36af84f056
Enhance MLFlow and MLFlowRepository with improved data handling and logging - Refactored MLFlow class to sort data by 'created_at' and drop duplicates for better input preparation. - Updated MLFlowRepository methods to include detailed logging for artifact downloads and model predictions. - Introduced LzmaPayloadCodec for efficient payload compression in the worker, optimizing data handling for large payloads. - Enhanced timestamp handling in treated data to ensure compatibility with model expectations.
350 lines
14 KiB
Python
350 lines
14 KiB
Python
from temporalio import activity, workflow
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with workflow.unsafe.imports_passed_through():
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from datetime import datetime
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from pandas import Timestamp, to_datetime
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from sientia_do.temporal.constants import DATETIME_FORMAT, DATETIME_FORMAT_WITH_TZ
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from sientia_do.temporal.activities.base import BaseActivity
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from sientia_do.notifications.handlers import CoreNotificationHandler as NotificationHandler
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from sientia_do.notifications.models import NotificationLevel
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from sientia_do.observability.logger import Logger
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from sientia_do.formatters import create_sample_dict
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from laborious.utils.repository.model_repository import MLFlowRepository
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from typing import Any
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import numpy as np
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from pandas import DataFrame
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import traceback
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class MLFlow(BaseActivity):
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"""
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MLFlow integration activities for model inference operations.
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This class provides activities for interacting with MLFlow models, including
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data transformation and prediction operations. It handles authentication,
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data preprocessing, and model management with configurable retention policies.
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The class implements comprehensive error handling and logging for all
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MLFlow operations, ensuring reliable model inference in production environments.
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Attributes:
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mlflow_host (str): MLFlow server hostname
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mlflow_port (int): MLFlow server port
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mlflow_username (str): MLFlow authentication username
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mlflow_password (str): MLFlow authentication password
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model_monitoring_repository (MLFlowRepository): Repository for MLFlow operations
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"""
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def __init__(self, mlflow_host: str, mlflow_port: int, mlflow_username: str,
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mlflow_password: str, logger: Logger, notification_handler: NotificationHandler):
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"""
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Initialize MLFlow activities with server configuration.
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Args:
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mlflow_host: MLFlow server hostname or IP address
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mlflow_port: MLFlow server port number
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mlflow_username: Username for MLFlow authentication
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mlflow_password: Password for MLFlow authentication
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logger: Logger instance for observability and debugging
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notification_handler: Notification handler for alerts and monitoring
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Raises:
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Exception: If MLFlowRepository initialization fails
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"""
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BaseActivity.__init__(
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self, logger, notification_handler, set_error_counter=True)
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self.mlflow_host = mlflow_host
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self.mlflow_port = mlflow_port
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self.mlflow_username = mlflow_username
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self.mlflow_password = mlflow_password
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self.model_monitoring_repository = MLFlowRepository(
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f"{mlflow_host}:{mlflow_port}", mlflow_username, mlflow_password, logger
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)
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@activity.defn(name="request_transform")
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async def request_transform(self, input_data: dict[str, Any]) -> dict[str, Any]:
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"""
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Transform input data using MLFlow models.
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This activity processes input data through MLFlow model transformation,
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including data preprocessing, format conversion, and validation. It handles
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data deduplication, pivoting, and cleanup to ensure optimal model performance.
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The transformation process includes:
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1. Data deduplication based on variable and timestamp
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2. Data pivoting for model input format
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3. Null value handling and cleanup
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4. MLFlow model transformation request
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5. Response validation and logging
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Args:
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input_data: Configuration and data for transformation
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Required keys:
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- metadata (dict): Workflow execution metadata
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- data (dict): Input data for transformation
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- model_name (str): Name of the MLFlow model to use
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- model_retention (int): Model retention period in minutes
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Returns:
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dict: Transformed data from MLFlow model
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Raises:
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Exception: If transformation fails or MLFlow model is unavailable
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"""
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metadata = input_data['metadata']
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self.info('Transforming data...', metadata)
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data = DataFrame(input_data['data'])
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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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self.debug("Raw input data:", metadata)
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self.debug(data.head(5).to_string(), metadata)
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# Sort by created_at in descending order and keep first occurrence of each variable/timestamp pair
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data = data.sort_values('created_at', ascending=False).drop_duplicates(
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subset=['variable', 'timestamp'], keep='first'
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)
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# Pivot data for model input format
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data = data.pivot(
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index='timestamp', columns='variable',
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values='value')
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data.fillna(np.nan, inplace=True)
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# data.reset_index(inplace=True)
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data.columns.name = None
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self.debug("Processed input data:", metadata)
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self.debug(data.head(5).to_string(), metadata)
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# Request transformation from MLFlow model
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response_data = self.model_monitoring_repository.transform(
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model_name, data, model_config, metadata
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)
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self.debug(
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f"Transform raw response data: \n {create_sample_dict(response_data, max_items=5, max_depth=5)}", metadata)
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self.debug(
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f"Transform response data: \n {create_sample_dict(response_data, max_items=5, max_depth=5)}", metadata)
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self.info("Data transformed successfully", metadata)
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return response_data
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@activity.defn(name="request_predict")
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async def request_predict(self, input_data: dict[str, Any]) -> dict[str, Any]:
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"""
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Execute predictions using MLFlow models.
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This activity performs ML model inference using MLFlow models with the
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transformed data. It handles data format conversion, null value processing,
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and model prediction requests with comprehensive error handling.
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The prediction process includes:
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1. Data format validation and cleanup
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2. Null value handling for model compatibility
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3. MLFlow model prediction request
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4. Response validation and logging
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5. Performance monitoring and metrics
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Args:
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input_data: Configuration and data for prediction
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Required keys:
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- metadata (dict): Workflow execution metadata
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- data (dict): Transformed data for prediction
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- model_name (str): Name of the MLFlow model to use
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- model_retention (int): Model retention period in minutes
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Returns:
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dict: Prediction results from MLFlow model
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Raises:
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Exception: If prediction fails or MLFlow model is unavailable
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"""
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metadata = input_data['metadata']
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self.info('Predicting data...', metadata)
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data = DataFrame(input_data['data'])
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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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self.debug(f"Input data for: \n {data.head(5).to_string()}", metadata)
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# Convert numpy.nan to None for model compatibility
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data.replace(np.nan, None, inplace=True)
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data['timestamp'] = data.index
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data['timestamp'] = to_datetime(
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data['timestamp'], format=DATETIME_FORMAT_WITH_TZ).dt.strftime(DATETIME_FORMAT)
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# Request prediction from MLFlow model
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response_data = self.model_monitoring_repository.predict(
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model_name, data, model_config, metadata
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)
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self.debug(
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f"Prediction response data: \n {create_sample_dict(response_data, max_items=5, max_depth=5)}", metadata)
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self.info("Data predicted successfully", metadata)
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return response_data
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@activity.defn(name="retrain_model")
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async def retrain_model(self, input_data: dict[str, Any]) -> dict[str, Any]:
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"""
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Retrain MLFlow models with updated training data.
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This activity orchestrates the complete model retraining process,
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including data preparation, model retraining execution, and result
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validation. It handles data preprocessing, column cleanup, and
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comprehensive error handling for production model management.
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The retraining process includes:
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1. Data timestamp extraction and validation
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2. Column cleanup and data preparation
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3. Data pivoting for model input format
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4. MLFlow model retraining execution
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5. Result validation and error handling
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Args:
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input_data (dict): Input data containing:
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- metadata (dict): Workflow execution metadata
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- data (dict[str, Any]): Training data for model retraining
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- model_name (str): Name of the MLFlow model to retrain
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Returns:
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dict: Retraining results containing:
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- status (str): Retraining operation status
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- timestamp (str): Timestamp of the retraining operation
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- experiment (str): MLFlow experiment identifier
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Raises:
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Exception: If retraining fails or encounters critical errors
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"""
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metadata = input_data['metadata']
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data = DataFrame(input_data['data'])
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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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self.info(f'Retraining model {model_name}...', metadata)
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timestamp = data['timestamp'].max()
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self.debug(f'Timestamp: {timestamp}', metadata)
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# Sort by created_at in descending order and keep first occurrence of each variable/timestamp pair
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data = data.sort_values('created_at', ascending=False).drop_duplicates(
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subset=['variable', 'timestamp'], keep='first'
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)
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data.drop(columns=['model_id'], inplace=True, errors='ignore')
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data.drop(columns=['created_at'], inplace=True, errors='ignore')
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# Pivot data for model input format
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data = data.pivot(
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index='timestamp', columns='variable',
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values='value')
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data.fillna(np.nan, inplace=True)
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# data.reset_index(inplace=True)
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data.columns.name = None
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data['timestamp'] = data.index
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data['timestamp'] = to_datetime(
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data['timestamp'], format=DATETIME_FORMAT_WITH_TZ).dt.strftime(DATETIME_FORMAT)
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data['timestamp'] = to_datetime(
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data['timestamp'], format=DATETIME_FORMAT)
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# data = data.dropna()
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data.columns.name = None
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retrain_output = self.model_monitoring_repository.retrain_model(
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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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metadata=metadata
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)
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if not retrain_output['success']:
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trace = retrain_output['traceback']
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self.send_notification(
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metadata=metadata,
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notification_id='RETRAIN_MODEL_ERROR',
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message=f"Error retraining model {model_name}: {retrain_output['message']}",
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block='retrain_model',
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level=NotificationLevel.ERROR,
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attachment_content=trace
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)
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self.error(trace, metadata=metadata)
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return {
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**retrain_output,
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'timestamp': timestamp
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}
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@activity.defn(name="update_production_model")
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async def update_production_model(self, input_data: dict[str, Any]) -> dict[Any, Any]:
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"""
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Update production model with newly trained model version.
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This activity manages the critical process of updating production
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models with newly trained versions. It handles model deployment,
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status tracking, and comprehensive reporting for operational
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visibility and audit trails.
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The update process includes:
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1. Production model update execution
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2. Status and metadata tracking
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3. Comprehensive reporting and logging
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4. Error handling and notification
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5. Audit trail maintenance
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Args:
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input_data (dict): Input data containing:
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- metadata (dict): Workflow execution metadata
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- model_name (str): Name of the MLFlow model to update
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- experiment (str): MLFlow experiment identifier
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- model_id (str): Unique identifier for the model version
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- timestamp (str): Timestamp of the update operation
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- status (str): Current status of the model update
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Returns:
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dict[Any, Any]: Comprehensive update report containing:
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- model_id (str): Model version identifier
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- model_name (str): Name of the updated model
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- timestamp (str): Update operation timestamp
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- status (str): Update operation status
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- Additional MLFlow response metadata
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Raises:
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Exception: If production model update fails
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"""
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metadata = input_data['metadata']
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model_name = input_data['model_name']
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experiment = input_data['experiment']
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self.info(
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f'Updating production model {model_name} from experiment {experiment}...', metadata)
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try:
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response = self.model_monitoring_repository.update_production_model(
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experiment=experiment,
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model_name=model_name
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)
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self.info(
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f'Production model {model_name} updated successfully', metadata)
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return response
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except Exception as e:
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trace = traceback.format_exc()
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self.send_notification(
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metadata=metadata,
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notification_id='UPDATE_PRODUCTION_MODEL_ERROR',
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message=f'Error updating production model {model_name}: {e}',
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block='update_production_model',
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level=NotificationLevel.ERROR,
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attachment_content=trace
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)
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self.error(trace, metadata=metadata)
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raise e
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