fenn 0.1.2

Friendly Environment for Neural Networks – a simple framework that automates the boring side of ML.

fenn: Friendly Environment for Neural Networks

Stop writing boilerplate. Start training.

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

Why fenn?

• Auto-Configuration: YAML files are automatically parsed and injected into your entrypoint with CLI override support. No more hardcoded hyperparameters or scattered config logic.

• Unified Logging: All logs, print statements, and experiment metadata are automatically captured to local files and remote tracking backends simultaneously with no manual setup required.

• Multi-Backend Monitoring: Native integration with industry-standard trackers like Weights & Biases (W&B) for centralized experiment tracking and TensorBoard for real-time metric visualization

• Instant Notifications: Get real-time alerts on Discord and Telegram when experiments start, complete, or fail—no polling or manual checks.

• Template Ready: Built-in support for reproducible, shareable experiment templates.

Installation

pip install fenn

Quickstart

Initialize a Project

Run the CLI tool to see which repositories are available and to download a template together with its configuration file. First, list the available repositories:

fenn list

Then, download one of the available templates (here base is just an example):

fenn pull base

This command downloads the selected template into the current directory and generates the corresponding configuration file, which can be customized before running or extending the project.

Configuration

fenn relies on a simple YAML structure to define hyperparameters, paths, logging options, and integrations. You can configure the fenn.yaml file with the hyperparameters and options for your project.

The structure of the fenn.yaml file is:

# ---------------------------------------
# Fenn Configuration (Modify Carefully)
# ---------------------------------------

project: base

# ---------------------------
# Logging & Tracking
# ---------------------------

logger:
  dir: logger

# ---------------------------------------
# Example of User Section
# ---------------------------------------

train:
    lr: 0.001

Write Your Code

Use the @app.entrypoint decorator. Your configuration variables are automatically passed via args.

from fenn import Fenn

app = Fenn()

@app.entrypoint
def main(args):
    # 'args' contains your fenn.yaml configurations
    print(f"Training with learning rate: {args['train']['lr']}")

    # Your logic here...

if __name__ == "__main__":
    app.run()

By default, fenn will look for a configuration file named fenn.yaml in the current directory. If you would like to use a different name, a different location, or have multiple configuration files for different configurations, you can call set_config_file() and update the path or the name of your configuration file. You must assign the filename before calling run().

app = Fenn()
app.set_config_file("my_file.yaml")
...
app.run()

Run It

python main.py

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.

Contributing

Contributions are welcome!

If you’re interested in helping, please feel free to join our discord server or the dedicated discussion page and ping there your availability.

We can then discuss a possible contribution together, answer any questions, and help you get started!

Please, before opening a pull request, consult our CONTRIBUTING.md

Thank you for your support!