Adds a model artifact to an existing model
Note
Before calling this function, you must have already added a model using
add_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_dir | str | 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 'predictor' — Just a predict endpoint is exposed. 'executor' — The model's internals are exposed. |
| 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. |
PROJECT_ID = 'example_project'
MODEL_ID = 'example_model'
client.add_model_artifact(
model_dir='model_dir/',
project_id=PROJECT_ID,
model_id=MODEL_ID
)