Axect/pytorch_template
A flexible PyTorch template for ML experiments with configuration management, logging, and hyperparameter optimization.
This helps machine learning researchers or practitioners quickly set up, run, and monitor deep learning experiments. You provide a single YAML configuration file describing your model, data, and training parameters, and it outputs trained models, detailed performance metrics (including real-time visualizations), and a comprehensive report on hyperparameter tuning. It's designed for individuals or small teams working on neural network development and optimization.
Use this if you need a structured, repeatable way to run PyTorch deep learning experiments, automatically log results, and efficiently tune hyperparameters without writing extensive boilerplate code for each new project.
Not ideal if you need to deploy models to production, are looking for a low-code/no-code ML platform, or primarily work with frameworks other than PyTorch.
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Language
Python
License
MIT
Category
Last pushed
Feb 26, 2026
Commits (30d)
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