def get_ml_client(workspace_name: str) -> MLClient:
cred = DefaultAzureCredential()
if workspace_name == "Alexander256V100":
rv = MLClient(cred, "79f57c16-00fe-48da-87d4-5192e86cd047",
"Alexander256", workspace_name)
elif workspace_name == "aml1p-ml-wus2":
rv = MLClient(cred, "48bbc269-ce89-4f6f-9a12-c6f91fcb772d", "aml1p-rg",
workspace_name)
else:
raise ValueError(f"Workspace {workspace_name} is not known")
return rv
def ml_datastore_attach_blob(
cmd,
resource_group_name,
workspace_name,
account_name,
container_name,
name,
account_key=None,
sas_token=None,
protocol=None,
endpoint=None,
):
subscription_id = get_subscription_id(cmd.cli_ctx)
ml_client = MLClient(
subscription_id=subscription_id,
resource_group_name=resource_group_name,
default_workspace_name=workspace_name,
credential=AzureCliCredential(),
)
return ml_client.datastores.attach_azure_blob_storage(
name,
container_name,
account_name,
account_key=account_key,
sas_token=sas_token,
protocol=protocol,
endpoint=endpoint,
)
def ml_model_create(cmd,
resource_group_name,
workspace_name,
name=None,
version=None,
file=None,
path=None,
params_override=[]):
subscription_id = get_subscription_id(cmd.cli_ctx)
ml_client = MLClient(
subscription_id=subscription_id,
resource_group_name=resource_group_name,
default_workspace_name=workspace_name,
credential=AzureCliCredential(),
)
if name is not None:
params_override.append({"name": name})
if version is not None:
params_override.append({"version": version})
if path is not None:
params_override.append({"asset_path": path})
try:
return ml_client.model.create_or_update(
file=file, params_override=params_override)
except Exception as err:
print_error_and_exit(str(err))
def ml_workspace_list(cmd, resource_group_name):
subscription_id = get_subscription_id(cmd.cli_ctx)
ml_client = MLClient(subscription_id=subscription_id,
resource_group_name=resource_group_name,
credential=AzureCliCredential())
return ml_client.workspaces.list()
def client() -> MLClient:
"""return a machine learning client using default e2e testing workspace"""
subscription_id = "4faaaf21-663f-4391-96fd-47197c630979"
resource_group_name = "static_sdk_cli_v2_test_e2e"
workspace_name = "sdk_vnext_cli"
return MLClient(subscription_id,
resource_group_name,
default_workspace_name=workspace_name)
def mock_machinelearning_client() -> MLClient:
yield MLClient(
subscription_id=Test_Subscription,
resource_group_name=Test_Resource_Group,
default_workspace_name=Test_Workspace_Name,
base_url=Test_Base_Url,
credential=Mock(spec_set=DefaultAzureCredential),
)
def ml_data_delete(cmd, resource_group_name, workspace_name, name, version):
subscription_id = get_subscription_id(cmd.cli_ctx)
ml_client = MLClient(
subscription_id=subscription_id,
resource_group_name=resource_group_name,
default_workspace_name=workspace_name,
credential=AzureCliCredential(),
)
return ml_client.data.delete(name=name, version=version)
def ml_data_list(cmd, resource_group_name, workspace_name, name=None):
subscription_id = get_subscription_id(cmd.cli_ctx)
ml_client = MLClient(
subscription_id=subscription_id,
resource_group_name=resource_group_name,
default_workspace_name=workspace_name,
credential=AzureCliCredential(),
)
return ml_client.data.list(name=name)
def ml_compute_show(cmd, resource_group_name, workspace_name, name):
subscription_id = get_subscription_id(cmd.cli_ctx)
ml_client = MLClient(
subscription_id=subscription_id,
resource_group_name=resource_group_name,
default_workspace_name=workspace_name,
credential=AzureCliCredential(),
)
return ml_client.computes.get(name=name)
def ml_datastore_detach(cmd, resource_group_name, workspace_name,
datastore_name):
subscription_id = get_subscription_id(cmd.cli_ctx)
ml_client = MLClient(
subscription_id=subscription_id,
resource_group_name=resource_group_name,
default_workspace_name=workspace_name,
credential=AzureCliCredential(),
)
return ml_client.datastores.delete(datastore_name)
def ml_environment_list(cmd, resource_group_name, workspace_name):
subscription_id = get_subscription_id(cmd.cli_ctx)
ml_client = MLClient(
subscription_id=subscription_id,
resource_group_name=resource_group_name,
default_workspace_name=workspace_name,
credential=AzureCliCredential(),
)
return ml_client.environments.list()
def ml_datastore_show(cmd,
resource_group_name,
workspace_name,
datastore_name,
include_secrets=False):
subscription_id = get_subscription_id(cmd.cli_ctx)
ml_client = MLClient(subscription_id=subscription_id,
resource_group_name=resource_group_name,
default_workspace_name=workspace_name)
return ml_client.datastores.show(datastore_name,
include_secrets=include_secrets)
def ml_endpoint_list(cmd,
resource_group_name,
workspace_name,
type=ONLINE_ENDPOINT_TYPE):
subscription_id = get_subscription_id(cmd.cli_ctx)
ml_client = MLClient(
subscription_id=subscription_id,
resource_group_name=resource_group_name,
default_workspace_name=workspace_name,
credential=AzureCliCredential(),
)
return ml_client.endpoints.list(type=type)
def ml_job_stream(cmd, resource_group_name, workspace_name, name):
subscription_id = get_subscription_id(cmd.cli_ctx)
ml_client = MLClient(
subscription_id=subscription_id,
resource_group_name=resource_group_name,
default_workspace_name=workspace_name,
credential=AzureCliCredential(),
)
try:
ml_client.jobs.stream_logs(job_name=name)
except Exception as err:
print_error_and_exit(str(err))
def ml_job_show(cmd, resource_group_name, workspace_name, name):
subscription_id = get_subscription_id(cmd.cli_ctx)
ml_client = MLClient(
subscription_id=subscription_id,
resource_group_name=resource_group_name,
default_workspace_name=workspace_name,
credential=AzureCliCredential(),
)
try:
job_yaml = ml_client.jobs.get(name)
return dump_with_warnings(job_yaml)
except Exception as err:
print_error_and_exit(str(err))
def ml_job_list(cmd, resource_group_name, workspace_name):
subscription_id = get_subscription_id(cmd.cli_ctx)
ml_client = MLClient(
subscription_id=subscription_id,
resource_group_name=resource_group_name,
default_workspace_name=workspace_name,
credential=AzureCliCredential(),
)
try:
job_list = ml_client.jobs.list()
return list(map(lambda x: dump_with_warnings(ml_client, x), job_list))
except Exception as err:
print_error_and_exit(str(err))
def ml_data_create(cmd,
resource_group_name,
workspace_name,
name=None,
version=None,
file=None):
subscription_id = get_subscription_id(cmd.cli_ctx)
ml_client = MLClient(
subscription_id=subscription_id,
resource_group_name=resource_group_name,
default_workspace_name=workspace_name,
credential=AzureCliCredential(),
)
return ml_client.data.create_or_update(name=name,
version=version,
yaml_path=file)
def _ml_job_update(cmd,
resource_group_name,
workspace_name,
parameters: Dict = None):
subscription_id = get_subscription_id(cmd.cli_ctx)
ml_client = MLClient(subscription_id=subscription_id,
resource_group_name=resource_group_name,
default_workspace_name=workspace_name,
credential=AzureCliCredential())
try:
rest_obj = ml_client.jobs._submit(yaml_job=parameters)
return rest_obj
except Exception as err:
print_error_and_exit(str(err))
def ml_datastore_list(
cmd,
resource_group_name,
workspace_name,
include_secrets=False,
skip_token=None,
count=None,
is_default=None,
names=None,
search_text=None,
order_by=None,
order_by_asc=None,
):
subscription_id = get_subscription_id(cmd.cli_ctx)
ml_client = MLClient(subscription_id=subscription_id,
resource_group_name=resource_group_name,
default_workspace_name=workspace_name)
return ml_client.datastores.list(include_secrets=include_secrets)
def ml_environment_show(cmd,
resource_group_name,
workspace_name,
environment_name,
environment_version=None):
subscription_id = get_subscription_id(cmd.cli_ctx)
ml_client = MLClient(
subscription_id=subscription_id,
resource_group_name=resource_group_name,
default_workspace_name=workspace_name,
credential=AzureCliCredential(),
)
if environment_version is None:
return ml_client.environments.get_latest_version(
environment_name=environment_name)
return ml_client.environments.get(environment_name=environment_name,
environment_version=environment_version)
def ml_endpoint_get_deployment_logs(cmd,
resource_group_name,
workspace_name,
name,
deployment,
tail,
type=ONLINE_ENDPOINT_TYPE,
container=None):
subscription_id = get_subscription_id(cmd.cli_ctx)
ml_client = MLClient(
subscription_id=subscription_id,
resource_group_name=resource_group_name,
default_workspace_name=workspace_name,
credential=AzureCliCredential(),
)
return ml_client.endpoints.get_deployment_logs(type=type,
endpoint_name=name,
deployment_name=deployment,
tail=tail,
container_type=container)
def ml_data_upload(cmd,
resource_group_name,
workspace_name,
name,
version,
path,
datastore=None,
description=None):
subscription_id = get_subscription_id(cmd.cli_ctx)
ml_client = MLClient(
subscription_id=subscription_id,
resource_group_name=resource_group_name,
default_workspace_name=workspace_name,
credential=AzureCliCredential(),
)
return ml_client.data.upload(name=name,
version=version,
description=description,
local_path=path,
linked_service_name=datastore)
def ml_job_create(cmd,
resource_group_name,
workspace_name,
name=None,
file=None,
save_as=None,
stream=False,
params_override=[]):
subscription_id = get_subscription_id(cmd.cli_ctx)
ml_client = MLClient(
subscription_id=subscription_id,
resource_group_name=resource_group_name,
default_workspace_name=workspace_name,
credential=AzureCliCredential(),
)
try:
if name is not None:
params_override.append({"name": name})
job = ml_client.jobs.submit(job_name=name,
save_as_name=save_as,
file=file,
params_override=params_override)
if stream:
ml_client.jobs.stream_logs(job)
return dump_with_warnings(ml_client, job)
except Exception as err:
if str(
err
) is not None and "Only tags and properties can be updated." in str(
err):
print_error_and_exit(
"(User error) A job with that name already exists. Use the --name option to provide a new name."
)
else:
print_error_and_exit(str(err))
def ml_environment_create(cmd,
resource_group_name,
workspace_name,
file,
environment_name=None,
params_override=None):
# TODO : Once EMS supports accepting a version will enable it
# environment_version=None
subscription_id = get_subscription_id(cmd.cli_ctx)
ml_client = MLClient(
subscription_id=subscription_id,
resource_group_name=resource_group_name,
default_workspace_name=workspace_name,
credential=AzureCliCredential(),
)
return ml_client.environments.create_or_update(
environment_name=environment_name,
environment_version=None,
file=file,
params_override=params_override)
def ml_code_create(cmd,
resource_group_name,
workspace_name,
name,
directory,
datastore_name=None,
version=None,
show_progress=True):
subscription_id = get_subscription_id(cmd.cli_ctx)
ml_client = MLClient(
subscription_id=subscription_id,
resource_group_name=resource_group_name,
default_workspace_name=workspace_name,
credential=AzureCliCredential(),
)
return ml_client.code.create(name=name,
directory=directory,
version=version,
datastore_name=datastore_name,
show_progress=show_progress)
def ml_job_download(cmd,
resource_group_name,
workspace_name,
name,
outputs=False,
download_path=None):
subscription_id = get_subscription_id(cmd.cli_ctx)
ml_client = MLClient(
subscription_id=subscription_id,
resource_group_name=resource_group_name,
default_workspace_name=workspace_name,
credential=AzureCliCredential(),
)
try:
if not download_path:
download_path = cmd.cli_ctx.local_context.current_dir
return ml_client.jobs.download(job_name=name,
logs_only=not outputs,
download_path=download_path)
except Exception as err:
print_error_and_exit(str(err))
def test_set_default_workspace_name(self, mock_machinelearning_client: MLClient) -> None:
default_ws = "default"
mock_machinelearning_client.default_workspace_name = default_ws
assert default_ws == mock_machinelearning_client.default_workspace_name