deploy_model_custom_trained_model_sample.py

# Copyright 2020 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

# [START aiplatform_deploy_model_custom_trained_model_sample]
from google.cloud import aiplatform


def deploy_model_custom_trained_model_sample(
    project: str,
    endpoint_id: str,
    model_name: str,
    deployed_model_display_name: str,
    location: str = "us-central1",
    api_endpoint: str = "us-central1-aiplatform.googleapis.com",
    timeout: int = 7200,
):
    # The AI Platform services require regional API endpoints.
    client_options = {"api_endpoint": api_endpoint}
    # Initialize client that will be used to create and send requests.
    # This client only needs to be created once, and can be reused for multiple requests.
    client = aiplatform.gapic.EndpointServiceClient(client_options=client_options)
    deployed_model = {
        # format: 'projects/{project}/locations/{location}/models/{model}'
        "model": model_name,
        "display_name": deployed_model_display_name,
        # `dedicated_resources` must be used for non-AutoML models
        "dedicated_resources": {
            "min_replica_count": 1,
            "machine_spec": {
                "machine_type": "n1-standard-2",
                # Accelerators can be used only if the model specifies a GPU image.
                # 'accelerator_type': aiplatform.gapic.AcceleratorType.NVIDIA_TESLA_K80,
                # 'accelerator_count': 1,
            },
        },
    }
    # key '0' assigns traffic for the newly deployed model
    # Traffic percentage values must add up to 100
    # Leave dictionary empty if endpoint should not accept any traffic
    traffic_split = {"0": 100}
    endpoint = client.endpoint_path(
        project=project, location=location, endpoint=endpoint_id
    )
    response = client.deploy_model(
        endpoint=endpoint, deployed_model=deployed_model, traffic_split=traffic_split
    )
    print("Long running operation:", response.operation.name)
    deploy_model_response = response.result(timeout=timeout)
    print("deploy_model_response:", deploy_model_response)


# [END aiplatform_deploy_model_custom_trained_model_sample]