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.datalabeling_v1beta1.services.data_labeling_service.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.datalabeling_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 field_mask_pb2 # type: ignore
from google.protobuf import timestamp_pb2 # type: ignore
from google.cloud.datalabeling_v1beta1.services.data_labeling_service import pagers
from google.cloud.datalabeling_v1beta1.types import data_labeling_service, data_payloads
from google.cloud.datalabeling_v1beta1.types import (
annotation_spec_set as gcd_annotation_spec_set,
)
from google.cloud.datalabeling_v1beta1.types import evaluation_job as gcd_evaluation_job
from google.cloud.datalabeling_v1beta1.types import instruction as gcd_instruction
from google.cloud.datalabeling_v1beta1.types import annotation
from google.cloud.datalabeling_v1beta1.types import annotation_spec_set
from google.cloud.datalabeling_v1beta1.types import dataset
from google.cloud.datalabeling_v1beta1.types import dataset as gcd_dataset
from google.cloud.datalabeling_v1beta1.types import evaluation
from google.cloud.datalabeling_v1beta1.types import evaluation_job
from google.cloud.datalabeling_v1beta1.types import human_annotation_config
from google.cloud.datalabeling_v1beta1.types import instruction
from google.cloud.datalabeling_v1beta1.types import operations
from .transports.base import DEFAULT_CLIENT_INFO, DataLabelingServiceTransport
from .transports.grpc import DataLabelingServiceGrpcTransport
from .transports.grpc_asyncio import DataLabelingServiceGrpcAsyncIOTransport
class DataLabelingServiceClientMeta(type):
"""Metaclass for the DataLabelingService 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[DataLabelingServiceTransport]]
_transport_registry["grpc"] = DataLabelingServiceGrpcTransport
_transport_registry["grpc_asyncio"] = DataLabelingServiceGrpcAsyncIOTransport
def get_transport_class(
cls,
label: Optional[str] = None,
) -> Type[DataLabelingServiceTransport]:
"""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 DataLabelingServiceClient(metaclass=DataLabelingServiceClientMeta):
"""Service for the AI Platform Data Labeling API."""
@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 = "datalabeling.googleapis.com"
DEFAULT_MTLS_ENDPOINT = _get_default_mtls_endpoint.__func__( # type: ignore
DEFAULT_ENDPOINT
)
_DEFAULT_ENDPOINT_TEMPLATE = "datalabeling.{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:
DataLabelingServiceClient: 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:
DataLabelingServiceClient: 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) -> DataLabelingServiceTransport:
"""Returns the transport used by the client instance.
Returns:
DataLabelingServiceTransport: The transport used by the client
instance.
"""
return self._transport
[docs] @staticmethod
def annotated_dataset_path(
project: str,
dataset: str,
annotated_dataset: str,
) -> str:
"""Returns a fully-qualified annotated_dataset string."""
return "projects/{project}/datasets/{dataset}/annotatedDatasets/{annotated_dataset}".format(
project=project,
dataset=dataset,
annotated_dataset=annotated_dataset,
)
[docs] @staticmethod
def parse_annotated_dataset_path(path: str) -> Dict[str, str]:
"""Parses a annotated_dataset path into its component segments."""
m = re.match(
r"^projects/(?P<project>.+?)/datasets/(?P<dataset>.+?)/annotatedDatasets/(?P<annotated_dataset>.+?)$",
path,
)
return m.groupdict() if m else {}
[docs] @staticmethod
def annotation_spec_set_path(
project: str,
annotation_spec_set: str,
) -> str:
"""Returns a fully-qualified annotation_spec_set string."""
return "projects/{project}/annotationSpecSets/{annotation_spec_set}".format(
project=project,
annotation_spec_set=annotation_spec_set,
)
[docs] @staticmethod
def parse_annotation_spec_set_path(path: str) -> Dict[str, str]:
"""Parses a annotation_spec_set path into its component segments."""
m = re.match(
r"^projects/(?P<project>.+?)/annotationSpecSets/(?P<annotation_spec_set>.+?)$",
path,
)
return m.groupdict() if m else {}
[docs] @staticmethod
def data_item_path(
project: str,
dataset: str,
data_item: str,
) -> str:
"""Returns a fully-qualified data_item string."""
return "projects/{project}/datasets/{dataset}/dataItems/{data_item}".format(
project=project,
dataset=dataset,
data_item=data_item,
)
[docs] @staticmethod
def parse_data_item_path(path: str) -> Dict[str, str]:
"""Parses a data_item path into its component segments."""
m = re.match(
r"^projects/(?P<project>.+?)/datasets/(?P<dataset>.+?)/dataItems/(?P<data_item>.+?)$",
path,
)
return m.groupdict() if m else {}
[docs] @staticmethod
def dataset_path(
project: str,
dataset: str,
) -> str:
"""Returns a fully-qualified dataset string."""
return "projects/{project}/datasets/{dataset}".format(
project=project,
dataset=dataset,
)
[docs] @staticmethod
def parse_dataset_path(path: str) -> Dict[str, str]:
"""Parses a dataset path into its component segments."""
m = re.match(r"^projects/(?P<project>.+?)/datasets/(?P<dataset>.+?)$", path)
return m.groupdict() if m else {}
[docs] @staticmethod
def evaluation_path(
project: str,
dataset: str,
evaluation: str,
) -> str:
"""Returns a fully-qualified evaluation string."""
return "projects/{project}/datasets/{dataset}/evaluations/{evaluation}".format(
project=project,
dataset=dataset,
evaluation=evaluation,
)
[docs] @staticmethod
def parse_evaluation_path(path: str) -> Dict[str, str]:
"""Parses a evaluation path into its component segments."""
m = re.match(
r"^projects/(?P<project>.+?)/datasets/(?P<dataset>.+?)/evaluations/(?P<evaluation>.+?)$",
path,
)
return m.groupdict() if m else {}
[docs] @staticmethod
def evaluation_job_path(
project: str,
evaluation_job: str,
) -> str:
"""Returns a fully-qualified evaluation_job string."""
return "projects/{project}/evaluationJobs/{evaluation_job}".format(
project=project,
evaluation_job=evaluation_job,
)
[docs] @staticmethod
def parse_evaluation_job_path(path: str) -> Dict[str, str]:
"""Parses a evaluation_job path into its component segments."""
m = re.match(
r"^projects/(?P<project>.+?)/evaluationJobs/(?P<evaluation_job>.+?)$", path
)
return m.groupdict() if m else {}
[docs] @staticmethod
def example_path(
project: str,
dataset: str,
annotated_dataset: str,
example: str,
) -> str:
"""Returns a fully-qualified example string."""
return "projects/{project}/datasets/{dataset}/annotatedDatasets/{annotated_dataset}/examples/{example}".format(
project=project,
dataset=dataset,
annotated_dataset=annotated_dataset,
example=example,
)
[docs] @staticmethod
def parse_example_path(path: str) -> Dict[str, str]:
"""Parses a example path into its component segments."""
m = re.match(
r"^projects/(?P<project>.+?)/datasets/(?P<dataset>.+?)/annotatedDatasets/(?P<annotated_dataset>.+?)/examples/(?P<example>.+?)$",
path,
)
return m.groupdict() if m else {}
[docs] @staticmethod
def instruction_path(
project: str,
instruction: str,
) -> str:
"""Returns a fully-qualified instruction string."""
return "projects/{project}/instructions/{instruction}".format(
project=project,
instruction=instruction,
)
[docs] @staticmethod
def parse_instruction_path(path: str) -> Dict[str, str]:
"""Parses a instruction path into its component segments."""
m = re.match(
r"^projects/(?P<project>.+?)/instructions/(?P<instruction>.+?)$", 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 = DataLabelingServiceClient._DEFAULT_UNIVERSE
if universe_domain != _default_universe:
raise MutualTLSChannelError(
f"mTLS is not supported in any universe other than {_default_universe}."
)
api_endpoint = DataLabelingServiceClient.DEFAULT_MTLS_ENDPOINT
else:
api_endpoint = DataLabelingServiceClient._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 = DataLabelingServiceClient._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,
DataLabelingServiceTransport,
Callable[..., DataLabelingServiceTransport],
]
] = None,
client_options: Optional[Union[client_options_lib.ClientOptions, dict]] = None,
client_info: gapic_v1.client_info.ClientInfo = DEFAULT_CLIENT_INFO,
) -> None:
"""Instantiates the data labeling service 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,DataLabelingServiceTransport,Callable[..., DataLabelingServiceTransport]]]):
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 DataLabelingServiceTransport 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,
) = DataLabelingServiceClient._read_environment_variables()
self._client_cert_source = DataLabelingServiceClient._get_client_cert_source(
self._client_options.client_cert_source, self._use_client_cert
)
self._universe_domain = DataLabelingServiceClient._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, DataLabelingServiceTransport)
if transport_provided:
# transport is a DataLabelingServiceTransport 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(DataLabelingServiceTransport, transport)
self._api_endpoint = self._transport.host
self._api_endpoint = (
self._api_endpoint
or DataLabelingServiceClient._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[DataLabelingServiceTransport],
Callable[..., DataLabelingServiceTransport],
] = (
DataLabelingServiceClient.get_transport_class(transport)
if isinstance(transport, str) or transport is None
else cast(Callable[..., DataLabelingServiceTransport], 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[data_labeling_service.CreateDatasetRequest, dict]
] = None,
*,
parent: Optional[str] = None,
dataset: Optional[gcd_dataset.Dataset] = None,
retry: OptionalRetry = gapic_v1.method.DEFAULT,
timeout: Union[float, object] = gapic_v1.method.DEFAULT,
metadata: Sequence[Tuple[str, str]] = (),
) -> gcd_dataset.Dataset:
r"""Creates dataset. If success return a Dataset
resource.
.. 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 datalabeling_v1beta1
def sample_create_dataset():
# Create a client
client = datalabeling_v1beta1.DataLabelingServiceClient()
# Initialize request argument(s)
request = datalabeling_v1beta1.CreateDatasetRequest(
parent="parent_value",
)
# Make the request
response = client.create_dataset(request=request)
# Handle the response
print(response)
Args:
request (Union[google.cloud.datalabeling_v1beta1.types.CreateDatasetRequest, dict]):
The request object. Request message for CreateDataset.
parent (str):
Required. Dataset resource parent, format:
projects/{project_id}
This corresponds to the ``parent`` field
on the ``request`` instance; if ``request`` is provided, this
should not be set.
dataset (google.cloud.datalabeling_v1beta1.types.Dataset):
Required. The dataset to be created.
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.datalabeling_v1beta1.types.Dataset:
Dataset is the resource to hold your
data. You can request multiple labeling
tasks for a dataset while each one will
generate an AnnotatedDataset.
"""
# 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, data_labeling_service.CreateDatasetRequest):
request = data_labeling_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[data_labeling_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 dataset by resource name.
.. 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 datalabeling_v1beta1
def sample_get_dataset():
# Create a client
client = datalabeling_v1beta1.DataLabelingServiceClient()
# Initialize request argument(s)
request = datalabeling_v1beta1.GetDatasetRequest(
name="name_value",
)
# Make the request
response = client.get_dataset(request=request)
# Handle the response
print(response)
Args:
request (Union[google.cloud.datalabeling_v1beta1.types.GetDatasetRequest, dict]):
The request object. Request message for GetDataSet.
name (str):
Required. Dataset resource name, format:
projects/{project_id}/datasets/{dataset_id}
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.datalabeling_v1beta1.types.Dataset:
Dataset is the resource to hold your
data. You can request multiple labeling
tasks for a dataset while each one will
generate an AnnotatedDataset.
"""
# 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, data_labeling_service.GetDatasetRequest):
request = data_labeling_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[data_labeling_service.ListDatasetsRequest, dict]
] = None,
*,
parent: Optional[str] = None,
filter: Optional[str] = None,
retry: OptionalRetry = gapic_v1.method.DEFAULT,
timeout: Union[float, object] = gapic_v1.method.DEFAULT,
metadata: Sequence[Tuple[str, str]] = (),
) -> pagers.ListDatasetsPager:
r"""Lists datasets under a project. Pagination is
supported.
.. 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 datalabeling_v1beta1
def sample_list_datasets():
# Create a client
client = datalabeling_v1beta1.DataLabelingServiceClient()
# Initialize request argument(s)
request = datalabeling_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.datalabeling_v1beta1.types.ListDatasetsRequest, dict]):
The request object. Request message for ListDataset.
parent (str):
Required. Dataset resource parent, format:
projects/{project_id}
This corresponds to the ``parent`` field
on the ``request`` instance; if ``request`` is provided, this
should not be set.
filter (str):
Optional. Filter on dataset is not
supported at this moment.
This corresponds to the ``filter`` field
on the ``request`` instance; if ``request`` is provided, this
should not be set.
retry (google.api_core.retry.Retry): Designation of what errors, if any,
should be retried.
timeout (float): The timeout for this request.
metadata (Sequence[Tuple[str, str]]): Strings which should be
sent along with the request as metadata.
Returns:
google.cloud.datalabeling_v1beta1.services.data_labeling_service.pagers.ListDatasetsPager:
Results of listing datasets within a
project.
Iterating over this object will yield
results and resolve additional pages
automatically.
"""
# Create or coerce a protobuf request object.
# - Quick check: If we got a request object, we should *not* have
# gotten any keyword arguments that map to the request.
has_flattened_params = any([parent, filter])
if request is not None and has_flattened_params:
raise ValueError(
"If the `request` argument is set, then none of "
"the individual field arguments should be set."
)
# - Use the request object if provided (there's no risk of modifying the input as
# there are no flattened fields), or create one.
if not isinstance(request, data_labeling_service.ListDatasetsRequest):
request = data_labeling_service.ListDatasetsRequest(request)
# If we have keyword arguments corresponding to fields on the
# request, apply these.
if parent is not None:
request.parent = parent
if filter is not None:
request.filter = filter
# Wrap the RPC method; this adds retry and timeout information,
# and friendly error handling.
rpc = self._transport._wrapped_methods[self._transport.list_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 delete_dataset(
self,
request: Optional[
Union[data_labeling_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]] = (),
) -> None:
r"""Deletes a dataset by resource name.
.. 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 datalabeling_v1beta1
def sample_delete_dataset():
# Create a client
client = datalabeling_v1beta1.DataLabelingServiceClient()
# Initialize request argument(s)
request = datalabeling_v1beta1.DeleteDatasetRequest(
name="name_value",
)
# Make the request
client.delete_dataset(request=request)
Args:
request (Union[google.cloud.datalabeling_v1beta1.types.DeleteDatasetRequest, dict]):
The request object. Request message for DeleteDataset.
name (str):
Required. Dataset resource name, format:
projects/{project_id}/datasets/{dataset_id}
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.
"""
# 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, data_labeling_service.DeleteDatasetRequest):
request = data_labeling_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.
rpc(
request,
retry=retry,
timeout=timeout,
metadata=metadata,
)
[docs] def import_data(
self,
request: Optional[Union[data_labeling_service.ImportDataRequest, dict]] = None,
*,
name: Optional[str] = None,
input_config: Optional[dataset.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 dataset based on source locations
defined in request. It can be called multiple times for
the same dataset. Each dataset can only have one long
running operation running on it. For example, no
labeling task (also long running operation) can be
started while importing is still ongoing. Vice versa.
.. 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 datalabeling_v1beta1
def sample_import_data():
# Create a client
client = datalabeling_v1beta1.DataLabelingServiceClient()
# Initialize request argument(s)
request = datalabeling_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.datalabeling_v1beta1.types.ImportDataRequest, dict]):
The request object. Request message for ImportData API.
name (str):
Required. Dataset resource name, format:
projects/{project_id}/datasets/{dataset_id}
This corresponds to the ``name`` field
on the ``request`` instance; if ``request`` is provided, this
should not be set.
input_config (google.cloud.datalabeling_v1beta1.types.InputConfig):
Required. Specify the input source of
the data.
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.cloud.datalabeling_v1beta1.types.ImportDataOperationResponse`
Response used for ImportData longrunning operation.
"""
# 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, data_labeling_service.ImportDataRequest):
request = data_labeling_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,
operations.ImportDataOperationResponse,
metadata_type=operations.ImportDataOperationMetadata,
)
# Done; return the response.
return response
[docs] def export_data(
self,
request: Optional[Union[data_labeling_service.ExportDataRequest, dict]] = None,
*,
name: Optional[str] = None,
annotated_dataset: Optional[str] = None,
filter: Optional[str] = None,
output_config: Optional[dataset.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 data and annotations from 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 datalabeling_v1beta1
def sample_export_data():
# Create a client
client = datalabeling_v1beta1.DataLabelingServiceClient()
# Initialize request argument(s)
request = datalabeling_v1beta1.ExportDataRequest(
name="name_value",
annotated_dataset="annotated_dataset_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.datalabeling_v1beta1.types.ExportDataRequest, dict]):
The request object. Request message for ExportData API.
name (str):
Required. Dataset resource name, format:
projects/{project_id}/datasets/{dataset_id}
This corresponds to the ``name`` field
on the ``request`` instance; if ``request`` is provided, this
should not be set.
annotated_dataset (str):
Required. Annotated dataset resource name. DataItem in
Dataset and their annotations in specified annotated
dataset will be exported. It's in format of
projects/{project_id}/datasets/{dataset_id}/annotatedDatasets/
{annotated_dataset_id}
This corresponds to the ``annotated_dataset`` field
on the ``request`` instance; if ``request`` is provided, this
should not be set.
filter (str):
Optional. Filter is not supported at
this moment.
This corresponds to the ``filter`` field
on the ``request`` instance; if ``request`` is provided, this
should not be set.
output_config (google.cloud.datalabeling_v1beta1.types.OutputConfig):
Required. Specify the output
destination.
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.cloud.datalabeling_v1beta1.types.ExportDataOperationResponse`
Response used for ExportDataset longrunning operation.
"""
# 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, annotated_dataset, filter, 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, data_labeling_service.ExportDataRequest):
request = data_labeling_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 annotated_dataset is not None:
request.annotated_dataset = annotated_dataset
if filter is not None:
request.filter = filter
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,
operations.ExportDataOperationResponse,
metadata_type=operations.ExportDataOperationMetadata,
)
# Done; return the response.
return response
[docs] def get_data_item(
self,
request: Optional[Union[data_labeling_service.GetDataItemRequest, 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.DataItem:
r"""Gets a data item in a dataset by resource name. This
API can be called after data are imported into 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 datalabeling_v1beta1
def sample_get_data_item():
# Create a client
client = datalabeling_v1beta1.DataLabelingServiceClient()
# Initialize request argument(s)
request = datalabeling_v1beta1.GetDataItemRequest(
name="name_value",
)
# Make the request
response = client.get_data_item(request=request)
# Handle the response
print(response)
Args:
request (Union[google.cloud.datalabeling_v1beta1.types.GetDataItemRequest, dict]):
The request object. Request message for GetDataItem.
name (str):
Required. The name of the data item to get, format:
projects/{project_id}/datasets/{dataset_id}/dataItems/{data_item_id}
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.datalabeling_v1beta1.types.DataItem:
DataItem is a piece of data, without
annotation. For example, an image.
"""
# 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, data_labeling_service.GetDataItemRequest):
request = data_labeling_service.GetDataItemRequest(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_data_item]
# 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_data_items(
self,
request: Optional[
Union[data_labeling_service.ListDataItemsRequest, dict]
] = None,
*,
parent: Optional[str] = None,
filter: Optional[str] = None,
retry: OptionalRetry = gapic_v1.method.DEFAULT,
timeout: Union[float, object] = gapic_v1.method.DEFAULT,
metadata: Sequence[Tuple[str, str]] = (),
) -> pagers.ListDataItemsPager:
r"""Lists data items in a dataset. This API can be called
after data are imported into dataset. Pagination is
supported.
.. 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 datalabeling_v1beta1
def sample_list_data_items():
# Create a client
client = datalabeling_v1beta1.DataLabelingServiceClient()
# Initialize request argument(s)
request = datalabeling_v1beta1.ListDataItemsRequest(
parent="parent_value",
)
# Make the request
page_result = client.list_data_items(request=request)
# Handle the response
for response in page_result:
print(response)
Args:
request (Union[google.cloud.datalabeling_v1beta1.types.ListDataItemsRequest, dict]):
The request object. Request message for ListDataItems.
parent (str):
Required. Name of the dataset to list data items,
format: projects/{project_id}/datasets/{dataset_id}
This corresponds to the ``parent`` field
on the ``request`` instance; if ``request`` is provided, this
should not be set.
filter (str):
Optional. Filter is not supported at
this moment.
This corresponds to the ``filter`` field
on the ``request`` instance; if ``request`` is provided, this
should not be set.
retry (google.api_core.retry.Retry): Designation of what errors, if any,
should be retried.
timeout (float): The timeout for this request.
metadata (Sequence[Tuple[str, str]]): Strings which should be
sent along with the request as metadata.
Returns:
google.cloud.datalabeling_v1beta1.services.data_labeling_service.pagers.ListDataItemsPager:
Results of listing data items in a
dataset.
Iterating over this object will yield
results and resolve additional pages
automatically.
"""
# Create or coerce a protobuf request object.
# - Quick check: If we got a request object, we should *not* have
# gotten any keyword arguments that map to the request.
has_flattened_params = any([parent, filter])
if request is not None and has_flattened_params:
raise ValueError(
"If the `request` argument is set, then none of "
"the individual field arguments should be set."
)
# - Use the request object if provided (there's no risk of modifying the input as
# there are no flattened fields), or create one.
if not isinstance(request, data_labeling_service.ListDataItemsRequest):
request = data_labeling_service.ListDataItemsRequest(request)
# If we have keyword arguments corresponding to fields on the
# request, apply these.
if parent is not None:
request.parent = parent
if filter is not None:
request.filter = filter
# Wrap the RPC method; this adds retry and timeout information,
# and friendly error handling.
rpc = self._transport._wrapped_methods[self._transport.list_data_items]
# 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.ListDataItemsPager(
method=rpc,
request=request,
response=response,
retry=retry,
timeout=timeout,
metadata=metadata,
)
# Done; return the response.
return response
[docs] def get_annotated_dataset(
self,
request: Optional[
Union[data_labeling_service.GetAnnotatedDatasetRequest, 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.AnnotatedDataset:
r"""Gets an annotated dataset by resource name.
.. 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 datalabeling_v1beta1
def sample_get_annotated_dataset():
# Create a client
client = datalabeling_v1beta1.DataLabelingServiceClient()
# Initialize request argument(s)
request = datalabeling_v1beta1.GetAnnotatedDatasetRequest(
name="name_value",
)
# Make the request
response = client.get_annotated_dataset(request=request)
# Handle the response
print(response)
Args:
request (Union[google.cloud.datalabeling_v1beta1.types.GetAnnotatedDatasetRequest, dict]):
The request object. Request message for
GetAnnotatedDataset.
name (str):
Required. Name of the annotated dataset to get, format:
projects/{project_id}/datasets/{dataset_id}/annotatedDatasets/
{annotated_dataset_id}
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.datalabeling_v1beta1.types.AnnotatedDataset:
AnnotatedDataset is a set holding
annotations for data in a Dataset. Each
labeling task will generate an
AnnotatedDataset under the Dataset that
the task is requested for.
"""
# 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, data_labeling_service.GetAnnotatedDatasetRequest):
request = data_labeling_service.GetAnnotatedDatasetRequest(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_annotated_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_annotated_datasets(
self,
request: Optional[
Union[data_labeling_service.ListAnnotatedDatasetsRequest, dict]
] = None,
*,
parent: Optional[str] = None,
filter: Optional[str] = None,
retry: OptionalRetry = gapic_v1.method.DEFAULT,
timeout: Union[float, object] = gapic_v1.method.DEFAULT,
metadata: Sequence[Tuple[str, str]] = (),
) -> pagers.ListAnnotatedDatasetsPager:
r"""Lists annotated datasets for a dataset. Pagination is
supported.
.. 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 datalabeling_v1beta1
def sample_list_annotated_datasets():
# Create a client
client = datalabeling_v1beta1.DataLabelingServiceClient()
# Initialize request argument(s)
request = datalabeling_v1beta1.ListAnnotatedDatasetsRequest(
parent="parent_value",
)
# Make the request
page_result = client.list_annotated_datasets(request=request)
# Handle the response
for response in page_result:
print(response)
Args:
request (Union[google.cloud.datalabeling_v1beta1.types.ListAnnotatedDatasetsRequest, dict]):
The request object. Request message for
ListAnnotatedDatasets.
parent (str):
Required. Name of the dataset to list annotated
datasets, format:
projects/{project_id}/datasets/{dataset_id}
This corresponds to the ``parent`` field
on the ``request`` instance; if ``request`` is provided, this
should not be set.
filter (str):
Optional. Filter is not supported at
this moment.
This corresponds to the ``filter`` field
on the ``request`` instance; if ``request`` is provided, this
should not be set.
retry (google.api_core.retry.Retry): Designation of what errors, if any,
should be retried.
timeout (float): The timeout for this request.
metadata (Sequence[Tuple[str, str]]): Strings which should be
sent along with the request as metadata.
Returns:
google.cloud.datalabeling_v1beta1.services.data_labeling_service.pagers.ListAnnotatedDatasetsPager:
Results of listing annotated datasets
for a dataset.
Iterating over this object will yield
results and resolve additional pages
automatically.
"""
# Create or coerce a protobuf request object.
# - Quick check: If we got a request object, we should *not* have
# gotten any keyword arguments that map to the request.
has_flattened_params = any([parent, filter])
if request is not None and has_flattened_params:
raise ValueError(
"If the `request` argument is set, then none of "
"the individual field arguments should be set."
)
# - Use the request object if provided (there's no risk of modifying the input as
# there are no flattened fields), or create one.
if not isinstance(request, data_labeling_service.ListAnnotatedDatasetsRequest):
request = data_labeling_service.ListAnnotatedDatasetsRequest(request)
# If we have keyword arguments corresponding to fields on the
# request, apply these.
if parent is not None:
request.parent = parent
if filter is not None:
request.filter = filter
# Wrap the RPC method; this adds retry and timeout information,
# and friendly error handling.
rpc = self._transport._wrapped_methods[self._transport.list_annotated_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.ListAnnotatedDatasetsPager(
method=rpc,
request=request,
response=response,
retry=retry,
timeout=timeout,
metadata=metadata,
)
# Done; return the response.
return response
[docs] def delete_annotated_dataset(
self,
request: Optional[
Union[data_labeling_service.DeleteAnnotatedDatasetRequest, dict]
] = None,
*,
retry: OptionalRetry = gapic_v1.method.DEFAULT,
timeout: Union[float, object] = gapic_v1.method.DEFAULT,
metadata: Sequence[Tuple[str, str]] = (),
) -> None:
r"""Deletes an annotated dataset by resource name.
.. 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 datalabeling_v1beta1
def sample_delete_annotated_dataset():
# Create a client
client = datalabeling_v1beta1.DataLabelingServiceClient()
# Initialize request argument(s)
request = datalabeling_v1beta1.DeleteAnnotatedDatasetRequest(
name="name_value",
)
# Make the request
client.delete_annotated_dataset(request=request)
Args:
request (Union[google.cloud.datalabeling_v1beta1.types.DeleteAnnotatedDatasetRequest, dict]):
The request object. Request message for
DeleteAnnotatedDataset.
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.
"""
# Create or coerce a protobuf request object.
# - 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, data_labeling_service.DeleteAnnotatedDatasetRequest):
request = data_labeling_service.DeleteAnnotatedDatasetRequest(request)
# Wrap the RPC method; this adds retry and timeout information,
# and friendly error handling.
rpc = self._transport._wrapped_methods[self._transport.delete_annotated_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.
rpc(
request,
retry=retry,
timeout=timeout,
metadata=metadata,
)
[docs] def label_image(
self,
request: Optional[Union[data_labeling_service.LabelImageRequest, dict]] = None,
*,
parent: Optional[str] = None,
basic_config: Optional[human_annotation_config.HumanAnnotationConfig] = None,
feature: Optional[data_labeling_service.LabelImageRequest.Feature] = None,
retry: OptionalRetry = gapic_v1.method.DEFAULT,
timeout: Union[float, object] = gapic_v1.method.DEFAULT,
metadata: Sequence[Tuple[str, str]] = (),
) -> operation.Operation:
r"""Starts a labeling task for image. The type of image
labeling task is configured by feature in the request.
.. 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 datalabeling_v1beta1
def sample_label_image():
# Create a client
client = datalabeling_v1beta1.DataLabelingServiceClient()
# Initialize request argument(s)
image_classification_config = datalabeling_v1beta1.ImageClassificationConfig()
image_classification_config.annotation_spec_set = "annotation_spec_set_value"
basic_config = datalabeling_v1beta1.HumanAnnotationConfig()
basic_config.instruction = "instruction_value"
basic_config.annotated_dataset_display_name = "annotated_dataset_display_name_value"
request = datalabeling_v1beta1.LabelImageRequest(
image_classification_config=image_classification_config,
parent="parent_value",
basic_config=basic_config,
feature="SEGMENTATION",
)
# Make the request
operation = client.label_image(request=request)
print("Waiting for operation to complete...")
response = operation.result()
# Handle the response
print(response)
Args:
request (Union[google.cloud.datalabeling_v1beta1.types.LabelImageRequest, dict]):
The request object. Request message for starting an image
labeling task.
parent (str):
Required. Name of the dataset to request labeling task,
format: projects/{project_id}/datasets/{dataset_id}
This corresponds to the ``parent`` field
on the ``request`` instance; if ``request`` is provided, this
should not be set.
basic_config (google.cloud.datalabeling_v1beta1.types.HumanAnnotationConfig):
Required. Basic human annotation
config.
This corresponds to the ``basic_config`` field
on the ``request`` instance; if ``request`` is provided, this
should not be set.
feature (google.cloud.datalabeling_v1beta1.types.LabelImageRequest.Feature):
Required. The type of image labeling
task.
This corresponds to the ``feature`` 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.datalabeling_v1beta1.types.AnnotatedDataset` AnnotatedDataset is a set holding annotations for data in a Dataset. Each
labeling task will generate an AnnotatedDataset under
the Dataset that the task is requested for.
"""
# 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, basic_config, feature])
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, data_labeling_service.LabelImageRequest):
request = data_labeling_service.LabelImageRequest(request)
# If we have keyword arguments corresponding to fields on the
# request, apply these.
if parent is not None:
request.parent = parent
if basic_config is not None:
request.basic_config = basic_config
if feature is not None:
request.feature = feature
# Wrap the RPC method; this adds retry and timeout information,
# and friendly error handling.
rpc = self._transport._wrapped_methods[self._transport.label_image]
# 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,
dataset.AnnotatedDataset,
metadata_type=operations.LabelOperationMetadata,
)
# Done; return the response.
return response
[docs] def label_video(
self,
request: Optional[Union[data_labeling_service.LabelVideoRequest, dict]] = None,
*,
parent: Optional[str] = None,
basic_config: Optional[human_annotation_config.HumanAnnotationConfig] = None,
feature: Optional[data_labeling_service.LabelVideoRequest.Feature] = None,
retry: OptionalRetry = gapic_v1.method.DEFAULT,
timeout: Union[float, object] = gapic_v1.method.DEFAULT,
metadata: Sequence[Tuple[str, str]] = (),
) -> operation.Operation:
r"""Starts a labeling task for video. The type of video
labeling task is configured by feature in the request.
.. 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 datalabeling_v1beta1
def sample_label_video():
# Create a client
client = datalabeling_v1beta1.DataLabelingServiceClient()
# Initialize request argument(s)
video_classification_config = datalabeling_v1beta1.VideoClassificationConfig()
video_classification_config.annotation_spec_set_configs.annotation_spec_set = "annotation_spec_set_value"
basic_config = datalabeling_v1beta1.HumanAnnotationConfig()
basic_config.instruction = "instruction_value"
basic_config.annotated_dataset_display_name = "annotated_dataset_display_name_value"
request = datalabeling_v1beta1.LabelVideoRequest(
video_classification_config=video_classification_config,
parent="parent_value",
basic_config=basic_config,
feature="EVENT",
)
# Make the request
operation = client.label_video(request=request)
print("Waiting for operation to complete...")
response = operation.result()
# Handle the response
print(response)
Args:
request (Union[google.cloud.datalabeling_v1beta1.types.LabelVideoRequest, dict]):
The request object. Request message for LabelVideo.
parent (str):
Required. Name of the dataset to request labeling task,
format: projects/{project_id}/datasets/{dataset_id}
This corresponds to the ``parent`` field
on the ``request`` instance; if ``request`` is provided, this
should not be set.
basic_config (google.cloud.datalabeling_v1beta1.types.HumanAnnotationConfig):
Required. Basic human annotation
config.
This corresponds to the ``basic_config`` field
on the ``request`` instance; if ``request`` is provided, this
should not be set.
feature (google.cloud.datalabeling_v1beta1.types.LabelVideoRequest.Feature):
Required. The type of video labeling
task.
This corresponds to the ``feature`` 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.datalabeling_v1beta1.types.AnnotatedDataset` AnnotatedDataset is a set holding annotations for data in a Dataset. Each
labeling task will generate an AnnotatedDataset under
the Dataset that the task is requested for.
"""
# 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, basic_config, feature])
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, data_labeling_service.LabelVideoRequest):
request = data_labeling_service.LabelVideoRequest(request)
# If we have keyword arguments corresponding to fields on the
# request, apply these.
if parent is not None:
request.parent = parent
if basic_config is not None:
request.basic_config = basic_config
if feature is not None:
request.feature = feature
# Wrap the RPC method; this adds retry and timeout information,
# and friendly error handling.
rpc = self._transport._wrapped_methods[self._transport.label_video]
# 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,
dataset.AnnotatedDataset,
metadata_type=operations.LabelOperationMetadata,
)
# Done; return the response.
return response
[docs] def label_text(
self,
request: Optional[Union[data_labeling_service.LabelTextRequest, dict]] = None,
*,
parent: Optional[str] = None,
basic_config: Optional[human_annotation_config.HumanAnnotationConfig] = None,
feature: Optional[data_labeling_service.LabelTextRequest.Feature] = None,
retry: OptionalRetry = gapic_v1.method.DEFAULT,
timeout: Union[float, object] = gapic_v1.method.DEFAULT,
metadata: Sequence[Tuple[str, str]] = (),
) -> operation.Operation:
r"""Starts a labeling task for text. The type of text
labeling task is configured by feature in the request.
.. 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 datalabeling_v1beta1
def sample_label_text():
# Create a client
client = datalabeling_v1beta1.DataLabelingServiceClient()
# Initialize request argument(s)
text_classification_config = datalabeling_v1beta1.TextClassificationConfig()
text_classification_config.annotation_spec_set = "annotation_spec_set_value"
basic_config = datalabeling_v1beta1.HumanAnnotationConfig()
basic_config.instruction = "instruction_value"
basic_config.annotated_dataset_display_name = "annotated_dataset_display_name_value"
request = datalabeling_v1beta1.LabelTextRequest(
text_classification_config=text_classification_config,
parent="parent_value",
basic_config=basic_config,
feature="TEXT_ENTITY_EXTRACTION",
)
# Make the request
operation = client.label_text(request=request)
print("Waiting for operation to complete...")
response = operation.result()
# Handle the response
print(response)
Args:
request (Union[google.cloud.datalabeling_v1beta1.types.LabelTextRequest, dict]):
The request object. Request message for LabelText.
parent (str):
Required. Name of the data set to request labeling task,
format: projects/{project_id}/datasets/{dataset_id}
This corresponds to the ``parent`` field
on the ``request`` instance; if ``request`` is provided, this
should not be set.
basic_config (google.cloud.datalabeling_v1beta1.types.HumanAnnotationConfig):
Required. Basic human annotation
config.
This corresponds to the ``basic_config`` field
on the ``request`` instance; if ``request`` is provided, this
should not be set.
feature (google.cloud.datalabeling_v1beta1.types.LabelTextRequest.Feature):
Required. The type of text labeling
task.
This corresponds to the ``feature`` 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.datalabeling_v1beta1.types.AnnotatedDataset` AnnotatedDataset is a set holding annotations for data in a Dataset. Each
labeling task will generate an AnnotatedDataset under
the Dataset that the task is requested for.
"""
# 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, basic_config, feature])
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, data_labeling_service.LabelTextRequest):
request = data_labeling_service.LabelTextRequest(request)
# If we have keyword arguments corresponding to fields on the
# request, apply these.
if parent is not None:
request.parent = parent
if basic_config is not None:
request.basic_config = basic_config
if feature is not None:
request.feature = feature
# Wrap the RPC method; this adds retry and timeout information,
# and friendly error handling.
rpc = self._transport._wrapped_methods[self._transport.label_text]
# 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,
dataset.AnnotatedDataset,
metadata_type=operations.LabelOperationMetadata,
)
# Done; return the response.
return response
[docs] def get_example(
self,
request: Optional[Union[data_labeling_service.GetExampleRequest, dict]] = None,
*,
name: Optional[str] = None,
filter: Optional[str] = None,
retry: OptionalRetry = gapic_v1.method.DEFAULT,
timeout: Union[float, object] = gapic_v1.method.DEFAULT,
metadata: Sequence[Tuple[str, str]] = (),
) -> dataset.Example:
r"""Gets an example by resource name, including both data
and annotation.
.. 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 datalabeling_v1beta1
def sample_get_example():
# Create a client
client = datalabeling_v1beta1.DataLabelingServiceClient()
# Initialize request argument(s)
request = datalabeling_v1beta1.GetExampleRequest(
name="name_value",
)
# Make the request
response = client.get_example(request=request)
# Handle the response
print(response)
Args:
request (Union[google.cloud.datalabeling_v1beta1.types.GetExampleRequest, dict]):
The request object. Request message for GetExample
name (str):
Required. Name of example, format:
projects/{project_id}/datasets/{dataset_id}/annotatedDatasets/
{annotated_dataset_id}/examples/{example_id}
This corresponds to the ``name`` field
on the ``request`` instance; if ``request`` is provided, this
should not be set.
filter (str):
Optional. An expression for filtering Examples. Filter
by annotation_spec.display_name is supported. Format
"annotation_spec.display_name = {display_name}"
This corresponds to the ``filter`` field
on the ``request`` instance; if ``request`` is provided, this
should not be set.
retry (google.api_core.retry.Retry): Designation of what errors, if any,
should be retried.
timeout (float): The timeout for this request.
metadata (Sequence[Tuple[str, str]]): Strings which should be
sent along with the request as metadata.
Returns:
google.cloud.datalabeling_v1beta1.types.Example:
An Example is a piece of data and its
annotation. For example, an image with
label "house".
"""
# 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, filter])
if request is not None and has_flattened_params:
raise ValueError(
"If the `request` argument is set, then none of "
"the individual field arguments should be set."
)
# - Use the request object if provided (there's no risk of modifying the input as
# there are no flattened fields), or create one.
if not isinstance(request, data_labeling_service.GetExampleRequest):
request = data_labeling_service.GetExampleRequest(request)
# If we have keyword arguments corresponding to fields on the
# request, apply these.
if name is not None:
request.name = name
if filter is not None:
request.filter = filter
# Wrap the RPC method; this adds retry and timeout information,
# and friendly error handling.
rpc = self._transport._wrapped_methods[self._transport.get_example]
# 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_examples(
self,
request: Optional[
Union[data_labeling_service.ListExamplesRequest, dict]
] = None,
*,
parent: Optional[str] = None,
filter: Optional[str] = None,
retry: OptionalRetry = gapic_v1.method.DEFAULT,
timeout: Union[float, object] = gapic_v1.method.DEFAULT,
metadata: Sequence[Tuple[str, str]] = (),
) -> pagers.ListExamplesPager:
r"""Lists examples in an annotated dataset. Pagination is
supported.
.. 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 datalabeling_v1beta1
def sample_list_examples():
# Create a client
client = datalabeling_v1beta1.DataLabelingServiceClient()
# Initialize request argument(s)
request = datalabeling_v1beta1.ListExamplesRequest(
parent="parent_value",
)
# Make the request
page_result = client.list_examples(request=request)
# Handle the response
for response in page_result:
print(response)
Args:
request (Union[google.cloud.datalabeling_v1beta1.types.ListExamplesRequest, dict]):
The request object. Request message for ListExamples.
parent (str):
Required. Example resource parent.
This corresponds to the ``parent`` field
on the ``request`` instance; if ``request`` is provided, this
should not be set.
filter (str):
Optional. An expression for filtering Examples. For
annotated datasets that have annotation spec set, filter
by annotation_spec.display_name is supported. Format
"annotation_spec.display_name = {display_name}"
This corresponds to the ``filter`` field
on the ``request`` instance; if ``request`` is provided, this
should not be set.
retry (google.api_core.retry.Retry): Designation of what errors, if any,
should be retried.
timeout (float): The timeout for this request.
metadata (Sequence[Tuple[str, str]]): Strings which should be
sent along with the request as metadata.
Returns:
google.cloud.datalabeling_v1beta1.services.data_labeling_service.pagers.ListExamplesPager:
Results of listing Examples in and
annotated dataset.
Iterating over this object will yield
results and resolve additional pages
automatically.
"""
# Create or coerce a protobuf request object.
# - Quick check: If we got a request object, we should *not* have
# gotten any keyword arguments that map to the request.
has_flattened_params = any([parent, filter])
if request is not None and has_flattened_params:
raise ValueError(
"If the `request` argument is set, then none of "
"the individual field arguments should be set."
)
# - Use the request object if provided (there's no risk of modifying the input as
# there are no flattened fields), or create one.
if not isinstance(request, data_labeling_service.ListExamplesRequest):
request = data_labeling_service.ListExamplesRequest(request)
# If we have keyword arguments corresponding to fields on the
# request, apply these.
if parent is not None:
request.parent = parent
if filter is not None:
request.filter = filter
# Wrap the RPC method; this adds retry and timeout information,
# and friendly error handling.
rpc = self._transport._wrapped_methods[self._transport.list_examples]
# 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.ListExamplesPager(
method=rpc,
request=request,
response=response,
retry=retry,
timeout=timeout,
metadata=metadata,
)
# Done; return the response.
return response
[docs] def create_annotation_spec_set(
self,
request: Optional[
Union[data_labeling_service.CreateAnnotationSpecSetRequest, dict]
] = None,
*,
parent: Optional[str] = None,
annotation_spec_set: Optional[gcd_annotation_spec_set.AnnotationSpecSet] = None,
retry: OptionalRetry = gapic_v1.method.DEFAULT,
timeout: Union[float, object] = gapic_v1.method.DEFAULT,
metadata: Sequence[Tuple[str, str]] = (),
) -> gcd_annotation_spec_set.AnnotationSpecSet:
r"""Creates an annotation spec set by providing a set of
labels.
.. 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 datalabeling_v1beta1
def sample_create_annotation_spec_set():
# Create a client
client = datalabeling_v1beta1.DataLabelingServiceClient()
# Initialize request argument(s)
request = datalabeling_v1beta1.CreateAnnotationSpecSetRequest(
parent="parent_value",
)
# Make the request
response = client.create_annotation_spec_set(request=request)
# Handle the response
print(response)
Args:
request (Union[google.cloud.datalabeling_v1beta1.types.CreateAnnotationSpecSetRequest, dict]):
The request object. Request message for
CreateAnnotationSpecSet.
parent (str):
Required. AnnotationSpecSet resource parent, format:
projects/{project_id}
This corresponds to the ``parent`` field
on the ``request`` instance; if ``request`` is provided, this
should not be set.
annotation_spec_set (google.cloud.datalabeling_v1beta1.types.AnnotationSpecSet):
Required. Annotation spec set to create. Annotation
specs must be included. Only one annotation spec will be
accepted for annotation specs with same display_name.
This corresponds to the ``annotation_spec_set`` 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.datalabeling_v1beta1.types.AnnotationSpecSet:
An AnnotationSpecSet is a collection
of label definitions. For example, in
image classification tasks, you define a
set of possible labels for images as an
AnnotationSpecSet. An AnnotationSpecSet
is immutable upon creation.
"""
# 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, annotation_spec_set])
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, data_labeling_service.CreateAnnotationSpecSetRequest
):
request = data_labeling_service.CreateAnnotationSpecSetRequest(request)
# If we have keyword arguments corresponding to fields on the
# request, apply these.
if parent is not None:
request.parent = parent
if annotation_spec_set is not None:
request.annotation_spec_set = annotation_spec_set
# Wrap the RPC method; this adds retry and timeout information,
# and friendly error handling.
rpc = self._transport._wrapped_methods[
self._transport.create_annotation_spec_set
]
# 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_annotation_spec_set(
self,
request: Optional[
Union[data_labeling_service.GetAnnotationSpecSetRequest, 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_set.AnnotationSpecSet:
r"""Gets an annotation spec set by resource name.
.. 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 datalabeling_v1beta1
def sample_get_annotation_spec_set():
# Create a client
client = datalabeling_v1beta1.DataLabelingServiceClient()
# Initialize request argument(s)
request = datalabeling_v1beta1.GetAnnotationSpecSetRequest(
name="name_value",
)
# Make the request
response = client.get_annotation_spec_set(request=request)
# Handle the response
print(response)
Args:
request (Union[google.cloud.datalabeling_v1beta1.types.GetAnnotationSpecSetRequest, dict]):
The request object. Request message for
GetAnnotationSpecSet.
name (str):
Required. AnnotationSpecSet resource name, format:
projects/{project_id}/annotationSpecSets/{annotation_spec_set_id}
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.datalabeling_v1beta1.types.AnnotationSpecSet:
An AnnotationSpecSet is a collection
of label definitions. For example, in
image classification tasks, you define a
set of possible labels for images as an
AnnotationSpecSet. An AnnotationSpecSet
is immutable upon creation.
"""
# 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, data_labeling_service.GetAnnotationSpecSetRequest):
request = data_labeling_service.GetAnnotationSpecSetRequest(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_set]
# 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_annotation_spec_sets(
self,
request: Optional[
Union[data_labeling_service.ListAnnotationSpecSetsRequest, dict]
] = None,
*,
parent: Optional[str] = None,
filter: Optional[str] = None,
retry: OptionalRetry = gapic_v1.method.DEFAULT,
timeout: Union[float, object] = gapic_v1.method.DEFAULT,
metadata: Sequence[Tuple[str, str]] = (),
) -> pagers.ListAnnotationSpecSetsPager:
r"""Lists annotation spec sets for a project. Pagination
is supported.
.. 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 datalabeling_v1beta1
def sample_list_annotation_spec_sets():
# Create a client
client = datalabeling_v1beta1.DataLabelingServiceClient()
# Initialize request argument(s)
request = datalabeling_v1beta1.ListAnnotationSpecSetsRequest(
parent="parent_value",
)
# Make the request
page_result = client.list_annotation_spec_sets(request=request)
# Handle the response
for response in page_result:
print(response)
Args:
request (Union[google.cloud.datalabeling_v1beta1.types.ListAnnotationSpecSetsRequest, dict]):
The request object. Request message for
ListAnnotationSpecSets.
parent (str):
Required. Parent of AnnotationSpecSet resource, format:
projects/{project_id}
This corresponds to the ``parent`` field
on the ``request`` instance; if ``request`` is provided, this
should not be set.
filter (str):
Optional. Filter is not supported at
this moment.
This corresponds to the ``filter`` field
on the ``request`` instance; if ``request`` is provided, this
should not be set.
retry (google.api_core.retry.Retry): Designation of what errors, if any,
should be retried.
timeout (float): The timeout for this request.
metadata (Sequence[Tuple[str, str]]): Strings which should be
sent along with the request as metadata.
Returns:
google.cloud.datalabeling_v1beta1.services.data_labeling_service.pagers.ListAnnotationSpecSetsPager:
Results of listing annotation spec
set under a project.
Iterating over this object will yield
results and resolve additional pages
automatically.
"""
# Create or coerce a protobuf request object.
# - Quick check: If we got a request object, we should *not* have
# gotten any keyword arguments that map to the request.
has_flattened_params = any([parent, filter])
if request is not None and has_flattened_params:
raise ValueError(
"If the `request` argument is set, then none of "
"the individual field arguments should be set."
)
# - Use the request object if provided (there's no risk of modifying the input as
# there are no flattened fields), or create one.
if not isinstance(request, data_labeling_service.ListAnnotationSpecSetsRequest):
request = data_labeling_service.ListAnnotationSpecSetsRequest(request)
# If we have keyword arguments corresponding to fields on the
# request, apply these.
if parent is not None:
request.parent = parent
if filter is not None:
request.filter = filter
# Wrap the RPC method; this adds retry and timeout information,
# and friendly error handling.
rpc = self._transport._wrapped_methods[
self._transport.list_annotation_spec_sets
]
# 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.ListAnnotationSpecSetsPager(
method=rpc,
request=request,
response=response,
retry=retry,
timeout=timeout,
metadata=metadata,
)
# Done; return the response.
return response
[docs] def delete_annotation_spec_set(
self,
request: Optional[
Union[data_labeling_service.DeleteAnnotationSpecSetRequest, 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]] = (),
) -> None:
r"""Deletes an annotation spec set by resource name.
.. 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 datalabeling_v1beta1
def sample_delete_annotation_spec_set():
# Create a client
client = datalabeling_v1beta1.DataLabelingServiceClient()
# Initialize request argument(s)
request = datalabeling_v1beta1.DeleteAnnotationSpecSetRequest(
name="name_value",
)
# Make the request
client.delete_annotation_spec_set(request=request)
Args:
request (Union[google.cloud.datalabeling_v1beta1.types.DeleteAnnotationSpecSetRequest, dict]):
The request object. Request message for
DeleteAnnotationSpecSet.
name (str):
Required. AnnotationSpec resource name, format:
``projects/{project_id}/annotationSpecSets/{annotation_spec_set_id}``.
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.
"""
# 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, data_labeling_service.DeleteAnnotationSpecSetRequest
):
request = data_labeling_service.DeleteAnnotationSpecSetRequest(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_annotation_spec_set
]
# 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.
rpc(
request,
retry=retry,
timeout=timeout,
metadata=metadata,
)
[docs] def create_instruction(
self,
request: Optional[
Union[data_labeling_service.CreateInstructionRequest, dict]
] = None,
*,
parent: Optional[str] = None,
instruction: Optional[gcd_instruction.Instruction] = None,
retry: OptionalRetry = gapic_v1.method.DEFAULT,
timeout: Union[float, object] = gapic_v1.method.DEFAULT,
metadata: Sequence[Tuple[str, str]] = (),
) -> operation.Operation:
r"""Creates an instruction for how data should be
labeled.
.. 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 datalabeling_v1beta1
def sample_create_instruction():
# Create a client
client = datalabeling_v1beta1.DataLabelingServiceClient()
# Initialize request argument(s)
request = datalabeling_v1beta1.CreateInstructionRequest(
parent="parent_value",
)
# Make the request
operation = client.create_instruction(request=request)
print("Waiting for operation to complete...")
response = operation.result()
# Handle the response
print(response)
Args:
request (Union[google.cloud.datalabeling_v1beta1.types.CreateInstructionRequest, dict]):
The request object. Request message for
CreateInstruction.
parent (str):
Required. Instruction resource parent, format:
projects/{project_id}
This corresponds to the ``parent`` field
on the ``request`` instance; if ``request`` is provided, this
should not be set.
instruction (google.cloud.datalabeling_v1beta1.types.Instruction):
Required. Instruction of how to
perform the labeling task.
This corresponds to the ``instruction`` 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.datalabeling_v1beta1.types.Instruction` Instruction of how to perform the labeling task for human operators.
Currently only PDF instruction is supported.
"""
# 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, instruction])
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, data_labeling_service.CreateInstructionRequest):
request = data_labeling_service.CreateInstructionRequest(request)
# If we have keyword arguments corresponding to fields on the
# request, apply these.
if parent is not None:
request.parent = parent
if instruction is not None:
request.instruction = instruction
# Wrap the RPC method; this adds retry and timeout information,
# and friendly error handling.
rpc = self._transport._wrapped_methods[self._transport.create_instruction]
# 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,
gcd_instruction.Instruction,
metadata_type=operations.CreateInstructionMetadata,
)
# Done; return the response.
return response
[docs] def get_instruction(
self,
request: Optional[
Union[data_labeling_service.GetInstructionRequest, 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]] = (),
) -> instruction.Instruction:
r"""Gets an instruction by resource name.
.. 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 datalabeling_v1beta1
def sample_get_instruction():
# Create a client
client = datalabeling_v1beta1.DataLabelingServiceClient()
# Initialize request argument(s)
request = datalabeling_v1beta1.GetInstructionRequest(
name="name_value",
)
# Make the request
response = client.get_instruction(request=request)
# Handle the response
print(response)
Args:
request (Union[google.cloud.datalabeling_v1beta1.types.GetInstructionRequest, dict]):
The request object. Request message for GetInstruction.
name (str):
Required. Instruction resource name, format:
projects/{project_id}/instructions/{instruction_id}
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.datalabeling_v1beta1.types.Instruction:
Instruction of how to perform the
labeling task for human operators.
Currently only PDF instruction is
supported.
"""
# 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, data_labeling_service.GetInstructionRequest):
request = data_labeling_service.GetInstructionRequest(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_instruction]
# 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_instructions(
self,
request: Optional[
Union[data_labeling_service.ListInstructionsRequest, dict]
] = None,
*,
parent: Optional[str] = None,
filter: Optional[str] = None,
retry: OptionalRetry = gapic_v1.method.DEFAULT,
timeout: Union[float, object] = gapic_v1.method.DEFAULT,
metadata: Sequence[Tuple[str, str]] = (),
) -> pagers.ListInstructionsPager:
r"""Lists instructions for a project. Pagination is
supported.
.. 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 datalabeling_v1beta1
def sample_list_instructions():
# Create a client
client = datalabeling_v1beta1.DataLabelingServiceClient()
# Initialize request argument(s)
request = datalabeling_v1beta1.ListInstructionsRequest(
parent="parent_value",
)
# Make the request
page_result = client.list_instructions(request=request)
# Handle the response
for response in page_result:
print(response)
Args:
request (Union[google.cloud.datalabeling_v1beta1.types.ListInstructionsRequest, dict]):
The request object. Request message for ListInstructions.
parent (str):
Required. Instruction resource parent, format:
projects/{project_id}
This corresponds to the ``parent`` field
on the ``request`` instance; if ``request`` is provided, this
should not be set.
filter (str):
Optional. Filter is not supported at
this moment.
This corresponds to the ``filter`` field
on the ``request`` instance; if ``request`` is provided, this
should not be set.
retry (google.api_core.retry.Retry): Designation of what errors, if any,
should be retried.
timeout (float): The timeout for this request.
metadata (Sequence[Tuple[str, str]]): Strings which should be
sent along with the request as metadata.
Returns:
google.cloud.datalabeling_v1beta1.services.data_labeling_service.pagers.ListInstructionsPager:
Results of listing instructions under
a project.
Iterating over this object will yield
results and resolve additional pages
automatically.
"""
# Create or coerce a protobuf request object.
# - Quick check: If we got a request object, we should *not* have
# gotten any keyword arguments that map to the request.
has_flattened_params = any([parent, filter])
if request is not None and has_flattened_params:
raise ValueError(
"If the `request` argument is set, then none of "
"the individual field arguments should be set."
)
# - Use the request object if provided (there's no risk of modifying the input as
# there are no flattened fields), or create one.
if not isinstance(request, data_labeling_service.ListInstructionsRequest):
request = data_labeling_service.ListInstructionsRequest(request)
# If we have keyword arguments corresponding to fields on the
# request, apply these.
if parent is not None:
request.parent = parent
if filter is not None:
request.filter = filter
# Wrap the RPC method; this adds retry and timeout information,
# and friendly error handling.
rpc = self._transport._wrapped_methods[self._transport.list_instructions]
# 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.ListInstructionsPager(
method=rpc,
request=request,
response=response,
retry=retry,
timeout=timeout,
metadata=metadata,
)
# Done; return the response.
return response
[docs] def delete_instruction(
self,
request: Optional[
Union[data_labeling_service.DeleteInstructionRequest, 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]] = (),
) -> None:
r"""Deletes an instruction object by resource name.
.. 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 datalabeling_v1beta1
def sample_delete_instruction():
# Create a client
client = datalabeling_v1beta1.DataLabelingServiceClient()
# Initialize request argument(s)
request = datalabeling_v1beta1.DeleteInstructionRequest(
name="name_value",
)
# Make the request
client.delete_instruction(request=request)
Args:
request (Union[google.cloud.datalabeling_v1beta1.types.DeleteInstructionRequest, dict]):
The request object. Request message for
DeleteInstruction.
name (str):
Required. Instruction resource name, format:
projects/{project_id}/instructions/{instruction_id}
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.
"""
# 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, data_labeling_service.DeleteInstructionRequest):
request = data_labeling_service.DeleteInstructionRequest(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_instruction]
# 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.
rpc(
request,
retry=retry,
timeout=timeout,
metadata=metadata,
)
[docs] def get_evaluation(
self,
request: Optional[
Union[data_labeling_service.GetEvaluationRequest, 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]] = (),
) -> evaluation.Evaluation:
r"""Gets an evaluation by resource name (to search, use
[projects.evaluations.search][google.cloud.datalabeling.v1beta1.DataLabelingService.SearchEvaluations]).
.. 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 datalabeling_v1beta1
def sample_get_evaluation():
# Create a client
client = datalabeling_v1beta1.DataLabelingServiceClient()
# Initialize request argument(s)
request = datalabeling_v1beta1.GetEvaluationRequest(
name="name_value",
)
# Make the request
response = client.get_evaluation(request=request)
# Handle the response
print(response)
Args:
request (Union[google.cloud.datalabeling_v1beta1.types.GetEvaluationRequest, dict]):
The request object. Request message for GetEvaluation.
name (str):
Required. Name of the evaluation. Format:
"projects/{project_id}/datasets/{dataset_id}/evaluations/{evaluation_id}'
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.datalabeling_v1beta1.types.Evaluation:
Describes an evaluation between a machine learning model's predictions and
ground truth labels. Created when an
[EvaluationJob][google.cloud.datalabeling.v1beta1.EvaluationJob]
runs successfully.
"""
# 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, data_labeling_service.GetEvaluationRequest):
request = data_labeling_service.GetEvaluationRequest(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_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 search_evaluations(
self,
request: Optional[
Union[data_labeling_service.SearchEvaluationsRequest, dict]
] = None,
*,
parent: Optional[str] = None,
filter: Optional[str] = None,
retry: OptionalRetry = gapic_v1.method.DEFAULT,
timeout: Union[float, object] = gapic_v1.method.DEFAULT,
metadata: Sequence[Tuple[str, str]] = (),
) -> pagers.SearchEvaluationsPager:
r"""Searches
[evaluations][google.cloud.datalabeling.v1beta1.Evaluation]
within 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 datalabeling_v1beta1
def sample_search_evaluations():
# Create a client
client = datalabeling_v1beta1.DataLabelingServiceClient()
# Initialize request argument(s)
request = datalabeling_v1beta1.SearchEvaluationsRequest(
parent="parent_value",
)
# Make the request
page_result = client.search_evaluations(request=request)
# Handle the response
for response in page_result:
print(response)
Args:
request (Union[google.cloud.datalabeling_v1beta1.types.SearchEvaluationsRequest, dict]):
The request object. Request message for SearchEvaluation.
parent (str):
Required. Evaluation search parent (project ID). Format:
"projects/{project_id}"
This corresponds to the ``parent`` field
on the ``request`` instance; if ``request`` is provided, this
should not be set.
filter (str):
Optional. To search evaluations, you can filter by the
following:
- evaluation\_job.evaluation_job_id (the last part of
[EvaluationJob.name][google.cloud.datalabeling.v1beta1.EvaluationJob.name])
- evaluation\_job.model_id (the {model_name} portion of
[EvaluationJob.modelVersion][google.cloud.datalabeling.v1beta1.EvaluationJob.model_version])
- evaluation\_job.evaluation_job_run_time_start
(Minimum threshold for the
[evaluationJobRunTime][google.cloud.datalabeling.v1beta1.Evaluation.evaluation_job_run_time]
that created the evaluation)
- evaluation\_job.evaluation_job_run_time_end (Maximum
threshold for the
[evaluationJobRunTime][google.cloud.datalabeling.v1beta1.Evaluation.evaluation_job_run_time]
that created the evaluation)
- evaluation\_job.job_state
([EvaluationJob.state][google.cloud.datalabeling.v1beta1.EvaluationJob.state])
- annotation\_spec.display_name (the Evaluation
contains a metric for the annotation spec with this
[displayName][google.cloud.datalabeling.v1beta1.AnnotationSpec.display_name])
To filter by multiple critiera, use the ``AND`` operator
or the ``OR`` operator. The following examples shows a
string that filters by several critiera:
"evaluation\ *job.evaluation_job_id =
{evaluation_job_id} AND evaluation*\ job.model_id =
{model_name} AND
evaluation\ *job.evaluation_job_run_time_start =
{timestamp_1} AND
evaluation*\ job.evaluation_job_run_time_end =
{timestamp_2} AND annotation\_spec.display_name =
{display_name}"
This corresponds to the ``filter`` field
on the ``request`` instance; if ``request`` is provided, this
should not be set.
retry (google.api_core.retry.Retry): Designation of what errors, if any,
should be retried.
timeout (float): The timeout for this request.
metadata (Sequence[Tuple[str, str]]): Strings which should be
sent along with the request as metadata.
Returns:
google.cloud.datalabeling_v1beta1.services.data_labeling_service.pagers.SearchEvaluationsPager:
Results of searching evaluations.
Iterating over this object will yield
results and resolve additional pages
automatically.
"""
# Create or coerce a protobuf request object.
# - Quick check: If we got a request object, we should *not* have
# gotten any keyword arguments that map to the request.
has_flattened_params = any([parent, filter])
if request is not None and has_flattened_params:
raise ValueError(
"If the `request` argument is set, then none of "
"the individual field arguments should be set."
)
# - Use the request object if provided (there's no risk of modifying the input as
# there are no flattened fields), or create one.
if not isinstance(request, data_labeling_service.SearchEvaluationsRequest):
request = data_labeling_service.SearchEvaluationsRequest(request)
# If we have keyword arguments corresponding to fields on the
# request, apply these.
if parent is not None:
request.parent = parent
if filter is not None:
request.filter = filter
# Wrap the RPC method; this adds retry and timeout information,
# and friendly error handling.
rpc = self._transport._wrapped_methods[self._transport.search_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.SearchEvaluationsPager(
method=rpc,
request=request,
response=response,
retry=retry,
timeout=timeout,
metadata=metadata,
)
# Done; return the response.
return response
[docs] def search_example_comparisons(
self,
request: Optional[
Union[data_labeling_service.SearchExampleComparisonsRequest, 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.SearchExampleComparisonsPager:
r"""Searches example comparisons from an evaluation. The
return format is a list of example comparisons that show
ground truth and prediction(s) for a single input.
Search by providing an evaluation ID.
.. 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 datalabeling_v1beta1
def sample_search_example_comparisons():
# Create a client
client = datalabeling_v1beta1.DataLabelingServiceClient()
# Initialize request argument(s)
request = datalabeling_v1beta1.SearchExampleComparisonsRequest(
parent="parent_value",
)
# Make the request
page_result = client.search_example_comparisons(request=request)
# Handle the response
for response in page_result:
print(response)
Args:
request (Union[google.cloud.datalabeling_v1beta1.types.SearchExampleComparisonsRequest, dict]):
The request object. Request message of
SearchExampleComparisons.
parent (str):
Required. Name of the
[Evaluation][google.cloud.datalabeling.v1beta1.Evaluation]
resource to search for example comparisons from. Format:
"projects/{project_id}/datasets/{dataset_id}/evaluations/{evaluation_id}"
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.datalabeling_v1beta1.services.data_labeling_service.pagers.SearchExampleComparisonsPager:
Results of searching example
comparisons.
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, data_labeling_service.SearchExampleComparisonsRequest
):
request = data_labeling_service.SearchExampleComparisonsRequest(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.search_example_comparisons
]
# 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.SearchExampleComparisonsPager(
method=rpc,
request=request,
response=response,
retry=retry,
timeout=timeout,
metadata=metadata,
)
# Done; return the response.
return response
[docs] def create_evaluation_job(
self,
request: Optional[
Union[data_labeling_service.CreateEvaluationJobRequest, dict]
] = None,
*,
parent: Optional[str] = None,
job: Optional[evaluation_job.EvaluationJob] = None,
retry: OptionalRetry = gapic_v1.method.DEFAULT,
timeout: Union[float, object] = gapic_v1.method.DEFAULT,
metadata: Sequence[Tuple[str, str]] = (),
) -> evaluation_job.EvaluationJob:
r"""Creates an evaluation job.
.. 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 datalabeling_v1beta1
def sample_create_evaluation_job():
# Create a client
client = datalabeling_v1beta1.DataLabelingServiceClient()
# Initialize request argument(s)
request = datalabeling_v1beta1.CreateEvaluationJobRequest(
parent="parent_value",
)
# Make the request
response = client.create_evaluation_job(request=request)
# Handle the response
print(response)
Args:
request (Union[google.cloud.datalabeling_v1beta1.types.CreateEvaluationJobRequest, dict]):
The request object. Request message for
CreateEvaluationJob.
parent (str):
Required. Evaluation job resource parent. Format:
"projects/{project_id}"
This corresponds to the ``parent`` field
on the ``request`` instance; if ``request`` is provided, this
should not be set.
job (google.cloud.datalabeling_v1beta1.types.EvaluationJob):
Required. The evaluation job to
create.
This corresponds to the ``job`` 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.datalabeling_v1beta1.types.EvaluationJob:
Defines an evaluation job that runs periodically to generate
[Evaluations][google.cloud.datalabeling.v1beta1.Evaluation].
[Creating an evaluation
job](/ml-engine/docs/continuous-evaluation/create-job)
is the starting point for using continuous
evaluation.
"""
# 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, job])
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, data_labeling_service.CreateEvaluationJobRequest):
request = data_labeling_service.CreateEvaluationJobRequest(request)
# If we have keyword arguments corresponding to fields on the
# request, apply these.
if parent is not None:
request.parent = parent
if job is not None:
request.job = job
# Wrap the RPC method; this adds retry and timeout information,
# and friendly error handling.
rpc = self._transport._wrapped_methods[self._transport.create_evaluation_job]
# 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 update_evaluation_job(
self,
request: Optional[
Union[data_labeling_service.UpdateEvaluationJobRequest, dict]
] = None,
*,
evaluation_job: Optional[gcd_evaluation_job.EvaluationJob] = None,
update_mask: Optional[field_mask_pb2.FieldMask] = None,
retry: OptionalRetry = gapic_v1.method.DEFAULT,
timeout: Union[float, object] = gapic_v1.method.DEFAULT,
metadata: Sequence[Tuple[str, str]] = (),
) -> gcd_evaluation_job.EvaluationJob:
r"""Updates an evaluation job. You can only update certain fields of
the job's
[EvaluationJobConfig][google.cloud.datalabeling.v1beta1.EvaluationJobConfig]:
``humanAnnotationConfig.instruction``, ``exampleCount``, and
``exampleSamplePercentage``.
If you want to change any other aspect of the evaluation job,
you must delete the job and create a new one.
.. 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 datalabeling_v1beta1
def sample_update_evaluation_job():
# Create a client
client = datalabeling_v1beta1.DataLabelingServiceClient()
# Initialize request argument(s)
request = datalabeling_v1beta1.UpdateEvaluationJobRequest(
)
# Make the request
response = client.update_evaluation_job(request=request)
# Handle the response
print(response)
Args:
request (Union[google.cloud.datalabeling_v1beta1.types.UpdateEvaluationJobRequest, dict]):
The request object. Request message for
UpdateEvaluationJob.
evaluation_job (google.cloud.datalabeling_v1beta1.types.EvaluationJob):
Required. Evaluation job that is
going to be updated.
This corresponds to the ``evaluation_job`` field
on the ``request`` instance; if ``request`` is provided, this
should not be set.
update_mask (google.protobuf.field_mask_pb2.FieldMask):
Optional. Mask for which fields to update. You can only
provide the following fields:
- ``evaluationJobConfig.humanAnnotationConfig.instruction``
- ``evaluationJobConfig.exampleCount``
- ``evaluationJobConfig.exampleSamplePercentage``
You can provide more than one of these fields by
separating them with commas.
This corresponds to the ``update_mask`` field
on the ``request`` instance; if ``request`` is provided, this
should not be set.
retry (google.api_core.retry.Retry): Designation of what errors, if any,
should be retried.
timeout (float): The timeout for this request.
metadata (Sequence[Tuple[str, str]]): Strings which should be
sent along with the request as metadata.
Returns:
google.cloud.datalabeling_v1beta1.types.EvaluationJob:
Defines an evaluation job that runs periodically to generate
[Evaluations][google.cloud.datalabeling.v1beta1.Evaluation].
[Creating an evaluation
job](/ml-engine/docs/continuous-evaluation/create-job)
is the starting point for using continuous
evaluation.
"""
# 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([evaluation_job, update_mask])
if request is not None and has_flattened_params:
raise ValueError(
"If the `request` argument is set, then none of "
"the individual field arguments should be set."
)
# - Use the request object if provided (there's no risk of modifying the input as
# there are no flattened fields), or create one.
if not isinstance(request, data_labeling_service.UpdateEvaluationJobRequest):
request = data_labeling_service.UpdateEvaluationJobRequest(request)
# If we have keyword arguments corresponding to fields on the
# request, apply these.
if evaluation_job is not None:
request.evaluation_job = evaluation_job
if update_mask is not None:
request.update_mask = update_mask
# Wrap the RPC method; this adds retry and timeout information,
# and friendly error handling.
rpc = self._transport._wrapped_methods[self._transport.update_evaluation_job]
# Certain fields should be provided within the metadata header;
# add these here.
metadata = tuple(metadata) + (
gapic_v1.routing_header.to_grpc_metadata(
(("evaluation_job.name", request.evaluation_job.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_evaluation_job(
self,
request: Optional[
Union[data_labeling_service.GetEvaluationJobRequest, 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]] = (),
) -> evaluation_job.EvaluationJob:
r"""Gets an evaluation job by resource name.
.. 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 datalabeling_v1beta1
def sample_get_evaluation_job():
# Create a client
client = datalabeling_v1beta1.DataLabelingServiceClient()
# Initialize request argument(s)
request = datalabeling_v1beta1.GetEvaluationJobRequest(
name="name_value",
)
# Make the request
response = client.get_evaluation_job(request=request)
# Handle the response
print(response)
Args:
request (Union[google.cloud.datalabeling_v1beta1.types.GetEvaluationJobRequest, dict]):
The request object. Request message for GetEvaluationJob.
name (str):
Required. Name of the evaluation job. Format:
"projects/{project_id}/evaluationJobs/{evaluation_job_id}"
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.datalabeling_v1beta1.types.EvaluationJob:
Defines an evaluation job that runs periodically to generate
[Evaluations][google.cloud.datalabeling.v1beta1.Evaluation].
[Creating an evaluation
job](/ml-engine/docs/continuous-evaluation/create-job)
is the starting point for using continuous
evaluation.
"""
# 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, data_labeling_service.GetEvaluationJobRequest):
request = data_labeling_service.GetEvaluationJobRequest(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_evaluation_job]
# 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 pause_evaluation_job(
self,
request: Optional[
Union[data_labeling_service.PauseEvaluationJobRequest, 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]] = (),
) -> None:
r"""Pauses an evaluation job. Pausing an evaluation job that is
already in a ``PAUSED`` state is a no-op.
.. 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 datalabeling_v1beta1
def sample_pause_evaluation_job():
# Create a client
client = datalabeling_v1beta1.DataLabelingServiceClient()
# Initialize request argument(s)
request = datalabeling_v1beta1.PauseEvaluationJobRequest(
name="name_value",
)
# Make the request
client.pause_evaluation_job(request=request)
Args:
request (Union[google.cloud.datalabeling_v1beta1.types.PauseEvaluationJobRequest, dict]):
The request object. Request message for
PauseEvaluationJob.
name (str):
Required. Name of the evaluation job that is going to be
paused. Format:
"projects/{project_id}/evaluationJobs/{evaluation_job_id}"
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.
"""
# 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, data_labeling_service.PauseEvaluationJobRequest):
request = data_labeling_service.PauseEvaluationJobRequest(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.pause_evaluation_job]
# 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.
rpc(
request,
retry=retry,
timeout=timeout,
metadata=metadata,
)
[docs] def resume_evaluation_job(
self,
request: Optional[
Union[data_labeling_service.ResumeEvaluationJobRequest, 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]] = (),
) -> None:
r"""Resumes a paused evaluation job. A deleted evaluation
job can't be resumed. Resuming a running or scheduled
evaluation job is a no-op.
.. 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 datalabeling_v1beta1
def sample_resume_evaluation_job():
# Create a client
client = datalabeling_v1beta1.DataLabelingServiceClient()
# Initialize request argument(s)
request = datalabeling_v1beta1.ResumeEvaluationJobRequest(
name="name_value",
)
# Make the request
client.resume_evaluation_job(request=request)
Args:
request (Union[google.cloud.datalabeling_v1beta1.types.ResumeEvaluationJobRequest, dict]):
The request object. Request message ResumeEvaluationJob.
name (str):
Required. Name of the evaluation job that is going to be
resumed. Format:
"projects/{project_id}/evaluationJobs/{evaluation_job_id}"
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.
"""
# 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, data_labeling_service.ResumeEvaluationJobRequest):
request = data_labeling_service.ResumeEvaluationJobRequest(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.resume_evaluation_job]
# 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.
rpc(
request,
retry=retry,
timeout=timeout,
metadata=metadata,
)
[docs] def delete_evaluation_job(
self,
request: Optional[
Union[data_labeling_service.DeleteEvaluationJobRequest, 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]] = (),
) -> None:
r"""Stops and deletes an evaluation job.
.. 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 datalabeling_v1beta1
def sample_delete_evaluation_job():
# Create a client
client = datalabeling_v1beta1.DataLabelingServiceClient()
# Initialize request argument(s)
request = datalabeling_v1beta1.DeleteEvaluationJobRequest(
name="name_value",
)
# Make the request
client.delete_evaluation_job(request=request)
Args:
request (Union[google.cloud.datalabeling_v1beta1.types.DeleteEvaluationJobRequest, dict]):
The request object. Request message DeleteEvaluationJob.
name (str):
Required. Name of the evaluation job that is going to be
deleted. Format:
"projects/{project_id}/evaluationJobs/{evaluation_job_id}"
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.
"""
# 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, data_labeling_service.DeleteEvaluationJobRequest):
request = data_labeling_service.DeleteEvaluationJobRequest(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_evaluation_job]
# 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.
rpc(
request,
retry=retry,
timeout=timeout,
metadata=metadata,
)
[docs] def list_evaluation_jobs(
self,
request: Optional[
Union[data_labeling_service.ListEvaluationJobsRequest, dict]
] = None,
*,
parent: Optional[str] = None,
filter: Optional[str] = None,
retry: OptionalRetry = gapic_v1.method.DEFAULT,
timeout: Union[float, object] = gapic_v1.method.DEFAULT,
metadata: Sequence[Tuple[str, str]] = (),
) -> pagers.ListEvaluationJobsPager:
r"""Lists all evaluation jobs within a project with
possible filters. Pagination is supported.
.. 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 datalabeling_v1beta1
def sample_list_evaluation_jobs():
# Create a client
client = datalabeling_v1beta1.DataLabelingServiceClient()
# Initialize request argument(s)
request = datalabeling_v1beta1.ListEvaluationJobsRequest(
parent="parent_value",
)
# Make the request
page_result = client.list_evaluation_jobs(request=request)
# Handle the response
for response in page_result:
print(response)
Args:
request (Union[google.cloud.datalabeling_v1beta1.types.ListEvaluationJobsRequest, dict]):
The request object. Request message for
ListEvaluationJobs.
parent (str):
Required. Evaluation job resource parent. Format:
"projects/{project_id}"
This corresponds to the ``parent`` field
on the ``request`` instance; if ``request`` is provided, this
should not be set.
filter (str):
Optional. You can filter the jobs to list by model_id
(also known as model_name, as described in
[EvaluationJob.modelVersion][google.cloud.datalabeling.v1beta1.EvaluationJob.model_version])
or by evaluation job state (as described in
[EvaluationJob.state][google.cloud.datalabeling.v1beta1.EvaluationJob.state]).
To filter by both criteria, use the ``AND`` operator or
the ``OR`` operator. For example, you can use the
following string for your filter:
"evaluation\ *job.model_id = {model_name} AND
evaluation*\ job.state = {evaluation_job_state}"
This corresponds to the ``filter`` field
on the ``request`` instance; if ``request`` is provided, this
should not be set.
retry (google.api_core.retry.Retry): Designation of what errors, if any,
should be retried.
timeout (float): The timeout for this request.
metadata (Sequence[Tuple[str, str]]): Strings which should be
sent along with the request as metadata.
Returns:
google.cloud.datalabeling_v1beta1.services.data_labeling_service.pagers.ListEvaluationJobsPager:
Results for listing evaluation jobs.
Iterating over this object will yield
results and resolve additional pages
automatically.
"""
# Create or coerce a protobuf request object.
# - Quick check: If we got a request object, we should *not* have
# gotten any keyword arguments that map to the request.
has_flattened_params = any([parent, filter])
if request is not None and has_flattened_params:
raise ValueError(
"If the `request` argument is set, then none of "
"the individual field arguments should be set."
)
# - Use the request object if provided (there's no risk of modifying the input as
# there are no flattened fields), or create one.
if not isinstance(request, data_labeling_service.ListEvaluationJobsRequest):
request = data_labeling_service.ListEvaluationJobsRequest(request)
# If we have keyword arguments corresponding to fields on the
# request, apply these.
if parent is not None:
request.parent = parent
if filter is not None:
request.filter = filter
# Wrap the RPC method; this adds retry and timeout information,
# and friendly error handling.
rpc = self._transport._wrapped_methods[self._transport.list_evaluation_jobs]
# 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.ListEvaluationJobsPager(
method=rpc,
request=request,
response=response,
retry=retry,
timeout=timeout,
metadata=metadata,
)
# Done; return the response.
return response
def __enter__(self) -> "DataLabelingServiceClient":
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__ = ("DataLabelingServiceClient",)
