SIENTIAPDE-1243: Initial commit of the model manager project, adding core files and configurations.
This commit introduces the initial project structure, including: - .env.example: Example environment configuration. - .github/workflows/quality-gate.yml: CI workflow for quality checks. - .gitignore: Specifies intentionally untracked files that Git should ignore. - Makefile: Automation of tasks like docker builds. - README.md: Project documentation. - Source code for model management, activities, utils, worker and workflows. - Test suite. - Dockerfile for the simulator. - sonar-project.properties: SonarQube configuration file. - values.yaml: Helm chart values for deployment.
This commit is contained in:
Bruno Domingues
296
model-manager/workflows/sub_workflows/prediction_process.py
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296
model-manager/workflows/sub_workflows/prediction_process.py
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from temporalio import workflow
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with workflow.unsafe.imports_passed_through():
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from laborious.activities.activities import Activities
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from typing import Any
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from sientia_do.temporal.policies import retry_policy
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from datetime import timedelta
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@workflow.defn(name="prediction_process")
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class PredictionProcess():
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"""
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Core prediction processing workflow for the Laborious system.
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This workflow implements the complete ML model inference pipeline, handling
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data quality validation, MLFlow model interactions, and prediction processing.
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It serves as the central orchestrator for all prediction operations and ensures
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data quality throughout the entire process.
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The workflow implements a robust data processing pipeline with:
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- Data quality validation using configurable filters
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- MLFlow model transformation and prediction
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- Response validation and quality assurance
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- Flexible decision path handling
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- Comprehensive error handling and retry policies
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Workflow Execution:
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1. Timestamp Retrieval: Gets last processed timestamp for incremental processing
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2. Input Data Gate: Applies data quality filters
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3. Path Decision: Determines processing path based on filter results
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4. MLFlow Transform: Requests data transformation using MLFlow models
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5. Response Validation: Filters transform responses for quality assurance
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6. MLFlow Prediction: Executes prediction using transformed data
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7. Content Validation: Filters prediction responses for final quality check
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8. Export Delegation: Delegates to FormatAndExportPrediction workflow
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"""
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@workflow.run
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async def run(self, input_data: dict[str, Any]):
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"""
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Execute the prediction process workflow.
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This method orchestrates the complete prediction processing pipeline by:
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1. Retrieving the last processed timestamp for incremental processing
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2. Applying data quality filters to validate input data
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3. Executing MLFlow model transformation and prediction
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4. Validating all responses for quality assurance
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5. Delegating to export workflow for data persistence
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The method implements comprehensive error handling and ensures all
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data quality requirements are met before proceeding with ML operations.
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Args:
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input_data: Complete configuration for the prediction process
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Required keys:
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- metadata (dict): Workflow execution metadata
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- data (dict): Input data for prediction processing
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- schema (dict): Data schema definition
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- table_name (str): Target table for predictions
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- model_id (str): ML model identifier
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- model_name (str): ML model name
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- input_filters (dict): Data quality filters
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- mlflow_transform_filters (dict): MLFlow transform filters
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- mlflow_predict_filters (dict): MLFlow prediction filters
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- model_retention (int): Model retention period in minutes
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- path_priority (list[str]): Decision path priority configuration
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- opc_output_config (dict): OPC server export configuration
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Returns:
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None: The workflow completes successfully when export workflow finishes
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Raises:
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Exception: If any required parameters are missing or if the workflow fails
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during data processing, MLFlow operations, or workflow delegation
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"""
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metadata = input_data['metadata']
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data = input_data['data']
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model_id = input_data['model_id']
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model_name = input_data['model_name']
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model_config = input_data.get('model_config', {})
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# Get last timestamp for incremental processing
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last_timestamp = await workflow.execute_local_activity_method(
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Activities.get_last_timestamp,
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{
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**metadata,
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'data': data
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},
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retry_policy=retry_policy,
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start_to_close_timeout=timedelta(minutes=1),
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)
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# Apply input data quality gates
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gate_input = {
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**metadata,
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'filters': input_data['input_filters'],
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'data': data,
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'path_priority': input_data['path_priority']
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}
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path_flag, confidence, comment = await workflow.execute_local_activity_method(
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Activities.input_gate,
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gate_input,
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retry_policy=retry_policy,
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start_to_close_timeout=timedelta(minutes=1),
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)
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# Handle path decision based on filter results
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if await self.path_flag_handler(
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data, path_flag, input_data, confidence, last_timestamp, comment
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):
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return
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# Request MLFlow model transformation
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response_data = await workflow.execute_local_activity_method(
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Activities.request_transform,
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{
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**metadata,
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'data': data,
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'model_name': model_name,
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'model_config': model_config
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},
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retry_policy=retry_policy,
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start_to_close_timeout=timedelta(minutes=5),
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)
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# Validate MLFlow transform response
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path_flag, confidence, comment = await workflow.execute_local_activity_method(
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Activities.mlflow_response_gate,
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{
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**metadata,
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'filters': input_data['mlflow_transform_filters'],
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'data': response_data,
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'type': 'transform',
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'path_priority': input_data['path_priority']
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},
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retry_policy=retry_policy,
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start_to_close_timeout=timedelta(minutes=1),
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)
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# Handle path decision based on transform validation
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if await self.path_flag_handler(
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data, path_flag, input_data, confidence, last_timestamp, comment
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):
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return
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transformed_data = response_data['content']
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path_flag, confidence, comment = await workflow.execute_local_activity_method(
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Activities.mlflow_content_gate,
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{
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**metadata,
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'filters': input_data['mlflow_transform_filters'],
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'data': transformed_data,
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'type': 'transform',
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'path_priority': input_data['path_priority']
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},
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retry_policy=retry_policy,
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start_to_close_timeout=timedelta(minutes=1),
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)
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if await self.path_flag_handler(
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data, path_flag, input_data, confidence, last_timestamp, comment
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):
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return
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response_data = await workflow.execute_local_activity_method(
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Activities.request_predict,
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{
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**metadata,
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'data': transformed_data,
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'model_name': model_name,
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'model_config': model_config
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},
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retry_policy=retry_policy,
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start_to_close_timeout=timedelta(minutes=5),
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)
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# Validate MLFlow prediction response
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path_flag, confidence, comment = await workflow.execute_local_activity_method(
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Activities.mlflow_response_gate,
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{
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**metadata,
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'filters': input_data['mlflow_predict_filters'],
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'data': response_data,
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'type': 'predict',
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'path_priority': input_data['path_priority']
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},
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retry_policy=retry_policy,
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start_to_close_timeout=timedelta(minutes=1),
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)
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# Handle path decision based on prediction validation
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if await self.path_flag_handler(
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data, path_flag, input_data, confidence, last_timestamp, comment
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):
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return
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# Delegate to export workflow for data persistence
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await workflow.execute_child_workflow(
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'format_and_export_prediction',
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{
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'metadata': metadata,
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'path_flag': path_flag,
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'data': response_data['content'],
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'prediction_confidence': confidence,
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'timestamp': last_timestamp,
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'model_id': model_id,
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'model_name': model_name,
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'model_config': model_config,
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'opc_output_config': input_data['opc_output_config'],
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'schema': input_data['schema'],
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'table_name': input_data['table_name'],
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'comment': comment,
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'prediction_store_policy': input_data['prediction_store_policy']
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}
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)
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async def path_flag_handler(self, data: dict, path_flag: str, input_data: dict,
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confidence: int, last_timestamp: str, comment: str) -> bool:
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"""
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Handle path decisions based on filter results and confidence levels.
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This method determines the appropriate action based on the path flag
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returned by data quality filters. It can stop processing, continue,
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or repeat operations based on the configured path priority.
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Args:
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data: Input data for processing
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path_flag: Path decision from filter (STOP, CONTINUE, REPEAT)
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input_data: Complete workflow input configuration
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confidence: Confidence level from filter validation
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last_timestamp: Last processed timestamp
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comment: Additional information about the filter result
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Returns:
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bool: True if processing should stop, False to continue
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Path Handling:
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- STOP: Terminates workflow execution
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- CONTINUE: Proceeds with normal processing
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- REPEAT: Repeats last prediction if available
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"""
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metadata = input_data['metadata']
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schema = input_data['schema']
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table_name = input_data['table_name']
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model_id = input_data['model_id']
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model_name = input_data['model_name']
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model_config = input_data.get('model_config', {})
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path_flag = path_flag.upper() if path_flag else ''
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if path_flag == 'STOP':
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# Stop processing and exit workflow
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return True
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elif path_flag == 'REPEAT':
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# Repeat last prediction if available
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await workflow.execute_activity_method(
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Activities.repeat_last_prediction,
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{
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**metadata,
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'schema': schema,
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'table_name': table_name,
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'model': model_id,
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'last_timestamp': last_timestamp
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},
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retry_policy=retry_policy,
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start_to_close_timeout=timedelta(minutes=1),
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)
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return True
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elif path_flag == 'CONTINUE':
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# call write workflow
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await workflow.execute_child_workflow(
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'format_and_export_prediction',
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{
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'metadata': metadata,
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'path_flag': path_flag,
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'data': data,
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'prediction_confidence': confidence,
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'timestamp': last_timestamp,
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'model_id': model_id,
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'model_name': model_name,
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'model_config': model_config,
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'schema': schema,
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'table_name': table_name,
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'comment': comment,
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'opc_output_config': input_data['opc_output_config'],
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'prediction_store_policy': input_data['prediction_store_policy']
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}
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)
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return True
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return False
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