| Name | lightlog JSON |
| Version |
0.1.0
JSON |
| download |
| home_page | None |
| Summary | A lightweight, flexible logging library for Python with a C++ core and support for distributed computing environments |
| upload_time | 2024-09-19 20:19:00 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.8 |
| license | MIT License Copyright (c) 2024 Misagh Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. |
| keywords |
logging
debug
development-tools
python
rank-aware-logging
print-redirection
file-logging
console-logging
multi-process
parallel-computing
|
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# LightLog
[](https://github.com/misaghsoltani/LightLog/actions/workflows/wheel.yml)
[](https://github.com/misaghsoltani/LightLog/actions/workflows/pip.yml)
<div align="center">
<img src="https://raw.githubusercontent.com/misaghsoltani/LightLog/master/images/lightlog.png" alt="*LightLog*" width="350">
</div>
_LightLog_ is a lightweight, flexible, and efficient logging package for Python, built on a C++ core.
Originally developed to meet the logging needs of a personal project, its development was driven by a desire for a streamlined logging solution that balances performance and ease of use, and it is now shared with the community in the hopes of being a useful resource.
## Table of Contents
- [LightLog](#lightlog)
- [Table of Contents](#table-of-contents)
- [Features](#features)
- [Installation](#installation)
- [Usage](#usage)
- [Basic Usage](#basic-usage)
- [Distributed Computing with Manual Rank Setting](#distributed-computing-with-manual-rank-setting)
- [Distributed Computing with Auto Rank Detection](#distributed-computing-with-auto-rank-detection)
- [Distributed Computing with Specified Environment](#distributed-computing-with-specified-environment)
- [Print Redirection](#print-redirection)
- [API Reference](#api-reference)
- [`Logger` Class](#logger-class)
- [Parameters](#parameters)
- [Methods](#methods)
- [Performance](#performance)
- [Benchmark](#benchmark)
- [Benchmark Environment](#benchmark-environment)
- [Benchmark Results](#benchmark-results)
- [Summary](#summary)
- [Contributing](#contributing)
- [License](#license)
## Features
- Lightweight and efficient C++ core with Python bindings
- Support for both file and console logging
- Multiple log levels (DEBUG, INFO, WARNING, ERROR, CRITICAL)
- Rank-based logging for distributed systems
- Auto-detection of distributed environments (MPI, PyTorch, Horovod, SLURM, NCCL)
- Ability to redirect Python's print() function to the logger
- Support for logging to multiple files
- Versatile usage: can be used as a
- Decorator to log function calls and outputs
- Context manager to log specific code blocks or scopes
- Traditional logger for explicit logging statements
## Installation
You can install _LightLog_ using pip:
```bash
pip install lightlog
```
## Usage
### Basic Usage
This section demonstrates the fundamental operations of the _LightLog_ library, including initializing a logger, logging messages with different levels, and using the default logging behavior.
```python
from lightlog import Logger
# Initialize logger
logger = Logger('LogName', 'log.txt')
# Log with warning level
logger.log("Print to screen and save to 'log.txt'", level=lightlog.WARNING)
# Output: 2024-09-8 17:34:16 | WARNING | LogName | Print to screen and save to 'log.txt'
# Log without specifying level
logger.log("Print to screen and save to 'log.txt'")
# Output: Print to screen and save to 'log.txt'
# Log with info level
logger.info("Print to screen and save to 'log.txt'")
# Output: 2024-09-8 17:34:48 | INFO | LogName | Print to screen and save to 'log.txt'
```
### Distributed Computing with Manual Rank Setting
This example shows how to use _LightLog_ in a distributed computing environment where you manually specify the rank and world size. This is useful when you want to control these parameters explicitly.
```python
from lightlog import Logger
# Initialize logger with manual rank setting
logger = Logger('LogName', 'log.txt', use_rank=True, rank=0, world_size=10)
# Log with warning level
logger.log("Print to screen and save to 'log.txt'", level=lightlog.WARNING)
# Output: [0/10] 2024-09-8 17:36:43 | WARNING | LogName | Print to screen and save to 'log.txt'
# Log without specifying level
logger.log("Print to screen and save to 'log.txt'")
# Output: [0/10] Print to screen and save to 'log.txt'
```
### Distributed Computing with Auto Rank Detection
This section illustrates how _LightLog_ can automatically detect rank and world size in an MPI environment. It uses environment variables to determine these values without manual specification.
Assume environment is 'mpirun', the environment variable `OMPI_COMM_WORLD_SIZE` is set to 34, and the environment variable `OMPI_COMM_WORLD_RANK` is set to 8
```python
from lightlog import Logger
# Initialize logger with auto rank detection
logger = Logger('LogName', 'log.txt', use_rank=True)
# Log without specifying level
logger.log("Print to screen and save to 'log.txt'")
# Output: [8/34] Print to screen and save to 'log.txt'
# Log with info level
logger.info("Print to screen and save to 'log.txt'")
# Output: [8/34] 2024-09-8 17:37:50 | INFO | LogName | Print to screen and save to 'log.txt'
```
### Distributed Computing with Specified Environment
This example demonstrates how to use _LightLog_ with a specific distributed computing environment, in this case, Torchrun. It shows how to configure the logger to detect rank and world size from Torchrun-specific environment variables.
Assume the environment is 'torchrun', the environment variable `RANK` is set to 8, and the environment variable `WORLD_SIZE` is set to 34
```python
from lightlog import Logger
# Initialize logger with specified environment
logger = Logger('LogName', 'log.txt', use_rank=True, auto_detect_env='torchrun')
# Log with critical level
logger.critical("Print to screen and save to 'log.txt'")
# Output: [8/34] 2024-09-8 17:38:56 | CRITICAL | LogName | Print to screen and save to 'log.txt'
```
### Print Redirection
This section shows how _LightLog_ can intercept and redirect print statements. This feature is useful when you want to capture all output, including from third-party libraries that use print statements, in your log file.
```python
from lightlog import Logger
# Initialize logger
logger = Logger('LogName', 'log.txt')
# Normal print
print("Print to the screen only")
# Output: Print to the screen only
# Redirect print to logger
logger.redirect_print()
print("Print to screen and save to 'log.txt'")
# Output: Print to screen and save to 'log.txt'
# Reset print to normal behavior
logger.reset_print()
print("Print to the screen only")
# Output: Print to the screen only
```
### Context Manager
The LightLog library allows for flexible logging configurations. You can use it directly or as a context manager for temporary logging redirection.
```python
import lightlog
# Initialize logger
logger = lightlog.Logger("ContextExample", "/path/to/log.txt", level=lightlog.NOTSET)
print("This prints to console")
# Using logger as a context manager
with logger:
print("This prints to console and log file")
print("This prints to console again")
```
In this example, only the print statement within the `with` block is captured by the logger and written to both the console and the log file.
### Function Decorator
The `@log_prints` decorator can be used to automatically redirect all print statements within a function to a logger.
```python
@lightlog.log_prints(name="FunctionLogger", file_path="/path/to/log.txt", level=lightlog.INFO)
def example_function():
print("This will be logged")
example_function()
```
When `example_function()` is called, its print statement will be logged at the INFO level.
### Class Decorator
You can also apply the `@log_prints` decorator to a class, which will affect all methods within the class.
```python
@lightlog.log_prints(name="ClassLogger", file_path="/path/to/log.txt")
class ExampleClass:
def __init__(self):
print("This will be logged")
def method(self):
print("This will also be logged")
my_instance = ExampleClass()
my_instance.method()
```
Both the `__init__` and `method` print statements will be captured by the logger.
### Using Existing Logger Instance
You can create a logger instance and reuse it with the decorator:
```python
logger = lightlog.Logger(name="ExistingLogger", file_path="/path/to/log.txt")
@lightlog.log_prints(logger_instance=logger)
def yet_another_example():
print("This will use the existing logger")
yet_another_example()
```
This approach allows you to maintain consistent logging configurations across multiple functions or classes.
> [!NOTE]
>
> - If `use_rank` is set to `True`, the rank and world size label will be added to the log output.
> - The `level` parameter in `Logger` initialization sets the minimum logging level. `NOTSET` means all messages will be logged.
> - When using the `@log_prints` decorator, you can specify logging parameters or use an existing logger instance.
> - The file path in these examples is set to "/path/to/log.txt". In a real scenario, you would replace this with the actual path where you want to store your log file.
These examples demonstrate how LightLog can be integrated into various parts of your Python code to provide flexible logging capabilities.
## API Reference
### `Logger` Class
The `Logger` class is designed to provide flexible and efficient logging, especially in distributed environments. Below is a detailed description of its parameters and methods.
#### Parameters
- **`name: str`**
The name of the logger instance, typically used to identify the source of log messages.
- **`file_path: Optional[str] = None`**
Path to the log file. If not provided, logs are printed to the console.
- **`mode: str = 'a'`**
Mode for opening the log file. `'a'` for appending, `'w'` for overwriting.
- **`level: int = lightlog.NOTSET`**
Logging level. Set to `0` for no filtering (i.e., log everything).
- **`use_rank: bool = False`**
Enables rank-based logging for distributed systems.
- **`rank: Optional[int] = None`**
Manually set the process rank (useful for distributed configurations).
- **`world_size: Optional[int] = None`**
Total number of processes in distributed environments.
- **`auto_detect_env: Optional[str] = None`**
Automatically detect the environment for logging. Supported values include `'all'`, `'mpirun'`, `'torchrun'`, etc.
- **`log_rank: Optional[int] = None`**
The rank on which logging is performed. All other ranks will suppress logs.
### Methods
- **`log(*args, sep=" ", end="\n", level=lightlog.NOTSET, use_rank=False, new_file_path=None)`**
General logging method to log messages at a specific level.
- `args`: Content of the log message.
- `sep`: Separator between `args` (default is `" "`).
- `end`: End character (default is newline).
- `level`: Custom logging level for the message.
- `use_rank`: Include the rank in the log message (default is `False`).
- `new_file_path`: Temporarily set a new log file path for this message.
- **`debug(*args, sep=" ", end="\n", use_rank=False, new_file_path=None)`**
Log a debug-level message.
- **`info(*args, sep=" ", end="\n", use_rank=False, new_file_path=None)`**
Log an informational message.
- **`warning(*args, sep=" ", end="\n", use_rank=False, new_file_path=None)`**
Log a warning message.
- **`error(*args, sep=" ", end="\n", use_rank=False, new_file_path=None)`**
Log an error message.
- **`critical(*args, sep=" ", end="\n", use_rank=False, new_file_path=None)`**
Log a critical message.
- **`flush()`**
Flush the log buffer to ensure all pending log messages are written to the file or console.
- **`close()`**
Close the logger, releasing any associated resources.
- **`reconfigure(name: Optional[str] = None, new_file_path: Optional[str] = None, mode: str = 'a', level: Optional[int] = None, use_rank: Optional[bool] = None, rank: Optional[int] = None, world_size: Optional[int] = None, auto_detect_env: Optional[str] = None, log_rank: Optional[int] = None) -> None`**
Dynamically reconfigure the logger with new settings.
- `name`: Change the logger's name.
- `new_file_path`: Set a new file path for logging.
- `mode`: Update the file open mode (`'a'` for append, `'w'` for overwrite).
- `level`: Set a new logging level.
- `use_rank`: Enable or disable rank-based logging.
- `rank`: Update the process rank.
- `world_size`: Adjust the total number of processes.
- `auto_detect_env`: Set the environment for auto-detection (e.g., `'mpirun'`, `'torchrun'`).
- `log_rank`: Specify the rank for active logging.
- **`redirect_print()`**
Redirect the standard `print()` function to use the logger for logging output.
- **`reset_print()`**
Restore the default behavior of the `print()` function, removing the logger redirection.
## Performance
_LightLog_ is optimized for speed and efficiency. Its C++ core ensures fast logging operations, while the Python interface provides ease of use. Below is a benchmark comparison between _LightLog_ and Python's built-in `logging` module.
You can view the full benchmark code at [`benchmark.py`](https://github.com/misaghsoltani/LightLog/blob/main/benchmark.py).
### Benchmark
#### Benchmark Environment
This benchmark was conducted on an **Apple M1 Max** chip. The test measures the time it takes to log 100,000 messages using both _LightLog_ and Python's built-in logger, repeated 10 times, with each run consisting of 10 iterations.
#### Benchmark Results
Below are the benchmark results comparing the two loggers across multiple runs:
```bash
$ python benchmark.py
2024-09-18 04:17:23,997 | light_logger | INFO | Log message 0
[...]
2024-09-18 04:17:54,587 | light_logger | INFO | Log message 99999
2024-09-18 04:17:54,587 | py_logger | INFO | Log message 0
[...]
2024-09-18 04:20:29,099 | py_logger | INFO | Log message 99999
---------- Benchmark Results -----------
Iterations: 100000
Runs per repeat: 10
Repeats: 10
LightLog times: ['3.125600 seconds', '3.039536 seconds', '3.036778 seconds', '3.040145 seconds', '3.293178 seconds', '3.218442 seconds', '2.954060 seconds', '2.987005 seconds', '2.937843 seconds', '2.956478 seconds']
LightLog average time: 3.058907 seconds
Built-in logging times: ['15.090291 seconds', '14.747250 seconds', '15.834340 seconds', '15.643067 seconds', '15.691300 seconds', '15.500472 seconds', '15.389877 seconds', '15.533213 seconds', '15.616875 seconds', '15.463194 seconds']
Built-in logging average time: 15.450988 seconds
----------------------------------------
```
<div align="center">
<!-- <img src="images/average_times.png" height="350" style="margin: 10px;"> -->
<img src="https://raw.githubusercontent.com/misaghsoltani/LightLog/master/images/average_times_same_scale.png" height="360" style="margin: 10px;">
<!-- <img src="images/individual_times.png" height="350" style="margin: 10px;"> -->
<img src="https://raw.githubusercontent.com/misaghsoltani/LightLog/master/images/individual_times_same_scale.png" height="350" style="margin: 10px;">
</div>
### Summary
The results show that **_LightLog_** is approximately **5x faster** than Python's built-in `logging` module, making it a more efficient choice for logging large volumes of messages.
## Contributing
If you find a bug or have a feature request, please open an issue on the GitHub repository. If you'd like to contribute code, please fork the repository and submit a pull request.
## License
_LightLog_ is released under the MIT License. See the LICENSE file for details.
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"keywords": "logging, debug, development-tools, python, rank-aware-logging, print-redirection, file-logging, console-logging, multi-process, parallel-computing",
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"author_email": "Misagh Soltani <msoltani@email.sc.edu>",
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"description": "# LightLog\n\n[](https://github.com/misaghsoltani/LightLog/actions/workflows/wheel.yml)\n[](https://github.com/misaghsoltani/LightLog/actions/workflows/pip.yml)\n\n<div align=\"center\">\n <img src=\"https://raw.githubusercontent.com/misaghsoltani/LightLog/master/images/lightlog.png\" alt=\"*LightLog*\" width=\"350\">\n</div>\n\n_LightLog_ is a lightweight, flexible, and efficient logging package for Python, built on a C++ core.\nOriginally developed to meet the logging needs of a personal project, its development was driven by a desire for a streamlined logging solution that balances performance and ease of use, and it is now shared with the community in the hopes of being a useful resource.\n\n## Table of Contents\n\n- [LightLog](#lightlog)\n - [Table of Contents](#table-of-contents)\n - [Features](#features)\n - [Installation](#installation)\n - [Usage](#usage)\n - [Basic Usage](#basic-usage)\n - [Distributed Computing with Manual Rank Setting](#distributed-computing-with-manual-rank-setting)\n - [Distributed Computing with Auto Rank Detection](#distributed-computing-with-auto-rank-detection)\n - [Distributed Computing with Specified Environment](#distributed-computing-with-specified-environment)\n - [Print Redirection](#print-redirection)\n - [API Reference](#api-reference)\n - [`Logger` Class](#logger-class)\n - [Parameters](#parameters)\n - [Methods](#methods)\n - [Performance](#performance)\n - [Benchmark](#benchmark)\n - [Benchmark Environment](#benchmark-environment)\n - [Benchmark Results](#benchmark-results)\n - [Summary](#summary)\n - [Contributing](#contributing)\n - [License](#license)\n\n## Features\n\n- Lightweight and efficient C++ core with Python bindings\n- Support for both file and console logging\n- Multiple log levels (DEBUG, INFO, WARNING, ERROR, CRITICAL)\n- Rank-based logging for distributed systems\n- Auto-detection of distributed environments (MPI, PyTorch, Horovod, SLURM, NCCL)\n- Ability to redirect Python's print() function to the logger\n- Support for logging to multiple files\n- Versatile usage: can be used as a\n - Decorator to log function calls and outputs\n - Context manager to log specific code blocks or scopes\n - Traditional logger for explicit logging statements\n\n## Installation\n\nYou can install _LightLog_ using pip:\n\n```bash\npip install lightlog\n```\n\n## Usage\n\n### Basic Usage\n\nThis section demonstrates the fundamental operations of the _LightLog_ library, including initializing a logger, logging messages with different levels, and using the default logging behavior.\n\n```python\nfrom lightlog import Logger\n\n# Initialize logger\nlogger = Logger('LogName', 'log.txt')\n\n# Log with warning level\nlogger.log(\"Print to screen and save to 'log.txt'\", level=lightlog.WARNING)\n# Output: 2024-09-8 17:34:16 | WARNING | LogName | Print to screen and save to 'log.txt'\n\n# Log without specifying level\nlogger.log(\"Print to screen and save to 'log.txt'\")\n# Output: Print to screen and save to 'log.txt'\n\n# Log with info level\nlogger.info(\"Print to screen and save to 'log.txt'\")\n# Output: 2024-09-8 17:34:48 | INFO | LogName | Print to screen and save to 'log.txt'\n```\n\n### Distributed Computing with Manual Rank Setting\n\nThis example shows how to use _LightLog_ in a distributed computing environment where you manually specify the rank and world size. This is useful when you want to control these parameters explicitly.\n\n```python\nfrom lightlog import Logger\n\n# Initialize logger with manual rank setting\nlogger = Logger('LogName', 'log.txt', use_rank=True, rank=0, world_size=10)\n\n# Log with warning level\nlogger.log(\"Print to screen and save to 'log.txt'\", level=lightlog.WARNING)\n# Output: [0/10] 2024-09-8 17:36:43 | WARNING | LogName | Print to screen and save to 'log.txt'\n\n# Log without specifying level\nlogger.log(\"Print to screen and save to 'log.txt'\")\n# Output: [0/10] Print to screen and save to 'log.txt'\n```\n\n### Distributed Computing with Auto Rank Detection\n\nThis section illustrates how _LightLog_ can automatically detect rank and world size in an MPI environment. It uses environment variables to determine these values without manual specification.\n\nAssume environment is 'mpirun', the environment variable `OMPI_COMM_WORLD_SIZE` is set to 34, and the environment variable `OMPI_COMM_WORLD_RANK` is set to 8\n\n```python\nfrom lightlog import Logger\n\n# Initialize logger with auto rank detection\nlogger = Logger('LogName', 'log.txt', use_rank=True)\n\n# Log without specifying level\nlogger.log(\"Print to screen and save to 'log.txt'\")\n# Output: [8/34] Print to screen and save to 'log.txt'\n\n# Log with info level\nlogger.info(\"Print to screen and save to 'log.txt'\")\n# Output: [8/34] 2024-09-8 17:37:50 | INFO | LogName | Print to screen and save to 'log.txt'\n```\n\n### Distributed Computing with Specified Environment\n\nThis example demonstrates how to use _LightLog_ with a specific distributed computing environment, in this case, Torchrun. It shows how to configure the logger to detect rank and world size from Torchrun-specific environment variables.\n\nAssume the environment is 'torchrun', the environment variable `RANK` is set to 8, and the environment variable `WORLD_SIZE` is set to 34\n\n```python\nfrom lightlog import Logger\n\n# Initialize logger with specified environment\nlogger = Logger('LogName', 'log.txt', use_rank=True, auto_detect_env='torchrun')\n\n# Log with critical level\nlogger.critical(\"Print to screen and save to 'log.txt'\")\n# Output: [8/34] 2024-09-8 17:38:56 | CRITICAL | LogName | Print to screen and save to 'log.txt'\n```\n\n### Print Redirection\n\nThis section shows how _LightLog_ can intercept and redirect print statements. This feature is useful when you want to capture all output, including from third-party libraries that use print statements, in your log file.\n\n```python\nfrom lightlog import Logger\n\n# Initialize logger\nlogger = Logger('LogName', 'log.txt')\n\n# Normal print\nprint(\"Print to the screen only\")\n# Output: Print to the screen only\n\n# Redirect print to logger\nlogger.redirect_print()\nprint(\"Print to screen and save to 'log.txt'\")\n# Output: Print to screen and save to 'log.txt'\n\n# Reset print to normal behavior\nlogger.reset_print()\nprint(\"Print to the screen only\")\n# Output: Print to the screen only\n```\n\n### Context Manager\nThe LightLog library allows for flexible logging configurations. You can use it directly or as a context manager for temporary logging redirection.\n\n```python\nimport lightlog\n\n# Initialize logger\nlogger = lightlog.Logger(\"ContextExample\", \"/path/to/log.txt\", level=lightlog.NOTSET)\n\nprint(\"This prints to console\")\n\n# Using logger as a context manager\nwith logger:\n print(\"This prints to console and log file\")\n\nprint(\"This prints to console again\")\n```\n\nIn this example, only the print statement within the `with` block is captured by the logger and written to both the console and the log file.\n\n### Function Decorator\nThe `@log_prints` decorator can be used to automatically redirect all print statements within a function to a logger.\n\n```python\n@lightlog.log_prints(name=\"FunctionLogger\", file_path=\"/path/to/log.txt\", level=lightlog.INFO)\ndef example_function():\n print(\"This will be logged\")\n\nexample_function()\n```\n\nWhen `example_function()` is called, its print statement will be logged at the INFO level.\n\n### Class Decorator\nYou can also apply the `@log_prints` decorator to a class, which will affect all methods within the class.\n\n```python\n@lightlog.log_prints(name=\"ClassLogger\", file_path=\"/path/to/log.txt\")\nclass ExampleClass:\n def __init__(self):\n print(\"This will be logged\")\n\n def method(self):\n print(\"This will also be logged\")\n\nmy_instance = ExampleClass()\nmy_instance.method()\n```\n\nBoth the `__init__` and `method` print statements will be captured by the logger.\n\n### Using Existing Logger Instance\nYou can create a logger instance and reuse it with the decorator:\n\n```python\nlogger = lightlog.Logger(name=\"ExistingLogger\", file_path=\"/path/to/log.txt\")\n\n@lightlog.log_prints(logger_instance=logger)\ndef yet_another_example():\n print(\"This will use the existing logger\")\n\nyet_another_example()\n```\n\nThis approach allows you to maintain consistent logging configurations across multiple functions or classes.\n\n> [!NOTE]\n>\n> - If `use_rank` is set to `True`, the rank and world size label will be added to the log output.\n> - The `level` parameter in `Logger` initialization sets the minimum logging level. `NOTSET` means all messages will be logged.\n> - When using the `@log_prints` decorator, you can specify logging parameters or use an existing logger instance.\n> - The file path in these examples is set to \"/path/to/log.txt\". In a real scenario, you would replace this with the actual path where you want to store your log file.\n\nThese examples demonstrate how LightLog can be integrated into various parts of your Python code to provide flexible logging capabilities.\n\n## API Reference\n\n### `Logger` Class\n\nThe `Logger` class is designed to provide flexible and efficient logging, especially in distributed environments. Below is a detailed description of its parameters and methods.\n\n#### Parameters\n\n- **`name: str`** \n The name of the logger instance, typically used to identify the source of log messages.\n\n- **`file_path: Optional[str] = None`** \n Path to the log file. If not provided, logs are printed to the console.\n\n- **`mode: str = 'a'`** \n Mode for opening the log file. `'a'` for appending, `'w'` for overwriting.\n\n- **`level: int = lightlog.NOTSET`** \n Logging level. Set to `0` for no filtering (i.e., log everything).\n\n- **`use_rank: bool = False`** \n Enables rank-based logging for distributed systems.\n\n- **`rank: Optional[int] = None`** \n Manually set the process rank (useful for distributed configurations).\n\n- **`world_size: Optional[int] = None`** \n Total number of processes in distributed environments.\n\n- **`auto_detect_env: Optional[str] = None`** \n Automatically detect the environment for logging. Supported values include `'all'`, `'mpirun'`, `'torchrun'`, etc.\n\n- **`log_rank: Optional[int] = None`** \n The rank on which logging is performed. All other ranks will suppress logs.\n\n### Methods\n\n- **`log(*args, sep=\" \", end=\"\\n\", level=lightlog.NOTSET, use_rank=False, new_file_path=None)`** \n General logging method to log messages at a specific level.\n\n - `args`: Content of the log message.\n - `sep`: Separator between `args` (default is `\" \"`).\n - `end`: End character (default is newline).\n - `level`: Custom logging level for the message.\n - `use_rank`: Include the rank in the log message (default is `False`).\n - `new_file_path`: Temporarily set a new log file path for this message.\n\n- **`debug(*args, sep=\" \", end=\"\\n\", use_rank=False, new_file_path=None)`** \n Log a debug-level message.\n\n- **`info(*args, sep=\" \", end=\"\\n\", use_rank=False, new_file_path=None)`** \n Log an informational message.\n\n- **`warning(*args, sep=\" \", end=\"\\n\", use_rank=False, new_file_path=None)`** \n Log a warning message.\n\n- **`error(*args, sep=\" \", end=\"\\n\", use_rank=False, new_file_path=None)`** \n Log an error message.\n\n- **`critical(*args, sep=\" \", end=\"\\n\", use_rank=False, new_file_path=None)`** \n Log a critical message.\n\n- **`flush()`** \n Flush the log buffer to ensure all pending log messages are written to the file or console.\n\n- **`close()`** \n Close the logger, releasing any associated resources.\n\n- **`reconfigure(name: Optional[str] = None, new_file_path: Optional[str] = None, mode: str = 'a', level: Optional[int] = None, use_rank: Optional[bool] = None, rank: Optional[int] = None, world_size: Optional[int] = None, auto_detect_env: Optional[str] = None, log_rank: Optional[int] = None) -> None`** \n Dynamically reconfigure the logger with new settings.\n\n - `name`: Change the logger's name.\n - `new_file_path`: Set a new file path for logging.\n - `mode`: Update the file open mode (`'a'` for append, `'w'` for overwrite).\n - `level`: Set a new logging level.\n - `use_rank`: Enable or disable rank-based logging.\n - `rank`: Update the process rank.\n - `world_size`: Adjust the total number of processes.\n - `auto_detect_env`: Set the environment for auto-detection (e.g., `'mpirun'`, `'torchrun'`).\n - `log_rank`: Specify the rank for active logging.\n\n- **`redirect_print()`** \n Redirect the standard `print()` function to use the logger for logging output.\n\n- **`reset_print()`** \n Restore the default behavior of the `print()` function, removing the logger redirection.\n\n## Performance\n\n_LightLog_ is optimized for speed and efficiency. Its C++ core ensures fast logging operations, while the Python interface provides ease of use. Below is a benchmark comparison between _LightLog_ and Python's built-in `logging` module.\n\nYou can view the full benchmark code at [`benchmark.py`](https://github.com/misaghsoltani/LightLog/blob/main/benchmark.py).\n\n### Benchmark\n\n#### Benchmark Environment\n\nThis benchmark was conducted on an **Apple M1 Max** chip. The test measures the time it takes to log 100,000 messages using both _LightLog_ and Python's built-in logger, repeated 10 times, with each run consisting of 10 iterations.\n\n#### Benchmark Results\n\nBelow are the benchmark results comparing the two loggers across multiple runs:\n\n```bash\n$ python benchmark.py\n2024-09-18 04:17:23,997 | light_logger | INFO | Log message 0\n[...]\n2024-09-18 04:17:54,587 | light_logger | INFO | Log message 99999\n2024-09-18 04:17:54,587 | py_logger | INFO | Log message 0\n[...]\n2024-09-18 04:20:29,099 | py_logger | INFO | Log message 99999\n---------- Benchmark Results -----------\nIterations: 100000\nRuns per repeat: 10\nRepeats: 10\nLightLog times: ['3.125600 seconds', '3.039536 seconds', '3.036778 seconds', '3.040145 seconds', '3.293178 seconds', '3.218442 seconds', '2.954060 seconds', '2.987005 seconds', '2.937843 seconds', '2.956478 seconds']\nLightLog average time: 3.058907 seconds\nBuilt-in logging times: ['15.090291 seconds', '14.747250 seconds', '15.834340 seconds', '15.643067 seconds', '15.691300 seconds', '15.500472 seconds', '15.389877 seconds', '15.533213 seconds', '15.616875 seconds', '15.463194 seconds']\nBuilt-in logging average time: 15.450988 seconds\n----------------------------------------\n```\n\n<div align=\"center\">\n <!-- <img src=\"images/average_times.png\" height=\"350\" style=\"margin: 10px;\"> -->\n <img src=\"https://raw.githubusercontent.com/misaghsoltani/LightLog/master/images/average_times_same_scale.png\" height=\"360\" style=\"margin: 10px;\"> \n <!-- <img src=\"images/individual_times.png\" height=\"350\" style=\"margin: 10px;\"> -->\n <img src=\"https://raw.githubusercontent.com/misaghsoltani/LightLog/master/images/individual_times_same_scale.png\" height=\"350\" style=\"margin: 10px;\"> \n</div>\n\n### Summary\n\nThe results show that **_LightLog_** is approximately **5x faster** than Python's built-in `logging` module, making it a more efficient choice for logging large volumes of messages.\n\n## Contributing\n\nIf you find a bug or have a feature request, please open an issue on the GitHub repository. If you'd like to contribute code, please fork the repository and submit a pull request.\n\n## License\n\n_LightLog_ is released under the MIT License. See the LICENSE file for details.\n",
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"license": "MIT License Copyright (c) 2024 Misagh Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the \"Software\"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.",
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