Adds a model artifact to an existing model
Input Parameter | Type | Default | Description |
|---|---|---|---|
project_id | str | None | The unique identifier for the project. |
model_id | str | None | A unique identifier for the model. |
model_artifact_directory | pathlib.Path | None | A path to the directory containing all of the model files needed to run the model. |
deployment_type | Optional [str] | 'predictor' | The type of deployment for the model. Can be one of |
image_uri | Optional [str] | None | A URI of the form '/:'. If specified, the image will be used to create a new runtime to serve the model. |
namespace | Optional [str] | 'default' | The Kubernetes namespace to use for the newly created runtime. image_uri must be specified. |
port | Optional [int] | 5100 | The port to use for the newly created runtime. image_uri must be specified. |
replicas | Optional [int] | 1 | The number of replicas running the model. image_uri must be specified. |
cpus | Optional [int] | 0.25 | The number of CPU cores reserved per replica. image_uri must be specified. |
memory | Optional [str] | '128m' | The amount of memory reserved per replica. image_uri must be specified. |
gpus | Optional [int] | 0 | The number of GPU cores reserved per replica. image_uri must be specified. |
await_deployment | Optional [bool] | True | If True, will block until deployment completes. |
import pathlib
PROJECT_ID = 'example_project'
MODEL_ID = 'example_model'
model_artifact_directory = pathlib.Path('model_dir')
client.add_model_artifact(
model_artifact_directory=model_artifact_directory,
project_id=PROJECT_ID,
model_id=MODEL_ID
)