Reverse Dependencies of cupy-cuda11x
The following projects have a declared dependency on cupy-cuda11x:
- anndata — Annotated data.
- ArtNex — ArtNex is a deep learning framework exploring the innovative fusion of art and technology.
- autoparis — Python package for Autoparis Workflow.
- biasgen — Utility functions to produce and visualize simulated bias fields generated according to Kern et. al's sinusoidal sensitivity model.
- campie — Python APIs to simulate various CAMs on GPUs at scale
- cuquantum-python-cu11 — NVIDIA cuQuantum Python
- gbtoolbox — Tools for neural network generalization bounds
- gt4py — Python library for generating high-performance implementations of stencil kernels for weather and climate modeling from a domain-specific language (DSL)
- hisel — no summary
- histo-tools — General histology tools.
- klongpy — High-Performance Klong array language with rich Python integration.
- lapgm — A spatially regularized Gaussian mixture model for MR bias field correction and intensity normalization.
- nettracer3d — Scripts for intializing and analyzing networks from segmentations of three dimensional images.
- neuralflow — Deep learning framework built with numpy (cupy)
- nifty-ls — A fast Lomb-Scargle periodogram. It's nifty, and uses a NUFFT.
- npnn — NumPy Neural Network
- nvtabular — no summary
- pyDVL — The Python Data Valuation Library
- pyxu — Modular and scalable computational imaging in Python with support for GPU/out-of-core computing.
- qibotn — A tensor-network translation module for Qibo
- qojpca — qojpca package
- quqcs — quqcs is an open source library for quantum compute simulating on NVIDIA GPU
- rapids-singlecell — running single cell analysis on Nvidia GPUs
- rippy — Reinsurance Pricing in Python!
- seedbank — Common infrastructure for initializing random number generators.
- SLAP2-UTILS — Code to support using a SLAP2 Microscope
- structural-diversity-index — A package for fast numerical computation of the structural diversity index of arbitrary networks
- thinc — A refreshing functional take on deep learning, compatible with your favorite libraries
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