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.client
# -*- coding: utf-8 -*-
# Copyright 2020 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# 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
from distutils import util
import os
import re
from typing import Callable, Dict, Optional, Sequence, Tuple, Type, Union
import pkg_resources
from google.api_core import client_options as client_options_lib # type: ignore
from google.api_core import exceptions as core_exceptions # type: ignore
from google.api_core import gapic_v1 # type: ignore
from google.api_core import retry as retries # type: ignore
from google.auth import credentials as ga_credentials # type: ignore
from google.auth.transport import mtls # type: ignore
from google.auth.transport.grpc import SslCredentials # type: ignore
from google.auth.exceptions import MutualTLSChannelError # type: ignore
from google.oauth2 import service_account # type: ignore
from google.api_core import operation # type: ignore
from google.api_core import operation_async # type: ignore
from google.cloud.automl_v1.services.auto_ml import pagers
from google.cloud.automl_v1.types import annotation_spec
from google.cloud.automl_v1.types import 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
from google.cloud.automl_v1.types import image
from google.cloud.automl_v1.types import io
from google.cloud.automl_v1.types import model
from google.cloud.automl_v1.types import model as gca_model
from google.cloud.automl_v1.types import model_evaluation
from google.cloud.automl_v1.types import operations
from google.cloud.automl_v1.types import service
from google.cloud.automl_v1.types import text
from google.cloud.automl_v1.types import text_extraction
from google.cloud.automl_v1.types import text_sentiment
from google.cloud.automl_v1.types import translation
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 .transports.base import AutoMlTransport, DEFAULT_CLIENT_INFO
from .transports.grpc import AutoMlGrpcTransport
from .transports.grpc_asyncio import AutoMlGrpcAsyncIOTransport
class AutoMlClientMeta(type):
"""Metaclass for the AutoMl client.
This provides class-level methods for building and retrieving
support objects (e.g. transport) without polluting the client instance
objects.
"""
_transport_registry = OrderedDict() # type: Dict[str, Type[AutoMlTransport]]
_transport_registry["grpc"] = AutoMlGrpcTransport
_transport_registry["grpc_asyncio"] = AutoMlGrpcAsyncIOTransport
def get_transport_class(cls, label: str = None,) -> Type[AutoMlTransport]:
"""Returns an appropriate transport class.
Args:
label: The name of the desired transport. If none is
provided, then the first transport in the registry is used.
Returns:
The transport class to use.
"""
# If a specific transport is requested, return that one.
if label:
return cls._transport_registry[label]
# No transport is requested; return the default (that is, the first one
# in the dictionary).
return next(iter(cls._transport_registry.values()))
[docs]class AutoMlClient(metaclass=AutoMlClientMeta):
"""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 kebab-case, either of those cases is accepted.
"""
@staticmethod
def _get_default_mtls_endpoint(api_endpoint):
"""Converts api endpoint to mTLS endpoint.
Convert "*.sandbox.googleapis.com" and "*.googleapis.com" to
"*.mtls.sandbox.googleapis.com" and "*.mtls.googleapis.com" respectively.
Args:
api_endpoint (Optional[str]): the api endpoint to convert.
Returns:
str: converted mTLS api endpoint.
"""
if not api_endpoint:
return api_endpoint
mtls_endpoint_re = re.compile(
r"(?P<name>[^.]+)(?P<mtls>\.mtls)?(?P<sandbox>\.sandbox)?(?P<googledomain>\.googleapis\.com)?"
)
m = mtls_endpoint_re.match(api_endpoint)
name, mtls, sandbox, googledomain = m.groups()
if mtls or not googledomain:
return api_endpoint
if sandbox:
return api_endpoint.replace(
"sandbox.googleapis.com", "mtls.sandbox.googleapis.com"
)
return api_endpoint.replace(".googleapis.com", ".mtls.googleapis.com")
DEFAULT_ENDPOINT = "automl.googleapis.com"
DEFAULT_MTLS_ENDPOINT = _get_default_mtls_endpoint.__func__( # type: ignore
DEFAULT_ENDPOINT
)
[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:
AutoMlClient: The constructed client.
"""
credentials = service_account.Credentials.from_service_account_info(info)
kwargs["credentials"] = credentials
return cls(*args, **kwargs)
[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:
AutoMlClient: The constructed client.
"""
credentials = service_account.Credentials.from_service_account_file(filename)
kwargs["credentials"] = credentials
return cls(*args, **kwargs)
from_service_account_json = from_service_account_file
@property
def transport(self) -> AutoMlTransport:
"""Returns the transport used by the client instance.
Returns:
AutoMlTransport: The transport used by the client
instance.
"""
return self._transport
[docs] @staticmethod
def annotation_spec_path(
project: str, location: str, dataset: str, annotation_spec: str,
) -> str:
"""Returns a fully-qualified annotation_spec string."""
return "projects/{project}/locations/{location}/datasets/{dataset}/annotationSpecs/{annotation_spec}".format(
project=project,
location=location,
dataset=dataset,
annotation_spec=annotation_spec,
)
[docs] @staticmethod
def parse_annotation_spec_path(path: str) -> Dict[str, str]:
"""Parses a annotation_spec path into its component segments."""
m = re.match(
r"^projects/(?P<project>.+?)/locations/(?P<location>.+?)/datasets/(?P<dataset>.+?)/annotationSpecs/(?P<annotation_spec>.+?)$",
path,
)
return m.groupdict() if m else {}
[docs] @staticmethod
def dataset_path(project: str, location: str, dataset: str,) -> str:
"""Returns a fully-qualified dataset string."""
return "projects/{project}/locations/{location}/datasets/{dataset}".format(
project=project, location=location, dataset=dataset,
)
[docs] @staticmethod
def parse_dataset_path(path: str) -> Dict[str, str]:
"""Parses a dataset path into its component segments."""
m = re.match(
r"^projects/(?P<project>.+?)/locations/(?P<location>.+?)/datasets/(?P<dataset>.+?)$",
path,
)
return m.groupdict() if m else {}
[docs] @staticmethod
def model_path(project: str, location: str, model: str,) -> str:
"""Returns a fully-qualified model string."""
return "projects/{project}/locations/{location}/models/{model}".format(
project=project, location=location, model=model,
)
[docs] @staticmethod
def parse_model_path(path: str) -> Dict[str, str]:
"""Parses a model path into its component segments."""
m = re.match(
r"^projects/(?P<project>.+?)/locations/(?P<location>.+?)/models/(?P<model>.+?)$",
path,
)
return m.groupdict() if m else {}
[docs] @staticmethod
def model_evaluation_path(
project: str, location: str, model: str, model_evaluation: str,
) -> str:
"""Returns a fully-qualified model_evaluation string."""
return "projects/{project}/locations/{location}/models/{model}/modelEvaluations/{model_evaluation}".format(
project=project,
location=location,
model=model,
model_evaluation=model_evaluation,
)
[docs] @staticmethod
def parse_model_evaluation_path(path: str) -> Dict[str, str]:
"""Parses a model_evaluation path into its component segments."""
m = re.match(
r"^projects/(?P<project>.+?)/locations/(?P<location>.+?)/models/(?P<model>.+?)/modelEvaluations/(?P<model_evaluation>.+?)$",
path,
)
return m.groupdict() if m else {}
[docs] @staticmethod
def common_billing_account_path(billing_account: str,) -> str:
"""Returns a fully-qualified billing_account string."""
return "billingAccounts/{billing_account}".format(
billing_account=billing_account,
)
[docs] @staticmethod
def parse_common_billing_account_path(path: str) -> Dict[str, str]:
"""Parse a billing_account path into its component segments."""
m = re.match(r"^billingAccounts/(?P<billing_account>.+?)$", path)
return m.groupdict() if m else {}
[docs] @staticmethod
def common_folder_path(folder: str,) -> str:
"""Returns a fully-qualified folder string."""
return "folders/{folder}".format(folder=folder,)
[docs] @staticmethod
def parse_common_folder_path(path: str) -> Dict[str, str]:
"""Parse a folder path into its component segments."""
m = re.match(r"^folders/(?P<folder>.+?)$", path)
return m.groupdict() if m else {}
[docs] @staticmethod
def common_organization_path(organization: str,) -> str:
"""Returns a fully-qualified organization string."""
return "organizations/{organization}".format(organization=organization,)
[docs] @staticmethod
def parse_common_organization_path(path: str) -> Dict[str, str]:
"""Parse a organization path into its component segments."""
m = re.match(r"^organizations/(?P<organization>.+?)$", path)
return m.groupdict() if m else {}
[docs] @staticmethod
def common_project_path(project: str,) -> str:
"""Returns a fully-qualified project string."""
return "projects/{project}".format(project=project,)
[docs] @staticmethod
def parse_common_project_path(path: str) -> Dict[str, str]:
"""Parse a project path into its component segments."""
m = re.match(r"^projects/(?P<project>.+?)$", path)
return m.groupdict() if m else {}
[docs] @staticmethod
def common_location_path(project: str, location: str,) -> str:
"""Returns a fully-qualified location string."""
return "projects/{project}/locations/{location}".format(
project=project, location=location,
)
[docs] @staticmethod
def parse_common_location_path(path: str) -> Dict[str, str]:
"""Parse a location path into its component segments."""
m = re.match(r"^projects/(?P<project>.+?)/locations/(?P<location>.+?)$", path)
return m.groupdict() if m else {}
def __init__(
self,
*,
credentials: Optional[ga_credentials.Credentials] = None,
transport: Union[str, AutoMlTransport, None] = None,
client_options: Optional[client_options_lib.ClientOptions] = None,
client_info: gapic_v1.client_info.ClientInfo = DEFAULT_CLIENT_INFO,
) -> None:
"""Instantiates the auto ml 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 (Union[str, AutoMlTransport]): The
transport to use. If set to None, a transport is chosen
automatically.
client_options (google.api_core.client_options.ClientOptions): Custom options for the
client. It won't take effect if a ``transport`` instance is provided.
(1) The ``api_endpoint`` property can be used to override the
default endpoint provided by the client. GOOGLE_API_USE_MTLS_ENDPOINT
environment variable can also be used to override the endpoint:
"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). However, the ``api_endpoint`` property takes
precedence if provided.
(2) If GOOGLE_API_USE_CLIENT_CERTIFICATE environment variable
is "true", then the ``client_cert_source`` property can be used
to provide client certificate for mutual TLS 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.
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.
"""
if isinstance(client_options, dict):
client_options = client_options_lib.from_dict(client_options)
if client_options is None:
client_options = client_options_lib.ClientOptions()
# Create SSL credentials for mutual TLS if needed.
use_client_cert = bool(
util.strtobool(os.getenv("GOOGLE_API_USE_CLIENT_CERTIFICATE", "false"))
)
client_cert_source_func = None
is_mtls = False
if use_client_cert:
if client_options.client_cert_source:
is_mtls = True
client_cert_source_func = client_options.client_cert_source
else:
is_mtls = mtls.has_default_client_cert_source()
if is_mtls:
client_cert_source_func = mtls.default_client_cert_source()
else:
client_cert_source_func = None
# Figure out which api endpoint to use.
if client_options.api_endpoint is not None:
api_endpoint = client_options.api_endpoint
else:
use_mtls_env = os.getenv("GOOGLE_API_USE_MTLS_ENDPOINT", "auto")
if use_mtls_env == "never":
api_endpoint = self.DEFAULT_ENDPOINT
elif use_mtls_env == "always":
api_endpoint = self.DEFAULT_MTLS_ENDPOINT
elif use_mtls_env == "auto":
if is_mtls:
api_endpoint = self.DEFAULT_MTLS_ENDPOINT
else:
api_endpoint = self.DEFAULT_ENDPOINT
else:
raise MutualTLSChannelError(
"Unsupported GOOGLE_API_USE_MTLS_ENDPOINT value. Accepted "
"values: never, auto, always"
)
# Save or instantiate the transport.
# Ordinarily, we provide the transport, but allowing a custom transport
# instance provides an extensibility point for unusual situations.
if isinstance(transport, AutoMlTransport):
# transport is a AutoMlTransport instance.
if credentials or client_options.credentials_file:
raise ValueError(
"When providing a transport instance, "
"provide its credentials directly."
)
if client_options.scopes:
raise ValueError(
"When providing a transport instance, provide its scopes "
"directly."
)
self._transport = transport
else:
Transport = type(self).get_transport_class(transport)
self._transport = Transport(
credentials=credentials,
credentials_file=client_options.credentials_file,
host=api_endpoint,
scopes=client_options.scopes,
client_cert_source_for_mtls=client_cert_source_func,
quota_project_id=client_options.quota_project_id,
client_info=client_info,
always_use_jwt_access=(
Transport == type(self).get_transport_class("grpc")
or Transport == type(self).get_transport_class("grpc_asyncio")
),
)
[docs] def create_dataset(
self,
request: service.CreateDatasetRequest = None,
*,
parent: str = None,
dataset: gca_dataset.Dataset = None,
retry: retries.Retry = gapic_v1.method.DEFAULT,
timeout: float = None,
metadata: Sequence[Tuple[str, str]] = (),
) -> operation.Operation:
r"""Creates a dataset.
Args:
request (google.cloud.automl_v1.types.CreateDatasetRequest):
The request object. Request message for
[AutoMl.CreateDataset][google.cloud.automl.v1.AutoMl.CreateDataset].
parent (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 (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.Retry): 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.Operation:
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.
# Sanity 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."
)
# Minor optimization to avoid making a copy if the user passes
# in a service.CreateDatasetRequest.
# There's no risk of modifying the input as we've already verified
# there are no flattened fields.
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._transport._wrapped_methods[self._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),)),
)
# Send the request.
response = rpc(request, retry=retry, timeout=timeout, metadata=metadata,)
# Wrap the response in an operation future.
response = operation.from_gapic(
response,
self._transport.operations_client,
gca_dataset.Dataset,
metadata_type=operations.OperationMetadata,
)
# Done; return the response.
return response
[docs] def get_dataset(
self,
request: service.GetDatasetRequest = None,
*,
name: str = None,
retry: retries.Retry = gapic_v1.method.DEFAULT,
timeout: float = None,
metadata: Sequence[Tuple[str, str]] = (),
) -> dataset.Dataset:
r"""Gets a dataset.
Args:
request (google.cloud.automl_v1.types.GetDatasetRequest):
The request object. Request message for
[AutoMl.GetDataset][google.cloud.automl.v1.AutoMl.GetDataset].
name (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.Retry): 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.
# Sanity 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."
)
# Minor optimization to avoid making a copy if the user passes
# in a service.GetDatasetRequest.
# There's no risk of modifying the input as we've already verified
# there are no flattened fields.
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._transport._wrapped_methods[self._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),)),
)
# Send the request.
response = rpc(request, retry=retry, timeout=timeout, metadata=metadata,)
# Done; return the response.
return response
[docs] def list_datasets(
self,
request: service.ListDatasetsRequest = None,
*,
parent: str = None,
retry: retries.Retry = gapic_v1.method.DEFAULT,
timeout: float = None,
metadata: Sequence[Tuple[str, str]] = (),
) -> pagers.ListDatasetsPager:
r"""Lists datasets in a project.
Args:
request (google.cloud.automl_v1.types.ListDatasetsRequest):
The request object. Request message for
[AutoMl.ListDatasets][google.cloud.automl.v1.AutoMl.ListDatasets].
parent (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.Retry): 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.ListDatasetsPager:
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.
# Sanity 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."
)
# Minor optimization to avoid making a copy if the user passes
# in a service.ListDatasetsRequest.
# There's no risk of modifying the input as we've already verified
# there are no flattened fields.
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._transport._wrapped_methods[self._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),)),
)
# Send the request.
response = rpc(request, retry=retry, timeout=timeout, metadata=metadata,)
# This method is paged; wrap the response in a pager, which provides
# an `__iter__` convenience method.
response = pagers.ListDatasetsPager(
method=rpc, request=request, response=response, metadata=metadata,
)
# Done; return the response.
return response
[docs] def update_dataset(
self,
request: service.UpdateDatasetRequest = None,
*,
dataset: gca_dataset.Dataset = None,
update_mask: field_mask_pb2.FieldMask = None,
retry: retries.Retry = gapic_v1.method.DEFAULT,
timeout: float = None,
metadata: Sequence[Tuple[str, str]] = (),
) -> gca_dataset.Dataset:
r"""Updates a dataset.
Args:
request (google.cloud.automl_v1.types.UpdateDatasetRequest):
The request object. Request message for
[AutoMl.UpdateDataset][google.cloud.automl.v1.AutoMl.UpdateDataset]
dataset (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 (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.Retry): 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.
# Sanity 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."
)
# Minor optimization to avoid making a copy if the user passes
# in a service.UpdateDatasetRequest.
# There's no risk of modifying the input as we've already verified
# there are no flattened fields.
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._transport._wrapped_methods[self._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),)
),
)
# Send the request.
response = rpc(request, retry=retry, timeout=timeout, metadata=metadata,)
# Done; return the response.
return response
[docs] def delete_dataset(
self,
request: service.DeleteDatasetRequest = None,
*,
name: str = None,
retry: retries.Retry = gapic_v1.method.DEFAULT,
timeout: float = None,
metadata: Sequence[Tuple[str, str]] = (),
) -> operation.Operation:
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.
Args:
request (google.cloud.automl_v1.types.DeleteDatasetRequest):
The request object. Request message for
[AutoMl.DeleteDataset][google.cloud.automl.v1.AutoMl.DeleteDataset].
name (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.Retry): 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.Operation:
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);
}
The JSON representation for Empty is empty JSON
object {}.
"""
# Create or coerce a protobuf request object.
# Sanity 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."
)
# Minor optimization to avoid making a copy if the user passes
# in a service.DeleteDatasetRequest.
# There's no risk of modifying the input as we've already verified
# there are no flattened fields.
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._transport._wrapped_methods[self._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),)),
)
# Send the request.
response = rpc(request, retry=retry, timeout=timeout, metadata=metadata,)
# Wrap the response in an operation future.
response = operation.from_gapic(
response,
self._transport.operations_client,
empty_pb2.Empty,
metadata_type=operations.OperationMetadata,
)
# Done; return the response.
return response
[docs] def import_data(
self,
request: service.ImportDataRequest = None,
*,
name: str = None,
input_config: io.InputConfig = None,
retry: retries.Retry = gapic_v1.method.DEFAULT,
timeout: float = None,
metadata: Sequence[Tuple[str, str]] = (),
) -> operation.Operation:
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.
Args:
request (google.cloud.automl_v1.types.ImportDataRequest):
The request object. Request message for
[AutoMl.ImportData][google.cloud.automl.v1.AutoMl.ImportData].
name (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 (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.Retry): 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.Operation:
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);
}
The JSON representation for Empty is empty JSON
object {}.
"""
# Create or coerce a protobuf request object.
# Sanity 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."
)
# Minor optimization to avoid making a copy if the user passes
# in a service.ImportDataRequest.
# There's no risk of modifying the input as we've already verified
# there are no flattened fields.
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._transport._wrapped_methods[self._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),)),
)
# Send the request.
response = rpc(request, retry=retry, timeout=timeout, metadata=metadata,)
# Wrap the response in an operation future.
response = operation.from_gapic(
response,
self._transport.operations_client,
empty_pb2.Empty,
metadata_type=operations.OperationMetadata,
)
# Done; return the response.
return response
[docs] def export_data(
self,
request: service.ExportDataRequest = None,
*,
name: str = None,
output_config: io.OutputConfig = None,
retry: retries.Retry = gapic_v1.method.DEFAULT,
timeout: float = None,
metadata: Sequence[Tuple[str, str]] = (),
) -> operation.Operation:
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.
Args:
request (google.cloud.automl_v1.types.ExportDataRequest):
The request object. Request message for
[AutoMl.ExportData][google.cloud.automl.v1.AutoMl.ExportData].
name (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 (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.Retry): 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.Operation:
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);
}
The JSON representation for Empty is empty JSON
object {}.
"""
# Create or coerce a protobuf request object.
# Sanity 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."
)
# Minor optimization to avoid making a copy if the user passes
# in a service.ExportDataRequest.
# There's no risk of modifying the input as we've already verified
# there are no flattened fields.
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._transport._wrapped_methods[self._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),)),
)
# Send the request.
response = rpc(request, retry=retry, timeout=timeout, metadata=metadata,)
# Wrap the response in an operation future.
response = operation.from_gapic(
response,
self._transport.operations_client,
empty_pb2.Empty,
metadata_type=operations.OperationMetadata,
)
# Done; return the response.
return response
[docs] def get_annotation_spec(
self,
request: service.GetAnnotationSpecRequest = None,
*,
name: str = None,
retry: retries.Retry = gapic_v1.method.DEFAULT,
timeout: float = None,
metadata: Sequence[Tuple[str, str]] = (),
) -> annotation_spec.AnnotationSpec:
r"""Gets an annotation spec.
Args:
request (google.cloud.automl_v1.types.GetAnnotationSpecRequest):
The request object. Request message for
[AutoMl.GetAnnotationSpec][google.cloud.automl.v1.AutoMl.GetAnnotationSpec].
name (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.Retry): 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.
# Sanity 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."
)
# Minor optimization to avoid making a copy if the user passes
# in a service.GetAnnotationSpecRequest.
# There's no risk of modifying the input as we've already verified
# there are no flattened fields.
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._transport._wrapped_methods[self._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),)),
)
# Send the request.
response = rpc(request, retry=retry, timeout=timeout, metadata=metadata,)
# Done; return the response.
return response
[docs] def create_model(
self,
request: service.CreateModelRequest = None,
*,
parent: str = None,
model: gca_model.Model = None,
retry: retries.Retry = gapic_v1.method.DEFAULT,
timeout: float = None,
metadata: Sequence[Tuple[str, str]] = (),
) -> operation.Operation:
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.
Args:
request (google.cloud.automl_v1.types.CreateModelRequest):
The request object. Request message for
[AutoMl.CreateModel][google.cloud.automl.v1.AutoMl.CreateModel].
parent (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 (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.Retry): 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.Operation:
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.
# Sanity 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."
)
# Minor optimization to avoid making a copy if the user passes
# in a service.CreateModelRequest.
# There's no risk of modifying the input as we've already verified
# there are no flattened fields.
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._transport._wrapped_methods[self._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),)),
)
# Send the request.
response = rpc(request, retry=retry, timeout=timeout, metadata=metadata,)
# Wrap the response in an operation future.
response = operation.from_gapic(
response,
self._transport.operations_client,
gca_model.Model,
metadata_type=operations.OperationMetadata,
)
# Done; return the response.
return response
[docs] def get_model(
self,
request: service.GetModelRequest = None,
*,
name: str = None,
retry: retries.Retry = gapic_v1.method.DEFAULT,
timeout: float = None,
metadata: Sequence[Tuple[str, str]] = (),
) -> model.Model:
r"""Gets a model.
Args:
request (google.cloud.automl_v1.types.GetModelRequest):
The request object. Request message for
[AutoMl.GetModel][google.cloud.automl.v1.AutoMl.GetModel].
name (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.Retry): 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.
# Sanity 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."
)
# Minor optimization to avoid making a copy if the user passes
# in a service.GetModelRequest.
# There's no risk of modifying the input as we've already verified
# there are no flattened fields.
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._transport._wrapped_methods[self._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),)),
)
# Send the request.
response = rpc(request, retry=retry, timeout=timeout, metadata=metadata,)
# Done; return the response.
return response
[docs] def list_models(
self,
request: service.ListModelsRequest = None,
*,
parent: str = None,
retry: retries.Retry = gapic_v1.method.DEFAULT,
timeout: float = None,
metadata: Sequence[Tuple[str, str]] = (),
) -> pagers.ListModelsPager:
r"""Lists models.
Args:
request (google.cloud.automl_v1.types.ListModelsRequest):
The request object. Request message for
[AutoMl.ListModels][google.cloud.automl.v1.AutoMl.ListModels].
parent (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.Retry): 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.ListModelsPager:
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.
# Sanity 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."
)
# Minor optimization to avoid making a copy if the user passes
# in a service.ListModelsRequest.
# There's no risk of modifying the input as we've already verified
# there are no flattened fields.
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._transport._wrapped_methods[self._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),)),
)
# Send the request.
response = rpc(request, retry=retry, timeout=timeout, metadata=metadata,)
# This method is paged; wrap the response in a pager, which provides
# an `__iter__` convenience method.
response = pagers.ListModelsPager(
method=rpc, request=request, response=response, metadata=metadata,
)
# Done; return the response.
return response
[docs] def delete_model(
self,
request: service.DeleteModelRequest = None,
*,
name: str = None,
retry: retries.Retry = gapic_v1.method.DEFAULT,
timeout: float = None,
metadata: Sequence[Tuple[str, str]] = (),
) -> operation.Operation:
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.
Args:
request (google.cloud.automl_v1.types.DeleteModelRequest):
The request object. Request message for
[AutoMl.DeleteModel][google.cloud.automl.v1.AutoMl.DeleteModel].
name (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.Retry): 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.Operation:
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);
}
The JSON representation for Empty is empty JSON
object {}.
"""
# Create or coerce a protobuf request object.
# Sanity 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."
)
# Minor optimization to avoid making a copy if the user passes
# in a service.DeleteModelRequest.
# There's no risk of modifying the input as we've already verified
# there are no flattened fields.
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._transport._wrapped_methods[self._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),)),
)
# Send the request.
response = rpc(request, retry=retry, timeout=timeout, metadata=metadata,)
# Wrap the response in an operation future.
response = operation.from_gapic(
response,
self._transport.operations_client,
empty_pb2.Empty,
metadata_type=operations.OperationMetadata,
)
# Done; return the response.
return response
[docs] def update_model(
self,
request: service.UpdateModelRequest = None,
*,
model: gca_model.Model = None,
update_mask: field_mask_pb2.FieldMask = None,
retry: retries.Retry = gapic_v1.method.DEFAULT,
timeout: float = None,
metadata: Sequence[Tuple[str, str]] = (),
) -> gca_model.Model:
r"""Updates a model.
Args:
request (google.cloud.automl_v1.types.UpdateModelRequest):
The request object. Request message for
[AutoMl.UpdateModel][google.cloud.automl.v1.AutoMl.UpdateModel]
model (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 (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.Retry): 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.
# Sanity 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."
)
# Minor optimization to avoid making a copy if the user passes
# in a service.UpdateModelRequest.
# There's no risk of modifying the input as we've already verified
# there are no flattened fields.
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._transport._wrapped_methods[self._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),)
),
)
# Send the request.
response = rpc(request, retry=retry, timeout=timeout, metadata=metadata,)
# Done; return the response.
return response
[docs] def deploy_model(
self,
request: service.DeployModelRequest = None,
*,
name: str = None,
retry: retries.Retry = gapic_v1.method.DEFAULT,
timeout: float = None,
metadata: Sequence[Tuple[str, str]] = (),
) -> operation.Operation:
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.
Args:
request (google.cloud.automl_v1.types.DeployModelRequest):
The request object. Request message for
[AutoMl.DeployModel][google.cloud.automl.v1.AutoMl.DeployModel].
name (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.Retry): 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.Operation:
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);
}
The JSON representation for Empty is empty JSON
object {}.
"""
# Create or coerce a protobuf request object.
# Sanity 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."
)
# Minor optimization to avoid making a copy if the user passes
# in a service.DeployModelRequest.
# There's no risk of modifying the input as we've already verified
# there are no flattened fields.
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._transport._wrapped_methods[self._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),)),
)
# Send the request.
response = rpc(request, retry=retry, timeout=timeout, metadata=metadata,)
# Wrap the response in an operation future.
response = operation.from_gapic(
response,
self._transport.operations_client,
empty_pb2.Empty,
metadata_type=operations.OperationMetadata,
)
# Done; return the response.
return response
[docs] def undeploy_model(
self,
request: service.UndeployModelRequest = None,
*,
name: str = None,
retry: retries.Retry = gapic_v1.method.DEFAULT,
timeout: float = None,
metadata: Sequence[Tuple[str, str]] = (),
) -> operation.Operation:
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.
Args:
request (google.cloud.automl_v1.types.UndeployModelRequest):
The request object. Request message for
[AutoMl.UndeployModel][google.cloud.automl.v1.AutoMl.UndeployModel].
name (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.Retry): 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.Operation:
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);
}
The JSON representation for Empty is empty JSON
object {}.
"""
# Create or coerce a protobuf request object.
# Sanity 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."
)
# Minor optimization to avoid making a copy if the user passes
# in a service.UndeployModelRequest.
# There's no risk of modifying the input as we've already verified
# there are no flattened fields.
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._transport._wrapped_methods[self._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),)),
)
# Send the request.
response = rpc(request, retry=retry, timeout=timeout, metadata=metadata,)
# Wrap the response in an operation future.
response = operation.from_gapic(
response,
self._transport.operations_client,
empty_pb2.Empty,
metadata_type=operations.OperationMetadata,
)
# Done; return the response.
return response
[docs] def export_model(
self,
request: service.ExportModelRequest = None,
*,
name: str = None,
output_config: io.ModelExportOutputConfig = None,
retry: retries.Retry = gapic_v1.method.DEFAULT,
timeout: float = None,
metadata: Sequence[Tuple[str, str]] = (),
) -> operation.Operation:
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.
Args:
request (google.cloud.automl_v1.types.ExportModelRequest):
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 (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 (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.Retry): 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.Operation:
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);
}
The JSON representation for Empty is empty JSON
object {}.
"""
# Create or coerce a protobuf request object.
# Sanity 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."
)
# Minor optimization to avoid making a copy if the user passes
# in a service.ExportModelRequest.
# There's no risk of modifying the input as we've already verified
# there are no flattened fields.
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._transport._wrapped_methods[self._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),)),
)
# Send the request.
response = rpc(request, retry=retry, timeout=timeout, metadata=metadata,)
# Wrap the response in an operation future.
response = operation.from_gapic(
response,
self._transport.operations_client,
empty_pb2.Empty,
metadata_type=operations.OperationMetadata,
)
# Done; return the response.
return response
[docs] def get_model_evaluation(
self,
request: service.GetModelEvaluationRequest = None,
*,
name: str = None,
retry: retries.Retry = gapic_v1.method.DEFAULT,
timeout: float = None,
metadata: Sequence[Tuple[str, str]] = (),
) -> model_evaluation.ModelEvaluation:
r"""Gets a model evaluation.
Args:
request (google.cloud.automl_v1.types.GetModelEvaluationRequest):
The request object. Request message for
[AutoMl.GetModelEvaluation][google.cloud.automl.v1.AutoMl.GetModelEvaluation].
name (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.Retry): 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.
# Sanity 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."
)
# Minor optimization to avoid making a copy if the user passes
# in a service.GetModelEvaluationRequest.
# There's no risk of modifying the input as we've already verified
# there are no flattened fields.
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._transport._wrapped_methods[self._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),)),
)
# Send the request.
response = rpc(request, retry=retry, timeout=timeout, metadata=metadata,)
# Done; return the response.
return response
[docs] def list_model_evaluations(
self,
request: service.ListModelEvaluationsRequest = None,
*,
parent: str = None,
filter: str = None,
retry: retries.Retry = gapic_v1.method.DEFAULT,
timeout: float = None,
metadata: Sequence[Tuple[str, str]] = (),
) -> pagers.ListModelEvaluationsPager:
r"""Lists model evaluations.
Args:
request (google.cloud.automl_v1.types.ListModelEvaluationsRequest):
The request object. Request message for
[AutoMl.ListModelEvaluations][google.cloud.automl.v1.AutoMl.ListModelEvaluations].
parent (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 (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.Retry): 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.ListModelEvaluationsPager:
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.
# Sanity 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."
)
# Minor optimization to avoid making a copy if the user passes
# in a service.ListModelEvaluationsRequest.
# There's no risk of modifying the input as we've already verified
# there are no flattened fields.
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._transport._wrapped_methods[self._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),)),
)
# Send the request.
response = rpc(request, retry=retry, timeout=timeout, metadata=metadata,)
# This method is paged; wrap the response in a pager, which provides
# an `__iter__` convenience method.
response = pagers.ListModelEvaluationsPager(
method=rpc, request=request, response=response, metadata=metadata,
)
# Done; return the response.
return response
try:
DEFAULT_CLIENT_INFO = gapic_v1.client_info.ClientInfo(
gapic_version=pkg_resources.get_distribution("google-cloud-automl",).version,
)
except pkg_resources.DistributionNotFound:
DEFAULT_CLIENT_INFO = gapic_v1.client_info.ClientInfo()
__all__ = ("AutoMlClient",)