async def sample_update_model():
# Create a client
client = aiplatform_v1beta1.ModelServiceAsyncClient()
# Initialize request argument(s)
model = aiplatform_v1beta1.Model()
model.display_name = "display_name_value"
request = aiplatform_v1beta1.UpdateModelRequest(model=model, )
# Make the request
response = await client.update_model(request=request)
# Handle the response
print(response)
async def sample_upload_model():
# Create a client
client = aiplatform_v1beta1.ModelServiceAsyncClient()
# Initialize request argument(s)
model = aiplatform_v1beta1.Model()
model.display_name = "display_name_value"
request = aiplatform_v1beta1.UploadModelRequest(
parent="parent_value",
model=model,
)
# Make the request
operation = client.upload_model(request=request)
print("Waiting for operation to complete...")
response = await operation.result()
# Handle the response
print(response)
def upload_model_explain_tabular_managed_container_sample(
project: str,
display_name: str,
container_spec_image_uri: str,
artifact_uri: str,
input_tensor_name: str,
output_tensor_name: str,
feature_names: list,
location: str = "us-central1",
api_endpoint: str = "us-central1-aiplatform.googleapis.com",
timeout: int = 300,
):
# 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_v1beta1.ModelServiceClient(
client_options=client_options)
# Container specification for deploying the model
container_spec = {
"image_uri": container_spec_image_uri,
"command": [],
"args": []
}
# The explainabilty method and corresponding parameters
parameters = aiplatform_v1beta1.ExplanationParameters(
{"xrai_attribution": {
"step_count": 1
}})
# The input tensor for feature attribution to the output
# For single input model, y = f(x), this will be the serving input layer.
input_metadata = aiplatform_v1beta1.ExplanationMetadata.InputMetadata({
"input_tensor_name":
input_tensor_name,
# Input is tabular data
"modality":
"numeric",
# Assign feature names to the inputs for explanation
"encoding":
"BAG_OF_FEATURES",
"index_feature_mapping":
feature_names,
})
# The output tensor to explain
# For single output model, y = f(x), this will be the serving output layer.
output_metadata = aiplatform_v1beta1.ExplanationMetadata.OutputMetadata(
{"output_tensor_name": output_tensor_name})
# Assemble the explanation metadata
metadata = aiplatform_v1beta1.ExplanationMetadata(
inputs={"features": input_metadata},
outputs={"prediction": output_metadata})
# Assemble the explanation specification
explanation_spec = aiplatform_v1beta1.ExplanationSpec(
parameters=parameters, metadata=metadata)
model = aiplatform_v1beta1.Model(
display_name=display_name,
# The Cloud Storage location of the custom model
artifact_uri=artifact_uri,
explanation_spec=explanation_spec,
container_spec=container_spec,
)
parent = f"projects/{project}/locations/{location}"
response = client.upload_model(parent=parent, model=model)
print("Long running operation:", response.operation.name)
upload_model_response = response.result(timeout=timeout)
print("upload_model_response:", upload_model_response)