Source: meta-torch-core-free
Section: science
Priority: optional
Maintainer: Debian Science Maintainers <debian-science-maintainers@lists.alioth.debian.org>
Uploaders: Mo Zhou <cdluminate@gmail.com>
Build-Depends: debhelper (>= 11),
# NOTE: the following dependencies are run-time dependencies.
# luajit < 2.1.0 suffers from compatibility problem
               luajit (>= 2.1.0~),
# Extra Torch modules and community modules can be installed via luarocks.
               luarocks,
# normal lua dependency
               lua-cjson,
               lua-filesystem,
               lua-penlight,
# the following is core and *free* components of Torch Framework
               lua-torch-cwrap,
               lua-torch-paths,
               lua-torch-sys,
               lua-torch-xlua,
               lua-torch-torch7,
               lua-torch-nn,
               lua-torch-graph,
               lua-torch-nngraph,
               lua-torch-optim,
               lua-torch-sundown,
               lua-torch-dok,
               lua-torch-trepl,
               torch-trepl,
               lua-torch-image
Standards-Version: 4.1.4
Homepage: http://torch.ch
Vcs-Browser: https://salsa.debian.org/science-team/meta-torch-core-free
Vcs-Git: https://salsa.debian.org/science-team/meta-torch-core-free.git

Package: torch-core-free
Section: metapackages
Architecture: any
Depends: ${misc:Depends},
# luajit < 2.1.0 suffers from compatibility problem
         luajit (>= 2.1.0~),
# Extra Torch modules and community modules can be installed via luarocks.
         luarocks,
# normal lua dependency
         lua-cjson,
         lua-filesystem,
         lua-penlight,
# the following is core and *free* components of Torch Framework
         lua-torch-cwrap,
         lua-torch-paths,
         lua-torch-sys,
         lua-torch-xlua,
         lua-torch-torch7,
         lua-torch-nn,
         lua-torch-graph,
         lua-torch-nngraph,
         lua-torch-optim,
         lua-torch-sundown,
         lua-torch-dok,
         lua-torch-trepl,
         torch-trepl,
         lua-torch-image,
# these core but *not free* components will be included in torch-core-contrib.
#        lua-torch-cutorch,
#        lua-torch-cunn,
# //// BLAS ////
# libblas3 is not suitable for machine learning and deep learning!
# So, we just silently pull a suitable BLAS for user, instead of
# telling the user why we have built Torch7 against libblas3 and
# how to install a suitable BLAS. I think the researchers won't
# blame us for forcing them to use a suitable one. Here we set
# the candidate BLAS in pre-depends to ensure that apt won't pull
# and install package libblas3.
         libopenblas-base | libatlas-base | libblas.so.3,
Suggests: lua-torch-paths-dev,
          lua-torch-torch7-dev,
          libtorch-th-dev,
          libtorch-luat-dev,
Recommends: lua-torch-cutorch
# Well, the following description is basically copied from its homepage.
Description: Scientific Computing Framework For Luajit (Core Components)
 Torch is a scientific computing framework with wide support for machine
 learning algorithms that puts GPUs first. It is easy to use and efficient,
 thanks to an easy and fast scripting language, LuaJIT, and an underlying
 C/CUDA implementation.
 .
 A summary of core features:
  * a powerful N-dimensional array
  * lots of routines for indexing, slicing, transposing, ...
  * amazing interface to C, via LuaJIT
  * linear algebra routines
  * neural network, and energy-based models
  * numeric optimization routines
  * Fast and efficient GPU support
  * Embeddable, with ports to iOS, Android and FPGA backends
 .
 The goal of Torch is to have maximum flexibility and speed in building
 your scientific algorithms while making the process extremely simple.
 Torch comes with a large ecosystem of community-driven packages in
 machine learning, computer vision, signal processing, parallel
 processing, image, video, audio and networking among others, and
 builds on top of the Lua community.
 .
 At the heart of Torch are the popular neural network and optimization
 libraries which are simple to use, while having maximum flexibility
 in implementing complex neural network topologies. You can build
 arbitrary graphs of neural networks, and parallelize them over CPUs
 and GPUs in an efficient manner.
 .
 This package is a metapackage, which pulls the following core and *free*
 modules for you: cwrap, paths, sys, xlua, torch7, nn, graph, nngraph,
 optim, sundown, dok, trepl, image.
 .
 Note that cutorch (CUDA backend for torch) and cunn (CUDA backend for
 neural network) are not present in this metapacakge - they will be
 shipped in the torch-core-contrib metapackage in the future.