How this analysis was created: This summary and feature list are AI-generated from collected project material and can contain mistakes. Stars, license and language are imported from GitHub. Inclusion does not mean that we have tested or audited this project. Check the source documentation for any feature you depend on. Learn more on our About page.
The main features of ag14774/diffdist are: Developer Tools, More to explore, PyTorch Utilities.
Projects with overlapping indexed features include: mariogeiger/hessian — hessian in pytorch. lyken17/pytorch-opcounter — pytorch-OpCounter is a profiling utility for PyTorch neural networks designed to quantify model efficiency by… kevinmusgrave/pytorch-metric-learning — PyTorch Metric Learning is an open-source library for training neural networks to produce similarity-preserving… awentzonline/pytorch-cns — Generalized Compressed Network Search with PyTorch. avelino/awesome-go — This project serves as a comprehensive language ecosystem index, functioning as a centralized, community-curated… adrientorris/awesome-blazor.
pytorch-OpCounter is a profiling utility for PyTorch neural networks designed to quantify model efficiency by calculating floating point operations and multiply-accumulate counts. It functions as a complexity analyzer to measure the computational cost and theoretical workload of different model architectures. The tool allows for the definition of custom operation counting rules to support third-party modules not covered by default. It uses forward hooks to intercept module calls and recursive traversal of the module tree to aggregate operations across child sub-modules. The project provides
PyTorch Metric Learning is an open-source library for training neural networks to produce similarity-preserving embedding spaces. It provides a modular framework where interchangeable loss functions, mining strategies, and evaluation tools can be composed to learn representations that map similar items to nearby points and dissimilar items to distant points in the embedding space. The library distinguishes itself through a highly configurable architecture that separates concerns across several interchangeable components. Users can assemble custom loss functions from pluggable distance metrics
An ML framework to accelerate research and its path to production.
