SIENTIAPDE-1243: Rename project from 'laborious' to 'model-manager' across codebase and configuration. This includes updating project names in environment variables, Makefiles, README, metrics, and Helm chart values.

.env.example

@@ -17,10 +17,10 @@ OPC_URL="opc.tcp://sientia-opc-simulator-opc.sientia.svc.cluster.local:4840"
 LOG_LEVEL="DEBUG"
 HTTP_METRICS_PORT="9090"
 HTTP_SDK_METRICS_PORT="9091"
-PROJECT_NAME="sientia-laborious"
+PROJECT_NAME="sientia-model-manager"
 
 TEMPORAL_HOST="temporal-frontend.temporal.svc.cluster.local:7233"
-TEMPORAL_NAMESPACE="laborious"
+TEMPORAL_NAMESPACE="model-manager"
 
 MONGODB_USERNAME="mongo_user"
 MONGODB_PASSWORD="mongo_db_password"

Makefile

@@ -1,5 +1,5 @@
 VERSION = 1.0.8
-name = sientia-laborious
+name = sientia-model-manager
 # ENVIRONMENT = production
 
 docker-hub:

README.md

@@ -21,7 +21,7 @@ A comprehensive AI model management platform for the complete machine learning l
 
 ## Architecture
 
-The Laborious system uses a Temporal-based workflow architecture with clear separation of concerns and robust error handling. The architecture is designed for high availability, scalability, and operational excellence in production ML environments.
+The Model Manager system uses a Temporal-based workflow architecture with clear separation of concerns and robust error handling. The architecture is designed for high availability, scalability, and operational excellence in production ML environments.
 
 
 ### Architecture Principles
@@ -428,7 +428,7 @@ flowchart LR
 
 ## 📦 How to Run
 
-### Running the Laborious Application
+### Running the Model Manager Application
 
 Use the provided script to run the application locally:
 
@@ -443,7 +443,7 @@ chmod +x run_local.sh
 The script will:
 - Activate the virtual environment
 - Load environment variables from `.env`
-- Start the laborious worker application
+- Start the model-manager worker application
 
 ### Running Tests and Coverage
 
@@ -495,7 +495,7 @@ if [ -f .env ]; then
     export $(cat .env | grep -v '^#' | xargs)
 fi
 
-# Start the laborious worker
+# Start the model-manager worker
 python -m model_manager.worker.worker
 ```
 
@@ -525,25 +525,25 @@ pytest tests/workflow/test_predictions_batch.py
 
 ## 📊 Monitoring and Metrics
 
-The Laborious system exposes comprehensive Prometheus metrics for operational visibility and performance monitoring:
+The Model Manager system exposes comprehensive Prometheus metrics for operational visibility and performance monitoring:
 
 ### Application Health Metrics
 - `app_up`: Application health status (1=healthy, 0=unhealthy)
   - Labels: `pod_id`
 
 ### Prediction Operation Metrics
-- `laborious_predictions_written_count`: Counter for successful prediction exports
+- `model_manager_predictions_written_count`: Counter for successful prediction exports
   - Labels: `pod_id`, `model_name`, `pipeline_name`
-- `laborious_prediction_confidence_monitor`: Gauge for current prediction confidence levels
+- `model_manager_prediction_confidence_monitor`: Gauge for current prediction confidence levels
   - Labels: `pod_id`, `model_name`, `pipeline_name`
-- `laborious_prediction_response_time_monitor`: Histogram for prediction response times
+- `model_manager_prediction_response_time_monitor`: Histogram for prediction response times
   - Labels: `pod_id`, `model_name`, `pipeline_name`
   - Buckets: [0.01, 0.05, 0.1, 0.2, 0.5, 1.0, 2.0, 5.0, 10.0]
 
 ### OPC Export Metrics
-- `laborious_prediction_opc_writing_count`: Counter for OPC server write operations
+- `model_manager_prediction_opc_writing_count`: Counter for OPC server write operations
   - Labels: `pod_id`, `model_name`, `pipeline_name`, `opc_server_id`
-- `laborious_prediction_opc_writing_response_time_monitor`: Histogram for OPC write response times
+- `model_manager_prediction_opc_writing_response_time_monitor`: Histogram for OPC write response times
   - Labels: `pod_id`, `model_name`, `pipeline_name`, `opc_server_id`
   - Buckets: [0.01, 0.05, 0.1, 0.2, 0.5, 1.0, 2.0, 5.0, 10.0]
 
@@ -559,7 +559,7 @@ The Laborious system exposes comprehensive Prometheus metrics for operational vi
 | Variable | Description | Default | Required |
 |----------|-------------|---------|----------|
 | `TEMPORAL_HOST` | Temporal server address | `localhost:7233` | Yes |
-| `TEMPORAL_NAMESPACE` | Temporal namespace | `laborious` | No |
+| `TEMPORAL_NAMESPACE` | Temporal namespace | `model-manager` | No |
 | `POSTGRES_HOST` | PostgreSQL hostname | `localhost` | Yes |
 | `POSTGRES_PORT` | PostgreSQL port | `5432` | Yes |
 | `POSTGRES_USER` | PostgreSQL username | `sientia` | Yes |
@@ -585,7 +585,7 @@ The Laborious system exposes comprehensive Prometheus metrics for operational vi
 | `MONGODB_DATABASE_NAME` | MongoDB database name | `sientia` | Yes |
 | `MONGODB_TTL_INDEX_HOURS` | MongoDB TTL index hours | `1` | No |
 | `LOG_LEVEL` | Application log level | `INFO` | No |
-| `PROJECT_NAME` | Project name for metrics | `laborious` | No |
+| `PROJECT_NAME` | Project name for metrics | `model-manager` | No |
 | `HTTP_METRICS_PORT` | Prometheus metrics port | `9090` | No |
 | `HTTP_SDK_METRICS_PORT` | Temporal SDK metrics port | `9091` | No |
 | `POD_ID` | Kubernetes pod identifier | `None` | No |
@@ -838,4 +838,4 @@ For support and questions:
 
 ---
 
-**Note**: The Laborious system is designed for production use in industrial ML environments. Ensure proper security configuration and network isolation for production deployments.
+**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/activities/activities.py

@@ -12,7 +12,7 @@ with workflow.unsafe.imports_passed_through():
 
 class Activities(Postgres, MLFlow, Gates, OPC):
     """
-    Main activities orchestrator for the Laborious system.
+    Main activities orchestrator for the Model Manager system.
 
     This class combines functionality from multiple activity classes to provide
     a unified interface for all workflow operations. It manages database connections,

model_manager/activities/gates.py

@@ -53,7 +53,7 @@ mlflow_content_filter_functions = {
 
 class Gates(BaseActivity):
     """
-    Data quality gates and filtering activities for the Laborious system.
+    Data quality gates and filtering activities for the Model Manager system.
 
     This class implements comprehensive data quality validation and filtering
     mechanisms that can be applied at different stages of the prediction pipeline.

model_manager/metrics.py

@@ -1,7 +1,7 @@
 """
-Laborious Metrics Module
+Model Manager Metrics Module
 
-This module defines all Prometheus metrics used by the Sientia DataOps Laborious system
+This module defines all Prometheus metrics used by the Sientia DataOps Model Manager system
 for monitoring and observability. The metrics provide insights into system performance,
 prediction quality, and operational health.
 
@@ -37,21 +37,21 @@ CORE_LABELS = ["pod_id", "model_name", "pipeline_name"]
 
 # Prediction operation metrics
 PREDICTIONS_WRITTEN_COUNT = Counter(
-    "laborious_predictions_written_count",
+    "model_manager_predictions_written_count",
     "Number of predictions written to the database table predictions",
     CORE_LABELS,
 )
 
 # Prediction quality metrics
 PREDICTION_CONFIDENCE_MONITOR = Gauge(
-    "laborious_prediction_confidence_monitor",
+    "model_manager_prediction_confidence_monitor",
     "Current confidence of each prediction",
     CORE_LABELS,
 )
 
 # Performance monitoring metrics
 PREDICTION_RESPONSE_TIME_MONITOR = Histogram(
-    "laborious_prediction_response_time_monitor",
+    "model_manager_prediction_response_time_monitor",
     "Current response time of each prediction",
     CORE_LABELS,
     buckets=[0.01, 0.05, 0.1, 0.2, 0.5, 1.0, 2.0, 5.0, 10.0]
@@ -59,13 +59,13 @@ PREDICTION_RESPONSE_TIME_MONITOR = Histogram(
 
 # OPC export metrics
 PREDICTION_OPC_WRITING_COUNT = Counter(
-    "laborious_prediction_opc_writing_count",
+    "model_manager_prediction_opc_writing_count",
     "Number of predictions written to the OPC server",
     [*CORE_LABELS, "opc_server_id"],
 )
 
 PREDICTION_OPC_WRITING_RESPONSE_TIME_MONITOR = Histogram(
-    "laborious_prediction_opc_writing_response_time_monitor",
+    "model_manager_prediction_opc_writing_response_time_monitor",
     "Current response time of each prediction written to the OPC server",
     [*CORE_LABELS, "opc_server_id"],
     buckets=[0.01, 0.05, 0.1, 0.2, 0.5, 1.0, 2.0, 5.0, 10.0]

model_manager/worker/worker.py

@@ -1,7 +1,7 @@
 """
-Laborious Worker Module
+Model Manager Worker Module
 
-This module provides the main worker implementation for the Sientia DataOps Laborious system.
+This module provides the main worker implementation for the Sientia DataOps Model Manager system.
 It orchestrates Temporal workers, manages task queues, and handles the lifecycle of
 prediction and retraining workflows.
 
@@ -18,11 +18,11 @@ Key Features:
 
 Environment Variables:
 - TEMPORAL_HOST: Temporal server address (default: localhost:7233)
-- TEMPORAL_NAMESPACE: Temporal namespace (default: laborious)
+- TEMPORAL_NAMESPACE: Temporal namespace (default: model_manager)
 - POD_ID: Kubernetes pod identifier for metrics
 - HTTP_METRICS_PORT: Prometheus metrics server port (default: 9090)
 - HTTP_SDK_METRICS_PORT: Temporal SDK metrics port (default: 9091)
-- PROJECT_NAME: Project name for notifications (default: laborious)
+- PROJECT_NAME: Project name for notifications (default: model_manager)
 """
 
 from temporalio import workflow, client
@@ -56,7 +56,7 @@ SDK_METRICS_PORT = int(os.getenv('HTTP_SDK_METRICS_PORT', "9091"))
 
 async def main():
     """
-    Main entry point for the Laborious worker application.
+    Main entry point for the Model Manager worker application.
 
     This function initializes and starts all components of the worker:
     1. Sets up logging and metadata
@@ -97,7 +97,7 @@ async def main():
         connection_string=mongo_config['connection_string'],
         database=mongo_config['database_name'],
         logger=logger,
-        project_name=os.getenv('PROJECT_NAME', 'laborious')
+        project_name=os.getenv('PROJECT_NAME', 'model-manager')
     )
 
     logger.custom_info('Starting Activities...', metadata)
@@ -127,7 +127,7 @@ async def main():
 
     temporal_client = await client.Client.connect(
         target_host=host,
-        namespace=os.getenv('TEMPORAL_NAMESPACE', 'laborious'),
+        namespace=os.getenv('TEMPORAL_NAMESPACE', 'model-manager'),
         runtime=new_runtime
     )
 
@@ -214,7 +214,7 @@ def start_prometheus_server():
     and sets the application health metric to indicate the service is running.
 
     The server exposes metrics that can be scraped by Prometheus for monitoring
-    the health and performance of the Laborious worker.
+    the health and performance of the Model Manager worker.
 
     Environment Variables:
         HTTP_METRICS_PORT: Port for the metrics server (default: 9090)

model_manager/workflows/minimal_retrain.py

@@ -10,7 +10,7 @@ with workflow.unsafe.imports_passed_through():
 @workflow.defn(name="minimal_retrain")
 class MinimalRetrain():
     """
-    Automated model retraining workflow for the Laborious system.
+    Automated model retraining workflow for the Model Manager system.
 
     This workflow implements a complete model retraining pipeline that loads
     training data, executes model retraining, updates production models,

model_manager/workflows/predictions_batch.py

@@ -10,7 +10,7 @@ with workflow.unsafe.imports_passed_through():
 @workflow.defn(name="predictions_batch")
 class PredictionsBatch():
     """
-    Main batch prediction workflow for the Laborious system.
+    Main batch prediction workflow for the Model Manager system.
 
     This workflow orchestrates the complete batch prediction process, handling
     data loading, configuration management, and workflow delegation. It serves

model_manager/workflows/sub_workflows/prediction_process.py

@@ -10,7 +10,7 @@ with workflow.unsafe.imports_passed_through():
 @workflow.defn(name="prediction_process")
 class PredictionProcess():
     """
-    Core prediction processing workflow for the Laborious system.
+    Core prediction processing workflow for the Model Manager system.
 
     This workflow implements the complete ML model inference pipeline, handling
     data quality validation, MLFlow model interactions, and prediction processing.

values.yaml

@@ -17,8 +17,8 @@ image:
 imagePullSecrets:
 - name: docker-hub-secret
 # This is to override the chart name.
-nameOverride: "sientia-laborious-worker"
-fullnameOverride: "sientia-laborious-worker"
+nameOverride: "sientia-model-manager-worker"
+fullnameOverride: "sientia-model-manager-worker"
 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 +31,7 @@ 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-laborious-worker"
+  name: "sientia-model-manager-worker"
 
 # This is for setting Kubernetes Annotations to a Pod.
 # For more information checkout: https://kubernetes.io/docs/concepts/overview/working-with-objects/annotations/
@@ -149,7 +149,7 @@ serviceMonitor:
 env:
   # Entrypoint variables 
   - name: GITHUB_REPO_URL
-    value: "git@github.com:Aignosi/sientia-dataops-laborious_temporal.git"
+    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
@@ -195,12 +195,12 @@ env:
   - name: HTTP_SDK_METRICS_PORT
     value: "9091"
   - name: PROJECT_NAME
-    value: "sientia-laborious"
+    value: "sientia-model-manager"
 
   - name: TEMPORAL_HOST
     value: "temporal-frontend.temporal.svc.cluster.local:7233"
   - name: TEMPORAL_NAMESPACE
-    value: "laborious"
+    value: "model-manager"
 
   - name: MONGODB_USERNAME
     value: "root"
@@ -215,15 +215,15 @@ env:
 
 ssh:
   enabled: true
-  secretName: git-ssh-key-sientia-laborious-worker
+  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-laborious-worker sientia/sientia-module -n sientia --create-namespace -f ./values.yaml --version 0.5.0
+# helm upgrade --install sientia-model-manager-worker sientia/sientia-module -n sientia --create-namespace -f ./values.yaml --version 0.5.0
 
-# kubectl create secret generic git-ssh-key-sientia-laborious-worker \
+# kubectl create secret generic git-ssh-key-sientia-model-manager-worker \
 #   --namespace sientia \
 #   --from-file=ssh-privatekey=git_key \
 #   --type=kubernetes.io/ssh-auth