本文整理汇总了Python中azureml.core.Workspace方法的典型用法代码示例。如果您正苦于以下问题:Python core.Workspace方法的具体用法?Python core.Workspace怎么用?Python core.Workspace使用的例子?那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类azureml.core
的用法示例。
在下文中一共展示了core.Workspace方法的8个代码示例,这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞,您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: get_workspace_kwargs
# 需要导入模块: from azureml import core [as 别名]
# 或者: from azureml.core import Workspace [as 别名]
def get_workspace_kwargs() -> dict:
"""Get AzureML keyword arguments from environment
The name of this environment variable is set in the Argo workflow template,
and its value should be in the format:
`<subscription_id>:<resource_group>:<workspace_name>`.
Returns
-------
workspace_kwargs: dict
AzureML Workspace configuration to use for remote MLFlow tracking. See
:func:`gordo.builder.mlflow_utils.get_mlflow_client`.
"""
return get_kwargs_from_secret(
"AZUREML_WORKSPACE_STR", ["subscription_id", "resource_group", "workspace_name"]
)
示例2: mlflow_context
# 需要导入模块: from azureml import core [as 别名]
# 或者: from azureml.core import Workspace [as 别名]
def mlflow_context(
name: str,
model_key: str = uuid4().hex,
workspace_kwargs: dict = {},
service_principal_kwargs: dict = {},
):
"""
Generate MLflow logger function with either a local or AzureML backend
Parameters
----------
name: str
The name of the log group to log to (e.g. a model name).
model_key: str
Unique ID of logging run.
workspace_kwargs: dict
AzureML Workspace configuration to use for remote MLFlow tracking. See
:func:`gordo.builder.mlflow_utils.get_mlflow_client`.
service_principal_kwargs: dict
AzureML ServicePrincipalAuthentication keyword arguments. See
:func:`gordo.builder.mlflow_utils.get_mlflow_client`
Example
-------
>>> with tempfile.TemporaryDirectory as tmp_dir:
... mlflow.set_tracking_uri(f"file:{tmp_dir}")
... with mlflow_context("log_group", "unique_key", {}, {}) as (mlflow_client, run_id):
... log_machine(machine) # doctest: +SKIP
"""
mlflow_client = get_mlflow_client(workspace_kwargs, service_principal_kwargs)
run_id = get_run_id(mlflow_client, experiment_name=name, model_key=model_key)
logger.info(
f"MLflow client configured to use {'AzureML' if workspace_kwargs else 'local backend'}"
)
yield mlflow_client, run_id
mlflow_client.set_terminated(run_id)
示例3: get_compute
# 需要导入模块: from azureml import core [as 别名]
# 或者: from azureml.core import Workspace [as 别名]
def get_compute(workspace: Workspace, compute_name: str, vm_size: str, for_batch_scoring: bool = False): # NOQA E501
try:
if compute_name in workspace.compute_targets:
compute_target = workspace.compute_targets[compute_name]
if compute_target and type(compute_target) is AmlCompute:
print("Found existing compute target " + compute_name + " so using it.") # NOQA
else:
e = Env()
compute_config = AmlCompute.provisioning_configuration(
vm_size=vm_size,
vm_priority=e.vm_priority if not for_batch_scoring else e.vm_priority_scoring, # NOQA E501
min_nodes=e.min_nodes if not for_batch_scoring else e.min_nodes_scoring, # NOQA E501
max_nodes=e.max_nodes if not for_batch_scoring else e.max_nodes_scoring, # NOQA E501
idle_seconds_before_scaledown="300"
# #Uncomment the below lines for VNet support
# vnet_resourcegroup_name=vnet_resourcegroup_name,
# vnet_name=vnet_name,
# subnet_name=subnet_name
)
compute_target = ComputeTarget.create(
workspace, compute_name, compute_config
)
compute_target.wait_for_completion(
show_output=True, min_node_count=None, timeout_in_minutes=10
)
return compute_target
except ComputeTargetException as ex:
print(ex)
print("An error occurred trying to provision compute.")
exit(1)
示例4: get_environment
# 需要导入模块: from azureml import core [as 别名]
# 或者: from azureml.core import Workspace [as 别名]
def get_environment(
workspace: Workspace,
environment_name: str,
conda_dependencies_file: str,
create_new: bool = False,
enable_docker: bool = None,
use_gpu: bool = False
):
try:
e = Env()
environments = Environment.list(workspace=workspace)
restored_environment = None
for env in environments:
if env == environment_name:
restored_environment = environments[environment_name]
if restored_environment is None or create_new:
new_env = Environment.from_conda_specification(
environment_name,
os.path.join(e.sources_directory_train, conda_dependencies_file), # NOQA: E501
) # NOQA: E501
restored_environment = new_env
if enable_docker is not None:
restored_environment.docker.enabled = enable_docker
restored_environment.docker.base_image = DEFAULT_GPU_IMAGE if use_gpu else DEFAULT_CPU_IMAGE # NOQA: E501
restored_environment.register(workspace)
if restored_environment is not None:
print(restored_environment)
return restored_environment
except Exception as e:
print(e)
exit(1)
示例5: register_dataset
# 需要导入模块: from azureml import core [as 别名]
# 或者: from azureml.core import Workspace [as 别名]
def register_dataset(
aml_workspace: Workspace,
dataset_name: str,
datastore_name: str,
file_path: str
) -> Dataset:
datastore = Datastore.get(aml_workspace, datastore_name)
dataset = Dataset.Tabular.from_delimited_files(path=(datastore, file_path))
dataset = dataset.register(workspace=aml_workspace,
name=dataset_name,
create_new_version=True)
return dataset
示例6: get_current_workspace
# 需要导入模块: from azureml import core [as 别名]
# 或者: from azureml.core import Workspace [as 别名]
def get_current_workspace() -> Workspace:
"""
Retrieves and returns the current workspace.
Will not work when ran locally.
Parameters:
None
Return:
The current workspace.
"""
run = Run.get_context(allow_offline=False)
experiment = run.experiment
return experiment.workspace
示例7: get_compute
# 需要导入模块: from azureml import core [as 别名]
# 或者: from azureml.core import Workspace [as 别名]
def get_compute(
workspace: Workspace,
dbcomputename: str,
resource_group: str,
dbworkspace: str,
dbaccesstoken: str
):
try:
databricks_compute = DatabricksCompute(
workspace=workspace,
name=dbcomputename)
print('Compute target {} already exists'.format(dbcomputename))
except ComputeTargetException:
print('Compute not found, will use below parameters to attach new one')
print('db_compute_name {}'.format(dbcomputename))
print('db_resource_group {}'.format(resource_group))
print('db_workspace_name {}'.format(dbworkspace))
config = DatabricksCompute.attach_configuration(
resource_group=resource_group,
workspace_name=dbworkspace,
access_token=dbaccesstoken)
databricks_compute = ComputeTarget.attach(
workspace,
dbcomputename,
config)
databricks_compute.wait_for_completion(True)
return databricks_compute
示例8: get_model
# 需要导入模块: from azureml import core [as 别名]
# 或者: from azureml.core import Workspace [as 别名]
def get_model(
model_name: str,
model_version: int = None, # If none, return latest model
tag_name: str = None,
tag_value: str = None,
aml_workspace: Workspace = None
) -> AMLModel:
"""
Retrieves and returns a model from the workspace by its name
and (optional) tag.
Parameters:
aml_workspace (Workspace): aml.core Workspace that the model lives.
model_name (str): name of the model we are looking for
(optional) model_version (str): model version. Latest if not provided.
(optional) tag (str): the tag value & name the model was registered under.
Return:
A single aml model from the workspace that matches the name and tag, or
None.
"""
if aml_workspace is None:
print("No workspace defined - using current experiment workspace.")
aml_workspace = get_current_workspace()
tags = None
if tag_name is not None or tag_value is not None:
# Both a name and value must be specified to use tags.
if tag_name is None or tag_value is None:
raise ValueError(
"model_tag_name and model_tag_value should both be supplied"
+ "or excluded" # NOQA: E501
)
tags = [[tag_name, tag_value]]
model = None
if model_version is not None:
# TODO(tcare): Finding a specific version currently expects exceptions
# to propagate in the case we can't find the model. This call may
# result in a WebserviceException that may or may not be due to the
# model not existing.
model = AMLModel(
aml_workspace,
name=model_name,
version=model_version,
tags=tags)
else:
models = AMLModel.list(
aml_workspace, name=model_name, tags=tags, latest=True)
if len(models) == 1:
model = models[0]
elif len(models) > 1:
raise Exception("Expected only one model")
return model