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feat(trainer): add structured and configurable logging support
This PR implements comprehensive logging support for the Kubeflow SDK as requested in Issue #85. ## Features Implemented ### Core Logging Infrastructure - **NullHandler Pattern**: Prevents logging noise by default, users can override with their own configuration - **Configurable Logging**: Support for console, detailed, and JSON output formats - **Structured Logging**: JSON formatter for log aggregation systems (ELK stack, Fluentd, etc.) - **Environment-based Configuration**: Configure logging via environment variables ### Key Components - `kubeflow/trainer/logging/config.py`: Centralized logging configuration - `kubeflow/trainer/logging/formatters.py`: Custom formatters including JSON structured logging - `kubeflow/trainer/logging/logging_test.py`: Comprehensive test suite (14 tests) ### Integration Points - **SDK Integration**: Added debug logging to TrainerClient for better observability - **Package Integration**: Exposed logging utilities through kubeflow.trainer.logging - **NullHandler Setup**: Configured at package level to prevent logging noise ## Issue #85 Requirements Fulfilled ✅ **Consistent use of Python's logging library instead of print statements** - All SDK operations now use proper Python logging - NullHandler pattern prevents unwanted output by default ✅ **Support for different logging levels (DEBUG, INFO, WARNING, ERROR)** - Full support for all standard Python logging levels - Configurable via setup_logging() function or environment variables ✅ **Ability for users to configure log formatting and destinations** - Console, detailed, and JSON output formats - File output support - Custom formatter support ✅ **Clear and actionable log messages for key SDK operations** - Debug messages in TrainerClient initialization - Backend selection logging - Job creation and ID logging ## Testing - **14 comprehensive unit tests** covering all logging functionality - **NullHandler pattern verification** with proper isolation - **SDK integration testing** with real TrainerClient usage - **Application integration examples** with file and console logging - **All tests pass** with proper linting compliance ## Usage Examples ### Basic Usage ```python from kubeflow.trainer import TrainerClient, setup_logging # Setup logging (optional - NullHandler prevents noise by default) setup_logging(level="DEBUG", format_type="console") # Use SDK - debug messages will appear if logging is configured client = TrainerClient() ``` ### JSON Logging for Production ```python setup_logging(level="INFO", format_type="json") # Logs will be in JSON format suitable for log aggregation ``` ### Environment Configuration ```bash export KUBEFLOW_LOG_LEVEL=DEBUG export KUBEFLOW_LOG_FORMAT=json ``` ## Breaking Changes None - this is a pure addition with backward compatibility. ## Migration Guide No migration required. Existing code will continue to work without changes. Users can optionally configure logging for better observability. Resolves #85 Signed-off-by: Jaeyeon Kim <[email protected]>
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kubeflow/__init__.py

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# See the License for the specific language governing permissions and
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# limitations under the License.
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import logging
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# Configure NullHandler for the kubeflow package to avoid logging noise
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# when users haven't configured logging. Users can override this by setting
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# their own logging configuration.
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logger = logging.getLogger(__name__)
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logger.addHandler(logging.NullHandler())
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__version__ = "0.1.0"

kubeflow/trainer/__init__.py

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# Import the Kubeflow Trainer constants.
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from kubeflow.trainer.constants.constants import DATASET_PATH, MODEL_PATH # noqa: F401
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# Import the Kubeflow Trainer logging utilities.
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from kubeflow.trainer.logging import get_logger, setup_logging # noqa: F401
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# Import the Kubeflow Trainer types.
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from kubeflow.trainer.types.types import (
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BuiltinTrainer,
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"TrainerType",
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"LocalProcessBackendConfig",
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"KubernetesBackendConfig",
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"get_logger",
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"setup_logging",
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]

kubeflow/trainer/api/trainer_client.py

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ValueError: Invalid backend configuration.
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"""
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logger.debug("Initializing TrainerClient with backend_config=%s", backend_config)
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# initialize training backend
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if not backend_config:
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backend_config = KubernetesBackendConfig()
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logger.debug("Using default KubernetesBackendConfig")
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if isinstance(backend_config, KubernetesBackendConfig):
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self.backend = KubernetesBackend(backend_config)
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logger.debug("Initialized Kubernetes backend")
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elif isinstance(backend_config, LocalProcessBackendConfig):
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self.backend = LocalProcessBackend(backend_config)
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logger.debug("Initialized LocalProcess backend")
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else:
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logger.error("Invalid backend config type: %s", type(backend_config))
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raise ValueError(f"Invalid backend config '{backend_config}'")
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def list_runtimes(self) -> list[types.Runtime]:
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TimeoutError: Timeout to create TrainJobs.
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RuntimeError: Failed to create TrainJobs.
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"""
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return self.backend.train(runtime=runtime, initializer=initializer, trainer=trainer)
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logger.debug(
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"Creating TrainJob with runtime=%s, initializer=%s, trainer=%s",
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runtime,
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initializer,
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trainer,
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)
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job_id = self.backend.train(runtime=runtime, initializer=initializer, trainer=trainer)
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logger.debug("Successfully created TrainJob with ID: %s", job_id)
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return job_id
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def list_jobs(self, runtime: Optional[types.Runtime] = None) -> list[types.TrainJob]:
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"""List of the created TrainJobs. If a runtime is specified, only TrainJobs associated with
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# Copyright 2025 The Kubeflow Authors.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""Kubeflow SDK logging module.
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This module provides structured and configurable logging support for the Kubeflow SDK.
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It includes centralized logger configuration, structured log messages, and context-aware logging.
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"""
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from .config import get_logger, setup_logging
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from .formatters import StructuredFormatter
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__all__ = ["get_logger", "setup_logging", "StructuredFormatter"]

kubeflow/trainer/logging/config.py

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# Copyright 2025 The Kubeflow Authors.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""Logging configuration for Kubeflow SDK."""
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import logging
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import logging.config
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import os
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from typing import Optional, Union
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def setup_logging(
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level: Union[str, int] = "INFO",
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format_type: str = "console",
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log_file: Optional[str] = None,
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) -> None:
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"""Setup logging configuration for Kubeflow SDK.
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Args:
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level: Logging level (DEBUG, INFO, WARNING, ERROR, CRITICAL)
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format_type: Output format type ('console', 'json', 'detailed')
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log_file: Optional log file path for file output
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"""
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# Convert string level to logging constant
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if isinstance(level, str):
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level = getattr(logging, level.upper(), logging.INFO)
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# Base configuration
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config = {
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"version": 1,
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"disable_existing_loggers": False,
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"formatters": {
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"console": {
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"format": "%(asctime)s - %(name)s - %(levelname)s - %(message)s",
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"datefmt": "%Y-%m-%d %H:%M:%S",
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},
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"detailed": {
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"format": (
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"%(asctime)s - %(name)s - %(levelname)s - %(filename)s:%(lineno)d - %(message)s"
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),
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"datefmt": "%Y-%m-%d %H:%M:%S",
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},
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"json": {
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"()": "kubeflow.trainer.logging.formatters.StructuredFormatter",
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},
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},
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"handlers": {
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"console": {
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"class": "logging.StreamHandler",
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"level": level,
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"formatter": format_type,
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"stream": "ext://sys.stdout",
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},
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},
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"loggers": {
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"kubeflow": {
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"level": level,
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"handlers": ["console"],
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"propagate": False,
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},
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},
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"root": {
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"level": level,
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"handlers": ["console"],
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},
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}
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# Add file handler if log_file is specified
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if log_file:
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config["handlers"]["file"] = {
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"class": "logging.FileHandler",
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"level": level,
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"formatter": format_type,
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"filename": log_file,
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"mode": "a",
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}
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config["loggers"]["kubeflow"]["handlers"].append("file")
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config["root"]["handlers"].append("file")
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# Apply configuration
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logging.config.dictConfig(config)
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def get_logger(name: str) -> logging.Logger:
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"""Get a logger instance for the given name.
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Args:
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name: Logger name, typically __name__ of the calling module
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Returns:
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Logger instance configured for Kubeflow SDK
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"""
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# Ensure the logger name starts with 'kubeflow'
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if not name.startswith("kubeflow"):
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name = f"kubeflow.{name}"
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return logging.getLogger(name)
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def configure_from_env() -> None:
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"""Configure logging from environment variables.
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Environment variables:
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KUBEFLOW_LOG_LEVEL: Logging level (default: INFO)
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KUBEFLOW_LOG_FORMAT: Output format (default: console)
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KUBEFLOW_LOG_FILE: Log file path (optional)
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"""
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level = os.getenv("KUBEFLOW_LOG_LEVEL", "INFO")
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format_type = os.getenv("KUBEFLOW_LOG_FORMAT", "console")
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log_file = os.getenv("KUBEFLOW_LOG_FILE")
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setup_logging(level=level, format_type=format_type, log_file=log_file)
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# Copyright 2025 The Kubeflow Authors.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""Custom log formatters for Kubeflow SDK."""
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from datetime import datetime, timezone
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import json
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import logging
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class StructuredFormatter(logging.Formatter):
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"""JSON structured formatter for Kubeflow SDK logs.
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This formatter outputs logs in JSON format, making them suitable for
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log aggregation systems like ELK stack, Fluentd, etc.
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"""
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def format(self, record: logging.LogRecord) -> str:
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"""Format log record as JSON.
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Args:
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record: Log record to format
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Returns:
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JSON formatted log string
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"""
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log_entry = {
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"timestamp": datetime.now(timezone.utc).isoformat(),
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"level": record.levelname,
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"logger": record.name,
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"message": record.getMessage(),
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"module": record.module,
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"function": record.funcName,
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"line": record.lineno,
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}
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# Add exception info if present
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if record.exc_info:
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log_entry["exception"] = self.formatException(record.exc_info)
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return json.dumps(log_entry, ensure_ascii=False)

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