{"repo":"LambdaSection/NeuralDBG","free":true,"listed":false,"github":"https://github.com/LambdaSection/NeuralDBG","clone":"git clone https://github.com/LambdaSection/NeuralDBG.git","description":"A causal inference engine for deep learning training that provides structured explanations of neural network training failures. Understand why your model failed during training through semantic analysis and abductive reasoning, not raw tensor inspection.","language":"Python","stars":24,"topics":["data-science","data-visualization","dsl","machine-learning","neural-networks","pytorch","tensorflow","visualization","automation","diagrams"],"license":"MIT","category":"machine-learning","readme_excerpt":"NeuralDBG Training failed? NeuralDBG tells you why . It hooks into your PyTorch training loop, detects what went wrong (vanishing gradients, exploding gradients, data anomalies), and pinpoints the exact layer and step — so you fix it in seconds, not hours. Overview NeuralDBG treats training as a semantic trace of learning dynamics rather than a black box. It extracts meaningful events and provides causal hypotheses about training failures, enabling researchers to: - Identify gradient health transitions (stable - vanishing/saturated) - Detect activation regime shifts (normal - saturated/dead) - Detect optimizer instability (loss plateaus, spikes, divergence) - Catch data anomalies (NaN, Inf, distribution shifts) - Track propagation of instabilities through network layers - Generate ranked causal explanations for training failures Unlike traditional monitoring tools (TensorBoard, Weights & Biases), NeuralDBG focuses on causal inference rather than metric tracking. Neural Suite Component Availability Role ----------- -------------- ------ NeuralDBG Public (PyPI) Causal diagnostics in your training loop Diagnostic Workspace Private beta Visual causal graphs and hypothesis explorer Neural Agent Private beta Auto-remediation from causal hypotheses Request early access: open an issue with label suite-access . Try it in 60 seconds (Colab) Or locally: python examples/quickstart interactive.py Public benchmark Reproducible causal accuracy on synthetic failures (details): Latest results","default_branch":null,"files":null,"tree":[],"storefront":"/r/LambdaSection","claimed":false,"request_supported":{"post":"https://gitbuyer.com/r/LambdaSection/NeuralDBG/request-supported","requests":0},"note":"indexed from public GitHub; nothing is for sale on this page. Clone it from GitHub. Paid listings live at /search."}