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_v1beta1.services.auto_ml.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 os
import re
from typing import (
Callable,
Dict,
Mapping,
MutableMapping,
MutableSequence,
Optional,
Sequence,
Tuple,
Type,
Union,
cast,
)
import warnings
from google.api_core import client_options as client_options_lib
from google.api_core import exceptions as core_exceptions
from google.api_core import gapic_v1
from google.api_core import retry as retries
from google.auth import credentials as ga_credentials # type: ignore
from google.auth.exceptions import MutualTLSChannelError # type: ignore
from google.auth.transport import mtls # type: ignore
from google.auth.transport.grpc import SslCredentials # type: ignore
from google.oauth2 import service_account # type: ignore
from google.cloud.automl_v1beta1 import gapic_version as package_version
try:
OptionalRetry = Union[retries.Retry, gapic_v1.method._MethodDefault, None]
except AttributeError: # pragma: NO COVER
OptionalRetry = Union[retries.Retry, 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 timestamp_pb2 # type: ignore
from google.cloud.automl_v1beta1.services.auto_ml import pagers
from google.cloud.automl_v1beta1.types import (
model_evaluation,
operations,
regression,
service,
)
from google.cloud.automl_v1beta1.types import (
tables,
text,
text_extraction,
text_sentiment,
translation,
video,
)
from google.cloud.automl_v1beta1.types import annotation_spec, classification
from google.cloud.automl_v1beta1.types import column_spec
from google.cloud.automl_v1beta1.types import column_spec as gca_column_spec
from google.cloud.automl_v1beta1.types import data_stats, data_types
from google.cloud.automl_v1beta1.types import dataset
from google.cloud.automl_v1beta1.types import dataset as gca_dataset
from google.cloud.automl_v1beta1.types import detection, image, io
from google.cloud.automl_v1beta1.types import model
from google.cloud.automl_v1beta1.types import model as gca_model
from google.cloud.automl_v1beta1.types import table_spec
from google.cloud.automl_v1beta1.types import table_spec as gca_table_spec
from .transports.base import DEFAULT_CLIENT_INFO, AutoMlTransport
from .transports.grpc import AutoMlGrpcTransport
from .transports.grpc_asyncio import AutoMlGrpcAsyncIOTransport
from .transports.rest import AutoMlRestTransport
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
_transport_registry["rest"] = AutoMlRestTransport
def get_transport_class(
cls,
label: Optional[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")
# Note: DEFAULT_ENDPOINT is deprecated. Use _DEFAULT_ENDPOINT_TEMPLATE instead.
DEFAULT_ENDPOINT = "automl.googleapis.com"
DEFAULT_MTLS_ENDPOINT = _get_default_mtls_endpoint.__func__( # type: ignore
DEFAULT_ENDPOINT
)
_DEFAULT_ENDPOINT_TEMPLATE = "automl.{UNIVERSE_DOMAIN}"
_DEFAULT_UNIVERSE = "googleapis.com"
[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>.+?)$",
path,
)
return m.groupdict() if m else {}
[docs] @staticmethod
def column_spec_path(
project: str,
location: str,
dataset: str,
table_spec: str,
column_spec: str,
) -> str:
"""Returns a fully-qualified column_spec string."""
return "projects/{project}/locations/{location}/datasets/{dataset}/tableSpecs/{table_spec}/columnSpecs/{column_spec}".format(
project=project,
location=location,
dataset=dataset,
table_spec=table_spec,
column_spec=column_spec,
)
[docs] @staticmethod
def parse_column_spec_path(path: str) -> Dict[str, str]:
"""Parses a column_spec path into its component segments."""
m = re.match(
r"^projects/(?P<project>.+?)/locations/(?P<location>.+?)/datasets/(?P<dataset>.+?)/tableSpecs/(?P<table_spec>.+?)/columnSpecs/(?P<column_spec>.+?)$",
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>.+?)$",
path,
)
return m.groupdict() if m else {}
[docs] @staticmethod
def table_spec_path(
project: str,
location: str,
dataset: str,
table_spec: str,
) -> str:
"""Returns a fully-qualified table_spec string."""
return "projects/{project}/locations/{location}/datasets/{dataset}/tableSpecs/{table_spec}".format(
project=project,
location=location,
dataset=dataset,
table_spec=table_spec,
)
[docs] @staticmethod
def parse_table_spec_path(path: str) -> Dict[str, str]:
"""Parses a table_spec path into its component segments."""
m = re.match(
r"^projects/(?P<project>.+?)/locations/(?P<location>.+?)/datasets/(?P<dataset>.+?)/tableSpecs/(?P<table_spec>.+?)$",
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 {}
[docs] @classmethod
def get_mtls_endpoint_and_cert_source(
cls, client_options: Optional[client_options_lib.ClientOptions] = None
):
"""Deprecated. Return the API endpoint and client cert source for mutual TLS.
The client cert source is determined in the following order:
(1) if `GOOGLE_API_USE_CLIENT_CERTIFICATE` environment variable is not "true", the
client cert source is None.
(2) if `client_options.client_cert_source` is provided, use the provided one; if the
default client cert source exists, use the default one; otherwise the client cert
source is None.
The API endpoint is determined in the following order:
(1) if `client_options.api_endpoint` if provided, use the provided one.
(2) if `GOOGLE_API_USE_CLIENT_CERTIFICATE` environment variable is "always", use the
default mTLS endpoint; if the environment variable is "never", use the default API
endpoint; otherwise if client cert source exists, use the default mTLS endpoint, otherwise
use the default API endpoint.
More details can be found at https://google.aip.dev/auth/4114.
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.
"""
warnings.warn(
"get_mtls_endpoint_and_cert_source is deprecated. Use the api_endpoint property instead.",
DeprecationWarning,
)
if client_options is None:
client_options = client_options_lib.ClientOptions()
use_client_cert = os.getenv("GOOGLE_API_USE_CLIENT_CERTIFICATE", "false")
use_mtls_endpoint = os.getenv("GOOGLE_API_USE_MTLS_ENDPOINT", "auto")
if use_client_cert not in ("true", "false"):
raise ValueError(
"Environment variable `GOOGLE_API_USE_CLIENT_CERTIFICATE` must be either `true` or `false`"
)
if use_mtls_endpoint not in ("auto", "never", "always"):
raise MutualTLSChannelError(
"Environment variable `GOOGLE_API_USE_MTLS_ENDPOINT` must be `never`, `auto` or `always`"
)
# Figure out the client cert source to use.
client_cert_source = None
if use_client_cert == "true":
if client_options.client_cert_source:
client_cert_source = client_options.client_cert_source
elif mtls.has_default_client_cert_source():
client_cert_source = mtls.default_client_cert_source()
# Figure out which api endpoint to use.
if client_options.api_endpoint is not None:
api_endpoint = client_options.api_endpoint
elif use_mtls_endpoint == "always" or (
use_mtls_endpoint == "auto" and client_cert_source
):
api_endpoint = cls.DEFAULT_MTLS_ENDPOINT
else:
api_endpoint = cls.DEFAULT_ENDPOINT
return api_endpoint, client_cert_source
@staticmethod
def _read_environment_variables():
"""Returns the environment variables used by the client.
Returns:
Tuple[bool, str, str]: returns the GOOGLE_API_USE_CLIENT_CERTIFICATE,
GOOGLE_API_USE_MTLS_ENDPOINT, and GOOGLE_CLOUD_UNIVERSE_DOMAIN environment variables.
Raises:
ValueError: If GOOGLE_API_USE_CLIENT_CERTIFICATE is not
any of ["true", "false"].
google.auth.exceptions.MutualTLSChannelError: If GOOGLE_API_USE_MTLS_ENDPOINT
is not any of ["auto", "never", "always"].
"""
use_client_cert = os.getenv(
"GOOGLE_API_USE_CLIENT_CERTIFICATE", "false"
).lower()
use_mtls_endpoint = os.getenv("GOOGLE_API_USE_MTLS_ENDPOINT", "auto").lower()
universe_domain_env = os.getenv("GOOGLE_CLOUD_UNIVERSE_DOMAIN")
if use_client_cert not in ("true", "false"):
raise ValueError(
"Environment variable `GOOGLE_API_USE_CLIENT_CERTIFICATE` must be either `true` or `false`"
)
if use_mtls_endpoint not in ("auto", "never", "always"):
raise MutualTLSChannelError(
"Environment variable `GOOGLE_API_USE_MTLS_ENDPOINT` must be `never`, `auto` or `always`"
)
return use_client_cert == "true", use_mtls_endpoint, universe_domain_env
@staticmethod
def _get_client_cert_source(provided_cert_source, use_cert_flag):
"""Return the client cert source to be used by the client.
Args:
provided_cert_source (bytes): The client certificate source provided.
use_cert_flag (bool): A flag indicating whether to use the client certificate.
Returns:
bytes or None: The client cert source to be used by the client.
"""
client_cert_source = None
if use_cert_flag:
if provided_cert_source:
client_cert_source = provided_cert_source
elif mtls.has_default_client_cert_source():
client_cert_source = mtls.default_client_cert_source()
return client_cert_source
@staticmethod
def _get_api_endpoint(
api_override, client_cert_source, universe_domain, use_mtls_endpoint
):
"""Return the API endpoint used by the client.
Args:
api_override (str): The API endpoint override. If specified, this is always
the return value of this function and the other arguments are not used.
client_cert_source (bytes): The client certificate source used by the client.
universe_domain (str): The universe domain used by the client.
use_mtls_endpoint (str): How to use the mTLS endpoint, which depends also on the other parameters.
Possible values are "always", "auto", or "never".
Returns:
str: The API endpoint to be used by the client.
"""
if api_override is not None:
api_endpoint = api_override
elif use_mtls_endpoint == "always" or (
use_mtls_endpoint == "auto" and client_cert_source
):
_default_universe = AutoMlClient._DEFAULT_UNIVERSE
if universe_domain != _default_universe:
raise MutualTLSChannelError(
f"mTLS is not supported in any universe other than {_default_universe}."
)
api_endpoint = AutoMlClient.DEFAULT_MTLS_ENDPOINT
else:
api_endpoint = AutoMlClient._DEFAULT_ENDPOINT_TEMPLATE.format(
UNIVERSE_DOMAIN=universe_domain
)
return api_endpoint
@staticmethod
def _get_universe_domain(
client_universe_domain: Optional[str], universe_domain_env: Optional[str]
) -> str:
"""Return the universe domain used by the client.
Args:
client_universe_domain (Optional[str]): The universe domain configured via the client options.
universe_domain_env (Optional[str]): The universe domain configured via the "GOOGLE_CLOUD_UNIVERSE_DOMAIN" environment variable.
Returns:
str: The universe domain to be used by the client.
Raises:
ValueError: If the universe domain is an empty string.
"""
universe_domain = AutoMlClient._DEFAULT_UNIVERSE
if client_universe_domain is not None:
universe_domain = client_universe_domain
elif universe_domain_env is not None:
universe_domain = universe_domain_env
if len(universe_domain.strip()) == 0:
raise ValueError("Universe Domain cannot be an empty string.")
return universe_domain
def _validate_universe_domain(self):
"""Validates client's and credentials' universe domains are consistent.
Returns:
bool: True iff the configured universe domain is valid.
Raises:
ValueError: If the configured universe domain is not valid.
"""
# NOTE (b/349488459): universe validation is disabled until further notice.
return True
@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._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._universe_domain
def __init__(
self,
*,
credentials: Optional[ga_credentials.Credentials] = None,
transport: Optional[
Union[str, AutoMlTransport, Callable[..., AutoMlTransport]]
] = None,
client_options: Optional[Union[client_options_lib.ClientOptions, dict]] = 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 (Optional[Union[str,AutoMlTransport,Callable[..., AutoMlTransport]]]):
The transport to use, or a Callable that constructs and returns a new transport.
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 the ``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_options = client_options
if isinstance(self._client_options, dict):
self._client_options = client_options_lib.from_dict(self._client_options)
if self._client_options is None:
self._client_options = client_options_lib.ClientOptions()
self._client_options = cast(
client_options_lib.ClientOptions, self._client_options
)
universe_domain_opt = getattr(self._client_options, "universe_domain", None)
(
self._use_client_cert,
self._use_mtls_endpoint,
self._universe_domain_env,
) = AutoMlClient._read_environment_variables()
self._client_cert_source = AutoMlClient._get_client_cert_source(
self._client_options.client_cert_source, self._use_client_cert
)
self._universe_domain = AutoMlClient._get_universe_domain(
universe_domain_opt, self._universe_domain_env
)
self._api_endpoint = None # updated below, depending on `transport`
# Initialize the universe domain validation.
self._is_universe_domain_valid = False
api_key_value = getattr(self._client_options, "api_key", None)
if api_key_value and credentials:
raise ValueError(
"client_options.api_key and credentials are mutually exclusive"
)
# Save or instantiate the transport.
# Ordinarily, we provide the transport, but allowing a custom transport
# instance provides an extensibility point for unusual situations.
transport_provided = isinstance(transport, AutoMlTransport)
if transport_provided:
# transport is a AutoMlTransport instance.
if credentials or self._client_options.credentials_file or api_key_value:
raise ValueError(
"When providing a transport instance, "
"provide its credentials directly."
)
if self._client_options.scopes:
raise ValueError(
"When providing a transport instance, provide its scopes "
"directly."
)
self._transport = cast(AutoMlTransport, transport)
self._api_endpoint = self._transport.host
self._api_endpoint = self._api_endpoint or AutoMlClient._get_api_endpoint(
self._client_options.api_endpoint,
self._client_cert_source,
self._universe_domain,
self._use_mtls_endpoint,
)
if not transport_provided:
import google.auth._default # type: ignore
if api_key_value and hasattr(
google.auth._default, "get_api_key_credentials"
):
credentials = google.auth._default.get_api_key_credentials(
api_key_value
)
transport_init: Union[
Type[AutoMlTransport], Callable[..., AutoMlTransport]
] = (
AutoMlClient.get_transport_class(transport)
if isinstance(transport, str) or transport is None
else cast(Callable[..., AutoMlTransport], transport)
)
# initialize with the provided callable or the passed in class
self._transport = transport_init(
credentials=credentials,
credentials_file=self._client_options.credentials_file,
host=self._api_endpoint,
scopes=self._client_options.scopes,
client_cert_source_for_mtls=self._client_cert_source,
quota_project_id=self._client_options.quota_project_id,
client_info=client_info,
always_use_jwt_access=True,
api_audience=self._client_options.api_audience,
)
[docs] 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]] = (),
) -> gca_dataset.Dataset:
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_v1beta1
def sample_create_dataset():
# Create a client
client = automl_v1beta1.AutoMlClient()
# Initialize request argument(s)
dataset = automl_v1beta1.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_v1beta1.CreateDatasetRequest(
parent="parent_value",
dataset=dataset,
)
# Make the request
response = client.create_dataset(request=request)
# Handle the response
print(response)
Args:
request (Union[google.cloud.automl_v1beta1.types.CreateDatasetRequest, dict]):
The request object. Request message for
[AutoMl.CreateDataset][google.cloud.automl.v1beta1.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_v1beta1.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.cloud.automl_v1beta1.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._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),)),
)
# Validate the universe domain.
self._validate_universe_domain()
# Send the request.
response = rpc(
request,
retry=retry,
timeout=timeout,
metadata=metadata,
)
# Done; return the response.
return response
[docs] 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_v1beta1
def sample_get_dataset():
# Create a client
client = automl_v1beta1.AutoMlClient()
# Initialize request argument(s)
request = automl_v1beta1.GetDatasetRequest(
name="name_value",
)
# Make the request
response = client.get_dataset(request=request)
# Handle the response
print(response)
Args:
request (Union[google.cloud.automl_v1beta1.types.GetDatasetRequest, dict]):
The request object. Request message for
[AutoMl.GetDataset][google.cloud.automl.v1beta1.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_v1beta1.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._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),)),
)
# Validate the universe domain.
self._validate_universe_domain()
# Send the request.
response = rpc(
request,
retry=retry,
timeout=timeout,
metadata=metadata,
)
# Done; return the response.
return response
[docs] 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.ListDatasetsPager:
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_v1beta1
def sample_list_datasets():
# Create a client
client = automl_v1beta1.AutoMlClient()
# Initialize request argument(s)
request = automl_v1beta1.ListDatasetsRequest(
parent="parent_value",
)
# Make the request
page_result = client.list_datasets(request=request)
# Handle the response
for response in page_result:
print(response)
Args:
request (Union[google.cloud.automl_v1beta1.types.ListDatasetsRequest, dict]):
The request object. Request message for
[AutoMl.ListDatasets][google.cloud.automl.v1beta1.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_v1beta1.services.auto_ml.pagers.ListDatasetsPager:
Response message for
[AutoMl.ListDatasets][google.cloud.automl.v1beta1.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._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),)),
)
# Validate the universe domain.
self._validate_universe_domain()
# 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,
retry=retry,
timeout=timeout,
metadata=metadata,
)
# Done; return the response.
return response
[docs] def update_dataset(
self,
request: Optional[Union[service.UpdateDatasetRequest, dict]] = 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]] = (),
) -> 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_v1beta1
def sample_update_dataset():
# Create a client
client = automl_v1beta1.AutoMlClient()
# Initialize request argument(s)
dataset = automl_v1beta1.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_v1beta1.UpdateDatasetRequest(
dataset=dataset,
)
# Make the request
response = client.update_dataset(request=request)
# Handle the response
print(response)
Args:
request (Union[google.cloud.automl_v1beta1.types.UpdateDatasetRequest, dict]):
The request object. Request message for
[AutoMl.UpdateDataset][google.cloud.automl.v1beta1.AutoMl.UpdateDataset]
dataset (google.cloud.automl_v1beta1.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.
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_v1beta1.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])
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
# 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),)
),
)
# Validate the universe domain.
self._validate_universe_domain()
# Send the request.
response = rpc(
request,
retry=retry,
timeout=timeout,
metadata=metadata,
)
# Done; return the response.
return response
[docs] 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.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.
.. 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_v1beta1
def sample_delete_dataset():
# Create a client
client = automl_v1beta1.AutoMlClient()
# Initialize request argument(s)
request = automl_v1beta1.DeleteDatasetRequest(
name="name_value",
)
# Make the request
operation = client.delete_dataset(request=request)
print("Waiting for operation to complete...")
response = operation.result()
# Handle the response
print(response)
Args:
request (Union[google.cloud.automl_v1beta1.types.DeleteDatasetRequest, dict]):
The request object. Request message for
[AutoMl.DeleteDataset][google.cloud.automl.v1beta1.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);
}
"""
# 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._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),)),
)
# Validate the universe domain.
self._validate_universe_domain()
# 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: 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.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.v1beta1.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_v1beta1
def sample_import_data():
# Create a client
client = automl_v1beta1.AutoMlClient()
# Initialize request argument(s)
request = automl_v1beta1.ImportDataRequest(
name="name_value",
)
# Make the request
operation = client.import_data(request=request)
print("Waiting for operation to complete...")
response = operation.result()
# Handle the response
print(response)
Args:
request (Union[google.cloud.automl_v1beta1.types.ImportDataRequest, dict]):
The request object. Request message for
[AutoMl.ImportData][google.cloud.automl.v1beta1.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_v1beta1.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);
}
"""
# 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._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),)),
)
# Validate the universe domain.
self._validate_universe_domain()
# 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: 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.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.
.. 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_v1beta1
def sample_export_data():
# Create a client
client = automl_v1beta1.AutoMlClient()
# Initialize request argument(s)
request = automl_v1beta1.ExportDataRequest(
name="name_value",
)
# Make the request
operation = client.export_data(request=request)
print("Waiting for operation to complete...")
response = operation.result()
# Handle the response
print(response)
Args:
request (Union[google.cloud.automl_v1beta1.types.ExportDataRequest, dict]):
The request object. Request message for
[AutoMl.ExportData][google.cloud.automl.v1beta1.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_v1beta1.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);
}
"""
# 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._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),)),
)
# Validate the universe domain.
self._validate_universe_domain()
# 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: 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_v1beta1
def sample_get_annotation_spec():
# Create a client
client = automl_v1beta1.AutoMlClient()
# Initialize request argument(s)
request = automl_v1beta1.GetAnnotationSpecRequest(
name="name_value",
)
# Make the request
response = client.get_annotation_spec(request=request)
# Handle the response
print(response)
Args:
request (Union[google.cloud.automl_v1beta1.types.GetAnnotationSpecRequest, dict]):
The request object. Request message for
[AutoMl.GetAnnotationSpec][google.cloud.automl.v1beta1.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_v1beta1.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._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),)),
)
# Validate the universe domain.
self._validate_universe_domain()
# Send the request.
response = rpc(
request,
retry=retry,
timeout=timeout,
metadata=metadata,
)
# Done; return the response.
return response
[docs] def get_table_spec(
self,
request: Optional[Union[service.GetTableSpecRequest, 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]] = (),
) -> table_spec.TableSpec:
r"""Gets a table 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_v1beta1
def sample_get_table_spec():
# Create a client
client = automl_v1beta1.AutoMlClient()
# Initialize request argument(s)
request = automl_v1beta1.GetTableSpecRequest(
name="name_value",
)
# Make the request
response = client.get_table_spec(request=request)
# Handle the response
print(response)
Args:
request (Union[google.cloud.automl_v1beta1.types.GetTableSpecRequest, dict]):
The request object. Request message for
[AutoMl.GetTableSpec][google.cloud.automl.v1beta1.AutoMl.GetTableSpec].
name (str):
Required. The resource name of the
table 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_v1beta1.types.TableSpec:
A specification of a relational table.
The table's schema is represented via its child
column specs. It is pre-populated as part of
ImportData by schema inference algorithm, the version
of which is a required parameter of ImportData
InputConfig. Note: While working with a table, at
times the schema may be inconsistent with the data in
the table (e.g. string in a FLOAT64 column). The
consistency validation is done upon creation of a
model. Used by: \* Tables
"""
# 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.GetTableSpecRequest):
request = service.GetTableSpecRequest(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_table_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._validate_universe_domain()
# Send the request.
response = rpc(
request,
retry=retry,
timeout=timeout,
metadata=metadata,
)
# Done; return the response.
return response
[docs] def list_table_specs(
self,
request: Optional[Union[service.ListTableSpecsRequest, 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.ListTableSpecsPager:
r"""Lists table specs in 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_v1beta1
def sample_list_table_specs():
# Create a client
client = automl_v1beta1.AutoMlClient()
# Initialize request argument(s)
request = automl_v1beta1.ListTableSpecsRequest(
parent="parent_value",
)
# Make the request
page_result = client.list_table_specs(request=request)
# Handle the response
for response in page_result:
print(response)
Args:
request (Union[google.cloud.automl_v1beta1.types.ListTableSpecsRequest, dict]):
The request object. Request message for
[AutoMl.ListTableSpecs][google.cloud.automl.v1beta1.AutoMl.ListTableSpecs].
parent (str):
Required. The resource name of the
dataset to list table specs from.
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_v1beta1.services.auto_ml.pagers.ListTableSpecsPager:
Response message for
[AutoMl.ListTableSpecs][google.cloud.automl.v1beta1.AutoMl.ListTableSpecs].
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.ListTableSpecsRequest):
request = service.ListTableSpecsRequest(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_table_specs]
# 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._validate_universe_domain()
# 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.ListTableSpecsPager(
method=rpc,
request=request,
response=response,
retry=retry,
timeout=timeout,
metadata=metadata,
)
# Done; return the response.
return response
[docs] def update_table_spec(
self,
request: Optional[Union[service.UpdateTableSpecRequest, dict]] = None,
*,
table_spec: Optional[gca_table_spec.TableSpec] = None,
retry: OptionalRetry = gapic_v1.method.DEFAULT,
timeout: Union[float, object] = gapic_v1.method.DEFAULT,
metadata: Sequence[Tuple[str, str]] = (),
) -> gca_table_spec.TableSpec:
r"""Updates a table 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_v1beta1
def sample_update_table_spec():
# Create a client
client = automl_v1beta1.AutoMlClient()
# Initialize request argument(s)
request = automl_v1beta1.UpdateTableSpecRequest(
)
# Make the request
response = client.update_table_spec(request=request)
# Handle the response
print(response)
Args:
request (Union[google.cloud.automl_v1beta1.types.UpdateTableSpecRequest, dict]):
The request object. Request message for
[AutoMl.UpdateTableSpec][google.cloud.automl.v1beta1.AutoMl.UpdateTableSpec]
table_spec (google.cloud.automl_v1beta1.types.TableSpec):
Required. The table spec which
replaces the resource on the server.
This corresponds to the ``table_spec`` 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_v1beta1.types.TableSpec:
A specification of a relational table.
The table's schema is represented via its child
column specs. It is pre-populated as part of
ImportData by schema inference algorithm, the version
of which is a required parameter of ImportData
InputConfig. Note: While working with a table, at
times the schema may be inconsistent with the data in
the table (e.g. string in a FLOAT64 column). The
consistency validation is done upon creation of a
model. Used by: \* Tables
"""
# 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([table_spec])
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.UpdateTableSpecRequest):
request = service.UpdateTableSpecRequest(request)
# If we have keyword arguments corresponding to fields on the
# request, apply these.
if table_spec is not None:
request.table_spec = table_spec
# Wrap the RPC method; this adds retry and timeout information,
# and friendly error handling.
rpc = self._transport._wrapped_methods[self._transport.update_table_spec]
# Certain fields should be provided within the metadata header;
# add these here.
metadata = tuple(metadata) + (
gapic_v1.routing_header.to_grpc_metadata(
(("table_spec.name", request.table_spec.name),)
),
)
# Validate the universe domain.
self._validate_universe_domain()
# Send the request.
response = rpc(
request,
retry=retry,
timeout=timeout,
metadata=metadata,
)
# Done; return the response.
return response
[docs] def get_column_spec(
self,
request: Optional[Union[service.GetColumnSpecRequest, 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]] = (),
) -> column_spec.ColumnSpec:
r"""Gets a column 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_v1beta1
def sample_get_column_spec():
# Create a client
client = automl_v1beta1.AutoMlClient()
# Initialize request argument(s)
request = automl_v1beta1.GetColumnSpecRequest(
name="name_value",
)
# Make the request
response = client.get_column_spec(request=request)
# Handle the response
print(response)
Args:
request (Union[google.cloud.automl_v1beta1.types.GetColumnSpecRequest, dict]):
The request object. Request message for
[AutoMl.GetColumnSpec][google.cloud.automl.v1beta1.AutoMl.GetColumnSpec].
name (str):
Required. The resource name of the
column 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_v1beta1.types.ColumnSpec:
A representation of a column in a relational table. When listing them, column specs are returned in the same order in which they were
given on import . Used by: \* Tables
"""
# 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.GetColumnSpecRequest):
request = service.GetColumnSpecRequest(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_column_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._validate_universe_domain()
# Send the request.
response = rpc(
request,
retry=retry,
timeout=timeout,
metadata=metadata,
)
# Done; return the response.
return response
[docs] def list_column_specs(
self,
request: Optional[Union[service.ListColumnSpecsRequest, 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.ListColumnSpecsPager:
r"""Lists column specs in a table 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_v1beta1
def sample_list_column_specs():
# Create a client
client = automl_v1beta1.AutoMlClient()
# Initialize request argument(s)
request = automl_v1beta1.ListColumnSpecsRequest(
parent="parent_value",
)
# Make the request
page_result = client.list_column_specs(request=request)
# Handle the response
for response in page_result:
print(response)
Args:
request (Union[google.cloud.automl_v1beta1.types.ListColumnSpecsRequest, dict]):
The request object. Request message for
[AutoMl.ListColumnSpecs][google.cloud.automl.v1beta1.AutoMl.ListColumnSpecs].
parent (str):
Required. The resource name of the
table spec to list column specs from.
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_v1beta1.services.auto_ml.pagers.ListColumnSpecsPager:
Response message for
[AutoMl.ListColumnSpecs][google.cloud.automl.v1beta1.AutoMl.ListColumnSpecs].
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.ListColumnSpecsRequest):
request = service.ListColumnSpecsRequest(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_column_specs]
# 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._validate_universe_domain()
# 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.ListColumnSpecsPager(
method=rpc,
request=request,
response=response,
retry=retry,
timeout=timeout,
metadata=metadata,
)
# Done; return the response.
return response
[docs] def update_column_spec(
self,
request: Optional[Union[service.UpdateColumnSpecRequest, dict]] = None,
*,
column_spec: Optional[gca_column_spec.ColumnSpec] = None,
retry: OptionalRetry = gapic_v1.method.DEFAULT,
timeout: Union[float, object] = gapic_v1.method.DEFAULT,
metadata: Sequence[Tuple[str, str]] = (),
) -> gca_column_spec.ColumnSpec:
r"""Updates a column 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_v1beta1
def sample_update_column_spec():
# Create a client
client = automl_v1beta1.AutoMlClient()
# Initialize request argument(s)
request = automl_v1beta1.UpdateColumnSpecRequest(
)
# Make the request
response = client.update_column_spec(request=request)
# Handle the response
print(response)
Args:
request (Union[google.cloud.automl_v1beta1.types.UpdateColumnSpecRequest, dict]):
The request object. Request message for
[AutoMl.UpdateColumnSpec][google.cloud.automl.v1beta1.AutoMl.UpdateColumnSpec]
column_spec (google.cloud.automl_v1beta1.types.ColumnSpec):
Required. The column spec which
replaces the resource on the server.
This corresponds to the ``column_spec`` 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_v1beta1.types.ColumnSpec:
A representation of a column in a relational table. When listing them, column specs are returned in the same order in which they were
given on import . Used by: \* Tables
"""
# 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([column_spec])
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.UpdateColumnSpecRequest):
request = service.UpdateColumnSpecRequest(request)
# If we have keyword arguments corresponding to fields on the
# request, apply these.
if column_spec is not None:
request.column_spec = column_spec
# Wrap the RPC method; this adds retry and timeout information,
# and friendly error handling.
rpc = self._transport._wrapped_methods[self._transport.update_column_spec]
# Certain fields should be provided within the metadata header;
# add these here.
metadata = tuple(metadata) + (
gapic_v1.routing_header.to_grpc_metadata(
(("column_spec.name", request.column_spec.name),)
),
)
# Validate the universe domain.
self._validate_universe_domain()
# Send the request.
response = rpc(
request,
retry=retry,
timeout=timeout,
metadata=metadata,
)
# Done; return the response.
return response
[docs] 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.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.
.. 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_v1beta1
def sample_create_model():
# Create a client
client = automl_v1beta1.AutoMlClient()
# Initialize request argument(s)
request = automl_v1beta1.CreateModelRequest(
parent="parent_value",
)
# Make the request
operation = client.create_model(request=request)
print("Waiting for operation to complete...")
response = operation.result()
# Handle the response
print(response)
Args:
request (Union[google.cloud.automl_v1beta1.types.CreateModelRequest, dict]):
The request object. Request message for
[AutoMl.CreateModel][google.cloud.automl.v1beta1.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_v1beta1.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_v1beta1.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._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),)),
)
# Validate the universe domain.
self._validate_universe_domain()
# 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: 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_v1beta1
def sample_get_model():
# Create a client
client = automl_v1beta1.AutoMlClient()
# Initialize request argument(s)
request = automl_v1beta1.GetModelRequest(
name="name_value",
)
# Make the request
response = client.get_model(request=request)
# Handle the response
print(response)
Args:
request (Union[google.cloud.automl_v1beta1.types.GetModelRequest, dict]):
The request object. Request message for
[AutoMl.GetModel][google.cloud.automl.v1beta1.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_v1beta1.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._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),)),
)
# Validate the universe domain.
self._validate_universe_domain()
# Send the request.
response = rpc(
request,
retry=retry,
timeout=timeout,
metadata=metadata,
)
# Done; return the response.
return response
[docs] 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.ListModelsPager:
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_v1beta1
def sample_list_models():
# Create a client
client = automl_v1beta1.AutoMlClient()
# Initialize request argument(s)
request = automl_v1beta1.ListModelsRequest(
parent="parent_value",
)
# Make the request
page_result = client.list_models(request=request)
# Handle the response
for response in page_result:
print(response)
Args:
request (Union[google.cloud.automl_v1beta1.types.ListModelsRequest, dict]):
The request object. Request message for
[AutoMl.ListModels][google.cloud.automl.v1beta1.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_v1beta1.services.auto_ml.pagers.ListModelsPager:
Response message for
[AutoMl.ListModels][google.cloud.automl.v1beta1.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._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),)),
)
# Validate the universe domain.
self._validate_universe_domain()
# 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,
retry=retry,
timeout=timeout,
metadata=metadata,
)
# Done; return the response.
return response
[docs] 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.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.
.. 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_v1beta1
def sample_delete_model():
# Create a client
client = automl_v1beta1.AutoMlClient()
# Initialize request argument(s)
request = automl_v1beta1.DeleteModelRequest(
name="name_value",
)
# Make the request
operation = client.delete_model(request=request)
print("Waiting for operation to complete...")
response = operation.result()
# Handle the response
print(response)
Args:
request (Union[google.cloud.automl_v1beta1.types.DeleteModelRequest, dict]):
The request object. Request message for
[AutoMl.DeleteModel][google.cloud.automl.v1beta1.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);
}
"""
# 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._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),)),
)
# Validate the universe domain.
self._validate_universe_domain()
# 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 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.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.v1beta1.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_v1beta1
def sample_deploy_model():
# Create a client
client = automl_v1beta1.AutoMlClient()
# Initialize request argument(s)
request = automl_v1beta1.DeployModelRequest(
name="name_value",
)
# Make the request
operation = client.deploy_model(request=request)
print("Waiting for operation to complete...")
response = operation.result()
# Handle the response
print(response)
Args:
request (Union[google.cloud.automl_v1beta1.types.DeployModelRequest, dict]):
The request object. Request message for
[AutoMl.DeployModel][google.cloud.automl.v1beta1.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);
}
"""
# 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._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),)),
)
# Validate the universe domain.
self._validate_universe_domain()
# 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: 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.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.
.. 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_v1beta1
def sample_undeploy_model():
# Create a client
client = automl_v1beta1.AutoMlClient()
# Initialize request argument(s)
request = automl_v1beta1.UndeployModelRequest(
name="name_value",
)
# Make the request
operation = client.undeploy_model(request=request)
print("Waiting for operation to complete...")
response = operation.result()
# Handle the response
print(response)
Args:
request (Union[google.cloud.automl_v1beta1.types.UndeployModelRequest, dict]):
The request object. Request message for
[AutoMl.UndeployModel][google.cloud.automl.v1beta1.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);
}
"""
# 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._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),)),
)
# Validate the universe domain.
self._validate_universe_domain()
# 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: 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.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.v1beta1.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_v1beta1
def sample_export_model():
# Create a client
client = automl_v1beta1.AutoMlClient()
# Initialize request argument(s)
request = automl_v1beta1.ExportModelRequest(
name="name_value",
)
# Make the request
operation = client.export_model(request=request)
print("Waiting for operation to complete...")
response = operation.result()
# Handle the response
print(response)
Args:
request (Union[google.cloud.automl_v1beta1.types.ExportModelRequest, dict]):
The request object. Request message for
[AutoMl.ExportModel][google.cloud.automl.v1beta1.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_v1beta1.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);
}
"""
# 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._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),)),
)
# Validate the universe domain.
self._validate_universe_domain()
# 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_evaluated_examples(
self,
request: Optional[Union[service.ExportEvaluatedExamplesRequest, dict]] = None,
*,
name: Optional[str] = None,
output_config: Optional[io.ExportEvaluatedExamplesOutputConfig] = None,
retry: OptionalRetry = gapic_v1.method.DEFAULT,
timeout: Union[float, object] = gapic_v1.method.DEFAULT,
metadata: Sequence[Tuple[str, str]] = (),
) -> operation.Operation:
r"""Exports examples on which the model was evaluated (i.e. which
were in the TEST set of the dataset the model was created from),
together with their ground truth annotations and the annotations
created (predicted) by the model. The examples, ground truth and
predictions are exported in the state they were at the moment
the model was evaluated.
This export is available only for 30 days since the model
evaluation is created.
Currently only available for Tables.
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_v1beta1
def sample_export_evaluated_examples():
# Create a client
client = automl_v1beta1.AutoMlClient()
# Initialize request argument(s)
request = automl_v1beta1.ExportEvaluatedExamplesRequest(
name="name_value",
)
# Make the request
operation = client.export_evaluated_examples(request=request)
print("Waiting for operation to complete...")
response = operation.result()
# Handle the response
print(response)
Args:
request (Union[google.cloud.automl_v1beta1.types.ExportEvaluatedExamplesRequest, dict]):
The request object. Request message for
[AutoMl.ExportEvaluatedExamples][google.cloud.automl.v1beta1.AutoMl.ExportEvaluatedExamples].
name (str):
Required. The resource name of the
model whose evaluated examples are to be
exported.
This corresponds to the ``name`` field
on the ``request`` instance; if ``request`` is provided, this
should not be set.
output_config (google.cloud.automl_v1beta1.types.ExportEvaluatedExamplesOutputConfig):
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);
}
"""
# 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.ExportEvaluatedExamplesRequest):
request = service.ExportEvaluatedExamplesRequest(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_evaluated_examples
]
# 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._validate_universe_domain()
# 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: 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_v1beta1
def sample_get_model_evaluation():
# Create a client
client = automl_v1beta1.AutoMlClient()
# Initialize request argument(s)
request = automl_v1beta1.GetModelEvaluationRequest(
name="name_value",
)
# Make the request
response = client.get_model_evaluation(request=request)
# Handle the response
print(response)
Args:
request (Union[google.cloud.automl_v1beta1.types.GetModelEvaluationRequest, dict]):
The request object. Request message for
[AutoMl.GetModelEvaluation][google.cloud.automl.v1beta1.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_v1beta1.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._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),)),
)
# Validate the universe domain.
self._validate_universe_domain()
# 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: Optional[Union[service.ListModelEvaluationsRequest, 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.ListModelEvaluationsPager:
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_v1beta1
def sample_list_model_evaluations():
# Create a client
client = automl_v1beta1.AutoMlClient()
# Initialize request argument(s)
request = automl_v1beta1.ListModelEvaluationsRequest(
parent="parent_value",
)
# Make the request
page_result = client.list_model_evaluations(request=request)
# Handle the response
for response in page_result:
print(response)
Args:
request (Union[google.cloud.automl_v1beta1.types.ListModelEvaluationsRequest, dict]):
The request object. Request message for
[AutoMl.ListModelEvaluations][google.cloud.automl.v1beta1.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.
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_v1beta1.services.auto_ml.pagers.ListModelEvaluationsPager:
Response message for
[AutoMl.ListModelEvaluations][google.cloud.automl.v1beta1.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])
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
# 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),)),
)
# Validate the universe domain.
self._validate_universe_domain()
# 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,
retry=retry,
timeout=timeout,
metadata=metadata,
)
# Done; return the response.
return response
def __enter__(self) -> "AutoMlClient":
return self
[docs] def __exit__(self, type, value, traceback):
"""Releases underlying transport's resources.
.. warning::
ONLY use as a context manager if the transport is NOT shared
with other clients! Exiting the with block will CLOSE the transport
and may cause errors in other clients!
"""
self.transport.close()
DEFAULT_CLIENT_INFO = gapic_v1.client_info.ClientInfo(
gapic_version=package_version.__version__
)
__all__ = ("AutoMlClient",)
