Class PredictionServiceClient (1.73.0)

A service for online predictions and explanations.

Return the API endpoint used by the client instance.

Returns the transport used by the client instance.

Return the universe domain used by the client instance.

Instantiates the prediction service client.

Releases underlying transport's resources.

Starts asynchronous cancellation on a long-running operation.

The server makes a best effort to cancel the operation, but success is not guaranteed. If the server doesn't support this method, it returns google.rpc.Code.UNIMPLEMENTED.

Returns a fully-qualified billing_account string.

Returns a fully-qualified folder string.

Returns a fully-qualified location string.

Returns a fully-qualified organization string.

Returns a fully-qualified project string.

Deletes a long-running operation.

This method indicates that the client is no longer interested in the operation result. It does not cancel the operation. If the server doesn't support this method, it returns google.rpc.Code.UNIMPLEMENTED.

Perform an unary online prediction request to a gRPC model server for Vertex first-party products and frameworks.

# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
#   client as shown in:
#   https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import aiplatform_v1

def sample_direct_predict():
    # Create a client
    client = aiplatform_v1.PredictionServiceClient()

    # Initialize request argument(s)
    request = aiplatform_v1.DirectPredictRequest(
        endpoint="endpoint_value",
    )

    # Make the request
    response = client.direct_predict(request=request)

    # Handle the response
    print(response)

Perform an unary online prediction request to a gRPC model server for custom containers.

# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
#   client as shown in:
#   https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import aiplatform_v1

def sample_direct_raw_predict():
    # Create a client
    client = aiplatform_v1.PredictionServiceClient()

    # Initialize request argument(s)
    request = aiplatform_v1.DirectRawPredictRequest(
        endpoint="endpoint_value",
    )

    # Make the request
    response = client.direct_raw_predict(request=request)

    # Handle the response
    print(response)

Returns a fully-qualified endpoint string.

Perform an online explanation.

If xref_deployed_model_id is specified, the corresponding DeployModel must have xref_explanation_spec populated. If xref_deployed_model_id is not specified, all DeployedModels must have xref_explanation_spec populated.

# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
#   client as shown in:
#   https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import aiplatform_v1

def sample_explain():
    # Create a client
    client = aiplatform_v1.PredictionServiceClient()

    # Initialize request argument(s)
    instances = aiplatform_v1.Value()
    instances.null_value = "NULL_VALUE"

    request = aiplatform_v1.ExplainRequest(
        endpoint="endpoint_value",
        instances=instances,
    )

    # Make the request
    response = client.explain(request=request)

    # Handle the response
    print(response)

Creates an instance of this client using the provided credentials file.

Creates an instance of this client using the provided credentials info.

Creates an instance of this client using the provided credentials file.

Generate content with multimodal inputs.

# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
#   client as shown in:
#   https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import aiplatform_v1

def sample_generate_content():
    # Create a client
    client = aiplatform_v1.PredictionServiceClient()

    # Initialize request argument(s)
    contents = aiplatform_v1.Content()
    contents.parts.text = "text_value"

    request = aiplatform_v1.GenerateContentRequest(
        model="model_value",
        contents=contents,
    )

    # Make the request
    response = client.generate_content(request=request)

    # Handle the response
    print(response)

Gets the IAM access control policy for a function.

Returns an empty policy if the function exists and does not have a policy set.

Gets information about a location.

Deprecated. Return the API endpoint and client cert source for mutual TLS.

The client cert source is determined in the following order: (1) if GOOGLE_API_USE_CLIENT_CERTIFICATE environment variable is not "true", the client cert source is None. (2) if client_options.client_cert_source is provided, use the provided one; if the default client cert source exists, use the default one; otherwise the client cert source is None.

The API endpoint is determined in the following order: (1) if client_options.api_endpoint if provided, use the provided one. (2) if GOOGLE_API_USE_CLIENT_CERTIFICATE environment variable is "always", use the default mTLS endpoint; if the environment variable is "never", use the default API endpoint; otherwise if client cert source exists, use the default mTLS endpoint, otherwise use the default API endpoint.

More details can be found at https://google.aip.dev/auth/4114.

Gets the latest state of a long-running operation.

Lists information about the supported locations for this service.

Lists operations that match the specified filter in the request.

Returns a fully-qualified model string.

Parse a billing_account path into its component segments.

Parse a folder path into its component segments.

Parse a location path into its component segments.

Parse a organization path into its component segments.

Parse a project path into its component segments.

Parses a endpoint path into its component segments.

Parses a model path into its component segments.

Perform an online prediction.

# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
#   client as shown in:
#   https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import aiplatform_v1

def sample_predict():
    # Create a client
    client = aiplatform_v1.PredictionServiceClient()

    # Initialize request argument(s)
    instances = aiplatform_v1.Value()
    instances.null_value = "NULL_VALUE"

    request = aiplatform_v1.PredictRequest(
        endpoint="endpoint_value",
        instances=instances,
    )

    # Make the request
    response = client.predict(request=request)

    # Handle the response
    print(response)

Perform an online prediction with an arbitrary HTTP payload.

The response includes the following HTTP headers:

• X-Vertex-AI-Endpoint-Id: ID of the xref_Endpoint that served this prediction.

• X-Vertex-AI-Deployed-Model-Id: ID of the Endpoint's xref_DeployedModel that served this prediction.

# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
#   client as shown in:
#   https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import aiplatform_v1

def sample_raw_predict():
    # Create a client
    client = aiplatform_v1.PredictionServiceClient()

    # Initialize request argument(s)
    request = aiplatform_v1.RawPredictRequest(
        endpoint="endpoint_value",
    )

    # Make the request
    response = client.raw_predict(request=request)

    # Handle the response
    print(response)

Perform a server-side streaming online prediction request for Vertex LLM streaming.

# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
#   client as shown in:
#   https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import aiplatform_v1

def sample_server_streaming_predict():
    # Create a client
    client = aiplatform_v1.PredictionServiceClient()

    # Initialize request argument(s)
    request = aiplatform_v1.StreamingPredictRequest(
        endpoint="endpoint_value",
    )

    # Make the request
    stream = client.server_streaming_predict(request=request)

    # Handle the response
    for response in stream:
        print(response)

Sets the IAM access control policy on the specified function.

Replaces any existing policy.

Perform a streaming online prediction request to a gRPC model server for Vertex first-party products and frameworks.

# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
#   client as shown in:
#   https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import aiplatform_v1

def sample_stream_direct_predict():
    # Create a client
    client = aiplatform_v1.PredictionServiceClient()

    # Initialize request argument(s)
    request = aiplatform_v1.StreamDirectPredictRequest(
        endpoint="endpoint_value",
    )

    # This method expects an iterator which contains
    # 'aiplatform_v1.StreamDirectPredictRequest' objects
    # Here we create a generator that yields a single `request` for
    # demonstrative purposes.
    requests = [request]

    def request_generator():
        for request in requests:
            yield request

    # Make the request
    stream = client.stream_direct_predict(requests=request_generator())

    # Handle the response
    for response in stream:
        print(response)

Perform a streaming online prediction request to a gRPC model server for custom containers.

# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
#   client as shown in:
#   https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import aiplatform_v1

def sample_stream_direct_raw_predict():
    # Create a client
    client = aiplatform_v1.PredictionServiceClient()

    # Initialize request argument(s)
    request = aiplatform_v1.StreamDirectRawPredictRequest(
        endpoint="endpoint_value",
    )

    # This method expects an iterator which contains
    # 'aiplatform_v1.StreamDirectRawPredictRequest' objects
    # Here we create a generator that yields a single `request` for
    # demonstrative purposes.
    requests = [request]

    def request_generator():
        for request in requests:
            yield request

    # Make the request
    stream = client.stream_direct_raw_predict(requests=request_generator())

    # Handle the response
    for response in stream:
        print(response)

Generate content with multimodal inputs with streaming support.

# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
#   client as shown in:
#   https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import aiplatform_v1

def sample_stream_generate_content():
    # Create a client
    client = aiplatform_v1.PredictionServiceClient()

    # Initialize request argument(s)
    contents = aiplatform_v1.Content()
    contents.parts.text = "text_value"

    request = aiplatform_v1.GenerateContentRequest(
        model="model_value",
        contents=contents,
    )

    # Make the request
    stream = client.stream_generate_content(request=request)

    # Handle the response
    for response in stream:
        print(response)

Perform a streaming online prediction with an arbitrary HTTP payload.

# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
#   client as shown in:
#   https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import aiplatform_v1

def sample_stream_raw_predict():
    # Create a client
    client = aiplatform_v1.PredictionServiceClient()

    # Initialize request argument(s)
    request = aiplatform_v1.StreamRawPredictRequest(
        endpoint="endpoint_value",
    )

    # Make the request
    stream = client.stream_raw_predict(request=request)

    # Handle the response
    for response in stream:
        print(response)

Perform a streaming online prediction request for Vertex first-party products and frameworks.

# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
#   client as shown in:
#   https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import aiplatform_v1

def sample_streaming_predict():
    # Create a client
    client = aiplatform_v1.PredictionServiceClient()

    # Initialize request argument(s)
    request = aiplatform_v1.StreamingPredictRequest(
        endpoint="endpoint_value",
    )

    # This method expects an iterator which contains
    # 'aiplatform_v1.StreamingPredictRequest' objects
    # Here we create a generator that yields a single `request` for
    # demonstrative purposes.
    requests = [request]

    def request_generator():
        for request in requests:
            yield request

    # Make the request
    stream = client.streaming_predict(requests=request_generator())

    # Handle the response
    for response in stream:
        print(response)

Perform a streaming online prediction request through gRPC.

# This snippet has been automatically generated and should be regarded as a
# code template only.
# It will require modifications to work:
# - It may require correct/in-range values for request initialization.
# - It may require specifying regional endpoints when creating the service
#   client as shown in:
#   https://googleapis.dev/python/google-api-core/latest/client_options.html
from google.cloud import aiplatform_v1

def sample_streaming_raw_predict():
    # Create a client
    client = aiplatform_v1.PredictionServiceClient()

    # Initialize request argument(s)
    request = aiplatform_v1.StreamingRawPredictRequest(
        endpoint="endpoint_value",
    )

    # This method expects an iterator which contains
    # 'aiplatform_v1.StreamingRawPredictRequest' objects
    # Here we create a generator that yields a single `request` for
    # demonstrative purposes.
    requests = [request]

    def request_generator():
        for request in requests:
            yield request

    # Make the request
    stream = client.streaming_raw_predict(requests=request_generator())

    # Handle the response
    for response in stream:
        print(response)

Tests the specified IAM permissions against the IAM access control policy for a function.

If the function does not exist, this will return an empty set of permissions, not a NOT_FOUND error.

Waits until the specified long-running operation is done or reaches at most a specified timeout, returning the latest state.

If the operation is already done, the latest state is immediately returned. If the timeout specified is greater than the default HTTP/RPC timeout, the HTTP/RPC timeout is used. If the server does not support this method, it returns google.rpc.Code.UNIMPLEMENTED.