SIENTIAPDE-1309: Update README with Helm instructions and refactor experiment status messages. Also, update values.yaml with new image and configurations.

2025-11-06 15:34:03 -03:00
parent a625ef6c19
commit c7f44a2423
9 changed files with 133 additions and 95 deletions
--- a/README.md
+++ b/README.md
@@ -1037,13 +1037,13 @@ For support and questions:

 ### Create image

-```shell
+```bash
 $ docker build --ssh default --no-cache --progress=plain -t aignosi.azurecr.io/sientia-dataops-model-manager:0.0.0 .
 ```

 ### Create container

-```shell
+```bash
 $ docker run --env-file .env --network="host" --name sientia-dataops-model-manager -d aignosi.azurecr.io/sientia-dataops-model-manager:0.0.0

 $ docker logs -f sientia-dataops-model-manager
@@ -1051,13 +1051,13 @@ $ docker logs -f sientia-dataops-model-manager

 ### Login using access token

-```shell
+```bash
 $ docker login -u bruno-aignosi -p LD/IyZ4vtDI7khRYnH4HzfdTx3toorg6hlCetJM54n+ACRDim3xO aignosi.azurecr.io
 ```

 ### Push image to repository

-```shell
+```bash
 $ docker push aignosi.azurecr.io/sientia-dataops-model-manager:0.0.0
 ```

@@ -1065,40 +1065,42 @@ $ docker push aignosi.azurecr.io/sientia-dataops-model-manager:0.0.0

 ### Reference

-https://www.baeldung.com/ops/kubernetes-helm
+https://aignosi-wiki.atlassian.net/wiki/spaces/IT1/pages/274563074/Como+utilizar+o+Helm+Repo+Privado

-### Create Helm Chart folder
+### Add Helm Chart repository

-```shell
-# inside project root folder
-$ helm create helm-chart
-```
+```bash
+$ helm repo add sientia \
+    https://raw.githubusercontent.com/Aignosi/sientia-dataops-helm-repo/refs/heads/main/ \
+    --username $GITHUB_USER \
+    --password $GITHUB_PASS

-### Helm Lint
+# Update repository
+$ helm repo update

-```shell
-# Firstly, this is a simple command that takes the path to a chart and runs a battery of tests to ensure that the chart is well-formed:
-$ helm lint ./helm-chart
-```
+# List repositories
+$ helm repo list

-### Helm Template
+# List versions of a specific chart
+$ helm search repo sientia --versions

-```shell
-# Also, we've this command to render the template locally for quick feedback:
-$ helm template ./helm-chart
+# List all charts available
+$ helm search repo sientia
+
+# List chart details
+$ helm show all sientia/sientia-module
 ```

 ### Helm Install

 ```shell
-# Once we've verified the chart to be fine, finally, we can run this command to install the chart into the Kubernetes cluster:
-$ helm upgrade --install mlops-bff ./helm-chart -n sientia-core
+$ helm upgrade --install sientia-dataops-model-manager sientia/sientia-module -n sientia --create-namespace -f ./values.yaml --version 0.5.0
 ```

 ### Uninstall Helm Chart

 ```shell
-$ helm uninstall mlops-bff -n sientia-core
+$ helm uninstall sientia-dataops-model-manager -n sientia
 ```

 ---
--- a/model_manager/utils/models/experiment_status.py
+++ b/model_manager/utils/models/experiment_status.py
@@ -15,20 +15,20 @@ class ExperimentStatus(str, Enum):

    Attributes:
        ORCHESTRATOR_VALIDATION_ERROR: Error in the parameters validation.
-        MAGE_WAITING_PROC: Initial status indicating experiment is registered and waiting for processing.
+        ORCHESTRATOR_WAITING_PROC: Initial status indicating experiment is registered and waiting for processing.
        TRAINING_SUCCESS: Training completed successfully with model and metrics calculated.
        TRAINING_ERROR: Training failed due to data issues, model errors, or other exceptions.
-        MLFLOW_SENT: Model successfully saved to MLFlow.
-        MLFLOW_SEND_ERROR: Model saving to MLFlow failed due to connection or serialization errors.
+        TRACKING_SENT: Model successfully saved to MLFlow.
+        TRACKING_SEND_ERROR: Model saving to MLFlow failed due to connection or serialization errors.
        FILE_DELETED: Cleanup completed successfully with all artifacts removed.
        FILE_DELETE_ERROR: Cleanup failed due to file system or MinIO errors.
    """

    ORCHESTRATOR_VALIDATION_ERROR = 'ORCHESTRATOR_VALIDATION_ERROR'
-    MAGE_WAITING_PROC = 'MAGE_WAITING_PROC'
+    ORCHESTRATOR_WAITING_PROC = 'ORCHESTRATOR_WAITING_PROC'
    TRAINING_SUCCESS = 'TRAINING_SUCCESS'
    TRAINING_ERROR = 'TRAINING_ERROR'
-    MLFLOW_SENT = 'MLFLOW_SENT'
-    MLFLOW_SEND_ERROR = 'MLFLOW_SEND_ERROR'
+    TRACKING_SENT = 'TRACKING_SENT'
+    TRACKING_SEND_ERROR = 'TRACKING_SEND_ERROR'
    FILE_DELETED = 'FILE_DELETED'
    FILE_DELETE_ERROR = 'FILE_DELETE_ERROR'
--- a/model_manager/workflows/train_model.py
+++ b/model_manager/workflows/train_model.py
@@ -163,7 +163,7 @@ class TrainModel:
        Validate and convert training parameters from dict to TrainModelParams.

        This method calls the validate_train_params activity to convert and validate
-        the input parameters. On success, updates DB status to MAGE_WAITING_PROC.
+        the input parameters. On success, updates DB status to ORCHESTRATOR_WAITING_PROC.
        On error, updates DB status to ORCHESTRATOR_VALIDATION_ERROR.

        Args:
@@ -192,7 +192,7 @@ class TrainModel:
                metadata=metadata,
                experiment_run_id=experiment_run_id,
                update_type=UpdateType.STATUS,
-                status=ExperimentStatus.MAGE_WAITING_PROC,
+                status=ExperimentStatus.ORCHESTRATOR_WAITING_PROC,
            )

            return train_params
@@ -246,7 +246,7 @@ class TrainModel:
                metadata=metadata,
                experiment_run_id=experiment_run_id,
                update_type=UpdateType.MODEL_SAVED,
-                status=ExperimentStatus.MLFLOW_SENT,
+                status=ExperimentStatus.TRACKING_SENT,
                run_name=train_result.get('run_name'),
            )

@@ -260,7 +260,7 @@ class TrainModel:
            status = ExperimentStatus.TRAINING_ERROR

            if isinstance(e, ModelTrainingError) and (e.model_trained and not e.model_saved):
-                status = ExperimentStatus.MLFLOW_SEND_ERROR
+                status = ExperimentStatus.TRACKING_SEND_ERROR

            await self._update_experiment_run(
                metadata=metadata,
--- a/scripts/run_training_test.py
+++ b/scripts/run_training_test.py
@@ -120,7 +120,7 @@ def insert_experiment_run(file_name: str, request_data: dict) -> int:
                (
                    request_data['experimentName'],
                    request_data['username'],
-                    'MAGE_REQUEST_SENT',
+                    'ORCHESTRATOR_REQUEST_SENT',
                    now,
                    now,
                    MINIO_BUCKET,
--- a/tests/utils/models/test_experiment_status.py
+++ b/tests/utils/models/test_experiment_status.py
@@ -5,11 +5,11 @@ from model_manager.utils.models.experiment_status import ExperimentStatus

 def test_experiment_status_values():
    """Test that all expected status values exist."""
-    assert ExperimentStatus.MAGE_WAITING_PROC == 'MAGE_WAITING_PROC'
+    assert ExperimentStatus.ORCHESTRATOR_WAITING_PROC == 'ORCHESTRATOR_WAITING_PROC'
    assert ExperimentStatus.TRAINING_SUCCESS == 'TRAINING_SUCCESS'
    assert ExperimentStatus.TRAINING_ERROR == 'TRAINING_ERROR'
-    assert ExperimentStatus.MLFLOW_SENT == 'MLFLOW_SENT'
-    assert ExperimentStatus.MLFLOW_SEND_ERROR == 'MLFLOW_SEND_ERROR'
+    assert ExperimentStatus.TRACKING_SENT == 'TRACKING_SENT'
+    assert ExperimentStatus.TRACKING_SEND_ERROR == 'TRACKING_SEND_ERROR'
    assert ExperimentStatus.FILE_DELETED == 'FILE_DELETED'
    assert ExperimentStatus.FILE_DELETE_ERROR == 'FILE_DELETE_ERROR'

@@ -28,11 +28,11 @@ def test_experiment_status_is_string():

 def test_experiment_status_membership():
    """Test membership checks for status values."""
-    assert 'MAGE_WAITING_PROC' in [s.value for s in ExperimentStatus]
+    assert 'ORCHESTRATOR_WAITING_PROC' in [s.value for s in ExperimentStatus]
    assert 'TRAINING_SUCCESS' in [s.value for s in ExperimentStatus]
    assert 'TRAINING_ERROR' in [s.value for s in ExperimentStatus]
-    assert 'MLFLOW_SENT' in [s.value for s in ExperimentStatus]
-    assert 'MLFLOW_SEND_ERROR' in [s.value for s in ExperimentStatus]
+    assert 'TRACKING_SENT' in [s.value for s in ExperimentStatus]
+    assert 'TRACKING_SEND_ERROR' in [s.value for s in ExperimentStatus]
    assert 'FILE_DELETED' in [s.value for s in ExperimentStatus]
    assert 'FILE_DELETE_ERROR' in [s.value for s in ExperimentStatus]

@@ -41,39 +41,43 @@ def test_experiment_status_iteration():
    """Test that enum can be iterated."""
    statuses = list(ExperimentStatus)
    assert len(statuses) == 8
-    assert ExperimentStatus.MAGE_WAITING_PROC in statuses
+    assert ExperimentStatus.ORCHESTRATOR_WAITING_PROC in statuses
    assert ExperimentStatus.TRAINING_SUCCESS in statuses
    assert ExperimentStatus.TRAINING_ERROR in statuses
-    assert ExperimentStatus.MLFLOW_SENT in statuses
-    assert ExperimentStatus.MLFLOW_SEND_ERROR in statuses
+    assert ExperimentStatus.TRACKING_SENT in statuses
+    assert ExperimentStatus.TRACKING_SEND_ERROR in statuses
    assert ExperimentStatus.FILE_DELETED in statuses
    assert ExperimentStatus.FILE_DELETE_ERROR in statuses


 def test_experiment_status_comparison():
    """Test that enum values can be compared with strings."""
-    assert ExperimentStatus.MAGE_WAITING_PROC == 'MAGE_WAITING_PROC'
+    assert ExperimentStatus.ORCHESTRATOR_WAITING_PROC == 'ORCHESTRATOR_WAITING_PROC'
    assert ExperimentStatus.TRAINING_SUCCESS == 'TRAINING_SUCCESS'
    assert ExperimentStatus.TRAINING_ERROR != 'TRAINING_SUCCESS'


 def test_experiment_status_access_by_name():
    """Test accessing enum members by name."""
-    assert ExperimentStatus['MAGE_WAITING_PROC'] == ExperimentStatus.MAGE_WAITING_PROC
+    assert (
+        ExperimentStatus['ORCHESTRATOR_WAITING_PROC'] == ExperimentStatus.ORCHESTRATOR_WAITING_PROC
+    )
    assert ExperimentStatus['TRAINING_SUCCESS'] == ExperimentStatus.TRAINING_SUCCESS
    assert ExperimentStatus['TRAINING_ERROR'] == ExperimentStatus.TRAINING_ERROR
-    assert ExperimentStatus['MLFLOW_SENT'] == ExperimentStatus.MLFLOW_SENT
-    assert ExperimentStatus['MLFLOW_SEND_ERROR'] == ExperimentStatus.MLFLOW_SEND_ERROR
+    assert ExperimentStatus['TRACKING_SENT'] == ExperimentStatus.TRACKING_SENT
+    assert ExperimentStatus['TRACKING_SEND_ERROR'] == ExperimentStatus.TRACKING_SEND_ERROR
    assert ExperimentStatus['FILE_DELETED'] == ExperimentStatus.FILE_DELETED
    assert ExperimentStatus['FILE_DELETE_ERROR'] == ExperimentStatus.FILE_DELETE_ERROR


 def test_experiment_status_access_by_value():
    """Test accessing enum members by value."""
-    assert ExperimentStatus('MAGE_WAITING_PROC') == ExperimentStatus.MAGE_WAITING_PROC
+    assert (
+        ExperimentStatus('ORCHESTRATOR_WAITING_PROC') == ExperimentStatus.ORCHESTRATOR_WAITING_PROC
+    )
    assert ExperimentStatus('TRAINING_SUCCESS') == ExperimentStatus.TRAINING_SUCCESS
    assert ExperimentStatus('TRAINING_ERROR') == ExperimentStatus.TRAINING_ERROR
-    assert ExperimentStatus('MLFLOW_SENT') == ExperimentStatus.MLFLOW_SENT
-    assert ExperimentStatus('MLFLOW_SEND_ERROR') == ExperimentStatus.MLFLOW_SEND_ERROR
+    assert ExperimentStatus('TRACKING_SENT') == ExperimentStatus.TRACKING_SENT
+    assert ExperimentStatus('TRACKING_SEND_ERROR') == ExperimentStatus.TRACKING_SEND_ERROR
    assert ExperimentStatus('FILE_DELETED') == ExperimentStatus.FILE_DELETED
    assert ExperimentStatus('FILE_DELETE_ERROR') == ExperimentStatus.FILE_DELETE_ERROR
--- a/tests/utils/models/test_init.py
+++ b/tests/utils/models/test_init.py
@@ -10,7 +10,7 @@ from model_manager.utils.models import (
 def test_experiment_status_import():
    """Test that ExperimentStatus can be imported from models package."""
    assert ExperimentStatus is not None
-    assert hasattr(ExperimentStatus, 'MAGE_WAITING_PROC')
+    assert hasattr(ExperimentStatus, 'ORCHESTRATOR_WAITING_PROC')
    assert hasattr(ExperimentStatus, 'TRAINING_SUCCESS')


--- a/tests/workflows/test_train_model.py
+++ b/tests/workflows/test_train_model.py
@@ -286,9 +286,9 @@ async def test_train_model_mlflow_error(mock_workflow_module, mock_train_params)
    with pytest.raises(ModelTrainingError):
        await workflow_instance._train_model(mock_train_params, 123, metadata)

-    # Verify MLFLOW_SEND_ERROR status was set
+    # Verify TRACKING_SEND_ERROR status was set
    call_args = mock_workflow_module.execute_activity_method.call_args_list[1]
-    assert call_args[0][1]['status'] == ExperimentStatus.MLFLOW_SEND_ERROR
+    assert call_args[0][1]['status'] == ExperimentStatus.TRACKING_SEND_ERROR


@pytest.mark.asyncio
@@ -359,14 +359,14 @@ async def test_update_experiment_run_status_only(mock_workflow_module):
        metadata=metadata,
        experiment_run_id=123,
        update_type=UpdateType.STATUS,
-        status=ExperimentStatus.MAGE_WAITING_PROC,
+        status=ExperimentStatus.ORCHESTRATOR_WAITING_PROC,
    )

    # Verify activity was called with correct parameters
    call_args = mock_workflow_module.execute_activity_method.call_args[0]
    assert call_args[1]['experiment_run_id'] == 123
    assert call_args[1]['update_type'] == UpdateType.STATUS
-    assert call_args[1]['status'] == ExperimentStatus.MAGE_WAITING_PROC
+    assert call_args[1]['status'] == ExperimentStatus.ORCHESTRATOR_WAITING_PROC
    assert 'error_message' not in call_args[1] or call_args[1].get('error_message') is None


@@ -411,7 +411,7 @@ async def test_update_experiment_run_with_run_name(mock_workflow_module):
        metadata=metadata,
        experiment_run_id=123,
        update_type=UpdateType.MODEL_SAVED,
-        status=ExperimentStatus.MLFLOW_SENT,
+        status=ExperimentStatus.TRACKING_SENT,
        run_name='test-run-123',
    )

@@ -438,9 +438,9 @@ async def test_run_complete_workflow_success(
    mock_workflow_module.execute_activity_method = AsyncMock(
        side_effect=[
            mock_train_params,  # validate_train_params
-            None,  # update status (MAGE_WAITING_PROC)
+            None,  # update status (ORCHESTRATOR_WAITING_PROC)
            train_result,  # train_model
-            None,  # update status (MLFLOW_SENT)
+            None,  # update status (TRACKING_SENT)
            None,  # cleanup_resources
            None,  # update status (FILE_DELETED)
        ]
@@ -490,7 +490,7 @@ async def test_run_workflow_training_error(
    mock_workflow_module.execute_activity_method = AsyncMock(
        side_effect=[
            mock_train_params,  # validate_train_params
-            None,  # update status (MAGE_WAITING_PROC)
+            None,  # update status (ORCHESTRATOR_WAITING_PROC)
            RuntimeError('Training failed'),  # train_model fails
            None,  # update status (TRAINING_ERROR)
        ]
@@ -521,9 +521,9 @@ async def test_run_workflow_cleanup_error(
    mock_workflow_module.execute_activity_method = AsyncMock(
        side_effect=[
            mock_train_params,  # validate_train_params
-            None,  # update status (MAGE_WAITING_PROC)
+            None,  # update status (ORCHESTRATOR_WAITING_PROC)
            train_result,  # train_model
-            None,  # update status (MLFLOW_SENT)
+            None,  # update status (TRACKING_SENT)
            RuntimeError('Cleanup failed'),  # cleanup_resources fails
            None,  # update status (FILE_DELETE_ERROR)
        ]
--- a/todo-list.txt
+++ b/todo-list.txt
@@ -1,3 +1,5 @@
+- adiconar volume mounts por causa dos reports.
+
 - Criar o dashboard do grafana.

 - Atualizar o .github/workflows/quality-gate.yml para usar os pipelines genéricos do github;
--- a/values.yaml
+++ b/values.yaml
@@ -7,18 +7,19 @@ replicaCount: 1

 # This sets the container image more information can be found here: https://kubernetes.io/docs/concepts/containers/images/
 image:
-  repository: aignosi.azurecr.io/sientia-module-courier
+  repository: aignosi.azurecr.io/sientia-dataops-model-manager
  # This sets the pull policy for images.
-  pullPolicy: Always
+  pullPolicy: IfNotPresent
  # Overrides the image tag whose default is the chart appVersion.
-  tag: "0.0.2"
+  tag: "0.0.0"

 # This is for the secrets for pulling an image from a private repository more information can be found here: https://kubernetes.io/docs/tasks/configure-pod-container/pull-image-private-registry/
 imagePullSecrets:
 - name: docker-hub-secret
+
 # This is to override the chart name.
-nameOverride: "sientia-model-manager-worker"
-fullnameOverride: "sientia-model-manager-worker"
+nameOverride: "sientia-dataops-model-manager"
+fullnameOverride: "sientia-dataops-model-manager"
 namespace: sientia

 # This section builds out the service account more information can be found here: https://kubernetes.io/docs/concepts/security/service-accounts/
@@ -31,7 +32,7 @@ serviceAccount:
  annotations: {}
  # The name of the service account to use.
  # If not set and create is true, a name is generated using the fullname template
-  name: "sientia-model-manager-worker"
+  name: "sientia-dataops-model-manager"

 # This is for setting Kubernetes Annotations to a Pod.
 # For more information checkout: https://kubernetes.io/docs/concepts/overview/working-with-objects/annotations/
@@ -68,22 +69,21 @@ resources: {}
 livenessProbe:
  exec:
    command:
-      - sh
+      - python3
      - -c
-      - pgrep -f "model_manager.worker.worker"
+      - "import requests; requests.get('http://localhost:9090/metrics')"
  initialDelaySeconds: 20
  periodSeconds: 30

 readinessProbe:
  exec:
    command:
-      - sh
+      - python3
      - -c
-      - pgrep -f "model_manager.worker.worker"
+      - "import requests; requests.get('http://localhost:9090/metrics')"
  initialDelaySeconds: 10
  periodSeconds: 15

-
 # This section is for setting up autoscaling more information can be found here: https://kubernetes.io/docs/concepts/workloads/autoscaling/
 autoscaling:
  enabled: false
@@ -105,6 +105,17 @@ volumeMounts: []
 #   mountPath: "/etc/foo"
 #   readOnly: true

+# Deployment strategy configuration
+# More information: https://kubernetes.io/docs/concepts/workloads/controllers/deployment/#strategy
+deploymentStrategy:
+  type: Recreate
+  # rollingUpdate:
+  #   maxSurge: 0
+  #   maxUnavailable: 1
+
+# Number of old ReplicaSets to retain
+revisionHistoryLimit: 2
+
 nodeSelector: {}

 tolerations: []
@@ -118,7 +129,6 @@ services:
    port: 9091
    targetPort: 9091
    name: sdk-metrics
-
  metrics:
    enabled: true
    type: ClusterIP
@@ -141,48 +151,32 @@ serviceMonitor:
    path: /metrics
    interval: 30s
    relabelings: []
-
  additionalLabels:
    release: kube-prometheus-stack

-
 env:
-  # Entrypoint variables 
-  - name: GITHUB_REPO_URL
-    value: "git@github.com:Aignosi/sientia-dataops-model-manager.git"
-  - name: GITHUB_BRANCH
-    value: SIENTIAPDE-1222-ajustar-a-library-para-fazer-o-download-do-courier
-  - name: PYTHON_APP
-    value: "model_manager.worker.worker"
-
-  # Application variables
  - name: POSTGRES_HOST
    value: "paradedb-rw.paradedb.svc.cluster.local"
  - name: POSTGRES_PORT
    value: "5432"
  - name: POSTGRES_USER
-    value: "sientia"
+    value: "postgres"
  - name: POSTGRES_PASSWORD
-    value: "sientia"
+    value: "nFqc81y6kwmr2zuAIx43DhiOosFCVPpeEfTtTWZflkNjB2j1KtEeIANkhFR9mAX3"
  - name: POSTGRES_DBNAME
-    value: "sientia"
+    value: "sientia-core-mlops-bff"
  - name: POSTGRES_MIN_CONNECTIONS
    value: "10"
  - name: POSTGRES_MAX_CONNECTIONS
    value: "30"

-  - name: MLFLOW_HOST
-    value: "http://sientia-tracker-mlflow-tracking.sientia-tracker.svc.cluster.local"
-  - name: MLFLOW_PORT
-    value: "80"
+  - name: MLFLOW_URL
+    value: "http://sientia-tracker-mlflow-tracking.sientia-tracker.svc.cluster.local:80"
  - name: MLFLOW_USERNAME
    value: "aignosi"
  - name: MLFLOW_PASSWORD
    value: "1L0FP50j3ncp123"

-  - name: KAFKA_BOOTSTRAP_SERVERS
-    value: "kafka.kafka.svc.cluster.local:9092"
-
  - name: LOG_LEVEL
    value: "DEBUG"
  - name: HTTP_METRICS_PORT
@@ -208,15 +202,51 @@ env:
  - name: MONGODB_TTL_INDEX_HOURS
    value: "1"

+  - name: MINIO_ENDPOINT_URL
+    value: "http://minio.minio.svc.cluster.local:9000"
+  - name: MINIO_ACCESS_KEY
+    value: "model-training-user"
+  - name: MINIO_SECRET_KEY
+    value: "modelTrainingUser123"
+  - name: MINIO_REGION
+    value: "us-east-1"
+  - name: MINIO_USE_SSL
+    value: "false"
+  - name: MINIO_MAX_RETRY_ATTEMPTS
+    value: "3"
+  - name: MINIO_RETRY_MODE
+    value: "adaptive"
+  - name: MINIO_CONNECT_TIMEOUT
+    value: "10"
+  - name: MINIO_READ_TIMEOUT
+    value: "60"
+
+  - name: TIMEOUT_VALIDATE_PARAMS
+    value: "30"
+  - name: TIMEOUT_TRAIN_MODEL
+    value: "2700"
+  - name: TIMEOUT_DELETE_FILE
+    value: "120"
+  - name: TIMEOUT_UPDATE_DATABASE
+    value: "30"
+
+  - name: EXTRA_PIP_REQUIREMENTS
+    value: "git+https://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@github.com/Aignosi/sientia-mlops-library.git"
+
+  - name: POD_ID
+    valueFrom:
+      fieldRef:
+        fieldPath: metadata.name
+
 ssh:
-  enabled: true
+  enabled: false
  secretName: git-ssh-key-sientia-model-manager-worker
  sshPath: /mnt/.ssh
  knownHostsPath: /mnt/known_hosts

 # kubectl create secret docker-registry docker-hub-secret --namespace sientia --docker-server=http://aignosi.azurecr.io --docker-username=aignosi --docker-password=5I5zpQ6sRaHqX1hD3dr+2mo647yO3FRc359/wu6gsP+ACRDRz5mp

-# helm upgrade --install sientia-model-manager-worker sientia/sientia-module -n sientia --create-namespace -f ./values.yaml --version 0.5.0
+# helm upgrade --install sientia-dataops-model-manager sientia/sientia-module -n sientia --create-namespace -f ./values.yaml --version 0.5.0

 # kubectl create secret generic git-ssh-key-sientia-model-manager-worker \
 #   --namespace sientia \