig proto yaml file.

• resource_config_uri (Uri): Path which points to a GiGLResourceConfig yaml

What does it do?

Takes in a template GbmlConfig and outputs a frozen GbmlConfig by populating all job related metadata paths in sharedConfig. These are mostly GCS paths which the following components read and write from, and use as an intermediary data communication medium. For example, the field sharedConfig.trainedModelMetadata is populated with a GCS URI, which indicates to the Trainer to write the trained model to this path, and to the Inferencer to read the model from this path

How do I run it?

Import GiGL

from gigl.src.config_populator.config_populator import ConfigPopulator
from gigl.common import UriFactory
from gigl.src.common.types import AppliedTaskIdentifier

config_populator = ConfigPopulator()

task_config_uri = config_populator.run(
    applied_task_identifier=AppliedTaskIdentifier("my_gigl_job_name"),
    task_config_uri=UriFactory.create_uri("gs://my-temp-assets-bucket/task_config.yaml"),
    resource_config_uri=UriFactory.create_uri("gs://my-temp-assets-bucket/resource_config.yaml")
)

Command Line

python -m \
    gigl.src.config_populator.config_populator \
    --job_name mau_sample_task_id \
    --template_uri gs://TEST ASSET PLACEHOLDER/example_task_assets/mau/configs/mau_template_gbml_config_20230213.yaml \
    --output_file_path_frozen_gbml_config_uri my-output-frozen-config_path.yaml

Notes:

• output_file_path_frozen_gbml_config_uri is the output of the run method as seen in the import gigl usage.

• Be sure to note the discrepency of template_uri for command line usage vs task_config_uri for import gigl usage.

Output

A frozen GbmlConfig URI.

Other

• If trainedModelMetadata.trainedModelUri exists and/or skipTraining = true, this indicates that we will be running the pipeline with a pre-trained model, and Config Populator will not overwrite these fields in the sharedConfig of the frozen GbmlConfig.

• Although sharedConfig is added to the frozen config by the config populator, you may add the field to your template config to enable any feature/optional flags.