ise-uiuc/NablaFuzz
Fuzzing Automatic Differentiation in Deep-Learning Libraries (ICSE'23)
This tool helps deep learning library developers find bugs in the automatic differentiation (AD) features of their frameworks. It takes your existing deep learning library (like PyTorch or TensorFlow) as input and automatically generates tests to expose inconsistencies in how gradients are computed. The output is a list of potential bugs, pinpointing where the library might be calculating gradients incorrectly. Developers of deep learning frameworks are the primary users.
No commits in the last 6 months.
Use this if you are a developer or researcher working on deep learning libraries and need to rigorously test the correctness of their automatic differentiation implementations.
Not ideal if you are an end-user building or training deep learning models and not directly involved in the development or maintenance of the underlying deep learning framework.
Stars
27
Forks
4
Language
Python
License
—
Category
Last pushed
Mar 02, 2024
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/ise-uiuc/NablaFuzz"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
NVIDIA/TransformerEngine
A library for accelerating Transformer models on NVIDIA GPUs, including using 8-bit and 4-bit...
mlcommons/inference
Reference implementations of MLPerf® inference benchmarks
mlcommons/training
Reference implementations of MLPerf® training benchmarks
datamade/usaddress
:us: a python library for parsing unstructured United States address strings into address components
GRAAL-Research/deepparse
Deepparse is a state-of-the-art library for parsing multinational street addresses using deep learning