_            _    _        _         _
      /\ \         /\ \ /\ \     /\_\      / /\
      \_\ \       /  \ \\ \ \   / / /     / /  \
      /\__ \     / /\ \ \\ \ \_/ / /     / / /\ \__
     / /_ \ \   / / /\ \ \\ \___/ /     / / /\ \___\
    / / /\ \ \ / / /  \ \_\\ \ \_/      \ \ \ \/___/
   / / /  \/_// / /   / / / \ \ \        \ \ \
  / / /      / / /   / / /   \ \ \   _    \ \ \
 / / /      / / /___/ / /     \ \ \ /_/\__/ / /
/_/ /      / / /____\/ /       \ \_\\ \/___/ /
\_\/       \/_________/         \/_/ \_____\/

Enter the query into the form above. You can look for specific version of a package by using @ symbol like this: gcc@10.

API method:

GET /api/packages?search=hello&page=1&limit=20

where search is your query, page is a page number and limit is a number of items on a single page. Pagination information (such as a number of pages and etc) is returned in response headers.

If you'd like to join our channel search send a patch to ~whereiseveryone/toys@lists.sr.ht adding your channel as an entry in channels.scm.

cccl 2.3.1
Channel: guix-hpc-non-free
Location: tainted/cccl.scm (tainted cccl)
Home page: https://github.com/NVIDIA/cccl
Licenses: ASL 2.0
Build system: cmake
Synopsis: CUDA C++ Core Libraries (CCCL)
Description:

This CUDA C++ Core Libraries (CCCL) unifies three essential CUDA C++ libraries into a single, convenient repository: Thrust, CUB and libcudacxx. The goal of CCCL is to provide CUDA C++ developers with building blocks that make it easier to write safe and efficient code. Bringing these libraries together streamlines your development process and broadens your ability to leverage the power of CUDA C++.

python-numpy-mkl 1.26.4
Dependencies: bash@5.2.37 intel-oneapi-mkl@2023.2.0
Channel: guix-hpc-non-free
Location: tainted/python-xyz.scm (tainted python-xyz)
Home page: https://numpy.org
Licenses: Modified BSD
Build system: pyproject
Synopsis: Fundamental package for scientific computing with Python (MKL build)
Description:

NumPy is the fundamental package for scientific computing with Python. It contains among other things: a powerful N-dimensional array object, sophisticated (broadcasting) functions, tools for integrating C/C++ and Fortran code, useful linear algebra, Fourier transform, and random number capabilities.

Page: 123
Total packages: 50