ml_research_tools 1.0.0

A collection of tools for machine learning research, including experiment management, Kubernetes utilities, and LaTeX processing.

ML Research Tools

A comprehensive toolkit for machine learning research workflows, designed to streamline common tasks in experimentation, documentation, and deployment processes.

Key Features

• LaTeX Tools

  • Grammar and style checker for LaTeX documents
  • Automatic suggestions for improving technical writing

• LLM Integration

  • Easy interaction with OpenAI and compatible LLMs
  • Chat and completion interfaces with robust error handling
  • Automatic retries and caching to reduce API costs
  • Support for multiple model presets and tiers

• Document Tools

  • Ask questions and get answers about documentation

• Experiment Management

  • Weights & Biases run logs downloader

• Kubernetes Tools

  • Pod port forwarding with automatic reconnection

• Caching System

  • Redis-based function result caching
  • Transparent caching with decorators

Installation

From PyPI (Recommended)

pip install ml_research_tools

From Source

git clone https://github.com/alexdremov/ml_research_tools.git
cd ml_research_tools
poetry install

Configuration

The toolkit can be configured through multiple methods, with a cascading priority:

1. Command-line arguments (highest priority)
2. Configuration file
3. Default values (lowest priority)

Configuration File

By default, the configuration is stored in ~/.config/ml_research_tools/config.yaml. If this file doesn't exist, it will be created with default values when the tool is first run.

Example configuration file:

logging:
  level: INFO
  file: /path/to/log/file.log
redis:
  host: localhost
  port: 6379
  db: 0
  password: optional_password
  enabled: true
  ttl: 604800  # 7 days in seconds
llm:
  default: "standard"  # Default preset to use
  presets:
    standard:
      base_url: https://api.openai.com/v1
      model: gpt-3.5-turbo
      max_tokens: 8000
      temperature: 0.01
      top_p: 1.0
      retry_attempts: 3
      retry_delay: 5
      api_key: null
      tier: standard
    premium:
      base_url: https://api.openai.com/v1
      model: gpt-4o
      max_tokens: 8000
      temperature: 0.01
      top_p: 1.0
      retry_attempts: 3
      retry_delay: 5
      api_key: null
      tier: premium

Command-line Arguments

Configuration can be overridden using command-line arguments:

ml_research_tools --log-level DEBUG --redis-host redis.example.com --llm-preset premium paper.tex latex-grammar paper.tex

Available configuration options:

Option	Description	Default
--config	Path to configuration file	~/.config/ml_research_tools/config.yaml
--log-level	Logging level	INFO
--log-file	Path to log file	None
--verbose	Enable verbose logging	False
--redis-host	Redis host	localhost
--redis-port	Redis port	6379
--redis-db	Redis database number	0
--redis-password	Redis password	None
--redis-disable	Disable Redis caching	Enabled
--redis-recache	Recache results	False
--llm-preset	LLM preset to use	standard
--llm-tier	LLM tier to use	None
--list-presets	List available LLM presets	False
--list-tools	List available tools	False

Development

Development Workflow

1. Clone the repository
2. Install development dependencies:

   poetry install --with dev
   

3. Run tests:

   poetry run pytest
   

4. Code quality tools:

   # Format code
   poetry run black .
   poetry run isort .
   
   # Check typing
   poetry run mypy .
   
   # Run linter
   poetry run ruff .
   

Adding a New Tool

1. Create a new module in the appropriate directory
2. Implement a class that inherits from BaseTool
3. Register arguments in the add_arguments method
4. Implement the execute method
5. Import the module in __init__.py to ensure discovery

Example:

from ml_research_tools.core.base_tool import BaseTool

class MyNewTool(BaseTool):
    name = "my-tool"
    description = "Description of my new tool"

    def add_arguments(self, parser):
        parser.add_argument("--option", help="An option for my tool")

    def execute(self, config, args):
        # Implementation
        return 0  # Success

License

This project is licensed under the MIT License - see the LICENSE file for details.

Documentation

The full documentation is available at https://alexdremov.github.io/ml_research_tools/.

To build the documentation locally:

poetry install --with docs
cd docs
poetry run make html

Then open docs/build/html/index.html in your browser.

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

1. Fork the repository
2. Create your feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add some amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

LLM Disclosure

This project is wildly LLM-written (though, widely reviewed by me)