As of January 1, 2020 this library no longer supports Python 2 on the latest released version.
Library versions released prior to that date will continue to be available. For more information please
visit Python 2 support on Google Cloud.
Source code for google.cloud.automl_v1.services.auto_ml.async_client
# -*- coding: utf-8 -*-
# Copyright 2024 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
#
# http://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.
#
from collections import OrderedDict
import re
from typing import (
Callable,
Dict,
Mapping,
MutableMapping,
MutableSequence,
Optional,
Sequence,
Tuple,
Type,
Union,
)
from google.api_core import exceptions as core_exceptions
from google.api_core import gapic_v1
from google.api_core import retry_async as retries
from google.api_core.client_options import ClientOptions
from google.auth import credentials as ga_credentials # type: ignore
from google.oauth2 import service_account # type: ignore
from google.cloud.automl_v1 import gapic_version as package_version
try:
OptionalRetry = Union[retries.AsyncRetry, gapic_v1.method._MethodDefault, None]
except AttributeError: # pragma: NO COVER
OptionalRetry = Union[retries.AsyncRetry, object, None] # type: ignore
from google.api_core import operation # type: ignore
from google.api_core import operation_async # type: ignore
from google.protobuf import empty_pb2 # type: ignore
from google.protobuf import field_mask_pb2 # type: ignore
from google.protobuf import timestamp_pb2 # type: ignore
from google.cloud.automl_v1.services.auto_ml import pagers
from google.cloud.automl_v1.types import (
model_evaluation,
operations,
service,
text,
text_extraction,
text_sentiment,
translation,
)
from google.cloud.automl_v1.types import annotation_spec, classification
from google.cloud.automl_v1.types import dataset
from google.cloud.automl_v1.types import dataset as gca_dataset
from google.cloud.automl_v1.types import detection, image, io
from google.cloud.automl_v1.types import model
from google.cloud.automl_v1.types import model as gca_model
from .client import AutoMlClient
from .transports.base import DEFAULT_CLIENT_INFO, AutoMlTransport
from .transports.grpc_asyncio import AutoMlGrpcAsyncIOTransport
[docs]class AutoMlAsyncClient:
"""AutoML Server API.
The resource names are assigned by the server. The server never
reuses names that it has created after the resources with those
names are deleted.
An ID of a resource is the last element of the item's resource name.
For
``projects/{project_id}/locations/{location_id}/datasets/{dataset_id}``,
then the id for the item is ``{dataset_id}``.
Currently the only supported ``location_id`` is "us-central1".
On any input that is documented to expect a string parameter in
snake_case or dash-case, either of those cases is accepted.
"""
_client: AutoMlClient
# Copy defaults from the synchronous client for use here.
# Note: DEFAULT_ENDPOINT is deprecated. Use _DEFAULT_ENDPOINT_TEMPLATE instead.
DEFAULT_ENDPOINT = AutoMlClient.DEFAULT_ENDPOINT
DEFAULT_MTLS_ENDPOINT = AutoMlClient.DEFAULT_MTLS_ENDPOINT
_DEFAULT_ENDPOINT_TEMPLATE = AutoMlClient._DEFAULT_ENDPOINT_TEMPLATE
_DEFAULT_UNIVERSE = AutoMlClient._DEFAULT_UNIVERSE
annotation_spec_path = staticmethod(AutoMlClient.annotation_spec_path)
parse_annotation_spec_path = staticmethod(AutoMlClient.parse_annotation_spec_path)
dataset_path = staticmethod(AutoMlClient.dataset_path)
parse_dataset_path = staticmethod(AutoMlClient.parse_dataset_path)
model_path = staticmethod(AutoMlClient.model_path)
parse_model_path = staticmethod(AutoMlClient.parse_model_path)
model_evaluation_path = staticmethod(AutoMlClient.model_evaluation_path)
parse_model_evaluation_path = staticmethod(AutoMlClient.parse_model_evaluation_path)
common_billing_account_path = staticmethod(AutoMlClient.common_billing_account_path)
parse_common_billing_account_path = staticmethod(
AutoMlClient.parse_common_billing_account_path
)
common_folder_path = staticmethod(AutoMlClient.common_folder_path)
parse_common_folder_path = staticmethod(AutoMlClient.parse_common_folder_path)
common_organization_path = staticmethod(AutoMlClient.common_organization_path)
parse_common_organization_path = staticmethod(
AutoMlClient.parse_common_organization_path
)
common_project_path = staticmethod(AutoMlClient.common_project_path)
parse_common_project_path = staticmethod(AutoMlClient.parse_common_project_path)
common_location_path = staticmethod(AutoMlClient.common_location_path)
parse_common_location_path = staticmethod(AutoMlClient.parse_common_location_path)
[docs] @classmethod
def from_service_account_info(cls, info: dict, *args, **kwargs):
"""Creates an instance of this client using the provided credentials
info.
Args:
info (dict): The service account private key info.
args: Additional arguments to pass to the constructor.
kwargs: Additional arguments to pass to the constructor.
Returns:
AutoMlAsyncClient: The constructed client.
"""
return AutoMlClient.from_service_account_info.__func__(AutoMlAsyncClient, info, *args, **kwargs) # type: ignore
[docs] @classmethod
def from_service_account_file(cls, filename: str, *args, **kwargs):
"""Creates an instance of this client using the provided credentials
file.
Args:
filename (str): The path to the service account private key json
file.
args: Additional arguments to pass to the constructor.
kwargs: Additional arguments to pass to the constructor.
Returns:
AutoMlAsyncClient: The constructed client.
"""
return AutoMlClient.from_service_account_file.__func__(AutoMlAsyncClient, filename, *args, **kwargs) # type: ignore
from_service_account_json = from_service_account_file
[docs] @classmethod
def get_mtls_endpoint_and_cert_source(
cls, client_options: Optional[ClientOptions] = None
):
"""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.
Args:
client_options (google.api_core.client_options.ClientOptions): Custom options for the
client. Only the `api_endpoint` and `client_cert_source` properties may be used
in this method.
Returns:
Tuple[str, Callable[[], Tuple[bytes, bytes]]]: returns the API endpoint and the
client cert source to use.
Raises:
google.auth.exceptions.MutualTLSChannelError: If any errors happen.
"""
return AutoMlClient.get_mtls_endpoint_and_cert_source(client_options) # type: ignore
@property
def transport(self) -> AutoMlTransport:
"""Returns the transport used by the client instance.
Returns:
AutoMlTransport: The transport used by the client instance.
"""
return self._client.transport
@property
def api_endpoint(self):
"""Return the API endpoint used by the client instance.
Returns:
str: The API endpoint used by the client instance.
"""
return self._client._api_endpoint
@property
def universe_domain(self) -> str:
"""Return the universe domain used by the client instance.
Returns:
str: The universe domain used
by the client instance.
"""
return self._client._universe_domain
get_transport_class = AutoMlClient.get_transport_class
def __init__(
self,
*,
credentials: Optional[ga_credentials.Credentials] = None,
transport: Optional[
Union[str, AutoMlTransport, Callable[..., AutoMlTransport]]
] = "grpc_asyncio",
client_options: Optional[ClientOptions] = None,
client_info: gapic_v1.client_info.ClientInfo = DEFAULT_CLIENT_INFO,
) -> None:
"""Instantiates the auto ml async client.
Args:
credentials (Optional[google.auth.credentials.Credentials]): The
authorization credentials to attach to requests. These
credentials identify the application to the service; if none
are specified, the client will attempt to ascertain the
credentials from the environment.
transport (Optional[Union[str,AutoMlTransport,Callable[..., AutoMlTransport]]]):
The transport to use, or a Callable that constructs and returns a new transport to use.
If a Callable is given, it will be called with the same set of initialization
arguments as used in the AutoMlTransport constructor.
If set to None, a transport is chosen automatically.
client_options (Optional[Union[google.api_core.client_options.ClientOptions, dict]]):
Custom options for the client.
1. The ``api_endpoint`` property can be used to override the
default endpoint provided by the client when ``transport`` is
not explicitly provided. Only if this property is not set and
``transport`` was not explicitly provided, the endpoint is
determined by the GOOGLE_API_USE_MTLS_ENDPOINT environment
variable, which have one of the following values:
"always" (always use the default mTLS endpoint), "never" (always
use the default regular endpoint) and "auto" (auto-switch to the
default mTLS endpoint if client certificate is present; this is
the default value).
2. If the GOOGLE_API_USE_CLIENT_CERTIFICATE environment variable
is "true", then the ``client_cert_source`` property can be used
to provide a client certificate for mTLS transport. If
not provided, the default SSL client certificate will be used if
present. If GOOGLE_API_USE_CLIENT_CERTIFICATE is "false" or not
set, no client certificate will be used.
3. The ``universe_domain`` property can be used to override the
default "googleapis.com" universe. Note that ``api_endpoint``
property still takes precedence; and ``universe_domain`` is
currently not supported for mTLS.
client_info (google.api_core.gapic_v1.client_info.ClientInfo):
The client info used to send a user-agent string along with
API requests. If ``None``, then default info will be used.
Generally, you only need to set this if you're developing
your own client library.
Raises:
google.auth.exceptions.MutualTlsChannelError: If mutual TLS transport
creation failed for any reason.
"""
self._client = AutoMlClient(
credentials=credentials,
transport=transport,
client_options=client_options,
client_info=client_info,
)
[docs] async def create_dataset(
self,
request: Optional[Union[service.CreateDatasetRequest, dict]] = None,
*,
parent: Optional[str] = None,
dataset: Optional[gca_dataset.Dataset] = None,
retry: OptionalRetry = gapic_v1.method.DEFAULT,
timeout: Union[float, object] = gapic_v1.method.DEFAULT,
metadata: Sequence[Tuple[str, str]] = (),
) -> operation_async.AsyncOperation:
r"""Creates a dataset.
.. code-block:: python
# 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 automl_v1
async def sample_create_dataset():
# Create a client
client = automl_v1.AutoMlAsyncClient()
# Initialize request argument(s)
dataset = automl_v1.Dataset()
dataset.translation_dataset_metadata.source_language_code = "source_language_code_value"
dataset.translation_dataset_metadata.target_language_code = "target_language_code_value"
request = automl_v1.CreateDatasetRequest(
parent="parent_value",
dataset=dataset,
)
# Make the request
operation = client.create_dataset(request=request)
print("Waiting for operation to complete...")
response = (await operation).result()
# Handle the response
print(response)
Args:
request (Optional[Union[google.cloud.automl_v1.types.CreateDatasetRequest, dict]]):
The request object. Request message for
[AutoMl.CreateDataset][google.cloud.automl.v1.AutoMl.CreateDataset].
parent (:class:`str`):
Required. The resource name of the
project to create the dataset for.
This corresponds to the ``parent`` field
on the ``request`` instance; if ``request`` is provided, this
should not be set.
dataset (:class:`google.cloud.automl_v1.types.Dataset`):
Required. The dataset to create.
This corresponds to the ``dataset`` field
on the ``request`` instance; if ``request`` is provided, this
should not be set.
retry (google.api_core.retry_async.AsyncRetry): Designation of what errors, if any,
should be retried.
timeout (float): The timeout for this request.
metadata (Sequence[Tuple[str, str]]): Strings which should be
sent along with the request as metadata.
Returns:
google.api_core.operation_async.AsyncOperation:
An object representing a long-running operation.
The result type for the operation will be :class:`google.cloud.automl_v1.types.Dataset` A workspace for solving a single, particular machine learning (ML) problem.
A workspace contains examples that may be annotated.
"""
# Create or coerce a protobuf request object.
# - Quick check: If we got a request object, we should *not* have
# gotten any keyword arguments that map to the request.
has_flattened_params = any([parent, dataset])
if request is not None and has_flattened_params:
raise ValueError(
"If the `request` argument is set, then none of "
"the individual field arguments should be set."
)
# - Use the request object if provided (there's no risk of modifying the input as
# there are no flattened fields), or create one.
if not isinstance(request, service.CreateDatasetRequest):
request = service.CreateDatasetRequest(request)
# If we have keyword arguments corresponding to fields on the
# request, apply these.
if parent is not None:
request.parent = parent
if dataset is not None:
request.dataset = dataset
# Wrap the RPC method; this adds retry and timeout information,
# and friendly error handling.
rpc = self._client._transport._wrapped_methods[
self._client._transport.create_dataset
]
# Certain fields should be provided within the metadata header;
# add these here.
metadata = tuple(metadata) + (
gapic_v1.routing_header.to_grpc_metadata((("parent", request.parent),)),
)
# Validate the universe domain.
self._client._validate_universe_domain()
# Send the request.
response = await rpc(
request,
retry=retry,
timeout=timeout,
metadata=metadata,
)
# Wrap the response in an operation future.
response = operation_async.from_gapic(
response,
self._client._transport.operations_client,
gca_dataset.Dataset,
metadata_type=operations.OperationMetadata,
)
# Done; return the response.
return response
[docs] async def get_dataset(
self,
request: Optional[Union[service.GetDatasetRequest, dict]] = None,
*,
name: Optional[str] = None,
retry: OptionalRetry = gapic_v1.method.DEFAULT,
timeout: Union[float, object] = gapic_v1.method.DEFAULT,
metadata: Sequence[Tuple[str, str]] = (),
) -> dataset.Dataset:
r"""Gets a dataset.
.. code-block:: python
# 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 automl_v1
async def sample_get_dataset():
# Create a client
client = automl_v1.AutoMlAsyncClient()
# Initialize request argument(s)
request = automl_v1.GetDatasetRequest(
name="name_value",
)
# Make the request
response = await client.get_dataset(request=request)
# Handle the response
print(response)
Args:
request (Optional[Union[google.cloud.automl_v1.types.GetDatasetRequest, dict]]):
The request object. Request message for
[AutoMl.GetDataset][google.cloud.automl.v1.AutoMl.GetDataset].
name (:class:`str`):
Required. The resource name of the
dataset to retrieve.
This corresponds to the ``name`` field
on the ``request`` instance; if ``request`` is provided, this
should not be set.
retry (google.api_core.retry_async.AsyncRetry): Designation of what errors, if any,
should be retried.
timeout (float): The timeout for this request.
metadata (Sequence[Tuple[str, str]]): Strings which should be
sent along with the request as metadata.
Returns:
google.cloud.automl_v1.types.Dataset:
A workspace for solving a single,
particular machine learning (ML)
problem. A workspace contains examples
that may be annotated.
"""
# Create or coerce a protobuf request object.
# - Quick check: If we got a request object, we should *not* have
# gotten any keyword arguments that map to the request.
has_flattened_params = any([name])
if request is not None and has_flattened_params:
raise ValueError(
"If the `request` argument is set, then none of "
"the individual field arguments should be set."
)
# - Use the request object if provided (there's no risk of modifying the input as
# there are no flattened fields), or create one.
if not isinstance(request, service.GetDatasetRequest):
request = service.GetDatasetRequest(request)
# If we have keyword arguments corresponding to fields on the
# request, apply these.
if name is not None:
request.name = name
# Wrap the RPC method; this adds retry and timeout information,
# and friendly error handling.
rpc = self._client._transport._wrapped_methods[
self._client._transport.get_dataset
]
# Certain fields should be provided within the metadata header;
# add these here.
metadata = tuple(metadata) + (
gapic_v1.routing_header.to_grpc_metadata((("name", request.name),)),
)
# Validate the universe domain.
self._client._validate_universe_domain()
# Send the request.
response = await rpc(
request,
retry=retry,
timeout=timeout,
metadata=metadata,
)
# Done; return the response.
return response
[docs] async def list_datasets(
self,
request: Optional[Union[service.ListDatasetsRequest, dict]] = None,
*,
parent: Optional[str] = None,
retry: OptionalRetry = gapic_v1.method.DEFAULT,
timeout: Union[float, object] = gapic_v1.method.DEFAULT,
metadata: Sequence[Tuple[str, str]] = (),
) -> pagers.ListDatasetsAsyncPager:
r"""Lists datasets in a project.
.. code-block:: python
# 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 automl_v1
async def sample_list_datasets():
# Create a client
client = automl_v1.AutoMlAsyncClient()
# Initialize request argument(s)
request = automl_v1.ListDatasetsRequest(
parent="parent_value",
)
# Make the request
page_result = client.list_datasets(request=request)
# Handle the response
async for response in page_result:
print(response)
Args:
request (Optional[Union[google.cloud.automl_v1.types.ListDatasetsRequest, dict]]):
The request object. Request message for
[AutoMl.ListDatasets][google.cloud.automl.v1.AutoMl.ListDatasets].
parent (:class:`str`):
Required. The resource name of the
project from which to list datasets.
This corresponds to the ``parent`` field
on the ``request`` instance; if ``request`` is provided, this
should not be set.
retry (google.api_core.retry_async.AsyncRetry): Designation of what errors, if any,
should be retried.
timeout (float): The timeout for this request.
metadata (Sequence[Tuple[str, str]]): Strings which should be
sent along with the request as metadata.
Returns:
google.cloud.automl_v1.services.auto_ml.pagers.ListDatasetsAsyncPager:
Response message for
[AutoMl.ListDatasets][google.cloud.automl.v1.AutoMl.ListDatasets].
Iterating over this object will yield results and
resolve additional pages automatically.
"""
# Create or coerce a protobuf request object.
# - Quick check: If we got a request object, we should *not* have
# gotten any keyword arguments that map to the request.
has_flattened_params = any([parent])
if request is not None and has_flattened_params:
raise ValueError(
"If the `request` argument is set, then none of "
"the individual field arguments should be set."
)
# - Use the request object if provided (there's no risk of modifying the input as
# there are no flattened fields), or create one.
if not isinstance(request, service.ListDatasetsRequest):
request = service.ListDatasetsRequest(request)
# If we have keyword arguments corresponding to fields on the
# request, apply these.
if parent is not None:
request.parent = parent
# Wrap the RPC method; this adds retry and timeout information,
# and friendly error handling.
rpc = self._client._transport._wrapped_methods[
self._client._transport.list_datasets
]
# Certain fields should be provided within the metadata header;
# add these here.
metadata = tuple(metadata) + (
gapic_v1.routing_header.to_grpc_metadata((("parent", request.parent),)),
)
# Validate the universe domain.
self._client._validate_universe_domain()
# Send the request.
response = await rpc(
request,
retry=retry,
timeout=timeout,
metadata=metadata,
)
# This method is paged; wrap the response in a pager, which provides
# an `__aiter__` convenience method.
response = pagers.ListDatasetsAsyncPager(
method=rpc,
request=request,
response=response,
retry=retry,
timeout=timeout,
metadata=metadata,
)
# Done; return the response.
return response
[docs] async def update_dataset(
self,
request: Optional[Union[service.UpdateDatasetRequest, dict]] = None,
*,
dataset: Optional[gca_dataset.Dataset] = None,
update_mask: Optional[field_mask_pb2.FieldMask] = None,
retry: OptionalRetry = gapic_v1.method.DEFAULT,
timeout: Union[float, object] = gapic_v1.method.DEFAULT,
metadata: Sequence[Tuple[str, str]] = (),
) -> gca_dataset.Dataset:
r"""Updates a dataset.
.. code-block:: python
# 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 automl_v1
async def sample_update_dataset():
# Create a client
client = automl_v1.AutoMlAsyncClient()
# Initialize request argument(s)
dataset = automl_v1.Dataset()
dataset.translation_dataset_metadata.source_language_code = "source_language_code_value"
dataset.translation_dataset_metadata.target_language_code = "target_language_code_value"
request = automl_v1.UpdateDatasetRequest(
dataset=dataset,
)
# Make the request
response = await client.update_dataset(request=request)
# Handle the response
print(response)
Args:
request (Optional[Union[google.cloud.automl_v1.types.UpdateDatasetRequest, dict]]):
The request object. Request message for
[AutoMl.UpdateDataset][google.cloud.automl.v1.AutoMl.UpdateDataset]
dataset (:class:`google.cloud.automl_v1.types.Dataset`):
Required. The dataset which replaces
the resource on the server.
This corresponds to the ``dataset`` field
on the ``request`` instance; if ``request`` is provided, this
should not be set.
update_mask (:class:`google.protobuf.field_mask_pb2.FieldMask`):
Required. The update mask applies to
the resource.
This corresponds to the ``update_mask`` field
on the ``request`` instance; if ``request`` is provided, this
should not be set.
retry (google.api_core.retry_async.AsyncRetry): Designation of what errors, if any,
should be retried.
timeout (float): The timeout for this request.
metadata (Sequence[Tuple[str, str]]): Strings which should be
sent along with the request as metadata.
Returns:
google.cloud.automl_v1.types.Dataset:
A workspace for solving a single,
particular machine learning (ML)
problem. A workspace contains examples
that may be annotated.
"""
# Create or coerce a protobuf request object.
# - Quick check: If we got a request object, we should *not* have
# gotten any keyword arguments that map to the request.
has_flattened_params = any([dataset, update_mask])
if request is not None and has_flattened_params:
raise ValueError(
"If the `request` argument is set, then none of "
"the individual field arguments should be set."
)
# - Use the request object if provided (there's no risk of modifying the input as
# there are no flattened fields), or create one.
if not isinstance(request, service.UpdateDatasetRequest):
request = service.UpdateDatasetRequest(request)
# If we have keyword arguments corresponding to fields on the
# request, apply these.
if dataset is not None:
request.dataset = dataset
if update_mask is not None:
request.update_mask = update_mask
# Wrap the RPC method; this adds retry and timeout information,
# and friendly error handling.
rpc = self._client._transport._wrapped_methods[
self._client._transport.update_dataset
]
# Certain fields should be provided within the metadata header;
# add these here.
metadata = tuple(metadata) + (
gapic_v1.routing_header.to_grpc_metadata(
(("dataset.name", request.dataset.name),)
),
)
# Validate the universe domain.
self._client._validate_universe_domain()
# Send the request.
response = await rpc(
request,
retry=retry,
timeout=timeout,
metadata=metadata,
)
# Done; return the response.
return response
[docs] async def delete_dataset(
self,
request: Optional[Union[service.DeleteDatasetRequest, dict]] = None,
*,
name: Optional[str] = None,
retry: OptionalRetry = gapic_v1.method.DEFAULT,
timeout: Union[float, object] = gapic_v1.method.DEFAULT,
metadata: Sequence[Tuple[str, str]] = (),
) -> operation_async.AsyncOperation:
r"""Deletes a dataset and all of its contents. Returns empty
response in the
[response][google.longrunning.Operation.response] field when it
completes, and ``delete_details`` in the
[metadata][google.longrunning.Operation.metadata] field.
.. code-block:: python
# 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 automl_v1
async def sample_delete_dataset():
# Create a client
client = automl_v1.AutoMlAsyncClient()
# Initialize request argument(s)
request = automl_v1.DeleteDatasetRequest(
name="name_value",
)
# Make the request
operation = client.delete_dataset(request=request)
print("Waiting for operation to complete...")
response = (await operation).result()
# Handle the response
print(response)
Args:
request (Optional[Union[google.cloud.automl_v1.types.DeleteDatasetRequest, dict]]):
The request object. Request message for
[AutoMl.DeleteDataset][google.cloud.automl.v1.AutoMl.DeleteDataset].
name (:class:`str`):
Required. The resource name of the
dataset to delete.
This corresponds to the ``name`` field
on the ``request`` instance; if ``request`` is provided, this
should not be set.
retry (google.api_core.retry_async.AsyncRetry): Designation of what errors, if any,
should be retried.
timeout (float): The timeout for this request.
metadata (Sequence[Tuple[str, str]]): Strings which should be
sent along with the request as metadata.
Returns:
google.api_core.operation_async.AsyncOperation:
An object representing a long-running operation.
The result type for the operation will be :class:`google.protobuf.empty_pb2.Empty` A generic empty message that you can re-use to avoid defining duplicated
empty messages in your APIs. A typical example is to
use it as the request or the response type of an API
method. For instance:
service Foo {
rpc Bar(google.protobuf.Empty) returns
(google.protobuf.Empty);
}
"""
# Create or coerce a protobuf request object.
# - Quick check: If we got a request object, we should *not* have
# gotten any keyword arguments that map to the request.
has_flattened_params = any([name])
if request is not None and has_flattened_params:
raise ValueError(
"If the `request` argument is set, then none of "
"the individual field arguments should be set."
)
# - Use the request object if provided (there's no risk of modifying the input as
# there are no flattened fields), or create one.
if not isinstance(request, service.DeleteDatasetRequest):
request = service.DeleteDatasetRequest(request)
# If we have keyword arguments corresponding to fields on the
# request, apply these.
if name is not None:
request.name = name
# Wrap the RPC method; this adds retry and timeout information,
# and friendly error handling.
rpc = self._client._transport._wrapped_methods[
self._client._transport.delete_dataset
]
# Certain fields should be provided within the metadata header;
# add these here.
metadata = tuple(metadata) + (
gapic_v1.routing_header.to_grpc_metadata((("name", request.name),)),
)
# Validate the universe domain.
self._client._validate_universe_domain()
# Send the request.
response = await rpc(
request,
retry=retry,
timeout=timeout,
metadata=metadata,
)
# Wrap the response in an operation future.
response = operation_async.from_gapic(
response,
self._client._transport.operations_client,
empty_pb2.Empty,
metadata_type=operations.OperationMetadata,
)
# Done; return the response.
return response
[docs] async def import_data(
self,
request: Optional[Union[service.ImportDataRequest, dict]] = None,
*,
name: Optional[str] = None,
input_config: Optional[io.InputConfig] = None,
retry: OptionalRetry = gapic_v1.method.DEFAULT,
timeout: Union[float, object] = gapic_v1.method.DEFAULT,
metadata: Sequence[Tuple[str, str]] = (),
) -> operation_async.AsyncOperation:
r"""Imports data into a dataset. For Tables this method can only be
called on an empty Dataset.
For Tables:
- A
[schema_inference_version][google.cloud.automl.v1.InputConfig.params]
parameter must be explicitly set. Returns an empty response
in the [response][google.longrunning.Operation.response]
field when it completes.
.. code-block:: python
# 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 automl_v1
async def sample_import_data():
# Create a client
client = automl_v1.AutoMlAsyncClient()
# Initialize request argument(s)
input_config = automl_v1.InputConfig()
input_config.gcs_source.input_uris = ['input_uris_value1', 'input_uris_value2']
request = automl_v1.ImportDataRequest(
name="name_value",
input_config=input_config,
)
# Make the request
operation = client.import_data(request=request)
print("Waiting for operation to complete...")
response = (await operation).result()
# Handle the response
print(response)
Args:
request (Optional[Union[google.cloud.automl_v1.types.ImportDataRequest, dict]]):
The request object. Request message for
[AutoMl.ImportData][google.cloud.automl.v1.AutoMl.ImportData].
name (:class:`str`):
Required. Dataset name. Dataset must
already exist. All imported annotations
and examples will be added.
This corresponds to the ``name`` field
on the ``request`` instance; if ``request`` is provided, this
should not be set.
input_config (:class:`google.cloud.automl_v1.types.InputConfig`):
Required. The desired input location
and its domain specific semantics, if
any.
This corresponds to the ``input_config`` field
on the ``request`` instance; if ``request`` is provided, this
should not be set.
retry (google.api_core.retry_async.AsyncRetry): Designation of what errors, if any,
should be retried.
timeout (float): The timeout for this request.
metadata (Sequence[Tuple[str, str]]): Strings which should be
sent along with the request as metadata.
Returns:
google.api_core.operation_async.AsyncOperation:
An object representing a long-running operation.
The result type for the operation will be :class:`google.protobuf.empty_pb2.Empty` A generic empty message that you can re-use to avoid defining duplicated
empty messages in your APIs. A typical example is to
use it as the request or the response type of an API
method. For instance:
service Foo {
rpc Bar(google.protobuf.Empty) returns
(google.protobuf.Empty);
}
"""
# Create or coerce a protobuf request object.
# - Quick check: If we got a request object, we should *not* have
# gotten any keyword arguments that map to the request.
has_flattened_params = any([name, input_config])
if request is not None and has_flattened_params:
raise ValueError(
"If the `request` argument is set, then none of "
"the individual field arguments should be set."
)
# - Use the request object if provided (there's no risk of modifying the input as
# there are no flattened fields), or create one.
if not isinstance(request, service.ImportDataRequest):
request = service.ImportDataRequest(request)
# If we have keyword arguments corresponding to fields on the
# request, apply these.
if name is not None:
request.name = name
if input_config is not None:
request.input_config = input_config
# Wrap the RPC method; this adds retry and timeout information,
# and friendly error handling.
rpc = self._client._transport._wrapped_methods[
self._client._transport.import_data
]
# Certain fields should be provided within the metadata header;
# add these here.
metadata = tuple(metadata) + (
gapic_v1.routing_header.to_grpc_metadata((("name", request.name),)),
)
# Validate the universe domain.
self._client._validate_universe_domain()
# Send the request.
response = await rpc(
request,
retry=retry,
timeout=timeout,
metadata=metadata,
)
# Wrap the response in an operation future.
response = operation_async.from_gapic(
response,
self._client._transport.operations_client,
empty_pb2.Empty,
metadata_type=operations.OperationMetadata,
)
# Done; return the response.
return response
[docs] async def export_data(
self,
request: Optional[Union[service.ExportDataRequest, dict]] = None,
*,
name: Optional[str] = None,
output_config: Optional[io.OutputConfig] = None,
retry: OptionalRetry = gapic_v1.method.DEFAULT,
timeout: Union[float, object] = gapic_v1.method.DEFAULT,
metadata: Sequence[Tuple[str, str]] = (),
) -> operation_async.AsyncOperation:
r"""Exports dataset's data to the provided output location. Returns
an empty response in the
[response][google.longrunning.Operation.response] field when it
completes.
.. code-block:: python
# 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 automl_v1
async def sample_export_data():
# Create a client
client = automl_v1.AutoMlAsyncClient()
# Initialize request argument(s)
output_config = automl_v1.OutputConfig()
output_config.gcs_destination.output_uri_prefix = "output_uri_prefix_value"
request = automl_v1.ExportDataRequest(
name="name_value",
output_config=output_config,
)
# Make the request
operation = client.export_data(request=request)
print("Waiting for operation to complete...")
response = (await operation).result()
# Handle the response
print(response)
Args:
request (Optional[Union[google.cloud.automl_v1.types.ExportDataRequest, dict]]):
The request object. Request message for
[AutoMl.ExportData][google.cloud.automl.v1.AutoMl.ExportData].
name (:class:`str`):
Required. The resource name of the
dataset.
This corresponds to the ``name`` field
on the ``request`` instance; if ``request`` is provided, this
should not be set.
output_config (:class:`google.cloud.automl_v1.types.OutputConfig`):
Required. The desired output
location.
This corresponds to the ``output_config`` field
on the ``request`` instance; if ``request`` is provided, this
should not be set.
retry (google.api_core.retry_async.AsyncRetry): Designation of what errors, if any,
should be retried.
timeout (float): The timeout for this request.
metadata (Sequence[Tuple[str, str]]): Strings which should be
sent along with the request as metadata.
Returns:
google.api_core.operation_async.AsyncOperation:
An object representing a long-running operation.
The result type for the operation will be :class:`google.protobuf.empty_pb2.Empty` A generic empty message that you can re-use to avoid defining duplicated
empty messages in your APIs. A typical example is to
use it as the request or the response type of an API
method. For instance:
service Foo {
rpc Bar(google.protobuf.Empty) returns
(google.protobuf.Empty);
}
"""
# Create or coerce a protobuf request object.
# - Quick check: If we got a request object, we should *not* have
# gotten any keyword arguments that map to the request.
has_flattened_params = any([name, output_config])
if request is not None and has_flattened_params:
raise ValueError(
"If the `request` argument is set, then none of "
"the individual field arguments should be set."
)
# - Use the request object if provided (there's no risk of modifying the input as
# there are no flattened fields), or create one.
if not isinstance(request, service.ExportDataRequest):
request = service.ExportDataRequest(request)
# If we have keyword arguments corresponding to fields on the
# request, apply these.
if name is not None:
request.name = name
if output_config is not None:
request.output_config = output_config
# Wrap the RPC method; this adds retry and timeout information,
# and friendly error handling.
rpc = self._client._transport._wrapped_methods[
self._client._transport.export_data
]
# Certain fields should be provided within the metadata header;
# add these here.
metadata = tuple(metadata) + (
gapic_v1.routing_header.to_grpc_metadata((("name", request.name),)),
)
# Validate the universe domain.
self._client._validate_universe_domain()
# Send the request.
response = await rpc(
request,
retry=retry,
timeout=timeout,
metadata=metadata,
)
# Wrap the response in an operation future.
response = operation_async.from_gapic(
response,
self._client._transport.operations_client,
empty_pb2.Empty,
metadata_type=operations.OperationMetadata,
)
# Done; return the response.
return response
[docs] async def get_annotation_spec(
self,
request: Optional[Union[service.GetAnnotationSpecRequest, dict]] = None,
*,
name: Optional[str] = None,
retry: OptionalRetry = gapic_v1.method.DEFAULT,
timeout: Union[float, object] = gapic_v1.method.DEFAULT,
metadata: Sequence[Tuple[str, str]] = (),
) -> annotation_spec.AnnotationSpec:
r"""Gets an annotation spec.
.. code-block:: python
# 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 automl_v1
async def sample_get_annotation_spec():
# Create a client
client = automl_v1.AutoMlAsyncClient()
# Initialize request argument(s)
request = automl_v1.GetAnnotationSpecRequest(
name="name_value",
)
# Make the request
response = await client.get_annotation_spec(request=request)
# Handle the response
print(response)
Args:
request (Optional[Union[google.cloud.automl_v1.types.GetAnnotationSpecRequest, dict]]):
The request object. Request message for
[AutoMl.GetAnnotationSpec][google.cloud.automl.v1.AutoMl.GetAnnotationSpec].
name (:class:`str`):
Required. The resource name of the
annotation spec to retrieve.
This corresponds to the ``name`` field
on the ``request`` instance; if ``request`` is provided, this
should not be set.
retry (google.api_core.retry_async.AsyncRetry): Designation of what errors, if any,
should be retried.
timeout (float): The timeout for this request.
metadata (Sequence[Tuple[str, str]]): Strings which should be
sent along with the request as metadata.
Returns:
google.cloud.automl_v1.types.AnnotationSpec:
A definition of an annotation spec.
"""
# Create or coerce a protobuf request object.
# - Quick check: If we got a request object, we should *not* have
# gotten any keyword arguments that map to the request.
has_flattened_params = any([name])
if request is not None and has_flattened_params:
raise ValueError(
"If the `request` argument is set, then none of "
"the individual field arguments should be set."
)
# - Use the request object if provided (there's no risk of modifying the input as
# there are no flattened fields), or create one.
if not isinstance(request, service.GetAnnotationSpecRequest):
request = service.GetAnnotationSpecRequest(request)
# If we have keyword arguments corresponding to fields on the
# request, apply these.
if name is not None:
request.name = name
# Wrap the RPC method; this adds retry and timeout information,
# and friendly error handling.
rpc = self._client._transport._wrapped_methods[
self._client._transport.get_annotation_spec
]
# Certain fields should be provided within the metadata header;
# add these here.
metadata = tuple(metadata) + (
gapic_v1.routing_header.to_grpc_metadata((("name", request.name),)),
)
# Validate the universe domain.
self._client._validate_universe_domain()
# Send the request.
response = await rpc(
request,
retry=retry,
timeout=timeout,
metadata=metadata,
)
# Done; return the response.
return response
[docs] async def create_model(
self,
request: Optional[Union[service.CreateModelRequest, dict]] = None,
*,
parent: Optional[str] = None,
model: Optional[gca_model.Model] = None,
retry: OptionalRetry = gapic_v1.method.DEFAULT,
timeout: Union[float, object] = gapic_v1.method.DEFAULT,
metadata: Sequence[Tuple[str, str]] = (),
) -> operation_async.AsyncOperation:
r"""Creates a model. Returns a Model in the
[response][google.longrunning.Operation.response] field when it
completes. When you create a model, several model evaluations
are created for it: a global evaluation, and one evaluation for
each annotation spec.
.. code-block:: python
# 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 automl_v1
async def sample_create_model():
# Create a client
client = automl_v1.AutoMlAsyncClient()
# Initialize request argument(s)
request = automl_v1.CreateModelRequest(
parent="parent_value",
)
# Make the request
operation = client.create_model(request=request)
print("Waiting for operation to complete...")
response = (await operation).result()
# Handle the response
print(response)
Args:
request (Optional[Union[google.cloud.automl_v1.types.CreateModelRequest, dict]]):
The request object. Request message for
[AutoMl.CreateModel][google.cloud.automl.v1.AutoMl.CreateModel].
parent (:class:`str`):
Required. Resource name of the parent
project where the model is being
created.
This corresponds to the ``parent`` field
on the ``request`` instance; if ``request`` is provided, this
should not be set.
model (:class:`google.cloud.automl_v1.types.Model`):
Required. The model to create.
This corresponds to the ``model`` field
on the ``request`` instance; if ``request`` is provided, this
should not be set.
retry (google.api_core.retry_async.AsyncRetry): Designation of what errors, if any,
should be retried.
timeout (float): The timeout for this request.
metadata (Sequence[Tuple[str, str]]): Strings which should be
sent along with the request as metadata.
Returns:
google.api_core.operation_async.AsyncOperation:
An object representing a long-running operation.
The result type for the operation will be
:class:`google.cloud.automl_v1.types.Model` API proto
representing a trained machine learning model.
"""
# Create or coerce a protobuf request object.
# - Quick check: If we got a request object, we should *not* have
# gotten any keyword arguments that map to the request.
has_flattened_params = any([parent, model])
if request is not None and has_flattened_params:
raise ValueError(
"If the `request` argument is set, then none of "
"the individual field arguments should be set."
)
# - Use the request object if provided (there's no risk of modifying the input as
# there are no flattened fields), or create one.
if not isinstance(request, service.CreateModelRequest):
request = service.CreateModelRequest(request)
# If we have keyword arguments corresponding to fields on the
# request, apply these.
if parent is not None:
request.parent = parent
if model is not None:
request.model = model
# Wrap the RPC method; this adds retry and timeout information,
# and friendly error handling.
rpc = self._client._transport._wrapped_methods[
self._client._transport.create_model
]
# Certain fields should be provided within the metadata header;
# add these here.
metadata = tuple(metadata) + (
gapic_v1.routing_header.to_grpc_metadata((("parent", request.parent),)),
)
# Validate the universe domain.
self._client._validate_universe_domain()
# Send the request.
response = await rpc(
request,
retry=retry,
timeout=timeout,
metadata=metadata,
)
# Wrap the response in an operation future.
response = operation_async.from_gapic(
response,
self._client._transport.operations_client,
gca_model.Model,
metadata_type=operations.OperationMetadata,
)
# Done; return the response.
return response
[docs] async def get_model(
self,
request: Optional[Union[service.GetModelRequest, dict]] = None,
*,
name: Optional[str] = None,
retry: OptionalRetry = gapic_v1.method.DEFAULT,
timeout: Union[float, object] = gapic_v1.method.DEFAULT,
metadata: Sequence[Tuple[str, str]] = (),
) -> model.Model:
r"""Gets a model.
.. code-block:: python
# 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 automl_v1
async def sample_get_model():
# Create a client
client = automl_v1.AutoMlAsyncClient()
# Initialize request argument(s)
request = automl_v1.GetModelRequest(
name="name_value",
)
# Make the request
response = await client.get_model(request=request)
# Handle the response
print(response)
Args:
request (Optional[Union[google.cloud.automl_v1.types.GetModelRequest, dict]]):
The request object. Request message for
[AutoMl.GetModel][google.cloud.automl.v1.AutoMl.GetModel].
name (:class:`str`):
Required. Resource name of the model.
This corresponds to the ``name`` field
on the ``request`` instance; if ``request`` is provided, this
should not be set.
retry (google.api_core.retry_async.AsyncRetry): Designation of what errors, if any,
should be retried.
timeout (float): The timeout for this request.
metadata (Sequence[Tuple[str, str]]): Strings which should be
sent along with the request as metadata.
Returns:
google.cloud.automl_v1.types.Model:
API proto representing a trained
machine learning model.
"""
# Create or coerce a protobuf request object.
# - Quick check: If we got a request object, we should *not* have
# gotten any keyword arguments that map to the request.
has_flattened_params = any([name])
if request is not None and has_flattened_params:
raise ValueError(
"If the `request` argument is set, then none of "
"the individual field arguments should be set."
)
# - Use the request object if provided (there's no risk of modifying the input as
# there are no flattened fields), or create one.
if not isinstance(request, service.GetModelRequest):
request = service.GetModelRequest(request)
# If we have keyword arguments corresponding to fields on the
# request, apply these.
if name is not None:
request.name = name
# Wrap the RPC method; this adds retry and timeout information,
# and friendly error handling.
rpc = self._client._transport._wrapped_methods[
self._client._transport.get_model
]
# Certain fields should be provided within the metadata header;
# add these here.
metadata = tuple(metadata) + (
gapic_v1.routing_header.to_grpc_metadata((("name", request.name),)),
)
# Validate the universe domain.
self._client._validate_universe_domain()
# Send the request.
response = await rpc(
request,
retry=retry,
timeout=timeout,
metadata=metadata,
)
# Done; return the response.
return response
[docs] async def list_models(
self,
request: Optional[Union[service.ListModelsRequest, dict]] = None,
*,
parent: Optional[str] = None,
retry: OptionalRetry = gapic_v1.method.DEFAULT,
timeout: Union[float, object] = gapic_v1.method.DEFAULT,
metadata: Sequence[Tuple[str, str]] = (),
) -> pagers.ListModelsAsyncPager:
r"""Lists models.
.. code-block:: python
# 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 automl_v1
async def sample_list_models():
# Create a client
client = automl_v1.AutoMlAsyncClient()
# Initialize request argument(s)
request = automl_v1.ListModelsRequest(
parent="parent_value",
)
# Make the request
page_result = client.list_models(request=request)
# Handle the response
async for response in page_result:
print(response)
Args:
request (Optional[Union[google.cloud.automl_v1.types.ListModelsRequest, dict]]):
The request object. Request message for
[AutoMl.ListModels][google.cloud.automl.v1.AutoMl.ListModels].
parent (:class:`str`):
Required. Resource name of the
project, from which to list the models.
This corresponds to the ``parent`` field
on the ``request`` instance; if ``request`` is provided, this
should not be set.
retry (google.api_core.retry_async.AsyncRetry): Designation of what errors, if any,
should be retried.
timeout (float): The timeout for this request.
metadata (Sequence[Tuple[str, str]]): Strings which should be
sent along with the request as metadata.
Returns:
google.cloud.automl_v1.services.auto_ml.pagers.ListModelsAsyncPager:
Response message for
[AutoMl.ListModels][google.cloud.automl.v1.AutoMl.ListModels].
Iterating over this object will yield results and
resolve additional pages automatically.
"""
# Create or coerce a protobuf request object.
# - Quick check: If we got a request object, we should *not* have
# gotten any keyword arguments that map to the request.
has_flattened_params = any([parent])
if request is not None and has_flattened_params:
raise ValueError(
"If the `request` argument is set, then none of "
"the individual field arguments should be set."
)
# - Use the request object if provided (there's no risk of modifying the input as
# there are no flattened fields), or create one.
if not isinstance(request, service.ListModelsRequest):
request = service.ListModelsRequest(request)
# If we have keyword arguments corresponding to fields on the
# request, apply these.
if parent is not None:
request.parent = parent
# Wrap the RPC method; this adds retry and timeout information,
# and friendly error handling.
rpc = self._client._transport._wrapped_methods[
self._client._transport.list_models
]
# Certain fields should be provided within the metadata header;
# add these here.
metadata = tuple(metadata) + (
gapic_v1.routing_header.to_grpc_metadata((("parent", request.parent),)),
)
# Validate the universe domain.
self._client._validate_universe_domain()
# Send the request.
response = await rpc(
request,
retry=retry,
timeout=timeout,
metadata=metadata,
)
# This method is paged; wrap the response in a pager, which provides
# an `__aiter__` convenience method.
response = pagers.ListModelsAsyncPager(
method=rpc,
request=request,
response=response,
retry=retry,
timeout=timeout,
metadata=metadata,
)
# Done; return the response.
return response
[docs] async def delete_model(
self,
request: Optional[Union[service.DeleteModelRequest, dict]] = None,
*,
name: Optional[str] = None,
retry: OptionalRetry = gapic_v1.method.DEFAULT,
timeout: Union[float, object] = gapic_v1.method.DEFAULT,
metadata: Sequence[Tuple[str, str]] = (),
) -> operation_async.AsyncOperation:
r"""Deletes a model. Returns ``google.protobuf.Empty`` in the
[response][google.longrunning.Operation.response] field when it
completes, and ``delete_details`` in the
[metadata][google.longrunning.Operation.metadata] field.
.. code-block:: python
# 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 automl_v1
async def sample_delete_model():
# Create a client
client = automl_v1.AutoMlAsyncClient()
# Initialize request argument(s)
request = automl_v1.DeleteModelRequest(
name="name_value",
)
# Make the request
operation = client.delete_model(request=request)
print("Waiting for operation to complete...")
response = (await operation).result()
# Handle the response
print(response)
Args:
request (Optional[Union[google.cloud.automl_v1.types.DeleteModelRequest, dict]]):
The request object. Request message for
[AutoMl.DeleteModel][google.cloud.automl.v1.AutoMl.DeleteModel].
name (:class:`str`):
Required. Resource name of the model
being deleted.
This corresponds to the ``name`` field
on the ``request`` instance; if ``request`` is provided, this
should not be set.
retry (google.api_core.retry_async.AsyncRetry): Designation of what errors, if any,
should be retried.
timeout (float): The timeout for this request.
metadata (Sequence[Tuple[str, str]]): Strings which should be
sent along with the request as metadata.
Returns:
google.api_core.operation_async.AsyncOperation:
An object representing a long-running operation.
The result type for the operation will be :class:`google.protobuf.empty_pb2.Empty` A generic empty message that you can re-use to avoid defining duplicated
empty messages in your APIs. A typical example is to
use it as the request or the response type of an API
method. For instance:
service Foo {
rpc Bar(google.protobuf.Empty) returns
(google.protobuf.Empty);
}
"""
# Create or coerce a protobuf request object.
# - Quick check: If we got a request object, we should *not* have
# gotten any keyword arguments that map to the request.
has_flattened_params = any([name])
if request is not None and has_flattened_params:
raise ValueError(
"If the `request` argument is set, then none of "
"the individual field arguments should be set."
)
# - Use the request object if provided (there's no risk of modifying the input as
# there are no flattened fields), or create one.
if not isinstance(request, service.DeleteModelRequest):
request = service.DeleteModelRequest(request)
# If we have keyword arguments corresponding to fields on the
# request, apply these.
if name is not None:
request.name = name
# Wrap the RPC method; this adds retry and timeout information,
# and friendly error handling.
rpc = self._client._transport._wrapped_methods[
self._client._transport.delete_model
]
# Certain fields should be provided within the metadata header;
# add these here.
metadata = tuple(metadata) + (
gapic_v1.routing_header.to_grpc_metadata((("name", request.name),)),
)
# Validate the universe domain.
self._client._validate_universe_domain()
# Send the request.
response = await rpc(
request,
retry=retry,
timeout=timeout,
metadata=metadata,
)
# Wrap the response in an operation future.
response = operation_async.from_gapic(
response,
self._client._transport.operations_client,
empty_pb2.Empty,
metadata_type=operations.OperationMetadata,
)
# Done; return the response.
return response
[docs] async def update_model(
self,
request: Optional[Union[service.UpdateModelRequest, dict]] = None,
*,
model: Optional[gca_model.Model] = None,
update_mask: Optional[field_mask_pb2.FieldMask] = None,
retry: OptionalRetry = gapic_v1.method.DEFAULT,
timeout: Union[float, object] = gapic_v1.method.DEFAULT,
metadata: Sequence[Tuple[str, str]] = (),
) -> gca_model.Model:
r"""Updates a model.
.. code-block:: python
# 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 automl_v1
async def sample_update_model():
# Create a client
client = automl_v1.AutoMlAsyncClient()
# Initialize request argument(s)
request = automl_v1.UpdateModelRequest(
)
# Make the request
response = await client.update_model(request=request)
# Handle the response
print(response)
Args:
request (Optional[Union[google.cloud.automl_v1.types.UpdateModelRequest, dict]]):
The request object. Request message for
[AutoMl.UpdateModel][google.cloud.automl.v1.AutoMl.UpdateModel]
model (:class:`google.cloud.automl_v1.types.Model`):
Required. The model which replaces
the resource on the server.
This corresponds to the ``model`` field
on the ``request`` instance; if ``request`` is provided, this
should not be set.
update_mask (:class:`google.protobuf.field_mask_pb2.FieldMask`):
Required. The update mask applies to
the resource.
This corresponds to the ``update_mask`` field
on the ``request`` instance; if ``request`` is provided, this
should not be set.
retry (google.api_core.retry_async.AsyncRetry): Designation of what errors, if any,
should be retried.
timeout (float): The timeout for this request.
metadata (Sequence[Tuple[str, str]]): Strings which should be
sent along with the request as metadata.
Returns:
google.cloud.automl_v1.types.Model:
API proto representing a trained
machine learning model.
"""
# Create or coerce a protobuf request object.
# - Quick check: If we got a request object, we should *not* have
# gotten any keyword arguments that map to the request.
has_flattened_params = any([model, update_mask])
if request is not None and has_flattened_params:
raise ValueError(
"If the `request` argument is set, then none of "
"the individual field arguments should be set."
)
# - Use the request object if provided (there's no risk of modifying the input as
# there are no flattened fields), or create one.
if not isinstance(request, service.UpdateModelRequest):
request = service.UpdateModelRequest(request)
# If we have keyword arguments corresponding to fields on the
# request, apply these.
if model is not None:
request.model = model
if update_mask is not None:
request.update_mask = update_mask
# Wrap the RPC method; this adds retry and timeout information,
# and friendly error handling.
rpc = self._client._transport._wrapped_methods[
self._client._transport.update_model
]
# Certain fields should be provided within the metadata header;
# add these here.
metadata = tuple(metadata) + (
gapic_v1.routing_header.to_grpc_metadata(
(("model.name", request.model.name),)
),
)
# Validate the universe domain.
self._client._validate_universe_domain()
# Send the request.
response = await rpc(
request,
retry=retry,
timeout=timeout,
metadata=metadata,
)
# Done; return the response.
return response
[docs] async def deploy_model(
self,
request: Optional[Union[service.DeployModelRequest, dict]] = None,
*,
name: Optional[str] = None,
retry: OptionalRetry = gapic_v1.method.DEFAULT,
timeout: Union[float, object] = gapic_v1.method.DEFAULT,
metadata: Sequence[Tuple[str, str]] = (),
) -> operation_async.AsyncOperation:
r"""Deploys a model. If a model is already deployed, deploying it
with the same parameters has no effect. Deploying with different
parametrs (as e.g. changing
[node_number][google.cloud.automl.v1p1beta.ImageObjectDetectionModelDeploymentMetadata.node_number])
will reset the deployment state without pausing the model's
availability.
Only applicable for Text Classification, Image Object Detection
, Tables, and Image Segmentation; all other domains manage
deployment automatically.
Returns an empty response in the
[response][google.longrunning.Operation.response] field when it
completes.
.. code-block:: python
# 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 automl_v1
async def sample_deploy_model():
# Create a client
client = automl_v1.AutoMlAsyncClient()
# Initialize request argument(s)
request = automl_v1.DeployModelRequest(
name="name_value",
)
# Make the request
operation = client.deploy_model(request=request)
print("Waiting for operation to complete...")
response = (await operation).result()
# Handle the response
print(response)
Args:
request (Optional[Union[google.cloud.automl_v1.types.DeployModelRequest, dict]]):
The request object. Request message for
[AutoMl.DeployModel][google.cloud.automl.v1.AutoMl.DeployModel].
name (:class:`str`):
Required. Resource name of the model
to deploy.
This corresponds to the ``name`` field
on the ``request`` instance; if ``request`` is provided, this
should not be set.
retry (google.api_core.retry_async.AsyncRetry): Designation of what errors, if any,
should be retried.
timeout (float): The timeout for this request.
metadata (Sequence[Tuple[str, str]]): Strings which should be
sent along with the request as metadata.
Returns:
google.api_core.operation_async.AsyncOperation:
An object representing a long-running operation.
The result type for the operation will be :class:`google.protobuf.empty_pb2.Empty` A generic empty message that you can re-use to avoid defining duplicated
empty messages in your APIs. A typical example is to
use it as the request or the response type of an API
method. For instance:
service Foo {
rpc Bar(google.protobuf.Empty) returns
(google.protobuf.Empty);
}
"""
# Create or coerce a protobuf request object.
# - Quick check: If we got a request object, we should *not* have
# gotten any keyword arguments that map to the request.
has_flattened_params = any([name])
if request is not None and has_flattened_params:
raise ValueError(
"If the `request` argument is set, then none of "
"the individual field arguments should be set."
)
# - Use the request object if provided (there's no risk of modifying the input as
# there are no flattened fields), or create one.
if not isinstance(request, service.DeployModelRequest):
request = service.DeployModelRequest(request)
# If we have keyword arguments corresponding to fields on the
# request, apply these.
if name is not None:
request.name = name
# Wrap the RPC method; this adds retry and timeout information,
# and friendly error handling.
rpc = self._client._transport._wrapped_methods[
self._client._transport.deploy_model
]
# Certain fields should be provided within the metadata header;
# add these here.
metadata = tuple(metadata) + (
gapic_v1.routing_header.to_grpc_metadata((("name", request.name),)),
)
# Validate the universe domain.
self._client._validate_universe_domain()
# Send the request.
response = await rpc(
request,
retry=retry,
timeout=timeout,
metadata=metadata,
)
# Wrap the response in an operation future.
response = operation_async.from_gapic(
response,
self._client._transport.operations_client,
empty_pb2.Empty,
metadata_type=operations.OperationMetadata,
)
# Done; return the response.
return response
[docs] async def undeploy_model(
self,
request: Optional[Union[service.UndeployModelRequest, dict]] = None,
*,
name: Optional[str] = None,
retry: OptionalRetry = gapic_v1.method.DEFAULT,
timeout: Union[float, object] = gapic_v1.method.DEFAULT,
metadata: Sequence[Tuple[str, str]] = (),
) -> operation_async.AsyncOperation:
r"""Undeploys a model. If the model is not deployed this method has
no effect.
Only applicable for Text Classification, Image Object Detection
and Tables; all other domains manage deployment automatically.
Returns an empty response in the
[response][google.longrunning.Operation.response] field when it
completes.
.. code-block:: python
# 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 automl_v1
async def sample_undeploy_model():
# Create a client
client = automl_v1.AutoMlAsyncClient()
# Initialize request argument(s)
request = automl_v1.UndeployModelRequest(
name="name_value",
)
# Make the request
operation = client.undeploy_model(request=request)
print("Waiting for operation to complete...")
response = (await operation).result()
# Handle the response
print(response)
Args:
request (Optional[Union[google.cloud.automl_v1.types.UndeployModelRequest, dict]]):
The request object. Request message for
[AutoMl.UndeployModel][google.cloud.automl.v1.AutoMl.UndeployModel].
name (:class:`str`):
Required. Resource name of the model
to undeploy.
This corresponds to the ``name`` field
on the ``request`` instance; if ``request`` is provided, this
should not be set.
retry (google.api_core.retry_async.AsyncRetry): Designation of what errors, if any,
should be retried.
timeout (float): The timeout for this request.
metadata (Sequence[Tuple[str, str]]): Strings which should be
sent along with the request as metadata.
Returns:
google.api_core.operation_async.AsyncOperation:
An object representing a long-running operation.
The result type for the operation will be :class:`google.protobuf.empty_pb2.Empty` A generic empty message that you can re-use to avoid defining duplicated
empty messages in your APIs. A typical example is to
use it as the request or the response type of an API
method. For instance:
service Foo {
rpc Bar(google.protobuf.Empty) returns
(google.protobuf.Empty);
}
"""
# Create or coerce a protobuf request object.
# - Quick check: If we got a request object, we should *not* have
# gotten any keyword arguments that map to the request.
has_flattened_params = any([name])
if request is not None and has_flattened_params:
raise ValueError(
"If the `request` argument is set, then none of "
"the individual field arguments should be set."
)
# - Use the request object if provided (there's no risk of modifying the input as
# there are no flattened fields), or create one.
if not isinstance(request, service.UndeployModelRequest):
request = service.UndeployModelRequest(request)
# If we have keyword arguments corresponding to fields on the
# request, apply these.
if name is not None:
request.name = name
# Wrap the RPC method; this adds retry and timeout information,
# and friendly error handling.
rpc = self._client._transport._wrapped_methods[
self._client._transport.undeploy_model
]
# Certain fields should be provided within the metadata header;
# add these here.
metadata = tuple(metadata) + (
gapic_v1.routing_header.to_grpc_metadata((("name", request.name),)),
)
# Validate the universe domain.
self._client._validate_universe_domain()
# Send the request.
response = await rpc(
request,
retry=retry,
timeout=timeout,
metadata=metadata,
)
# Wrap the response in an operation future.
response = operation_async.from_gapic(
response,
self._client._transport.operations_client,
empty_pb2.Empty,
metadata_type=operations.OperationMetadata,
)
# Done; return the response.
return response
[docs] async def export_model(
self,
request: Optional[Union[service.ExportModelRequest, dict]] = None,
*,
name: Optional[str] = None,
output_config: Optional[io.ModelExportOutputConfig] = None,
retry: OptionalRetry = gapic_v1.method.DEFAULT,
timeout: Union[float, object] = gapic_v1.method.DEFAULT,
metadata: Sequence[Tuple[str, str]] = (),
) -> operation_async.AsyncOperation:
r"""Exports a trained, "export-able", model to a user specified
Google Cloud Storage location. A model is considered export-able
if and only if it has an export format defined for it in
[ModelExportOutputConfig][google.cloud.automl.v1.ModelExportOutputConfig].
Returns an empty response in the
[response][google.longrunning.Operation.response] field when it
completes.
.. code-block:: python
# 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 automl_v1
async def sample_export_model():
# Create a client
client = automl_v1.AutoMlAsyncClient()
# Initialize request argument(s)
output_config = automl_v1.ModelExportOutputConfig()
output_config.gcs_destination.output_uri_prefix = "output_uri_prefix_value"
request = automl_v1.ExportModelRequest(
name="name_value",
output_config=output_config,
)
# Make the request
operation = client.export_model(request=request)
print("Waiting for operation to complete...")
response = (await operation).result()
# Handle the response
print(response)
Args:
request (Optional[Union[google.cloud.automl_v1.types.ExportModelRequest, dict]]):
The request object. Request message for
[AutoMl.ExportModel][google.cloud.automl.v1.AutoMl.ExportModel].
Models need to be enabled for exporting, otherwise an
error code will be returned.
name (:class:`str`):
Required. The resource name of the
model to export.
This corresponds to the ``name`` field
on the ``request`` instance; if ``request`` is provided, this
should not be set.
output_config (:class:`google.cloud.automl_v1.types.ModelExportOutputConfig`):
Required. The desired output location
and configuration.
This corresponds to the ``output_config`` field
on the ``request`` instance; if ``request`` is provided, this
should not be set.
retry (google.api_core.retry_async.AsyncRetry): Designation of what errors, if any,
should be retried.
timeout (float): The timeout for this request.
metadata (Sequence[Tuple[str, str]]): Strings which should be
sent along with the request as metadata.
Returns:
google.api_core.operation_async.AsyncOperation:
An object representing a long-running operation.
The result type for the operation will be :class:`google.protobuf.empty_pb2.Empty` A generic empty message that you can re-use to avoid defining duplicated
empty messages in your APIs. A typical example is to
use it as the request or the response type of an API
method. For instance:
service Foo {
rpc Bar(google.protobuf.Empty) returns
(google.protobuf.Empty);
}
"""
# Create or coerce a protobuf request object.
# - Quick check: If we got a request object, we should *not* have
# gotten any keyword arguments that map to the request.
has_flattened_params = any([name, output_config])
if request is not None and has_flattened_params:
raise ValueError(
"If the `request` argument is set, then none of "
"the individual field arguments should be set."
)
# - Use the request object if provided (there's no risk of modifying the input as
# there are no flattened fields), or create one.
if not isinstance(request, service.ExportModelRequest):
request = service.ExportModelRequest(request)
# If we have keyword arguments corresponding to fields on the
# request, apply these.
if name is not None:
request.name = name
if output_config is not None:
request.output_config = output_config
# Wrap the RPC method; this adds retry and timeout information,
# and friendly error handling.
rpc = self._client._transport._wrapped_methods[
self._client._transport.export_model
]
# Certain fields should be provided within the metadata header;
# add these here.
metadata = tuple(metadata) + (
gapic_v1.routing_header.to_grpc_metadata((("name", request.name),)),
)
# Validate the universe domain.
self._client._validate_universe_domain()
# Send the request.
response = await rpc(
request,
retry=retry,
timeout=timeout,
metadata=metadata,
)
# Wrap the response in an operation future.
response = operation_async.from_gapic(
response,
self._client._transport.operations_client,
empty_pb2.Empty,
metadata_type=operations.OperationMetadata,
)
# Done; return the response.
return response
[docs] async def get_model_evaluation(
self,
request: Optional[Union[service.GetModelEvaluationRequest, dict]] = None,
*,
name: Optional[str] = None,
retry: OptionalRetry = gapic_v1.method.DEFAULT,
timeout: Union[float, object] = gapic_v1.method.DEFAULT,
metadata: Sequence[Tuple[str, str]] = (),
) -> model_evaluation.ModelEvaluation:
r"""Gets a model evaluation.
.. code-block:: python
# 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 automl_v1
async def sample_get_model_evaluation():
# Create a client
client = automl_v1.AutoMlAsyncClient()
# Initialize request argument(s)
request = automl_v1.GetModelEvaluationRequest(
name="name_value",
)
# Make the request
response = await client.get_model_evaluation(request=request)
# Handle the response
print(response)
Args:
request (Optional[Union[google.cloud.automl_v1.types.GetModelEvaluationRequest, dict]]):
The request object. Request message for
[AutoMl.GetModelEvaluation][google.cloud.automl.v1.AutoMl.GetModelEvaluation].
name (:class:`str`):
Required. Resource name for the model
evaluation.
This corresponds to the ``name`` field
on the ``request`` instance; if ``request`` is provided, this
should not be set.
retry (google.api_core.retry_async.AsyncRetry): Designation of what errors, if any,
should be retried.
timeout (float): The timeout for this request.
metadata (Sequence[Tuple[str, str]]): Strings which should be
sent along with the request as metadata.
Returns:
google.cloud.automl_v1.types.ModelEvaluation:
Evaluation results of a model.
"""
# Create or coerce a protobuf request object.
# - Quick check: If we got a request object, we should *not* have
# gotten any keyword arguments that map to the request.
has_flattened_params = any([name])
if request is not None and has_flattened_params:
raise ValueError(
"If the `request` argument is set, then none of "
"the individual field arguments should be set."
)
# - Use the request object if provided (there's no risk of modifying the input as
# there are no flattened fields), or create one.
if not isinstance(request, service.GetModelEvaluationRequest):
request = service.GetModelEvaluationRequest(request)
# If we have keyword arguments corresponding to fields on the
# request, apply these.
if name is not None:
request.name = name
# Wrap the RPC method; this adds retry and timeout information,
# and friendly error handling.
rpc = self._client._transport._wrapped_methods[
self._client._transport.get_model_evaluation
]
# Certain fields should be provided within the metadata header;
# add these here.
metadata = tuple(metadata) + (
gapic_v1.routing_header.to_grpc_metadata((("name", request.name),)),
)
# Validate the universe domain.
self._client._validate_universe_domain()
# Send the request.
response = await rpc(
request,
retry=retry,
timeout=timeout,
metadata=metadata,
)
# Done; return the response.
return response
[docs] async def list_model_evaluations(
self,
request: Optional[Union[service.ListModelEvaluationsRequest, dict]] = None,
*,
parent: Optional[str] = None,
filter: Optional[str] = None,
retry: OptionalRetry = gapic_v1.method.DEFAULT,
timeout: Union[float, object] = gapic_v1.method.DEFAULT,
metadata: Sequence[Tuple[str, str]] = (),
) -> pagers.ListModelEvaluationsAsyncPager:
r"""Lists model evaluations.
.. code-block:: python
# 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 automl_v1
async def sample_list_model_evaluations():
# Create a client
client = automl_v1.AutoMlAsyncClient()
# Initialize request argument(s)
request = automl_v1.ListModelEvaluationsRequest(
parent="parent_value",
filter="filter_value",
)
# Make the request
page_result = client.list_model_evaluations(request=request)
# Handle the response
async for response in page_result:
print(response)
Args:
request (Optional[Union[google.cloud.automl_v1.types.ListModelEvaluationsRequest, dict]]):
The request object. Request message for
[AutoMl.ListModelEvaluations][google.cloud.automl.v1.AutoMl.ListModelEvaluations].
parent (:class:`str`):
Required. Resource name of the model
to list the model evaluations for. If
modelId is set as "-", this will list
model evaluations from across all models
of the parent location.
This corresponds to the ``parent`` field
on the ``request`` instance; if ``request`` is provided, this
should not be set.
filter (:class:`str`):
Required. An expression for filtering the results of the
request.
- ``annotation_spec_id`` - for =, != or existence. See
example below for the last.
Some examples of using the filter are:
- ``annotation_spec_id!=4`` --> The model evaluation
was done for annotation spec with ID different than
4.
- ``NOT annotation_spec_id:*`` --> The model evaluation
was done for aggregate of all annotation specs.
This corresponds to the ``filter`` field
on the ``request`` instance; if ``request`` is provided, this
should not be set.
retry (google.api_core.retry_async.AsyncRetry): Designation of what errors, if any,
should be retried.
timeout (float): The timeout for this request.
metadata (Sequence[Tuple[str, str]]): Strings which should be
sent along with the request as metadata.
Returns:
google.cloud.automl_v1.services.auto_ml.pagers.ListModelEvaluationsAsyncPager:
Response message for
[AutoMl.ListModelEvaluations][google.cloud.automl.v1.AutoMl.ListModelEvaluations].
Iterating over this object will yield results and
resolve additional pages automatically.
"""
# Create or coerce a protobuf request object.
# - Quick check: If we got a request object, we should *not* have
# gotten any keyword arguments that map to the request.
has_flattened_params = any([parent, filter])
if request is not None and has_flattened_params:
raise ValueError(
"If the `request` argument is set, then none of "
"the individual field arguments should be set."
)
# - Use the request object if provided (there's no risk of modifying the input as
# there are no flattened fields), or create one.
if not isinstance(request, service.ListModelEvaluationsRequest):
request = service.ListModelEvaluationsRequest(request)
# If we have keyword arguments corresponding to fields on the
# request, apply these.
if parent is not None:
request.parent = parent
if filter is not None:
request.filter = filter
# Wrap the RPC method; this adds retry and timeout information,
# and friendly error handling.
rpc = self._client._transport._wrapped_methods[
self._client._transport.list_model_evaluations
]
# Certain fields should be provided within the metadata header;
# add these here.
metadata = tuple(metadata) + (
gapic_v1.routing_header.to_grpc_metadata((("parent", request.parent),)),
)
# Validate the universe domain.
self._client._validate_universe_domain()
# Send the request.
response = await rpc(
request,
retry=retry,
timeout=timeout,
metadata=metadata,
)
# This method is paged; wrap the response in a pager, which provides
# an `__aiter__` convenience method.
response = pagers.ListModelEvaluationsAsyncPager(
method=rpc,
request=request,
response=response,
retry=retry,
timeout=timeout,
metadata=metadata,
)
# Done; return the response.
return response
async def __aenter__(self) -> "AutoMlAsyncClient":
return self
async def __aexit__(self, exc_type, exc, tb):
await self.transport.close()
DEFAULT_CLIENT_INFO = gapic_v1.client_info.ClientInfo(
gapic_version=package_version.__version__
)
__all__ = ("AutoMlAsyncClient",)
