Deploys a model for serving with AI Platform.
tfx.extensions.google_cloud_ai_platform.runner.deploy_model_for_aip_prediction(
api: discovery.Resource,
serving_path: Text,
model_version: Text,
ai_platform_serving_args: Dict[Text, Any],
job_labels: Dict[Text, Text],
skip_model_creation: bool = False,
set_default_version: bool = True
) -> None
Args |
api
|
Google API client resource.
|
serving_path
|
The path to the model. Must be a GCS URI.
|
model_version
|
Version of the model being deployed. Must be different from
what is currently being served.
|
ai_platform_serving_args
|
Dictionary containing arguments for pushing to AI
Platform. The full set of parameters supported can be found at
https://cloud.google.com/ml-engine/reference/rest/v1/projects.models.versions#Version
Most keys are forwarded as-is, but following keys are handled specially:
- name: this must be empty (and will be filled by pusher).
- deployment_uri: this must be empty (and will be filled by pusher).
- python_version: when left empty, this will be filled by python version
of the environment being used.
- runtime_version: when left empty, this will be filled by TensorFlow
version from the environment.
- labels: a list of job labels will be merged with user's input.
|
job_labels
|
The dict of labels that will be attached to this job. They are
merged with optional labels from ai_platform_serving_args .
|
skip_model_creation
|
If true, the method assuem model already exist in
AI platform, therefore skipping model creation.
|
set_default_version
|
Whether set the newly deployed model version as the
default version.
|
Raises |
RuntimeError
|
if an error is encountered when trying to push.
|