SebChw/Actually-Robust-Training
Actually Robust Training - Tool Inspired by Andrej Karpathy "Recipe for training neural networks". It allows you to decompose your Deep Learning pipeline into modular and insightful "Steps". Additionally it has many features for testing and debugging neural nets.
When training deep neural networks with PyTorch, this tool helps you follow best practices and ensures your model training process is robust and explainable. It breaks down your deep learning pipeline into a series of modular steps, allowing you to feed in your data and model to get a well-debugged and reproducible training outcome. This is for machine learning engineers, data scientists, and deep learning practitioners who build and train neural networks.
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Use this if you are training deep learning models with PyTorch and want to ensure correctness, reproducibility, and robust debugging throughout your experiment lifecycle.
Not ideal if you are not working with PyTorch for deep learning or are looking for a no-code solution for model training.
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Language
Python
License
MIT
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Last pushed
Apr 13, 2024
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