Merge pull request #13 from Aignosi/feature/SIENTIAPDE-1309

SIENTIAPDE-1309: Enhance Project Configuration, Documentation, and Deployment

.dockerignore

@@ -202,3 +202,24 @@ bin/local/
 # Cache directories
 .cache/
 cache/
+
+# Development and configuration files
+.env.example
+requirements-dev.txt
+pyproject.toml
+sonar-project.properties
+todo-list.txt
+validate.sh
+run_local.sh
+LICENSE
+
+# Helm charts (development only)
+sientia-module/
+*.yaml
+
+# Local directories
+data/
+logs/
+models/
+temp/
+scripts/

.gitignore

@@ -235,7 +235,12 @@ scouter/pipelines/**/triggers.yaml
 # Ignore temporary files
 *.swp
 
+# Ignore test run reports in model_manager/reports/temp (but keep temp folder and .gitkeep)
+model_manager/reports/temp/*
+!model_manager/reports/temp/.gitkeep
+
 # Miscellaneous
 git_key*
 git_log
 tmp/
+sientia-module/

Dockerfile

@@ -28,7 +28,7 @@ ENV PATH="/opt/venv/bin:$PATH"
 RUN pip install --upgrade pip setuptools wheel
 
 # Copy requirements files for better Docker layer caching
-COPY requirements.txt requirements-dev.txt ./
+COPY requirements.txt ./
 
 # Install only production dependencies with no cache
 RUN --mount=type=ssh echo "=== Installing dependencies ===" && \

README.md

@@ -59,6 +59,8 @@ An enterprise-grade ML model training orchestration platform built on Temporal.
   - [Code Quality Standards](#code-quality-standards)
 - [License](#license)
 - [Support](#support)
+- [Docker](#docker)
+- [Helm Chart](#helm-chart)
 
 ## Features
 
@@ -1035,13 +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
@@ -1049,16 +1051,64 @@ $ 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
 ```
 
+## Helm Chart
+
+### Reference
+
+https://aignosi-wiki.atlassian.net/wiki/spaces/IT1/pages/274563074/Como+utilizar+o+Helm+Repo+Privado
+
+### Add Helm Chart repository
+
+```bash
+$ helm repo add sientia \
+    https://raw.githubusercontent.com/Aignosi/sientia-dataops-helm-repo/refs/heads/main/ \
+    --username $GITHUB_USER \
+    --password $GITHUB_PASS
+
+# Update repository
+$ helm repo update
+
+# List repositories
+$ helm repo list
+
+# List versions of a specific chart
+$ helm search repo sientia --versions
+
+# List all charts available
+$ helm search repo sientia
+
+# List chart details
+$ helm show all sientia/sientia-module
+
+# Download chart to current directory
+$ helm pull sientia/sientia-module --version 0.6.0 --untar
+
+# Remove chart directory
+$ rm -rf sientia-module
+```
+
+### Helm Install
+
+```shell
+$ helm upgrade --install sientia-dataops-model-manager sientia/sientia-module -n sientia --create-namespace -f ./values.yaml --version 0.6.0
+```
+
+### Uninstall Helm Chart
+
+```shell
+$ helm uninstall sientia-dataops-model-manager -n sientia
+```
+
 ---
 
 **Note**: The Model Manager system is designed for production use in industrial ML environments. Ensure proper security configuration and network isolation for production deployments.

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'

model_manager/utils/repository/model_repository.py

@@ -306,7 +306,7 @@ class ModelRepository:
         """
         # Use microsecond precision to reduce collision probability
         timestamp = datetime.now().strftime('%Y%m%d_%H%M%S_%f')
-        run_dir = path.join(base_path, f'{run_name}_{timestamp}')
+        run_dir = path.join(base_path, 'temp', f'{run_name}_{timestamp}')
 
         try:
             makedirs(run_dir, exist_ok=True)

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,

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,

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

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')
 
 

tests/utils/repository/test_model_repository.py

@@ -289,7 +289,7 @@ def test_create_run_directory_success(
 
     result = repo._create_run_directory('/tmp/reports', 'test_run')  # noqa: S108
 
-    expected_path = os.path.join('/tmp/reports', 'test_run_20240101_120000_123456')  # noqa: S108
+    expected_path = os.path.join('/tmp/reports/temp', 'test_run_20240101_120000_123456')  # noqa: S108
     assert result == expected_path
     mock_makedirs.assert_called_once_with(expected_path, exist_ok=True)
 

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)
         ]

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"
 
-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/
+# 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/
@@ -51,7 +52,6 @@ securityContext: {}
   # runAsNonRoot: true
   # runAsUser: 1000
 
-
 resources: {}
   # We usually recommend not to specify default resources and to leave this as a conscious
   # choice for the user. This also increases chances charts run on environments with little
@@ -68,22 +68,21 @@ 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
@@ -93,17 +92,26 @@ autoscaling:
   # targetMemoryUtilizationPercentage: 80
 
 # Additional volumes on the output Deployment definition.
-volumes: []
-# - name: foo
-#   secret:
-#     secretName: mysecret
-#     optional: false
+volumes:
+  - name: reports-volume
+    emptyDir:
+      sizeLimit: 1Gi
 
 # Additional volumeMounts on the output Deployment definition.
-volumeMounts: []
-# - name: foo
-#   mountPath: "/etc/foo"
-#   readOnly: true
+volumeMounts:
+  - name: reports-volume
+    mountPath: "/app/model_manager/reports/temp"
+
+# 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: {}
 
@@ -118,7 +126,6 @@ services:
     port: 9091
     targetPort: 9091
     name: sdk-metrics
-
   metrics:
     enabled: true
     type: ClusterIP
@@ -141,48 +148,32 @@ 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 +199,51 @@ 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 0.6.0
 
 # kubectl create secret generic git-ssh-key-sientia-model-manager-worker \
 #   --namespace sientia \