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SIENTIAPDE-1712

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Enhance README and Implement Drift Detection and Metrics Workflows

- Added new sections in README for Drift Workflow and Simple Metrics Workflow, detailing their execution flows and functionalities.
- Introduced `drift.py` for data drift detection, comparing current data against reference datasets.
- Added `simple_metrics.py` for calculating regression metrics (RMSE, MSE, MAE, R²).
- Updated `values.yaml` to include configuration for MinIO retention hours and offload threshold.
- Refactored `minio_dataframe_payload.py` to use the new offload threshold environment variable.
- Adjusted tests to reflect changes in environment variable handling for MinIO offload threshold.
This commit is contained in:
vitor-aignosi
2026-03-20 09:22:08 -03:00
parent 5d0d049082
commit d43f08d272
4 changed files with 176 additions and 51 deletions
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217
README.md
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@@ -19,6 +19,8 @@ A comprehensive, Temporal-based ML orchestration system for industrial data proc
- [Prediction Process Workflow](#2-prediction-process-workflow-prediction_processpy) - [Prediction Process Workflow](#2-prediction-process-workflow-prediction_processpy)
- [Format and Export Prediction Workflow](#3-format-and-export-prediction-workflow-format_and_export_predictionpy) - [Format and Export Prediction Workflow](#3-format-and-export-prediction-workflow-format_and_export_predictionpy)
- [Minimal Retrain Workflow](#4-minimal-retrain-workflow-minimal_retrainpy) - [Minimal Retrain Workflow](#4-minimal-retrain-workflow-minimal_retrainpy)
- [Drift Workflow](#5-drift-workflow-driftpy)
- [Simple Metrics Workflow](#6-simple-metrics-workflow-simple_metricspy)
- [Installation & Setup](#installation--setup) - [Installation & Setup](#installation--setup)
- [Prerequisites](#prerequisites) - [Prerequisites](#prerequisites)
- [Environment Setup](#environment-setup) - [Environment Setup](#environment-setup)
@@ -77,13 +79,16 @@ A comprehensive, Temporal-based ML orchestration system for industrial data proc
### Advanced Capabilities ### Advanced Capabilities
- **Incremental Data Processing**: Timestamp-based loading to avoid reprocessing - **Incremental Data Processing**: Timestamp-based loading to avoid reprocessing
- **Configurable Data Retention**: Model retention policies with automatic cleanup - **Configurable Data Retention**: Model retention policies with automatic cleanup
- **MinIO Payload Offload**: Automatic offload of large DataFrames to MinIO with retention cleanup
- **Data Drift Detection**: Univariate and multivariate drift monitoring against reference data
- **Regression Metrics**: Automated RMSE, MSE, MAE, R² calculation and export
- **Notification System**: Integrated alerting via MongoDB - **Notification System**: Integrated alerting via MongoDB
- **Scalable Architecture**: Kubernetes-ready with horizontal scaling - **Scalable Architecture**: Kubernetes-ready with horizontal scaling
- **Model Retraining**: Automated retraining workflows with production model updates - **Model Retraining**: Automated retraining workflows with production model updates
### Development & Quality Assurance ### Development & Quality Assurance
- **Code Quality Tools**: Ruff (lint/format), mypy (types), Bandit (security) - **Code Quality Tools**: Ruff (lint/format), mypy (types), Bandit (security)
- **Automated Validation**: `validate.sh` and CI quality gates - **Automated Validation**: CI quality gates and individual tool commands
- **Comprehensive Testing**: pytest with async support and high coverage - **Comprehensive Testing**: pytest with async support and high coverage
- **Type Safety**: Static type checking with mypy - **Type Safety**: Static type checking with mypy
- **Coverage Visualization**: Coverage Gutters integration - **Coverage Visualization**: Coverage Gutters integration
@@ -129,6 +134,8 @@ Laborious uses a Temporal-based architecture with strong separation of concerns
- `sub_workflows/prediction_process.py`: Core prediction pipeline - `sub_workflows/prediction_process.py`: Core prediction pipeline
- `sub_workflows/format_and_export_prediction.py`: Formatting and export - `sub_workflows/format_and_export_prediction.py`: Formatting and export
- `minimal_retrain.py`: Automated model retraining and production update - `minimal_retrain.py`: Automated model retraining and production update
- `drift.py`: Data drift detection and monitoring
- `simple_metrics.py`: Regression metrics calculation (RMSE, MSE, MAE, R²)
#### **Activities (`laborious/activities/`)** #### **Activities (`laborious/activities/`)**
- `gates.py`: Data quality validation, filtering, and data formatting operations - `gates.py`: Data quality validation, filtering, and data formatting operations
@@ -139,16 +146,26 @@ Laborious uses a Temporal-based architecture with strong separation of concerns
- MLFlow model transformation and prediction - MLFlow model transformation and prediction
- Model retraining and production updates - Model retraining and production updates
- Reference data retrieval from MLflow Model Registry - Reference data retrieval from MLflow Model Registry
- `storage.py`: PostgreSQL queries and MinIO-aware data loading
- `load_query_with_minio_offload`: SQL load with automatic MinIO offload
- `export_payload_to_postgres`: Resolve MinIO payloads and export to Postgres
- `cleanup_minio_objects_expired`: Retention-based MinIO object cleanup
- `query_to_minio`: Legacy parquet upload for retraining data
- `model_metrics.py`: Drift detection and regression metrics
- Univariate and multivariate drift calculation
- Simple metrics (RMSE, MSE, MAE, R²)
- `opc.py`: OPC UA export to industrial systems (optional) - `opc.py`: OPC UA export to industrial systems (optional)
- `api.py`: PI Web API export operations (optional) - `api.py`: PI Web API export operations (optional)
- Prediction and confidence data writing to PI Web API - Prediction and confidence data writing to PI Web API
- Error handling and notification integration - Error handling and notification integration
- `activities.py`: Aggregates activity interfaces - `activities.py`: Aggregates all activity interfaces (Storage, MLFlow, Gates, OPC, ModelMetrics, API)
#### **Data Services (`laborious/utils/`)** #### **Data Services (`laborious/utils/`)**
- `connectors_config.py`: Env-driven configuration builders - `connectors_config.py`: Env-driven configuration builders
- `models/minio_dataframe_payload.py`: MinIO-offloaded DataFrame payload model
- `repository/model_repository.py`: MLFlow operations and retraining - `repository/model_repository.py`: MLFlow operations and retraining
- `repository/opc_repository.py`: OPC communication and writes - `repository/opc_repository.py`: OPC communication and writes
- `repository/minio_manager.py`: MinIO object storage operations
- `filters/conditional_filters.py` and `filters/mlflow_filters.py` - `filters/conditional_filters.py` and `filters/mlflow_filters.py`
### Data Flow Architecture ### Data Flow Architecture
@@ -167,6 +184,18 @@ Training Data → Model Retraining → Quality Validation →
Production Update → Notification & Monitoring Production Update → Notification & Monitoring
``` ```
#### **3. Drift Detection Pipeline**
```
Target Data (PostgreSQL) + Reference Data (MLFlow) →
Drift Calculation (univariate + multivariate) → PostgreSQL Export
```
#### **4. Simple Metrics Pipeline**
```
Predictions + Targets (PostgreSQL JOIN) →
Metrics Calculation (RMSE, MSE, MAE, R²) → PostgreSQL Export
```
### Security Architecture ### Security Architecture
#### **Authentication & Authorization** #### **Authentication & Authorization**
@@ -262,20 +291,21 @@ The **PredictionProcess** workflow implements the core prediction pipeline for M
- **Prediction Export**: Delegates prediction formatting and export operations - **Prediction Export**: Delegates prediction formatting and export operations
#### Execution Flow #### Execution Flow
1. **Timestamp Retrieval**: Gets the last processed timestamp for incremental processing 1. **Input Data Gate**: Applies configured filters for data quality validation
2. **Input Data Gate**: Applies configured filters for data quality validation 2. **Path Decision**: Determines processing path based on filter results
3. **Path Decision**: Determines processing path based on filter results 3. **MLFlow Transform**: Requests data transformation using MLFlow models
4. **MLFlow Transform**: Requests data transformation using MLFlow models 4. **Response Validation**: Filters transform responses for quality assurance
5. **Response Validation**: Filters transform responses for quality assurance 5. **Content Validation**: Filters transformed data content for quality check
6. **MLFlow Prediction**: Executes prediction using transformed data 6. **MLFlow Prediction**: Executes prediction using transformed data
7. **Content Validation**: Filters prediction responses for final quality check 7. **Prediction Response Validation**: Filters prediction responses for final quality check
8. **Export Delegation**: Delegates to FormatAndExportPrediction workflow 8. **Export Delegation**: Delegates to FormatAndExportPrediction workflow
9. **MinIO Cleanup**: Cleans up expired offloaded payloads (if any, in `finally` block)
#### Key Features #### Key Features
- **Configurable Quality Gates**: Multiple filter types with policy-based configuration - **Configurable Quality Gates**: Multiple filter types with policy-based configuration
- **Flexible Path Handling**: Configurable decision paths (STOP, CONTINUE, REPEAT) - **Flexible Path Handling**: Configurable decision paths (STOP, CONTINUE, REPEAT)
- **MLFlow Integration**: Comprehensive model management and inference - **MLFlow Integration**: Comprehensive model management and inference
- **Incremental Processing**: Timestamp-based data processing optimization - **MinIO Cleanup**: Automatic retention-based cleanup of offloaded payloads
- **Comprehensive Monitoring**: Detailed metrics and error reporting - **Comprehensive Monitoring**: Detailed metrics and error reporting
#### Input Parameters #### Input Parameters
@@ -320,12 +350,12 @@ The **PredictionProcess** workflow implements the core prediction pipeline for M
#### Architecture Diagram #### Architecture Diagram
```mermaid ```mermaid
flowchart LR flowchart LR
A[1. get_last_timestamp] --> B[2. input_gate] --> C[3. request_transform] --> D[4. mlflow_response_gate] --> E[5. mlflow_content_gate] --> F[6. request_predict] --> G[7. mlflow_response_gate] --> H[8. format_and_export_prediction🔃] A[1. input_gate] --> B[2. request_transform] --> C[3. mlflow_response_gate] --> D[4. mlflow_content_gate] --> E[5. request_predict] --> F[6. mlflow_response_gate] --> G[7. format_and_export_prediction🔃]
G --> H[8. cleanup_minio_objects_expired]
A -.-> Redis[(Redis)] B -.-> MLFlow[MLFlow]
C -.-> MLFlow[MLFlow] E -.-> MLFlow[MLFlow]
F -.-> MLFlow[MLFlow] H -.-> MinIO[(MinIO)]
G -.-> Filters[MLFlow Filters]
``` ```
### 3. Format and Export Prediction Workflow (`format_and_export_prediction.py`) ### 3. Format and Export Prediction Workflow (`format_and_export_prediction.py`)
@@ -403,6 +433,76 @@ flowchart LR
D -.-> PostgreSQL[(PostgreSQL)] D -.-> PostgreSQL[(PostgreSQL)]
``` ```
### 5. Drift Workflow (`drift.py`)
The **Drift** workflow detects data drift by comparing current data against a reference dataset from the MLflow Model Registry.
#### Execution Flow
1. **Data Loading**: Loads target data and reference data in parallel
2. **Drift Calculation**: Calculates univariate and multivariate drift metrics
3. **Data Export**: Exports drift metrics to PostgreSQL
#### Architecture Diagram
```mermaid
flowchart LR
A[1. load_custom_query] --> C[3. calculate_drift] --> D[4. export_data_to_postgres]
B[2. get_reference_data] --> C
A -.-> Database[(Database)]
B -.-> MLFlow[MLFlow]
D -.-> PostgreSQL[(PostgreSQL)]
```
#### Input Parameters
```json
{
"schedule_name": "hourly_drift",
"model_name": "temperature_model",
"model_id": "temp_001",
"schema": "sientia_data",
"source_table_name": "laborious_data",
"target_table_name": "drift_metrics",
"interval": 60,
"model_config": { "target": "temperature" },
"drift_metrics": ["kolmogorov_smirnov", "jensen_shannon", "wasserstein"],
"chunk_period": "min"
}
```
### 6. Simple Metrics Workflow (`simple_metrics.py`)
The **SimpleMetrics** workflow calculates regression metrics (RMSE, MSE, MAE, R²) by comparing predictions against actual target values.
#### Execution Flow
1. **Data Loading**: Loads prediction vs target data via a JOIN query
2. **Metrics Calculation**: Calculates configured regression metrics
3. **Data Export**: Exports metrics to PostgreSQL
#### Architecture Diagram
```mermaid
flowchart LR
A[1. load_custom_query] --> B[2. calculate_simple_metrics] --> C[3. export_data_to_postgres]
A -.-> Database[(Database)]
C -.-> PostgreSQL[(PostgreSQL)]
```
#### Input Parameters
```json
{
"schedule_name": "hourly_metrics",
"model_name": "temperature_model",
"model_id": "temp_001",
"schema": "sientia_data",
"predictions_table_name": "predictions",
"data_table_name": "laborious_data",
"target_table_name": "simple_metrics",
"interval_minutes": 60,
"model_config": { "target": "temperature" },
"metrics": ["rmse", "mse", "mae", "r2"]
}
```
## 📋 Prerequisites ## 📋 Prerequisites
- Python 3.11+ - Python 3.11+
@@ -527,8 +627,8 @@ pytest
pytest --cov=laborious --cov-report=html pytest --cov=laborious --cov-report=html
# Run specific test categories # Run specific test categories
pytest tests/activities/ pytest tests/laborious/activities/
pytest tests/workflow/ pytest tests/laborious/workflows/
``` ```
### Manual Application Execution ### Manual Application Execution
@@ -569,18 +669,7 @@ pip install -r requirements-dev.txt
### Complete Validation ### Complete Validation
Option 1 (recommended): Run each validation step individually:
```bash
./validate.sh
```
The script runs, in order:
1. Format check (Ruff)
2. Linting (Ruff)
3. Type checking (mypy)
4. Security analysis (Bandit)
5. Tests with coverage (pytest)
Option 2 (individual commands):
```bash ```bash
ruff format --check laborious/ tests/ ruff format --check laborious/ tests/
ruff check laborious/ tests/ ruff check laborious/ tests/
@@ -606,7 +695,7 @@ The workflow at `.github/workflows/quality-gate.yml` executes validations on eac
### Best Practices ### Best Practices
- Run `./validate.sh` before committing - Run all validation steps before committing
- Use `ruff check --watch` for continuous feedback - Use `ruff check --watch` for continuous feedback
- Add type hints and tests for new code - Add type hints and tests for new code
@@ -615,15 +704,34 @@ The workflow at `.github/workflows/quality-gate.yml` executes validations on eac
### Test Structure ### Test Structure
``` ```
tests/ tests/
├── activities/ # Activity implementation tests ├── conftest.py # Global fixtures and env setup
│ ├── test_gates.py # Data quality gates and formatting tests ├── laborious/
│ ├── test_mlflow.py # MLFlow operations and reference data tests │ ├── activities/ # Activity implementation tests
│ └── ... # Other activity tests │ │ ├── test_activities.py # Activities aggregator tests
├── workflows/ # Workflow orchestration tests │ │ ├── test_gates.py # Data quality gates and formatting tests
│ └── subworkflows/ # Sub-workflow tests │ │ ├── test_mlflow.py # MLFlow operations and reference data tests
│ └── test_format_and_export_prediction.py # Export workflow tests │ │ ├── test_storage.py # Storage and MinIO offload tests
├── utils/ # Utility function tests │ │ ├── test_model_metrics.py # Drift and simple metrics tests
└── integration/ # End-to-end workflow tests │ │ ├── test_opc.py # OPC operations tests
│ │ └── test_api.py # PI Web API operations tests
│ ├── workflows/ # Workflow orchestration tests
│ │ ├── test_predictions_batch.py
│ │ ├── test_minimal_retrain.py
│ │ ├── test_drift.py
│ │ ├── test_simple_metrics.py
│ │ └── subworkflows/
│ │ ├── test_prediction_process.py
│ │ └── test_format_and_export_prediction.py
│ └── utils/ # Utility function tests
│ ├── test_connectors_config.py
│ ├── models/
│ │ └── test_minio_dataframe_payload.py
│ ├── filters/
│ │ ├── test_conditional_filters.py
│ │ └── test_mlflow_filters.py
│ └── repository/
│ ├── test_model_repository.py
│ └── test_opc_repository.py
``` ```
### Test Coverage ### Test Coverage
@@ -643,8 +751,8 @@ pip install pytest pytest-cov pytest-asyncio
pytest --cov=laborious --cov-report=html pytest --cov=laborious --cov-report=html
# Run specific test modules # Run specific test modules
pytest tests/activities/test_gates.py pytest tests/laborious/activities/test_gates.py
pytest tests/workflow/test_predictions_batch.py pytest tests/laborious/workflows/test_predictions_batch.py
``` ```
## 📊 Monitoring and Metrics ## 📊 Monitoring and Metrics
@@ -716,8 +824,13 @@ The Laborious system exposes comprehensive Prometheus metrics for operational vi
| `HTTP_METRICS_PORT` | Prometheus metrics port | `9090` | No | | `HTTP_METRICS_PORT` | Prometheus metrics port | `9090` | No |
| `HTTP_SDK_METRICS_PORT` | Temporal SDK metrics port | `9091` | No | | `HTTP_SDK_METRICS_PORT` | Temporal SDK metrics port | `9091` | No |
| `POD_ID` | Kubernetes pod identifier | `None` | No | | `POD_ID` | Kubernetes pod identifier | `None` | No |
| `SIENTIA_MINIO_RETENTION_HOURS` | Retention window for offloaded MinIO objects | `168` | No | | `MINIO_ENDPOINT_URL` | MinIO endpoint URL | `http://localhost:9000` | Yes |
| `SIENTIA_MINIO_OFFLOAD_THRESHOLD_BYTES` | Offload threshold for DataFrame-derived payloads | `int(1.5 * 1024 * 1024)` | No | | `MINIO_ACCESS_KEY` | MinIO access key | `minioadmin` | Yes |
| `MINIO_SECRET_KEY` | MinIO secret key | `minioadmin` | Yes |
| `MINIO_REGION_NAME` | MinIO region name | `us-east-1` | No |
| `MINIO_DEFAULT_BUCKET` | Default MinIO bucket | `laborious` | No |
| `MINIO_RETENTION_HOURS` | Retention window (hours) for offloaded MinIO objects | `24` | No |
| `SIENTIA_MINIO_OFFLOAD_THRESHOLD_MEGABYTES` | Offload threshold for DataFrame-derived payloads | `int(1.5 * 1024 * 1024)` | No |
@@ -728,7 +841,7 @@ Laborious uses MinIO to prevent Temporal workflow history from carrying very lar
Whenever a payload exceeds a configurable size threshold, it is stored as a parquet file in MinIO and the workflow history only keeps a lightweight reference. Whenever a payload exceeds a configurable size threshold, it is stored as a parquet file in MinIO and the workflow history only keeps a lightweight reference.
Notes: Notes:
- `SIENTIA_MINIO_OFFLOAD_THRESHOLD_BYTES` supports: - `SIENTIA_MINIO_OFFLOAD_THRESHOLD_MEGABYTES` supports:
- Integer bytes (e.g. `"1572864"`) - Integer bytes (e.g. `"1572864"`)
- Float MiB (e.g. `"1.5"`), converted to bytes as `MiB * 1024 * 1024` - Float MiB (e.g. `"1.5"`), converted to bytes as `MiB * 1024 * 1024`
- Fallback behavior uses `1.5 MiB` when the env var is missing or invalid. - Fallback behavior uses `1.5 MiB` when the env var is missing or invalid.
@@ -751,7 +864,7 @@ After evaluation, the payload is serialized for Temporal as a flat dict:
- `data` is omitted / set to `None`. - `data` is omitted / set to `None`.
When an activity needs pandas operations, it resolves references using: When an activity needs pandas operations, it resolves references using:
- `MinioDataFramePayload.dataframe_from_wire(...)` - `MinioDataFramePayload.retrieve(minio_repo)` — downloads from MinIO or returns inline data as a DataFrame
#### MinIO Object Naming (Retention Parsing) #### MinIO Object Naming (Retention Parsing)
@@ -952,27 +1065,35 @@ This is the configuration created by the Orchestrator in Temporal.
``` ```
laborious/ laborious/
├── activities/ # Temporal activity implementations ├── activities/ # Temporal activity implementations
│ ├── activities.py # Main activities orchestrator │ ├── activities.py # Main activities aggregator
│ ├── gates.py # Data quality gates and filtering │ ├── gates.py # Data quality gates and filtering
│ ├── mlflow.py # MLFlow model operations │ ├── mlflow.py # MLFlow model operations
│ ├── storage.py # PostgreSQL queries and MinIO offload
│ ├── model_metrics.py # Drift and regression metrics
│ ├── opc.py # OPC server operations │ ├── opc.py # OPC server operations
│ └── api.py # PI Web API operations │ └── api.py # PI Web API operations
├── workflows/ # Temporal workflow definitions ├── workflows/ # Temporal workflow definitions
│ ├── predictions_batch.py # Main batch prediction workflow │ ├── predictions_batch.py # Main batch prediction workflow
│ ├── minimal_retrain.py # Model retraining workflow │ ├── minimal_retrain.py # Model retraining workflow
│ ├── drift.py # Data drift detection workflow
│ ├── simple_metrics.py # Regression metrics workflow
│ └── sub_workflows/ # Sub-workflow implementations │ └── sub_workflows/ # Sub-workflow implementations
│ ├── prediction_process.py # Core prediction workflow │ ├── prediction_process.py # Core prediction workflow
│ └── format_and_export_prediction.py # Export workflow │ └── format_and_export_prediction.py # Export workflow
├── worker/ # Worker implementation ├── worker/ # Worker implementation
│ └── worker.py # Main worker orchestrator │ ├── worker.py # Main worker orchestrator
│ └── prepare_worker.py # Worker factory with autoscaling config
├── utils/ # Utility functions ├── utils/ # Utility functions
│ ├── connectors_config.py # Database configuration │ ├── connectors_config.py # Environment-driven config builders
│ ├── models/ # Data models
│ │ └── minio_dataframe_payload.py # MinIO-offloaded DataFrame payload
│ ├── filters/ # Data quality filters │ ├── filters/ # Data quality filters
│ │ ├── conditional_filters.py # Conditional data filters │ │ ├── conditional_filters.py # Conditional data filters
│ │ └── mlflow_filters.py # MLFlow response filters │ │ └── mlflow_filters.py # MLFlow response filters
│ └── repository/ # Data access layer │ └── repository/ # Data access layer
│ ├── model_repository.py # MLFlow model operations │ ├── model_repository.py # MLFlow model operations
│ └── opc_repository.py # OPC server operations │ ├── opc_repository.py # OPC server operations
│ └── minio_manager.py # MinIO object storage operations
├── metrics.py # Prometheus metrics definitions ├── metrics.py # Prometheus metrics definitions
└── __init__.py └── __init__.py
``` ```

2
laborious/utils/models/minio_dataframe_payload.py
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@@ -31,7 +31,7 @@ _OBJECT_TIMESTAMP_PATTERN = re.compile(
) )
OFFLOAD_THRESHOLD_BYTES = int( OFFLOAD_THRESHOLD_BYTES = int(
float(getenv('SIENTIA_MINIO_OFFLOAD_THRESHOLD_BYTES', '1.5')) * 1024 * 1024 float(getenv('SIENTIA_MINIO_OFFLOAD_THRESHOLD_MEGABYTES', '1.5')) * 1024 * 1024
) )
# Relative prefix used for storing offloaded training datasets in MinIO. # Relative prefix used for storing offloaded training datasets in MinIO.

4
tests/conftest.py
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@@ -2,9 +2,9 @@ import os
import sys import sys
from unittest.mock import MagicMock from unittest.mock import MagicMock
# The production code converts SIENTIA_MINIO_OFFLOAD_THRESHOLD_BYTES to int at import-time. # The production code converts SIENTIA_MINIO_OFFLOAD_THRESHOLD_MEGABYTES to int at import-time.
# Tests must set it to a valid integer string to avoid import errors. # Tests must set it to a valid integer string to avoid import errors.
os.environ.setdefault('SIENTIA_MINIO_OFFLOAD_THRESHOLD_BYTES', '1') os.environ.setdefault('SIENTIA_MINIO_OFFLOAD_THRESHOLD_MEGABYTES', '1')
class DummyMinioDataFramePayload: class DummyMinioDataFramePayload:

4
values.yaml
View File

@@ -231,6 +231,10 @@ env:
value: "sa-east-1" value: "sa-east-1"
- name: MINIO_DEFAULT_BUCKET - name: MINIO_DEFAULT_BUCKET
value: "sientia" value: "sientia"
- name: MINIO_RETENTION_HOURS
value: "24"
- name: SIENTIA_MINIO_OFFLOAD_THRESHOLD_MEGABYTES
value: "1.5"
- name: PI_WEB_API_BASE_URL - name: PI_WEB_API_BASE_URL
value: "https://pivision.votorantimcimentos.com/piwebapi" value: "https://pivision.votorantimcimentos.com/piwebapi"