# pylint: disable=too-many-lines
# coding=utf-8
# --------------------------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License. See License.txt in the project root for license information.
# Code generated by Microsoft (R) AutoRest Code Generator.
# Changes may cause incorrect behavior and will be lost if the code is regenerated.
# --------------------------------------------------------------------------
import sys
from typing import Any, Callable, Dict, IO, Iterable, Optional, TypeVar, Union, cast, overload
import urllib.parse
from azure.core.exceptions import (
ClientAuthenticationError,
HttpResponseError,
ResourceExistsError,
ResourceNotFoundError,
ResourceNotModifiedError,
map_error,
)
from azure.core.paging import ItemPaged
from azure.core.pipeline import PipelineResponse
from azure.core.pipeline.transport import HttpResponse
from azure.core.polling import LROPoller, NoPolling, PollingMethod
from azure.core.rest import HttpRequest
from azure.core.tracing.decorator import distributed_trace
from azure.core.utils import case_insensitive_dict
from azure.mgmt.core.exceptions import ARMErrorFormat
from azure.mgmt.core.polling.arm_polling import ARMPolling
from .. import models as _models
from .._serialization import Serializer
from .._vendor import _convert_request, _format_url_section
if sys.version_info >= (3, 8):
from typing import Literal # pylint: disable=no-name-in-module, ungrouped-imports
else:
from typing_extensions import Literal # type: ignore # pylint: disable=ungrouped-imports
T = TypeVar("T")
ClsType = Optional[Callable[[PipelineResponse[HttpRequest, HttpResponse], T, Dict[str, Any]], Any]]
_SERIALIZER = Serializer()
_SERIALIZER.client_side_validation = False
def build_create_or_update_request(
resource_group_name: str, cluster_name: str, subscription_id: str, **kwargs: Any
) -> HttpRequest:
_headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
_params = case_insensitive_dict(kwargs.pop("params", {}) or {})
api_version = kwargs.pop(
"api_version", _params.pop("api-version", "2017-08-01-preview")
) # type: Literal["2017-08-01-preview"]
content_type = kwargs.pop("content_type", _headers.pop("Content-Type", None)) # type: Optional[str]
accept = _headers.pop("Accept", "application/json")
# Construct URL
_url = kwargs.pop(
"template_url",
"/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningCompute/operationalizationClusters/{clusterName}",
) # pylint: disable=line-too-long
path_format_arguments = {
"subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, "str"),
"resourceGroupName": _SERIALIZER.url(
"resource_group_name", resource_group_name, "str", max_length=90, min_length=1, pattern=r"^[-\w\._\(\)]+$"
),
"clusterName": _SERIALIZER.url(
"cluster_name",
cluster_name,
"str",
max_length=90,
min_length=1,
pattern=r"^[a-zA-Z][-\w\._\(\)]+[a-zA-Z0-9]$",
),
}
_url = _format_url_section(_url, **path_format_arguments)
# Construct parameters
_params["api-version"] = _SERIALIZER.query("api_version", api_version, "str")
# Construct headers
if content_type is not None:
_headers["Content-Type"] = _SERIALIZER.header("content_type", content_type, "str")
_headers["Accept"] = _SERIALIZER.header("accept", accept, "str")
return HttpRequest(method="PUT", url=_url, params=_params, headers=_headers, **kwargs)
def build_get_request(resource_group_name: str, cluster_name: str, subscription_id: str, **kwargs: Any) -> HttpRequest:
_headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
_params = case_insensitive_dict(kwargs.pop("params", {}) or {})
api_version = kwargs.pop(
"api_version", _params.pop("api-version", "2017-08-01-preview")
) # type: Literal["2017-08-01-preview"]
accept = _headers.pop("Accept", "application/json")
# Construct URL
_url = kwargs.pop(
"template_url",
"/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningCompute/operationalizationClusters/{clusterName}",
) # pylint: disable=line-too-long
path_format_arguments = {
"subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, "str"),
"resourceGroupName": _SERIALIZER.url(
"resource_group_name", resource_group_name, "str", max_length=90, min_length=1, pattern=r"^[-\w\._\(\)]+$"
),
"clusterName": _SERIALIZER.url(
"cluster_name",
cluster_name,
"str",
max_length=90,
min_length=1,
pattern=r"^[a-zA-Z][-\w\._\(\)]+[a-zA-Z0-9]$",
),
}
_url = _format_url_section(_url, **path_format_arguments)
# Construct parameters
_params["api-version"] = _SERIALIZER.query("api_version", api_version, "str")
# Construct headers
_headers["Accept"] = _SERIALIZER.header("accept", accept, "str")
return HttpRequest(method="GET", url=_url, params=_params, headers=_headers, **kwargs)
def build_update_request(
resource_group_name: str, cluster_name: str, subscription_id: str, **kwargs: Any
) -> HttpRequest:
_headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
_params = case_insensitive_dict(kwargs.pop("params", {}) or {})
api_version = kwargs.pop(
"api_version", _params.pop("api-version", "2017-08-01-preview")
) # type: Literal["2017-08-01-preview"]
content_type = kwargs.pop("content_type", _headers.pop("Content-Type", None)) # type: Optional[str]
accept = _headers.pop("Accept", "application/json")
# Construct URL
_url = kwargs.pop(
"template_url",
"/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningCompute/operationalizationClusters/{clusterName}",
) # pylint: disable=line-too-long
path_format_arguments = {
"subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, "str"),
"resourceGroupName": _SERIALIZER.url(
"resource_group_name", resource_group_name, "str", max_length=90, min_length=1, pattern=r"^[-\w\._\(\)]+$"
),
"clusterName": _SERIALIZER.url(
"cluster_name",
cluster_name,
"str",
max_length=90,
min_length=1,
pattern=r"^[a-zA-Z][-\w\._\(\)]+[a-zA-Z0-9]$",
),
}
_url = _format_url_section(_url, **path_format_arguments)
# Construct parameters
_params["api-version"] = _SERIALIZER.query("api_version", api_version, "str")
# Construct headers
if content_type is not None:
_headers["Content-Type"] = _SERIALIZER.header("content_type", content_type, "str")
_headers["Accept"] = _SERIALIZER.header("accept", accept, "str")
return HttpRequest(method="PATCH", url=_url, params=_params, headers=_headers, **kwargs)
def build_delete_request(
resource_group_name: str,
cluster_name: str,
subscription_id: str,
*,
delete_all: Optional[bool] = None,
**kwargs: Any
) -> HttpRequest:
_headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
_params = case_insensitive_dict(kwargs.pop("params", {}) or {})
api_version = kwargs.pop(
"api_version", _params.pop("api-version", "2017-08-01-preview")
) # type: Literal["2017-08-01-preview"]
accept = _headers.pop("Accept", "application/json")
# Construct URL
_url = kwargs.pop(
"template_url",
"/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningCompute/operationalizationClusters/{clusterName}",
) # pylint: disable=line-too-long
path_format_arguments = {
"subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, "str"),
"resourceGroupName": _SERIALIZER.url(
"resource_group_name", resource_group_name, "str", max_length=90, min_length=1, pattern=r"^[-\w\._\(\)]+$"
),
"clusterName": _SERIALIZER.url(
"cluster_name",
cluster_name,
"str",
max_length=90,
min_length=1,
pattern=r"^[a-zA-Z][-\w\._\(\)]+[a-zA-Z0-9]$",
),
}
_url = _format_url_section(_url, **path_format_arguments)
# Construct parameters
_params["api-version"] = _SERIALIZER.query("api_version", api_version, "str")
if delete_all is not None:
_params["deleteAll"] = _SERIALIZER.query("delete_all", delete_all, "bool")
# Construct headers
_headers["Accept"] = _SERIALIZER.header("accept", accept, "str")
return HttpRequest(method="DELETE", url=_url, params=_params, headers=_headers, **kwargs)
def build_list_keys_request(
resource_group_name: str, cluster_name: str, subscription_id: str, **kwargs: Any
) -> HttpRequest:
_headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
_params = case_insensitive_dict(kwargs.pop("params", {}) or {})
api_version = kwargs.pop(
"api_version", _params.pop("api-version", "2017-08-01-preview")
) # type: Literal["2017-08-01-preview"]
accept = _headers.pop("Accept", "application/json")
# Construct URL
_url = kwargs.pop(
"template_url",
"/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningCompute/operationalizationClusters/{clusterName}/listKeys",
) # pylint: disable=line-too-long
path_format_arguments = {
"subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, "str"),
"resourceGroupName": _SERIALIZER.url(
"resource_group_name", resource_group_name, "str", max_length=90, min_length=1, pattern=r"^[-\w\._\(\)]+$"
),
"clusterName": _SERIALIZER.url(
"cluster_name",
cluster_name,
"str",
max_length=90,
min_length=1,
pattern=r"^[a-zA-Z][-\w\._\(\)]+[a-zA-Z0-9]$",
),
}
_url = _format_url_section(_url, **path_format_arguments)
# Construct parameters
_params["api-version"] = _SERIALIZER.query("api_version", api_version, "str")
# Construct headers
_headers["Accept"] = _SERIALIZER.header("accept", accept, "str")
return HttpRequest(method="POST", url=_url, params=_params, headers=_headers, **kwargs)
def build_check_system_services_updates_available_request(
resource_group_name: str, cluster_name: str, subscription_id: str, **kwargs: Any
) -> HttpRequest:
_headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
_params = case_insensitive_dict(kwargs.pop("params", {}) or {})
api_version = kwargs.pop(
"api_version", _params.pop("api-version", "2017-08-01-preview")
) # type: Literal["2017-08-01-preview"]
accept = _headers.pop("Accept", "application/json")
# Construct URL
_url = kwargs.pop(
"template_url",
"/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningCompute/operationalizationClusters/{clusterName}/checkSystemServicesUpdatesAvailable",
) # pylint: disable=line-too-long
path_format_arguments = {
"subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, "str"),
"resourceGroupName": _SERIALIZER.url(
"resource_group_name", resource_group_name, "str", max_length=90, min_length=1, pattern=r"^[-\w\._\(\)]+$"
),
"clusterName": _SERIALIZER.url(
"cluster_name",
cluster_name,
"str",
max_length=90,
min_length=1,
pattern=r"^[a-zA-Z][-\w\._\(\)]+[a-zA-Z0-9]$",
),
}
_url = _format_url_section(_url, **path_format_arguments)
# Construct parameters
_params["api-version"] = _SERIALIZER.query("api_version", api_version, "str")
# Construct headers
_headers["Accept"] = _SERIALIZER.header("accept", accept, "str")
return HttpRequest(method="POST", url=_url, params=_params, headers=_headers, **kwargs)
def build_update_system_services_request(
resource_group_name: str, cluster_name: str, subscription_id: str, **kwargs: Any
) -> HttpRequest:
_headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
_params = case_insensitive_dict(kwargs.pop("params", {}) or {})
api_version = kwargs.pop(
"api_version", _params.pop("api-version", "2017-08-01-preview")
) # type: Literal["2017-08-01-preview"]
accept = _headers.pop("Accept", "application/json")
# Construct URL
_url = kwargs.pop(
"template_url",
"/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningCompute/operationalizationClusters/{clusterName}/updateSystemServices",
) # pylint: disable=line-too-long
path_format_arguments = {
"subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, "str"),
"resourceGroupName": _SERIALIZER.url(
"resource_group_name", resource_group_name, "str", max_length=90, min_length=1, pattern=r"^[-\w\._\(\)]+$"
),
"clusterName": _SERIALIZER.url(
"cluster_name",
cluster_name,
"str",
max_length=90,
min_length=1,
pattern=r"^[a-zA-Z][-\w\._\(\)]+[a-zA-Z0-9]$",
),
}
_url = _format_url_section(_url, **path_format_arguments)
# Construct parameters
_params["api-version"] = _SERIALIZER.query("api_version", api_version, "str")
# Construct headers
_headers["Accept"] = _SERIALIZER.header("accept", accept, "str")
return HttpRequest(method="POST", url=_url, params=_params, headers=_headers, **kwargs)
def build_list_by_resource_group_request(
resource_group_name: str, subscription_id: str, *, skiptoken: Optional[str] = None, **kwargs: Any
) -> HttpRequest:
_headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
_params = case_insensitive_dict(kwargs.pop("params", {}) or {})
api_version = kwargs.pop(
"api_version", _params.pop("api-version", "2017-08-01-preview")
) # type: Literal["2017-08-01-preview"]
accept = _headers.pop("Accept", "application/json")
# Construct URL
_url = kwargs.pop(
"template_url",
"/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningCompute/operationalizationClusters",
) # pylint: disable=line-too-long
path_format_arguments = {
"subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, "str"),
"resourceGroupName": _SERIALIZER.url(
"resource_group_name", resource_group_name, "str", max_length=90, min_length=1, pattern=r"^[-\w\._\(\)]+$"
),
}
_url = _format_url_section(_url, **path_format_arguments)
# Construct parameters
_params["api-version"] = _SERIALIZER.query("api_version", api_version, "str")
if skiptoken is not None:
_params["$skiptoken"] = _SERIALIZER.query("skiptoken", skiptoken, "str")
# Construct headers
_headers["Accept"] = _SERIALIZER.header("accept", accept, "str")
return HttpRequest(method="GET", url=_url, params=_params, headers=_headers, **kwargs)
def build_list_by_subscription_id_request(
subscription_id: str, *, skiptoken: Optional[str] = None, **kwargs: Any
) -> HttpRequest:
_headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
_params = case_insensitive_dict(kwargs.pop("params", {}) or {})
api_version = kwargs.pop(
"api_version", _params.pop("api-version", "2017-08-01-preview")
) # type: Literal["2017-08-01-preview"]
accept = _headers.pop("Accept", "application/json")
# Construct URL
_url = kwargs.pop(
"template_url",
"/subscriptions/{subscriptionId}/providers/Microsoft.MachineLearningCompute/operationalizationClusters",
) # pylint: disable=line-too-long
path_format_arguments = {
"subscriptionId": _SERIALIZER.url("subscription_id", subscription_id, "str"),
}
_url = _format_url_section(_url, **path_format_arguments)
# Construct parameters
_params["api-version"] = _SERIALIZER.query("api_version", api_version, "str")
if skiptoken is not None:
_params["$skiptoken"] = _SERIALIZER.query("skiptoken", skiptoken, "str")
# Construct headers
_headers["Accept"] = _SERIALIZER.header("accept", accept, "str")
return HttpRequest(method="GET", url=_url, params=_params, headers=_headers, **kwargs)
[docs]class OperationalizationClustersOperations:
"""
.. warning::
**DO NOT** instantiate this class directly.
Instead, you should access the following operations through
:class:`~azure.mgmt.machinelearningcompute.MachineLearningComputeManagementClient`'s
:attr:`operationalization_clusters` attribute.
"""
models = _models
def __init__(self, *args, **kwargs):
input_args = list(args)
self._client = input_args.pop(0) if input_args else kwargs.pop("client")
self._config = input_args.pop(0) if input_args else kwargs.pop("config")
self._serialize = input_args.pop(0) if input_args else kwargs.pop("serializer")
self._deserialize = input_args.pop(0) if input_args else kwargs.pop("deserializer")
def _create_or_update_initial(
self,
resource_group_name: str,
cluster_name: str,
parameters: Union[_models.OperationalizationCluster, IO],
**kwargs: Any
) -> _models.OperationalizationCluster:
error_map = {
401: ClientAuthenticationError,
404: ResourceNotFoundError,
409: ResourceExistsError,
304: ResourceNotModifiedError,
}
error_map.update(kwargs.pop("error_map", {}) or {})
_headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
_params = case_insensitive_dict(kwargs.pop("params", {}) or {})
api_version = kwargs.pop(
"api_version", _params.pop("api-version", self._config.api_version)
) # type: Literal["2017-08-01-preview"]
content_type = kwargs.pop("content_type", _headers.pop("Content-Type", None)) # type: Optional[str]
cls = kwargs.pop("cls", None) # type: ClsType[_models.OperationalizationCluster]
content_type = content_type or "application/json"
_json = None
_content = None
if isinstance(parameters, (IO, bytes)):
_content = parameters
else:
_json = self._serialize.body(parameters, "OperationalizationCluster")
request = build_create_or_update_request(
resource_group_name=resource_group_name,
cluster_name=cluster_name,
subscription_id=self._config.subscription_id,
api_version=api_version,
content_type=content_type,
json=_json,
content=_content,
template_url=self._create_or_update_initial.metadata["url"],
headers=_headers,
params=_params,
)
request = _convert_request(request)
request.url = self._client.format_url(request.url) # type: ignore
pipeline_response = self._client._pipeline.run( # type: ignore # pylint: disable=protected-access
request, stream=False, **kwargs
)
response = pipeline_response.http_response
if response.status_code not in [200, 201]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
error = self._deserialize.failsafe_deserialize(_models.ErrorResponseWrapper, pipeline_response)
raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
if response.status_code == 200:
deserialized = self._deserialize("OperationalizationCluster", pipeline_response)
if response.status_code == 201:
deserialized = self._deserialize("OperationalizationCluster", pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
_create_or_update_initial.metadata = {"url": "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningCompute/operationalizationClusters/{clusterName}"} # type: ignore
@overload
def begin_create_or_update(
self,
resource_group_name: str,
cluster_name: str,
parameters: _models.OperationalizationCluster,
*,
content_type: str = "application/json",
**kwargs: Any
) -> LROPoller[_models.OperationalizationCluster]:
"""Create or update an operationalization cluster.
:param resource_group_name: Name of the resource group in which the cluster is located.
Required.
:type resource_group_name: str
:param cluster_name: The name of the cluster. Required.
:type cluster_name: str
:param parameters: Parameters supplied to create or update an Operationalization cluster.
Required.
:type parameters: ~azure.mgmt.machinelearningcompute.models.OperationalizationCluster
:keyword content_type: Body Parameter content-type. Content type parameter for JSON body.
Default value is "application/json".
:paramtype content_type: str
:keyword callable cls: A custom type or function that will be passed the direct response
:keyword str continuation_token: A continuation token to restart a poller from a saved state.
:keyword polling: By default, your polling method will be ARMPolling. Pass in False for this
operation to not poll, or pass in your own initialized polling object for a personal polling
strategy.
:paramtype polling: bool or ~azure.core.polling.PollingMethod
:keyword int polling_interval: Default waiting time between two polls for LRO operations if no
Retry-After header is present.
:return: An instance of LROPoller that returns either OperationalizationCluster or the result
of cls(response)
:rtype:
~azure.core.polling.LROPoller[~azure.mgmt.machinelearningcompute.models.OperationalizationCluster]
:raises ~azure.core.exceptions.HttpResponseError:
"""
@overload
def begin_create_or_update(
self,
resource_group_name: str,
cluster_name: str,
parameters: IO,
*,
content_type: str = "application/json",
**kwargs: Any
) -> LROPoller[_models.OperationalizationCluster]:
"""Create or update an operationalization cluster.
:param resource_group_name: Name of the resource group in which the cluster is located.
Required.
:type resource_group_name: str
:param cluster_name: The name of the cluster. Required.
:type cluster_name: str
:param parameters: Parameters supplied to create or update an Operationalization cluster.
Required.
:type parameters: IO
:keyword content_type: Body Parameter content-type. Content type parameter for binary body.
Default value is "application/json".
:paramtype content_type: str
:keyword callable cls: A custom type or function that will be passed the direct response
:keyword str continuation_token: A continuation token to restart a poller from a saved state.
:keyword polling: By default, your polling method will be ARMPolling. Pass in False for this
operation to not poll, or pass in your own initialized polling object for a personal polling
strategy.
:paramtype polling: bool or ~azure.core.polling.PollingMethod
:keyword int polling_interval: Default waiting time between two polls for LRO operations if no
Retry-After header is present.
:return: An instance of LROPoller that returns either OperationalizationCluster or the result
of cls(response)
:rtype:
~azure.core.polling.LROPoller[~azure.mgmt.machinelearningcompute.models.OperationalizationCluster]
:raises ~azure.core.exceptions.HttpResponseError:
"""
[docs] @distributed_trace
def begin_create_or_update(
self,
resource_group_name: str,
cluster_name: str,
parameters: Union[_models.OperationalizationCluster, IO],
**kwargs: Any
) -> LROPoller[_models.OperationalizationCluster]:
"""Create or update an operationalization cluster.
:param resource_group_name: Name of the resource group in which the cluster is located.
Required.
:type resource_group_name: str
:param cluster_name: The name of the cluster. Required.
:type cluster_name: str
:param parameters: Parameters supplied to create or update an Operationalization cluster. Is
either a model type or a IO type. Required.
:type parameters: ~azure.mgmt.machinelearningcompute.models.OperationalizationCluster or IO
:keyword content_type: Body Parameter content-type. Known values are: 'application/json'.
Default value is None.
:paramtype content_type: str
:keyword callable cls: A custom type or function that will be passed the direct response
:keyword str continuation_token: A continuation token to restart a poller from a saved state.
:keyword polling: By default, your polling method will be ARMPolling. Pass in False for this
operation to not poll, or pass in your own initialized polling object for a personal polling
strategy.
:paramtype polling: bool or ~azure.core.polling.PollingMethod
:keyword int polling_interval: Default waiting time between two polls for LRO operations if no
Retry-After header is present.
:return: An instance of LROPoller that returns either OperationalizationCluster or the result
of cls(response)
:rtype:
~azure.core.polling.LROPoller[~azure.mgmt.machinelearningcompute.models.OperationalizationCluster]
:raises ~azure.core.exceptions.HttpResponseError:
"""
_headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
_params = case_insensitive_dict(kwargs.pop("params", {}) or {})
api_version = kwargs.pop(
"api_version", _params.pop("api-version", self._config.api_version)
) # type: Literal["2017-08-01-preview"]
content_type = kwargs.pop("content_type", _headers.pop("Content-Type", None)) # type: Optional[str]
cls = kwargs.pop("cls", None) # type: ClsType[_models.OperationalizationCluster]
polling = kwargs.pop("polling", True) # type: Union[bool, PollingMethod]
lro_delay = kwargs.pop("polling_interval", self._config.polling_interval)
cont_token = kwargs.pop("continuation_token", None) # type: Optional[str]
if cont_token is None:
raw_result = self._create_or_update_initial( # type: ignore
resource_group_name=resource_group_name,
cluster_name=cluster_name,
parameters=parameters,
api_version=api_version,
content_type=content_type,
cls=lambda x, y, z: x,
headers=_headers,
params=_params,
**kwargs
)
kwargs.pop("error_map", None)
def get_long_running_output(pipeline_response):
deserialized = self._deserialize("OperationalizationCluster", pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
if polling is True:
polling_method = cast(PollingMethod, ARMPolling(lro_delay, **kwargs)) # type: PollingMethod
elif polling is False:
polling_method = cast(PollingMethod, NoPolling())
else:
polling_method = polling
if cont_token:
return LROPoller.from_continuation_token(
polling_method=polling_method,
continuation_token=cont_token,
client=self._client,
deserialization_callback=get_long_running_output,
)
return LROPoller(self._client, raw_result, get_long_running_output, polling_method)
begin_create_or_update.metadata = {"url": "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningCompute/operationalizationClusters/{clusterName}"} # type: ignore
[docs] @distributed_trace
def get(self, resource_group_name: str, cluster_name: str, **kwargs: Any) -> _models.OperationalizationCluster:
"""Gets the operationalization cluster resource view. Note that the credentials are not returned
by this call. Call ListKeys to get them.
:param resource_group_name: Name of the resource group in which the cluster is located.
Required.
:type resource_group_name: str
:param cluster_name: The name of the cluster. Required.
:type cluster_name: str
:keyword callable cls: A custom type or function that will be passed the direct response
:return: OperationalizationCluster or the result of cls(response)
:rtype: ~azure.mgmt.machinelearningcompute.models.OperationalizationCluster
:raises ~azure.core.exceptions.HttpResponseError:
"""
error_map = {
401: ClientAuthenticationError,
404: ResourceNotFoundError,
409: ResourceExistsError,
304: ResourceNotModifiedError,
}
error_map.update(kwargs.pop("error_map", {}) or {})
_headers = kwargs.pop("headers", {}) or {}
_params = case_insensitive_dict(kwargs.pop("params", {}) or {})
api_version = kwargs.pop(
"api_version", _params.pop("api-version", self._config.api_version)
) # type: Literal["2017-08-01-preview"]
cls = kwargs.pop("cls", None) # type: ClsType[_models.OperationalizationCluster]
request = build_get_request(
resource_group_name=resource_group_name,
cluster_name=cluster_name,
subscription_id=self._config.subscription_id,
api_version=api_version,
template_url=self.get.metadata["url"],
headers=_headers,
params=_params,
)
request = _convert_request(request)
request.url = self._client.format_url(request.url) # type: ignore
pipeline_response = self._client._pipeline.run( # type: ignore # pylint: disable=protected-access
request, stream=False, **kwargs
)
response = pipeline_response.http_response
if response.status_code not in [200]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
error = self._deserialize.failsafe_deserialize(_models.ErrorResponseWrapper, pipeline_response)
raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
deserialized = self._deserialize("OperationalizationCluster", pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
get.metadata = {"url": "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningCompute/operationalizationClusters/{clusterName}"} # type: ignore
@overload
def update(
self,
resource_group_name: str,
cluster_name: str,
parameters: _models.OperationalizationClusterUpdateParameters,
*,
content_type: str = "application/json",
**kwargs: Any
) -> _models.OperationalizationCluster:
"""The PATCH operation can be used to update only the tags for a cluster. Use PUT operation to
update other properties.
:param resource_group_name: Name of the resource group in which the cluster is located.
Required.
:type resource_group_name: str
:param cluster_name: The name of the cluster. Required.
:type cluster_name: str
:param parameters: The parameters supplied to patch the cluster. Required.
:type parameters:
~azure.mgmt.machinelearningcompute.models.OperationalizationClusterUpdateParameters
:keyword content_type: Body Parameter content-type. Content type parameter for JSON body.
Default value is "application/json".
:paramtype content_type: str
:keyword callable cls: A custom type or function that will be passed the direct response
:return: OperationalizationCluster or the result of cls(response)
:rtype: ~azure.mgmt.machinelearningcompute.models.OperationalizationCluster
:raises ~azure.core.exceptions.HttpResponseError:
"""
@overload
def update(
self,
resource_group_name: str,
cluster_name: str,
parameters: IO,
*,
content_type: str = "application/json",
**kwargs: Any
) -> _models.OperationalizationCluster:
"""The PATCH operation can be used to update only the tags for a cluster. Use PUT operation to
update other properties.
:param resource_group_name: Name of the resource group in which the cluster is located.
Required.
:type resource_group_name: str
:param cluster_name: The name of the cluster. Required.
:type cluster_name: str
:param parameters: The parameters supplied to patch the cluster. Required.
:type parameters: IO
:keyword content_type: Body Parameter content-type. Content type parameter for binary body.
Default value is "application/json".
:paramtype content_type: str
:keyword callable cls: A custom type or function that will be passed the direct response
:return: OperationalizationCluster or the result of cls(response)
:rtype: ~azure.mgmt.machinelearningcompute.models.OperationalizationCluster
:raises ~azure.core.exceptions.HttpResponseError:
"""
[docs] @distributed_trace
def update(
self,
resource_group_name: str,
cluster_name: str,
parameters: Union[_models.OperationalizationClusterUpdateParameters, IO],
**kwargs: Any
) -> _models.OperationalizationCluster:
"""The PATCH operation can be used to update only the tags for a cluster. Use PUT operation to
update other properties.
:param resource_group_name: Name of the resource group in which the cluster is located.
Required.
:type resource_group_name: str
:param cluster_name: The name of the cluster. Required.
:type cluster_name: str
:param parameters: The parameters supplied to patch the cluster. Is either a model type or a IO
type. Required.
:type parameters:
~azure.mgmt.machinelearningcompute.models.OperationalizationClusterUpdateParameters or IO
:keyword content_type: Body Parameter content-type. Known values are: 'application/json'.
Default value is None.
:paramtype content_type: str
:keyword callable cls: A custom type or function that will be passed the direct response
:return: OperationalizationCluster or the result of cls(response)
:rtype: ~azure.mgmt.machinelearningcompute.models.OperationalizationCluster
:raises ~azure.core.exceptions.HttpResponseError:
"""
error_map = {
401: ClientAuthenticationError,
404: ResourceNotFoundError,
409: ResourceExistsError,
304: ResourceNotModifiedError,
}
error_map.update(kwargs.pop("error_map", {}) or {})
_headers = case_insensitive_dict(kwargs.pop("headers", {}) or {})
_params = case_insensitive_dict(kwargs.pop("params", {}) or {})
api_version = kwargs.pop(
"api_version", _params.pop("api-version", self._config.api_version)
) # type: Literal["2017-08-01-preview"]
content_type = kwargs.pop("content_type", _headers.pop("Content-Type", None)) # type: Optional[str]
cls = kwargs.pop("cls", None) # type: ClsType[_models.OperationalizationCluster]
content_type = content_type or "application/json"
_json = None
_content = None
if isinstance(parameters, (IO, bytes)):
_content = parameters
else:
_json = self._serialize.body(parameters, "OperationalizationClusterUpdateParameters")
request = build_update_request(
resource_group_name=resource_group_name,
cluster_name=cluster_name,
subscription_id=self._config.subscription_id,
api_version=api_version,
content_type=content_type,
json=_json,
content=_content,
template_url=self.update.metadata["url"],
headers=_headers,
params=_params,
)
request = _convert_request(request)
request.url = self._client.format_url(request.url) # type: ignore
pipeline_response = self._client._pipeline.run( # type: ignore # pylint: disable=protected-access
request, stream=False, **kwargs
)
response = pipeline_response.http_response
if response.status_code not in [200]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
error = self._deserialize.failsafe_deserialize(_models.ErrorResponseWrapper, pipeline_response)
raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
deserialized = self._deserialize("OperationalizationCluster", pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
update.metadata = {"url": "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningCompute/operationalizationClusters/{clusterName}"} # type: ignore
def _delete_initial( # pylint: disable=inconsistent-return-statements
self, resource_group_name: str, cluster_name: str, delete_all: Optional[bool] = None, **kwargs: Any
) -> None:
error_map = {
401: ClientAuthenticationError,
404: ResourceNotFoundError,
409: ResourceExistsError,
304: ResourceNotModifiedError,
}
error_map.update(kwargs.pop("error_map", {}) or {})
_headers = kwargs.pop("headers", {}) or {}
_params = case_insensitive_dict(kwargs.pop("params", {}) or {})
api_version = kwargs.pop(
"api_version", _params.pop("api-version", self._config.api_version)
) # type: Literal["2017-08-01-preview"]
cls = kwargs.pop("cls", None) # type: ClsType[None]
request = build_delete_request(
resource_group_name=resource_group_name,
cluster_name=cluster_name,
subscription_id=self._config.subscription_id,
delete_all=delete_all,
api_version=api_version,
template_url=self._delete_initial.metadata["url"],
headers=_headers,
params=_params,
)
request = _convert_request(request)
request.url = self._client.format_url(request.url) # type: ignore
pipeline_response = self._client._pipeline.run( # type: ignore # pylint: disable=protected-access
request, stream=False, **kwargs
)
response = pipeline_response.http_response
if response.status_code not in [202, 204]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
error = self._deserialize.failsafe_deserialize(_models.ErrorResponseWrapper, pipeline_response)
raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)
response_headers = {}
if response.status_code == 202:
response_headers["Location"] = self._deserialize("str", response.headers.get("Location"))
if cls:
return cls(pipeline_response, None, response_headers)
_delete_initial.metadata = {"url": "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningCompute/operationalizationClusters/{clusterName}"} # type: ignore
[docs] @distributed_trace
def begin_delete(
self, resource_group_name: str, cluster_name: str, delete_all: Optional[bool] = None, **kwargs: Any
) -> LROPoller[None]:
"""Deletes the specified cluster.
:param resource_group_name: Name of the resource group in which the cluster is located.
Required.
:type resource_group_name: str
:param cluster_name: The name of the cluster. Required.
:type cluster_name: str
:param delete_all: If true, deletes all resources associated with this cluster. Default value
is None.
:type delete_all: bool
:keyword callable cls: A custom type or function that will be passed the direct response
:keyword str continuation_token: A continuation token to restart a poller from a saved state.
:keyword polling: By default, your polling method will be ARMPolling. Pass in False for this
operation to not poll, or pass in your own initialized polling object for a personal polling
strategy.
:paramtype polling: bool or ~azure.core.polling.PollingMethod
:keyword int polling_interval: Default waiting time between two polls for LRO operations if no
Retry-After header is present.
:return: An instance of LROPoller that returns either None or the result of cls(response)
:rtype: ~azure.core.polling.LROPoller[None]
:raises ~azure.core.exceptions.HttpResponseError:
"""
_headers = kwargs.pop("headers", {}) or {}
_params = case_insensitive_dict(kwargs.pop("params", {}) or {})
api_version = kwargs.pop(
"api_version", _params.pop("api-version", self._config.api_version)
) # type: Literal["2017-08-01-preview"]
cls = kwargs.pop("cls", None) # type: ClsType[None]
polling = kwargs.pop("polling", True) # type: Union[bool, PollingMethod]
lro_delay = kwargs.pop("polling_interval", self._config.polling_interval)
cont_token = kwargs.pop("continuation_token", None) # type: Optional[str]
if cont_token is None:
raw_result = self._delete_initial( # type: ignore
resource_group_name=resource_group_name,
cluster_name=cluster_name,
delete_all=delete_all,
api_version=api_version,
cls=lambda x, y, z: x,
headers=_headers,
params=_params,
**kwargs
)
kwargs.pop("error_map", None)
def get_long_running_output(pipeline_response): # pylint: disable=inconsistent-return-statements
if cls:
return cls(pipeline_response, None, {})
if polling is True:
polling_method = cast(PollingMethod, ARMPolling(lro_delay, **kwargs)) # type: PollingMethod
elif polling is False:
polling_method = cast(PollingMethod, NoPolling())
else:
polling_method = polling
if cont_token:
return LROPoller.from_continuation_token(
polling_method=polling_method,
continuation_token=cont_token,
client=self._client,
deserialization_callback=get_long_running_output,
)
return LROPoller(self._client, raw_result, get_long_running_output, polling_method)
begin_delete.metadata = {"url": "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningCompute/operationalizationClusters/{clusterName}"} # type: ignore
[docs] @distributed_trace
def list_keys(
self, resource_group_name: str, cluster_name: str, **kwargs: Any
) -> _models.OperationalizationClusterCredentials:
"""Gets the credentials for the specified cluster such as Storage, ACR and ACS credentials. This
is a long running operation because it fetches keys from dependencies.
:param resource_group_name: Name of the resource group in which the cluster is located.
Required.
:type resource_group_name: str
:param cluster_name: The name of the cluster. Required.
:type cluster_name: str
:keyword callable cls: A custom type or function that will be passed the direct response
:return: OperationalizationClusterCredentials or the result of cls(response)
:rtype: ~azure.mgmt.machinelearningcompute.models.OperationalizationClusterCredentials
:raises ~azure.core.exceptions.HttpResponseError:
"""
error_map = {
401: ClientAuthenticationError,
404: ResourceNotFoundError,
409: ResourceExistsError,
304: ResourceNotModifiedError,
}
error_map.update(kwargs.pop("error_map", {}) or {})
_headers = kwargs.pop("headers", {}) or {}
_params = case_insensitive_dict(kwargs.pop("params", {}) or {})
api_version = kwargs.pop(
"api_version", _params.pop("api-version", self._config.api_version)
) # type: Literal["2017-08-01-preview"]
cls = kwargs.pop("cls", None) # type: ClsType[_models.OperationalizationClusterCredentials]
request = build_list_keys_request(
resource_group_name=resource_group_name,
cluster_name=cluster_name,
subscription_id=self._config.subscription_id,
api_version=api_version,
template_url=self.list_keys.metadata["url"],
headers=_headers,
params=_params,
)
request = _convert_request(request)
request.url = self._client.format_url(request.url) # type: ignore
pipeline_response = self._client._pipeline.run( # type: ignore # pylint: disable=protected-access
request, stream=False, **kwargs
)
response = pipeline_response.http_response
if response.status_code not in [200]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
raise HttpResponseError(response=response, error_format=ARMErrorFormat)
deserialized = self._deserialize("OperationalizationClusterCredentials", pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
list_keys.metadata = {"url": "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningCompute/operationalizationClusters/{clusterName}/listKeys"} # type: ignore
[docs] @distributed_trace
def check_system_services_updates_available(
self, resource_group_name: str, cluster_name: str, **kwargs: Any
) -> _models.CheckSystemServicesUpdatesAvailableResponse:
"""Checks if updates are available for system services in the cluster.
:param resource_group_name: Name of the resource group in which the cluster is located.
Required.
:type resource_group_name: str
:param cluster_name: The name of the cluster. Required.
:type cluster_name: str
:keyword callable cls: A custom type or function that will be passed the direct response
:return: CheckSystemServicesUpdatesAvailableResponse or the result of cls(response)
:rtype: ~azure.mgmt.machinelearningcompute.models.CheckSystemServicesUpdatesAvailableResponse
:raises ~azure.core.exceptions.HttpResponseError:
"""
error_map = {
401: ClientAuthenticationError,
404: ResourceNotFoundError,
409: ResourceExistsError,
304: ResourceNotModifiedError,
}
error_map.update(kwargs.pop("error_map", {}) or {})
_headers = kwargs.pop("headers", {}) or {}
_params = case_insensitive_dict(kwargs.pop("params", {}) or {})
api_version = kwargs.pop(
"api_version", _params.pop("api-version", self._config.api_version)
) # type: Literal["2017-08-01-preview"]
cls = kwargs.pop("cls", None) # type: ClsType[_models.CheckSystemServicesUpdatesAvailableResponse]
request = build_check_system_services_updates_available_request(
resource_group_name=resource_group_name,
cluster_name=cluster_name,
subscription_id=self._config.subscription_id,
api_version=api_version,
template_url=self.check_system_services_updates_available.metadata["url"],
headers=_headers,
params=_params,
)
request = _convert_request(request)
request.url = self._client.format_url(request.url) # type: ignore
pipeline_response = self._client._pipeline.run( # type: ignore # pylint: disable=protected-access
request, stream=False, **kwargs
)
response = pipeline_response.http_response
if response.status_code not in [200]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
raise HttpResponseError(response=response, error_format=ARMErrorFormat)
deserialized = self._deserialize("CheckSystemServicesUpdatesAvailableResponse", pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
check_system_services_updates_available.metadata = {"url": "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningCompute/operationalizationClusters/{clusterName}/checkSystemServicesUpdatesAvailable"} # type: ignore
def _update_system_services_initial(
self, resource_group_name: str, cluster_name: str, **kwargs: Any
) -> Optional[_models.UpdateSystemServicesResponse]:
error_map = {
401: ClientAuthenticationError,
404: ResourceNotFoundError,
409: ResourceExistsError,
304: ResourceNotModifiedError,
}
error_map.update(kwargs.pop("error_map", {}) or {})
_headers = kwargs.pop("headers", {}) or {}
_params = case_insensitive_dict(kwargs.pop("params", {}) or {})
api_version = kwargs.pop(
"api_version", _params.pop("api-version", self._config.api_version)
) # type: Literal["2017-08-01-preview"]
cls = kwargs.pop("cls", None) # type: ClsType[Optional[_models.UpdateSystemServicesResponse]]
request = build_update_system_services_request(
resource_group_name=resource_group_name,
cluster_name=cluster_name,
subscription_id=self._config.subscription_id,
api_version=api_version,
template_url=self._update_system_services_initial.metadata["url"],
headers=_headers,
params=_params,
)
request = _convert_request(request)
request.url = self._client.format_url(request.url) # type: ignore
pipeline_response = self._client._pipeline.run( # type: ignore # pylint: disable=protected-access
request, stream=False, **kwargs
)
response = pipeline_response.http_response
if response.status_code not in [200, 202]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
raise HttpResponseError(response=response, error_format=ARMErrorFormat)
deserialized = None
response_headers = {}
if response.status_code == 200:
deserialized = self._deserialize("UpdateSystemServicesResponse", pipeline_response)
if response.status_code == 202:
response_headers["Location"] = self._deserialize("str", response.headers.get("Location"))
if cls:
return cls(pipeline_response, deserialized, response_headers)
return deserialized
_update_system_services_initial.metadata = {"url": "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningCompute/operationalizationClusters/{clusterName}/updateSystemServices"} # type: ignore
[docs] @distributed_trace
def begin_update_system_services(
self, resource_group_name: str, cluster_name: str, **kwargs: Any
) -> LROPoller[_models.UpdateSystemServicesResponse]:
"""Updates system services in a cluster.
:param resource_group_name: Name of the resource group in which the cluster is located.
Required.
:type resource_group_name: str
:param cluster_name: The name of the cluster. Required.
:type cluster_name: str
:keyword callable cls: A custom type or function that will be passed the direct response
:keyword str continuation_token: A continuation token to restart a poller from a saved state.
:keyword polling: By default, your polling method will be ARMPolling. Pass in False for this
operation to not poll, or pass in your own initialized polling object for a personal polling
strategy.
:paramtype polling: bool or ~azure.core.polling.PollingMethod
:keyword int polling_interval: Default waiting time between two polls for LRO operations if no
Retry-After header is present.
:return: An instance of LROPoller that returns either UpdateSystemServicesResponse or the
result of cls(response)
:rtype:
~azure.core.polling.LROPoller[~azure.mgmt.machinelearningcompute.models.UpdateSystemServicesResponse]
:raises ~azure.core.exceptions.HttpResponseError:
"""
_headers = kwargs.pop("headers", {}) or {}
_params = case_insensitive_dict(kwargs.pop("params", {}) or {})
api_version = kwargs.pop(
"api_version", _params.pop("api-version", self._config.api_version)
) # type: Literal["2017-08-01-preview"]
cls = kwargs.pop("cls", None) # type: ClsType[_models.UpdateSystemServicesResponse]
polling = kwargs.pop("polling", True) # type: Union[bool, PollingMethod]
lro_delay = kwargs.pop("polling_interval", self._config.polling_interval)
cont_token = kwargs.pop("continuation_token", None) # type: Optional[str]
if cont_token is None:
raw_result = self._update_system_services_initial( # type: ignore
resource_group_name=resource_group_name,
cluster_name=cluster_name,
api_version=api_version,
cls=lambda x, y, z: x,
headers=_headers,
params=_params,
**kwargs
)
kwargs.pop("error_map", None)
def get_long_running_output(pipeline_response):
deserialized = self._deserialize("UpdateSystemServicesResponse", pipeline_response)
if cls:
return cls(pipeline_response, deserialized, {})
return deserialized
if polling is True:
polling_method = cast(PollingMethod, ARMPolling(lro_delay, **kwargs)) # type: PollingMethod
elif polling is False:
polling_method = cast(PollingMethod, NoPolling())
else:
polling_method = polling
if cont_token:
return LROPoller.from_continuation_token(
polling_method=polling_method,
continuation_token=cont_token,
client=self._client,
deserialization_callback=get_long_running_output,
)
return LROPoller(self._client, raw_result, get_long_running_output, polling_method)
begin_update_system_services.metadata = {"url": "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningCompute/operationalizationClusters/{clusterName}/updateSystemServices"} # type: ignore
[docs] @distributed_trace
def list_by_resource_group(
self, resource_group_name: str, skiptoken: Optional[str] = None, **kwargs: Any
) -> Iterable["_models.OperationalizationCluster"]:
"""Gets the clusters in the specified resource group.
:param resource_group_name: Name of the resource group in which the cluster is located.
Required.
:type resource_group_name: str
:param skiptoken: Continuation token for pagination. Default value is None.
:type skiptoken: str
:keyword callable cls: A custom type or function that will be passed the direct response
:return: An iterator like instance of either OperationalizationCluster or the result of
cls(response)
:rtype:
~azure.core.paging.ItemPaged[~azure.mgmt.machinelearningcompute.models.OperationalizationCluster]
:raises ~azure.core.exceptions.HttpResponseError:
"""
_headers = kwargs.pop("headers", {}) or {}
_params = case_insensitive_dict(kwargs.pop("params", {}) or {})
api_version = kwargs.pop(
"api_version", _params.pop("api-version", self._config.api_version)
) # type: Literal["2017-08-01-preview"]
cls = kwargs.pop("cls", None) # type: ClsType[_models.PaginatedOperationalizationClustersList]
error_map = {
401: ClientAuthenticationError,
404: ResourceNotFoundError,
409: ResourceExistsError,
304: ResourceNotModifiedError,
}
error_map.update(kwargs.pop("error_map", {}) or {})
def prepare_request(next_link=None):
if not next_link:
request = build_list_by_resource_group_request(
resource_group_name=resource_group_name,
subscription_id=self._config.subscription_id,
skiptoken=skiptoken,
api_version=api_version,
template_url=self.list_by_resource_group.metadata["url"],
headers=_headers,
params=_params,
)
request = _convert_request(request)
request.url = self._client.format_url(request.url) # type: ignore
else:
# make call to next link with the client's api-version
_parsed_next_link = urllib.parse.urlparse(next_link)
_next_request_params = case_insensitive_dict(
{
key: [urllib.parse.quote(v) for v in value]
for key, value in urllib.parse.parse_qs(_parsed_next_link.query).items()
}
)
_next_request_params["api-version"] = self._config.api_version
request = HttpRequest(
"GET", urllib.parse.urljoin(next_link, _parsed_next_link.path), params=_next_request_params
)
request = _convert_request(request)
request.url = self._client.format_url(request.url) # type: ignore
request.method = "GET"
return request
def extract_data(pipeline_response):
deserialized = self._deserialize("PaginatedOperationalizationClustersList", pipeline_response)
list_of_elem = deserialized.value
if cls:
list_of_elem = cls(list_of_elem)
return deserialized.next_link or None, iter(list_of_elem)
def get_next(next_link=None):
request = prepare_request(next_link)
pipeline_response = self._client._pipeline.run( # type: ignore # pylint: disable=protected-access
request, stream=False, **kwargs
)
response = pipeline_response.http_response
if response.status_code not in [200]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
raise HttpResponseError(response=response, error_format=ARMErrorFormat)
return pipeline_response
return ItemPaged(get_next, extract_data)
list_by_resource_group.metadata = {"url": "/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningCompute/operationalizationClusters"} # type: ignore
[docs] @distributed_trace
def list_by_subscription_id(
self, skiptoken: Optional[str] = None, **kwargs: Any
) -> Iterable["_models.OperationalizationCluster"]:
"""Gets the operationalization clusters in the specified subscription.
:param skiptoken: Continuation token for pagination. Default value is None.
:type skiptoken: str
:keyword callable cls: A custom type or function that will be passed the direct response
:return: An iterator like instance of either OperationalizationCluster or the result of
cls(response)
:rtype:
~azure.core.paging.ItemPaged[~azure.mgmt.machinelearningcompute.models.OperationalizationCluster]
:raises ~azure.core.exceptions.HttpResponseError:
"""
_headers = kwargs.pop("headers", {}) or {}
_params = case_insensitive_dict(kwargs.pop("params", {}) or {})
api_version = kwargs.pop(
"api_version", _params.pop("api-version", self._config.api_version)
) # type: Literal["2017-08-01-preview"]
cls = kwargs.pop("cls", None) # type: ClsType[_models.PaginatedOperationalizationClustersList]
error_map = {
401: ClientAuthenticationError,
404: ResourceNotFoundError,
409: ResourceExistsError,
304: ResourceNotModifiedError,
}
error_map.update(kwargs.pop("error_map", {}) or {})
def prepare_request(next_link=None):
if not next_link:
request = build_list_by_subscription_id_request(
subscription_id=self._config.subscription_id,
skiptoken=skiptoken,
api_version=api_version,
template_url=self.list_by_subscription_id.metadata["url"],
headers=_headers,
params=_params,
)
request = _convert_request(request)
request.url = self._client.format_url(request.url) # type: ignore
else:
# make call to next link with the client's api-version
_parsed_next_link = urllib.parse.urlparse(next_link)
_next_request_params = case_insensitive_dict(
{
key: [urllib.parse.quote(v) for v in value]
for key, value in urllib.parse.parse_qs(_parsed_next_link.query).items()
}
)
_next_request_params["api-version"] = self._config.api_version
request = HttpRequest(
"GET", urllib.parse.urljoin(next_link, _parsed_next_link.path), params=_next_request_params
)
request = _convert_request(request)
request.url = self._client.format_url(request.url) # type: ignore
request.method = "GET"
return request
def extract_data(pipeline_response):
deserialized = self._deserialize("PaginatedOperationalizationClustersList", pipeline_response)
list_of_elem = deserialized.value
if cls:
list_of_elem = cls(list_of_elem)
return deserialized.next_link or None, iter(list_of_elem)
def get_next(next_link=None):
request = prepare_request(next_link)
pipeline_response = self._client._pipeline.run( # type: ignore # pylint: disable=protected-access
request, stream=False, **kwargs
)
response = pipeline_response.http_response
if response.status_code not in [200]:
map_error(status_code=response.status_code, response=response, error_map=error_map)
raise HttpResponseError(response=response, error_format=ARMErrorFormat)
return pipeline_response
return ItemPaged(get_next, extract_data)
list_by_subscription_id.metadata = {"url": "/subscriptions/{subscriptionId}/providers/Microsoft.MachineLearningCompute/operationalizationClusters"} # type: ignore