client.upload_model

🚧
Deprecated
This client method is being deprecated and will not be supported in future versions of the client. Please use client.add_model_artifact() going forward.

For more information, see Uploading a Model Artifact.

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.
artifact_path	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 '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.

import pathlib

PROJECT_ID = 'example_project'
MODEL_ID = 'example_model'

artifact_path = pathlib.Path('model_dir')

client.upload_model_package(
    artifact_path=artifact_path,
    project_id=PROJECT_ID,
    model_id=MODEL_ID
)

🚧Deprecated

🚧
Deprecated