[AQUA Telemetry] Update MD Tracking

ads/aqua/modeldeployment/constants.py

@@ -1,5 +1,5 @@
 #!/usr/bin/env python
-# Copyright (c) 2024 Oracle and/or its affiliates.
+# Copyright (c) 2024, 2025 Oracle and/or its affiliates.
 # Licensed under the Universal Permissive License v 1.0 as shown at https://oss.oracle.com/licenses/upl/
 
 """
@@ -8,3 +8,6 @@
 
 This module contains constants used in Aqua Model Deployment.
 """
+
+DEFAULT_WAIT_TIME = 12000
+DEFAULT_POLL_INTERVAL = 10

ads/aqua/modeldeployment/deployment.py

@@ -55,6 +55,7 @@
     extract_base_model_from_ft,
     extract_fine_tune_artifacts_path,
 )
+from ads.aqua.modeldeployment.constants import DEFAULT_POLL_INTERVAL, DEFAULT_WAIT_TIME
 from ads.aqua.modeldeployment.entities import (
     AquaDeployment,
     AquaDeploymentConfig,
@@ -65,12 +66,16 @@
     ModelDeploymentConfigSummary,
 )
 from ads.aqua.modeldeployment.utils import MultiModelDeploymentConfigLoader
+from ads.common.decorator.threaded import thread_pool
 from ads.common.object_storage_details import ObjectStorageDetails
 from ads.common.utils import UNKNOWN, get_log_links
+from ads.common.work_request import DataScienceWorkRequest
 from ads.config import (
     AQUA_DEPLOYMENT_CONTAINER_CMD_VAR_METADATA_NAME,
     AQUA_DEPLOYMENT_CONTAINER_METADATA_NAME,
     AQUA_DEPLOYMENT_CONTAINER_URI_METADATA_NAME,
+    AQUA_TELEMETRY_BUCKET,
+    AQUA_TELEMETRY_BUCKET_NS,
     COMPARTMENT_OCID,
     PROJECT_OCID,
 )
@@ -508,6 +513,9 @@ def _create(
                     if key not in env_var:
                         env_var.update(env)
 
+        env_var.update({"AQUA_TELEMETRY_BUCKET_NS": AQUA_TELEMETRY_BUCKET_NS})
+        env_var.update({"AQUA_TELEMETRY_BUCKET": AQUA_TELEMETRY_BUCKET})
+
         logger.info(f"Env vars used for deploying {aqua_model.id} :{env_var}")
 
         tags = {**tags, **(create_deployment_details.freeform_tags or {})}
@@ -788,7 +796,15 @@ def _create_deployment(
 
         deployment_id = deployment.id
         logger.info(
-            f"Aqua model deployment {deployment_id} created for model {aqua_model_id}."
+            f"Aqua model deployment {deployment_id} created for model {aqua_model_id}. Work request Id is {deployment.dsc_model_deployment.workflow_req_id}"
+        )
+
+        thread_pool.submit(
+            self.get_deployment_status,
+            deployment_id,
+            deployment.dsc_model_deployment.workflow_req_id,
+            model_type,
+            model_name,
         )
 
         # we arbitrarily choose last 8 characters of OCID to identify MD in telemetry
@@ -1313,3 +1329,62 @@ def list_shapes(self, **kwargs) -> List[ComputeShapeSummary]:
             )
             for oci_shape in oci_shapes
         ]
+
+    def get_deployment_status(
+        self,
+        model_deployment_id: str,
+        work_request_id: str,
+        model_type: str,
+        model_name: str,
+    ) -> None:
+        """Waits for the data science  model deployment to be completed and log its status in telemetry.
+
+        Parameters
+        ----------
+
+        model_deployment_id: str
+            The id of the deployed aqua model.
+        work_request_id: str
+            The work request Id of the model deployment.
+        model_type: str
+            The type of aqua model to be deployed. Allowed values are: `custom`, `service` and `multi_model`.
+
+        Returns
+        -------
+        AquaDeployment
+            An Aqua deployment instance.
+        """
+        ocid = get_ocid_substring(model_deployment_id, key_len=8)
+        telemetry_kwargs = {"ocid": ocid}
+
+        data_science_work_request: DataScienceWorkRequest = DataScienceWorkRequest(
+            work_request_id
+        )
+
+        try:
+            data_science_work_request.wait_work_request(
+                progress_bar_description="Creating model deployment",
+                max_wait_time=DEFAULT_WAIT_TIME,
+                poll_interval=DEFAULT_POLL_INTERVAL,
+            )
+        except Exception:
+            if data_science_work_request._error_message:
+                error_str = ""
+                for error in data_science_work_request._error_message:
+                    error_str = error_str + " " + error.message
+
+            self.telemetry.record_event(
+                category=f"aqua/{model_type}/deployment/status",
+                action="FAILED",
+                detail=error_str,
+                value=model_name,
+                **telemetry_kwargs,
+            )
+
+        else:
+            self.telemetry.record_event_async(
+                category=f"aqua/{model_type}/deployment/status",
+                action="SUCCEEDED",
+                value=model_name,
+                **telemetry_kwargs,
+            )

ads/common/work_request.py

@@ -1,7 +1,5 @@
 #!/usr/bin/env python
-# -*- coding: utf-8; -*-
-
-# Copyright (c) 2024 Oracle and/or its affiliates.
+# Copyright (c) 2024, 2025 Oracle and/or its affiliates.
 # Licensed under the Universal Permissive License v 1.0 as shown at https://oss.oracle.com/licenses/upl/
 
 import logging
@@ -12,6 +10,7 @@
 import oci
 from oci import Signer
 from tqdm.auto import tqdm
+
 from ads.common.oci_datascience import OCIDataScienceMixin
 
 logger = logging.getLogger(__name__)
@@ -20,10 +19,10 @@
 DEFAULT_WAIT_TIME = 1200
 DEFAULT_POLL_INTERVAL = 10
 WORK_REQUEST_PERCENTAGE = 100
-# default tqdm progress bar format: 
+# default tqdm progress bar format:
 # {l_bar}{bar}| {n_fmt}/{total_fmt} [{elapsed}<{remaining}, ' '{rate_fmt}{postfix}]
 # customize the bar format to remove the {n_fmt}/{total_fmt} from the right side
-DEFAULT_BAR_FORMAT = '{l_bar}{bar}| [{elapsed}<{remaining}, ' '{rate_fmt}{postfix}]'
+DEFAULT_BAR_FORMAT = "{l_bar}{bar}| [{elapsed}<{remaining}, " "{rate_fmt}{postfix}]"
 
 
 class DataScienceWorkRequest(OCIDataScienceMixin):
@@ -32,13 +31,13 @@ class DataScienceWorkRequest(OCIDataScienceMixin):
     """
 
     def __init__(
-        self, 
-        id: str, 
+        self,
+        id: str,
         description: str = "Processing",
-        config: dict = None, 
-        signer: Signer = None, 
-        client_kwargs: dict = None, 
-        **kwargs
+        config: dict = None,
+        signer: Signer = None,
+        client_kwargs: dict = None,
+        **kwargs,
     ) -> None:
         """Initializes ADSWorkRequest object.
 
@@ -49,41 +48,43 @@ def __init__(
         description: str
             Progress bar initial step description (Defaults to `Processing`).
         config : dict, optional
-            OCI API key config dictionary to initialize 
+            OCI API key config dictionary to initialize
             oci.data_science.DataScienceClient (Defaults to None).
         signer : oci.signer.Signer, optional
-            OCI authentication signer to initialize 
+            OCI authentication signer to initialize
             oci.data_science.DataScienceClient (Defaults to None).
         client_kwargs : dict, optional
-            Additional client keyword arguments to initialize 
+            Additional client keyword arguments to initialize
             oci.data_science.DataScienceClient (Defaults to None).
         kwargs:
-            Additional keyword arguments to initialize 
+            Additional keyword arguments to initialize
             oci.data_science.DataScienceClient.
         """
         self.id = id
         self._description = description
         self._percentage = 0
         self._status = None
+        self._error_message = ""
         super().__init__(config, signer, client_kwargs, **kwargs)
-
 
     def _sync(self):
         """Fetches the latest work request information to ADSWorkRequest object."""
         work_request = self.client.get_work_request(self.id).data
-        work_request_logs = self.client.list_work_request_logs(
-            self.id
-        ).data
+        work_request_logs = self.client.list_work_request_logs(self.id).data
 
-        self._percentage= work_request.percent_complete
+        self._percentage = work_request.percent_complete
         self._status = work_request.status
-        self._description = work_request_logs[-1].message if work_request_logs else "Processing"
+        self._description = (
+            work_request_logs[-1].message if work_request_logs else "Processing"
+        )
+        if work_request.status == "FAILED":
+            self._error_message = self.client.list_work_request_errors(self.id).data
 
     def watch(
-        self, 
+        self,
         progress_callback: Callable,
-        max_wait_time: int=DEFAULT_WAIT_TIME,
-        poll_interval: int=DEFAULT_POLL_INTERVAL,
+        max_wait_time: int = DEFAULT_WAIT_TIME,
+        poll_interval: int = DEFAULT_POLL_INTERVAL,
     ):
         """Updates the progress bar with realtime message and percentage until the process is completed.
 
@@ -92,10 +93,10 @@ def watch(
         progress_callback: Callable
             Progress bar callback function.
             It must accept `(percent_change, description)` where `percent_change` is the
-            work request percent complete and `description` is the latest work request log message. 
+            work request percent complete and `description` is the latest work request log message.
         max_wait_time: int
             Maximum amount of time to wait in seconds (Defaults to 1200).
-            Negative implies infinite wait time. 
+            Negative implies infinite wait time.
         poll_interval: int
             Poll interval in seconds (Defaults to 10).
 
@@ -107,7 +108,6 @@ def watch(
 
         start_time = time.time()
         while self._percentage < 100:
-
             seconds_since = time.time() - start_time
             if max_wait_time > 0 and seconds_since >= max_wait_time:
                 logger.error(f"Exceeded max wait time of {max_wait_time} seconds.")
@@ -124,12 +124,14 @@ def watch(
             percent_change = self._percentage - previous_percent_complete
             previous_percent_complete = self._percentage
             progress_callback(
-                percent_change=percent_change,
-                description=self._description
+                percent_change=percent_change, description=self._description
             )
 
             if self._status in WORK_REQUEST_STOP_STATE:
-                if self._status != oci.work_requests.models.WorkRequest.STATUS_SUCCEEDED:
+                if (
+                    self._status
+                    != oci.work_requests.models.WorkRequest.STATUS_SUCCEEDED
+                ):
                     if self._description:
                         raise Exception(self._description)
                     else:
@@ -145,12 +147,12 @@ def watch(
 
     def wait_work_request(
         self,
-        progress_bar_description: str="Processing",
-        max_wait_time: int=DEFAULT_WAIT_TIME,
-        poll_interval: int=DEFAULT_POLL_INTERVAL
+        progress_bar_description: str = "Processing",
+        max_wait_time: int = DEFAULT_WAIT_TIME,
+        poll_interval: int = DEFAULT_POLL_INTERVAL,
     ):
         """Waits for the work request progress bar to be completed.
-        
+
         Parameters
         ----------
         progress_bar_description: str
@@ -160,7 +162,7 @@ def wait_work_request(
             Negative implies infinite wait time.
         poll_interval: int
             Poll interval in seconds (Defaults to 10).
-        
+
         Returns
         -------
         None
@@ -172,7 +174,7 @@ def wait_work_request(
             mininterval=0,
             file=sys.stdout,
             desc=progress_bar_description,
-            bar_format=DEFAULT_BAR_FORMAT
+            bar_format=DEFAULT_BAR_FORMAT,
         ) as pbar:
 
             def progress_callback(percent_change, description):
@@ -184,6 +186,5 @@ def progress_callback(percent_change, description):
             self.watch(
                 progress_callback=progress_callback,
                 max_wait_time=max_wait_time,
-                poll_interval=poll_interval
+                poll_interval=poll_interval,
             )
-

ads/model/service/oci_datascience_model_deployment.py

@@ -185,9 +185,9 @@ def activate(
                 self.id,
             )
 
+
+            self.workflow_req_id = response.headers.get("opc-work-request-id", None)
             if wait_for_completion:
-                self.workflow_req_id = response.headers.get("opc-work-request-id", None)
-
                 try:
                     DataScienceWorkRequest(self.workflow_req_id).wait_work_request(
                         progress_bar_description="Activating model deployment",
@@ -233,11 +233,9 @@ def create(
         response = self.client.create_model_deployment(create_model_deployment_details)
         self.update_from_oci_model(response.data)
         logger.info(f"Creating model deployment `{self.id}`.")
-        print(f"Model Deployment OCID: {self.id}")
 
+        self.workflow_req_id = response.headers.get("opc-work-request-id", None)
         if wait_for_completion:
-            self.workflow_req_id = response.headers.get("opc-work-request-id", None)
-
             try:
                 DataScienceWorkRequest(self.workflow_req_id).wait_work_request(
                     progress_bar_description="Creating model deployment",
@@ -287,10 +285,8 @@ def deactivate(
             response = self.client.deactivate_model_deployment(
                 self.id,
             )
-
+            self.workflow_req_id = response.headers.get("opc-work-request-id", None)
             if wait_for_completion:
-                self.workflow_req_id = response.headers.get("opc-work-request-id", None)
-
                 try:
                     DataScienceWorkRequest(self.workflow_req_id).wait_work_request(
                         progress_bar_description="Deactivating model deployment",
@@ -355,10 +351,9 @@ def delete(
         response = self.client.delete_model_deployment(
             self.id,
         )
-
+
+        self.workflow_req_id = response.headers.get("opc-work-request-id", None)
         if wait_for_completion:
-            self.workflow_req_id = response.headers.get("opc-work-request-id", None)
-
             try:
                 DataScienceWorkRequest(self.workflow_req_id).wait_work_request(
                     progress_bar_description="Deleting model deployment",