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Welcome to fenn (Friendly Environment for Neural Networks), a lightweight Python framework designed to strip away the repetitive boilerplate of Machine Learning development.

Stop writing boilerplate. Start training.

fenn is a lightweight Python framework that automates the boring stuff in Machine Learning projects so you can focus on the model. It handles configuration parsing, logging setup, and experiment tracking in a minimal, opinionated way.

Why fenn?

In a typical ML project, developers often spend hours setting up logging directories, writing YAML parsers, and manually connecting experiment trackers. Fenn automates this entire lifecycle:

  • Auto-Configuration: YAML files are automatically parsed and injected into your entrypoint. You get full parametrization support without writing a single line of argparse.
  • Unified Logging: All logs, print statements, and experiment metadata are captured to local files and remote backends simultaneously.
  • Multi-Backend Monitoring: Native integration with Weights & Biases (W&B) and TensorBoard.
  • Instant Notifications: Get real-time alerts on Discord and Telegram when experiments start, finish, or crash.
  • Template Ready: Download and use reproducible experiment templates to jumpstart new projects.

Roadmap

  • ML Templates: Automated creation of standard project structures.
  • Model Tools: Utilities for Neural Network creation, training, and testing.
  • Notifications: Email notification system for completed training runs.
  • Data Tools: Data exploration and visualization helpers.
  • Analysis: Result analysis tools (diagrams, confusion matrices, etc.).
  • Integrations: Support for TensorBoard and similar tracking tools.
  • Testing: Comprehensive unit and integration tests for the framework.