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[API] Use a common dependency in model monitoring endpoints #5296

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merged 2 commits into from
Mar 19, 2024
Merged

[API] Use a common dependency in model monitoring endpoints #5296

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614ff66
Change fastapi imports to `from ... import`
jond01 Mar 17, 2024
838bddc
Move the common parameters to a shared dependency
jond01 Mar 17, 2024
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127 changes: 69 additions & 58 deletions server/api/api/endpoints/model_monitoring.py
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Original file line number Diff line number Diff line change
Expand Up @@ -11,9 +11,11 @@
# 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.
#

import fastapi
from dataclasses import dataclass
from typing import Annotated

from fastapi import APIRouter, Depends
from sqlalchemy.orm import Session

import mlrun.common.schemas
Expand All @@ -23,16 +25,50 @@
from server.api.api.endpoints.functions import process_model_monitoring_secret
from server.api.crud.model_monitoring.deployment import MonitoringDeployment

router = fastapi.APIRouter(prefix="/projects/{project}/model-monitoring")
router = APIRouter(prefix="/projects/{project}/model-monitoring")


@dataclass
class _CommonParams:
"""Common parameters for model monitoring endpoints"""

project: str
auth_info: mlrun.common.schemas.AuthInfo
db_session: Session


async def _common_parameters(
project: str,
auth_info: Annotated[
mlrun.common.schemas.AuthInfo, Depends(deps.authenticate_request)
],
db_session: Annotated[Session, Depends(deps.get_db_session)],
) -> _CommonParams:
"""
Verify authorization and return common parameters.

:param project: Project name.
:param auth_info: The auth info of the request.
:param db_session: A session that manages the current dialog with the database.
:returns: A `_CommonParameters` object that contains the input data.
"""
await server.api.utils.auth.verifier.AuthVerifier().query_project_resource_permissions(
resource_type=mlrun.common.schemas.AuthorizationResourceTypes.function,
project_name=project,
resource_name=mlrun.common.schemas.model_monitoring.MonitoringFunctionNames.APPLICATION_CONTROLLER,
action=mlrun.common.schemas.AuthorizationAction.store,
auth_info=auth_info,
)
return _CommonParams(
project=project,
auth_info=auth_info,
db_session=db_session,
)


@router.post("/enable-model-monitoring")
async def enable_model_monitoring(
project: str,
auth_info: mlrun.common.schemas.AuthInfo = fastapi.Depends(
deps.authenticate_request
),
db_session: Session = fastapi.Depends(deps.get_db_session),
commons: Annotated[_CommonParams, Depends(_common_parameters)],
base_period: int = 10,
image: str = "mlrun/mlrun",
):
Expand All @@ -44,91 +80,66 @@ async def enable_model_monitoring(
And the stream function goal is to monitor the log of the data stream. It is triggered when a new log entry
is detected. It processes the new events into statistics that are then written to statistics databases.


:param project: Project name.
:param auth_info: The auth info of the request.
:param db_session: A session that manages the current dialog with the database.
:param base_period: The time period in minutes in which the model monitoring controller function
triggers. By default, the base period is 10 minutes.
:param image: The image of the model monitoring controller, writer & monitoring
stream functions, which are real time nuclio functions.
By default, the image is mlrun/mlrun.
:param commons: The common parameters of the request.
:param base_period: The time period in minutes in which the model monitoring controller function
triggers. By default, the base period is 10 minutes.
:param image: The image of the model monitoring controller, writer & monitoring
stream functions, which are real time nuclio functions.
By default, the image is mlrun/mlrun.
"""

await server.api.utils.auth.verifier.AuthVerifier().query_project_resource_permissions(
resource_type=mlrun.common.schemas.AuthorizationResourceTypes.function,
project_name=project,
resource_name=mlrun.common.schemas.model_monitoring.MonitoringFunctionNames.APPLICATION_CONTROLLER,
action=mlrun.common.schemas.AuthorizationAction.store,
auth_info=auth_info,
)

model_monitoring_access_key = None
if not mlrun.mlconf.is_ce_mode():
# Generate V3IO Access Key
model_monitoring_access_key = process_model_monitoring_secret(
db_session,
project,
commons.db_session,
commons.project,
mlrun.common.schemas.model_monitoring.ProjectSecretKeys.ACCESS_KEY,
)

return MonitoringDeployment().deploy_monitoring_functions(
project=project,
project=commons.project,
model_monitoring_access_key=model_monitoring_access_key,
db_session=db_session,
auth_info=auth_info,
db_session=commons.db_session,
auth_info=commons.auth_info,
image=image,
base_period=base_period,
)


@router.post("/model-monitoring-controller")
async def update_model_monitoring_controller(
project: str,
auth_info: mlrun.common.schemas.AuthInfo = fastapi.Depends(
deps.authenticate_request
),
db_session: Session = fastapi.Depends(deps.get_db_session),
commons: Annotated[_CommonParams, Depends(_common_parameters)],
base_period: int = 10,
image: str = "mlrun/mlrun",
):
"""
Redeploy model monitoring application controller function.
The main goal of the controller function is to handle the monitoring processing and triggering applications.

:param project: Project name.
:param auth_info: The auth info of the request.
:param db_session: A session that manages the current dialog with the database.
:param base_period: The time period in minutes in which the model monitoring controller function
triggers. By default, the base period is 10 minutes.
:param image: The default image of the model monitoring controller job. Note that the writer
function, which is a real time nuclio functino, will be deployed with the same
image. By default, the image is mlrun/mlrun.
:param commons: The common parameters of the request.
:param base_period: The time period in minutes in which the model monitoring controller function
triggers. By default, the base period is 10 minutes.
:param image: The default image of the model monitoring controller job. Note that the writer
function, which is a real time nuclio functino, will be deployed with the same
image. By default, the image is mlrun/mlrun.
"""

await server.api.utils.auth.verifier.AuthVerifier().query_project_resource_permissions(
resource_type=mlrun.common.schemas.AuthorizationResourceTypes.function,
project_name=project,
resource_name=mlrun.common.schemas.model_monitoring.MonitoringFunctionNames.APPLICATION_CONTROLLER,
action=mlrun.common.schemas.AuthorizationAction.store,
auth_info=auth_info,
)

model_monitoring_access_key = None
if not mlrun.mlconf.is_ce_mode():
# Generate V3IO Access Key
model_monitoring_access_key = process_model_monitoring_secret(
db_session,
project,
commons.db_session,
commons.project,
mlrun.common.schemas.model_monitoring.ProjectSecretKeys.ACCESS_KEY,
)
try:
# validate that the model monitoring stream has not yet been deployed
mlrun.runtimes.nuclio.function.get_nuclio_deploy_status(
name=mlrun.common.schemas.model_monitoring.MonitoringFunctionNames.APPLICATION_CONTROLLER,
project=project,
project=commons.project,
tag="",
auth_info=auth_info,
auth_info=commons.auth_info,
)

except mlrun.errors.MLRunNotFoundError:
Expand All @@ -138,10 +149,10 @@ async def update_model_monitoring_controller(
)

return MonitoringDeployment().deploy_model_monitoring_controller(
project=project,
project=commons.project,
model_monitoring_access_key=model_monitoring_access_key,
db_session=db_session,
auth_info=auth_info,
db_session=commons.db_session,
auth_info=commons.auth_info,
controller_image=image,
base_period=base_period,
overwrite=True,
Expand Down