advlog-core 0.2.0

Advanced logging library with Rich features, progress tracking, and ML training utilities

advlog

A Python logging library with colored terminal output, file management, progress tracking, and ML training utilities, built on top of Rich.

Personal project notice: This package was written for personal development use. Update frequency and feature scope may be limited. Contributions and suggestions are welcome.

Features

• Rich Console Output — Colored terminal logging via the Rich library
• Flexible Configuration — LoggerConfig dataclass for per-logger overrides; simple initialize() / get_logger() API for common cases
• File Logging — Separate or merged file strategies with configurable rotation
• Progress Tracking — Integrated progress bars via ProgressTracker
• Multi-Module Coordination — LoggerManager for consistent logging across modules
• ML/Training Utilities — TrainingLogger plugin for experiment logging
• Smart File Naming — Timestamped, daily, and incremental log file naming

Installation

pip install advlog-core

With all optional features:

pip install "advlog-core[all]"

The PyPI package name is advlog-core; import as import advlog.

Quick Start

One-liner

from advlog import get_logger

log = get_logger(__name__)
log.info("Application started")
log.warning("High memory usage detected")
log.error("Database connection failed")

Initialize once, use everywhere

from advlog import initialize, get_logger, get_progress

initialize(
    output_dir="./logs",
    session_name="myapp",
    log_level="DEBUG",
    show_location=True,
)

log = get_logger(__name__)
log.info("Initialized")

progress = get_progress()
with progress:
    task = progress.add_task("Processing", total=100)
    for i in range(100):
        progress.update(task, advance=1)

Per-logger configuration with LoggerConfig

from advlog import LoggerManager, LoggerConfig

manager = LoggerManager(shared_console=True, shared_file="logs/app.log")

# Default registration
api_logger = manager.register_logger("api")

# Override with LoggerConfig
db_logger = manager.register_logger(config=LoggerConfig(
    name="database",
    log_level="DEBUG",
    log_file="logs/db.log",
    show_location=True,
))

api_logger.info("API server started")
db_logger.debug("Executing query: SELECT ...")

Register a group of loggers at once

from advlog import create_logger_group

loggers = create_logger_group(
    ["api", "database", "auth"],
    shared_console=True,
    file_strategy="merged",
)

loggers["api"].info("Request received")
loggers["database"].debug("Query executed")
loggers["auth"].warning("Invalid login attempt")

Formatters

Four formatters are available:

Class	Description
RichColorFormatter	Wraps messages in Rich color markup by log level
PlainFormatter	Plain text, no color codes
IndentedFormatter	Indents continuation lines of multi-line messages
JSONFormatter	Outputs each record as a JSON object

from advlog import JSONFormatter
import logging

handler = logging.StreamHandler()
handler.setFormatter(JSONFormatter())
logging.getLogger("myapp").addHandler(handler)

File Logging

from advlog import create_file_handler

handler = create_file_handler(
    log_file="logs/app.log",
    file_mode="a",
    show_location=True,
)

Smart log file naming

from advlog import LogNamingStrategy

# Timestamp-based:  logs/2026-03-06/20260306_143022_app_prod.log
log_file = LogNamingStrategy.timestamped("app", suffix="prod")

# Daily directory:  logs/2026-03-06/myapp_errors.log
log_file = LogNamingStrategy.daily("myapp", suffix="errors")

# Incremental (no conflicts):  logs/backup_data.log  →  backup_data_1.log ...
log_file = LogNamingStrategy.incremental("backup", suffix="data")

Progress Tracking

from advlog import ProgressTracker

with ProgressTracker() as progress:
    download = progress.add_task("Downloading", total=100)
    process  = progress.add_task("Processing",  total=50)

    for _ in range(100):
        progress.update(download, advance=1)

    for _ in range(50):
        progress.update(process, advance=1)

ML Training Utilities

from advlog import TrainingLogger
import logging

logger = logging.getLogger(__name__)
trainer = TrainingLogger(logger)

trainer.log_train_step(
    epoch=1,
    total_epochs=10,
    batch=100,
    total_batches=1000,
    loss_dict={"loss": 0.4231, "accuracy": 0.872},
    learning_rate=1e-3,
    time_elapsed=45.2,
)

Exception Logging

from advlog import setup_exception_logging

# Install a sys.excepthook that logs all uncaught exceptions
setup_exception_logging()

Real-World Example

See examples/real_world_demo/ for a complete multi-module application demonstrating global initialization, cross-module loggers, progress tracking, error handling, and organized file output.

python -m examples.real_world_demo.main

Dependencies

Required

• Rich — Terminal formatting and colored output

Optional

Extra tag	Package	Feature
config	omegaconf	Advanced config file parsing
torch	torchinfo	Model structure visualization
distributed	accelerate	Distributed/multi-GPU training support

pip install "advlog-core[config]"
pip install "advlog-core[torch]"
pip install "advlog-core[distributed]"

Contributing

Contributions are welcome — please feel free to open an issue or submit a pull request.

License

Apache License 2.0 — see LICENSE for details.

Acknowledgments

• Rich for the terminal formatting library
• Python logging for the logging infrastructure