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A conda-smithy repository for mlpack.

License: BSD 3-Clause "New" or "Revised" License

Shell 53.19% Batchfile 46.81%

mlpack-feedstock's Introduction

About mlpack-feedstock

Feedstock license: BSD-3-Clause

Home: http://www.mlpack.org

Package license: BSD-3-Clause

Summary: mlpack: a fast, header-only machine learning library

Development: https://github.com/mlpack/mlpack

Documentation: http://www.mlpack.org/docs.html

mlpack is an intuitive, fast, and flexible C++ machine learning library with bindings to other languages. It is meant to be a machine learning analog to LAPACK, and aims to implement a wide array of machine learning methods and functions as a "swiss army knife" for machine learning researchers. In addition to its powerful C++ interface, mlpack also provides command-line programs and Python bindings.

Current build status

Azure

Variant	Status
linux_64_numpy1.22python3.10.____cpython
linux_64_numpy1.22python3.8.____cpython
linux_64_numpy1.22python3.9.____73_pypy
linux_64_numpy1.22python3.9.____cpython
linux_64_numpy1.23python3.11.____cpython
linux_64_numpy1.26python3.12.____cpython
osx_64_numpy1.22python3.10.____cpython
osx_64_numpy1.22python3.8.____cpython
osx_64_numpy1.22python3.9.____73_pypy
osx_64_numpy1.22python3.9.____cpython
osx_64_numpy1.23python3.11.____cpython
osx_64_numpy1.26python3.12.____cpython
win_64_numpy1.22python3.10.____cpython
win_64_numpy1.22python3.8.____cpython
win_64_numpy1.22python3.9.____73_pypy
win_64_numpy1.22python3.9.____cpython
win_64_numpy1.23python3.11.____cpython
win_64_numpy1.26python3.12.____cpython

Current release info

Name	Downloads	Version	Platforms

Installing mlpack

Installing mlpack from the conda-forge channel can be achieved by adding conda-forge to your channels with:

conda config --add channels conda-forge
conda config --set channel_priority strict

Once the conda-forge channel has been enabled, mlpack can be installed with conda:

conda install mlpack

or with mamba:

mamba install mlpack

It is possible to list all of the versions of mlpack available on your platform with conda:

conda search mlpack --channel conda-forge

or with mamba:

mamba search mlpack --channel conda-forge

Alternatively, mamba repoquery may provide more information:

# Search all versions available on your platform:
mamba repoquery search mlpack --channel conda-forge

# List packages depending on `mlpack`:
mamba repoquery whoneeds mlpack --channel conda-forge

# List dependencies of `mlpack`:
mamba repoquery depends mlpack --channel conda-forge

About conda-forge

conda-forge is a community-led conda channel of installable packages. In order to provide high-quality builds, the process has been automated into the conda-forge GitHub organization. The conda-forge organization contains one repository for each of the installable packages. Such a repository is known as a feedstock.

A feedstock is made up of a conda recipe (the instructions on what and how to build the package) and the necessary configurations for automatic building using freely available continuous integration services. Thanks to the awesome service provided by Azure, GitHub, CircleCI, AppVeyor, Drone, and TravisCI it is possible to build and upload installable packages to the conda-forge anaconda.org channel for Linux, Windows and OSX respectively.

To manage the continuous integration and simplify feedstock maintenance conda-smithy has been developed. Using the conda-forge.yml within this repository, it is possible to re-render all of this feedstock's supporting files (e.g. the CI configuration files) with conda smithy rerender.

For more information please check the conda-forge documentation.

Terminology

feedstock - the conda recipe (raw material), supporting scripts and CI configuration.

conda-smithy - the tool which helps orchestrate the feedstock. Its primary use is in the construction of the CI .yml files and simplify the management of many feedstocks.

conda-forge - the place where the feedstock and smithy live and work to produce the finished article (built conda distributions)

Updating mlpack-feedstock

If you would like to improve the mlpack recipe or build a new package version, please fork this repository and submit a PR. Upon submission, your changes will be run on the appropriate platforms to give the reviewer an opportunity to confirm that the changes result in a successful build. Once merged, the recipe will be re-built and uploaded automatically to the conda-forge channel, whereupon the built conda packages will be available for everybody to install and use from the conda-forge channel. Note that all branches in the conda-forge/mlpack-feedstock are immediately built and any created packages are uploaded, so PRs should be based on branches in forks and branches in the main repository should only be used to build distinct package versions.

In order to produce a uniquely identifiable distribution:

If the version of a package is not being increased, please add or increase the build/number.
If the version of a package is being increased, please remember to return the build/number back to 0.

Feedstock Maintainers

mlpack-feedstock's People

Contributors

Watchers

Forkers

jakirkham-feedstocks regro-cf-autotick-bot zoq geekypathak21 marcelotrevisani rcurtin

mlpack-feedstock's Issues

Python ImportError: libmlpack.so.3: undefined symbol: wrapper_dgbsv_

Issue:

I am getting the following error when trying to import the mlpack Python module (installed from conda-forge with conda install mlpack):

Python 3.6.5 | packaged by conda-forge | (default, Apr  6 2018, 13:39:56) 
[GCC 4.8.2 20140120 (Red Hat 4.8.2-15)] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> import mlpack
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/tmp/ml/lib/python3.6/site-packages/mlpack/__init__.py", line 11, in <module>
    from .test_python_binding import test_python_binding
ImportError: /tmp/ml/lib/python3.6/site-packages/mlpack/../../../libmlpack.so.3: undefined symbol: wrapper_dgbsv_

The non-python CLI programs like mkl_knn, mlpack_random_forest, mlpack_preprocess_split etc. work fine. The examples in http://www.mlpack.org/docs/mlpack-3.0.2/doxygen/cli_quickstart.html run without errors and give the expected outputs, so the mentioned ImportError might be a python-specific problem.

Since the Circle CI build runs fine, I guess the problem is related to the different linux distribution on the CI server. I have encountered similar "undefined symbol" issues when conda packages had an incomplete dependency list and the build environment fell back to linux distribution-native versions of these dependencies.

I just tried building the mlpack feedstock locally with conda build and this also doesn't work. It fails with the following linking error, which I don't understand: https://pastebin.com/GZdL8D15

Environment (conda list):

$ conda list
# packages in environment at /tmp/ml:
#
# Name                    Version                   Build  Channel
armadillo                 8.200.0         blas_openblas_201  [blas_openblas]  conda-forge
arpack                    3.6.1           blas_openblas_0  [blas_openblas]  conda-forge
blas                      1.1                    openblas    conda-forge
boost                     1.67.0           py36h3e44d54_0    conda-forge
boost-cpp                 1.67.0               h3a22d5f_0    conda-forge
bzip2                     1.0.6                h470a237_2    conda-forge
ca-certificates           2018.4.16                     0    conda-forge
certifi                   2018.4.16                py36_0    conda-forge
icu                       58.2                 hfc679d8_0    conda-forge
libgcc                    7.2.0                h69d50b8_2  
libgcc-ng                 7.2.0                hdf63c60_3  
libgfortran               3.0.0                         1  
libstdcxx-ng              7.2.0                hdf63c60_3  
mlpack                    3.0.2            py36h5c5fb89_0    conda-forge
ncurses                   5.9                          10    conda-forge
numpy                     1.14.5          py36_blas_openblashd3ea46f_201  [blas_openblas]  conda-forge
openblas                  0.2.20                        8    conda-forge
openssl                   1.0.2o                        0    conda-forge
pandas                    0.23.3                   py36_0    conda-forge
pip                       9.0.3                    py36_0    conda-forge
python                    3.6.5                         1    conda-forge
python-dateutil           2.7.3                      py_0    conda-forge
pytz                      2018.5                     py_0    conda-forge
readline                  7.0                           0    conda-forge
setuptools                40.0.0                   py36_0    conda-forge
six                       1.11.0                   py36_1    conda-forge
sqlite                    3.20.1                        2    conda-forge
superlu                   5.2.1           blas_openblash49db2b8_203  [blas_openblas]  conda-forge
tk                        8.6.8                         0    conda-forge
wheel                     0.31.1                   py36_0    conda-forge
xz                        5.2.3                         0    conda-forge
zlib                      1.2.11               h470a237_3    conda-forge

Details about conda and system ( conda info ):

$ conda info

     active environment : /tmp/ml
    active env location : /tmp/ml
       user config file : /home/m4/.condarc
 populated config files : /home/m4/.condarc
          conda version : 4.5.8
    conda-build version : 3.11.0
         python version : 3.6.5.final.0
       base environment : /home/m4/anaconda3  (writable)
           channel URLs : https://conda.anaconda.org/pytorch/linux-64
                          https://conda.anaconda.org/pytorch/noarch
                          https://conda.anaconda.org/conda-forge/linux-64
                          https://conda.anaconda.org/conda-forge/noarch
                          https://repo.anaconda.com/pkgs/main/linux-64
                          https://repo.anaconda.com/pkgs/main/noarch
                          https://repo.anaconda.com/pkgs/free/linux-64
                          https://repo.anaconda.com/pkgs/free/noarch
                          https://repo.anaconda.com/pkgs/r/linux-64
                          https://repo.anaconda.com/pkgs/r/noarch
                          https://repo.anaconda.com/pkgs/pro/linux-64
                          https://repo.anaconda.com/pkgs/pro/noarch
          package cache : /home/m4/anaconda3/pkgs
                          /home/m4/.conda/pkgs
       envs directories : /home/m4/anaconda3/envs
                          /home/m4/.conda/envs
               platform : linux-64
             user-agent : conda/4.5.8 requests/2.19.1 CPython/3.6.5 Linux/4.17.8-1-ARCH arch/ glibc/2.27
                UID:GID : 1000:1000
             netrc file : None
           offline mode : False

Remove upper limit on armadillo dependency

Solution to issue cannot be found in the documentation.

I checked the documentation.

Issue

This package currently has an upper limit on its armadillo dependency of armadillo <10.0a0. However, based on this exchange, it seems that this should not be a restriction on this package.

Installed packages

N/A

Environment info

N/A

Compiling mlpack cpp files using shared library. library not found

Issue:
I have installed mlpack via conda and importing it into python works. But when I try to compile cpp files using g++, I receive the error mlpack not found. I have included the location of libmlpack.so in my LD_LIBRARY_PATH and compiled with -lmlpack.

Environment (conda list):

$ conda list

Name Version Build Channel

_libgcc_mutex 0.1 conda_forge conda-forge
_openmp_mutex 4.5 0_gnu conda-forge
armadillo 9.200.7 hf4e8f56_0 conda-forge
arpack 3.7.0 hc6cf775_1 conda-forge
binutils-meta 1.0.4 0 conda-forge
binutils_impl_linux-64 2.34 h53a641e_5 conda-forge
binutils_linux-64 2.34 hc952b39_20 conda-forge
boost 1.72.0 py38h9de70de_0 conda-forge
boost-cpp 1.72.0 h7b93d67_1 conda-forge
bzip2 1.0.8 h516909a_2 conda-forge
c-compiler 1.0.4 h516909a_0 conda-forge
ca-certificates 2020.4.5.2 hecda079_0 conda-forge
certifi 2020.4.5.2 py38h32f6830_0 conda-forge
compilers 1.0.4 0 conda-forge
cxx-compiler 1.0.4 hc9558a2_0 conda-forge
ensmallen 2.12.1 hc0d2d6d_0 conda-forge
fortran-compiler 1.0.4 he991be0_0 conda-forge
gcc_impl_linux-64 7.5.0 hd420e75_6 conda-forge
gcc_linux-64 7.5.0 h09487f9_20 conda-forge
gfortran_impl_linux-64 7.5.0 hdf63c60_6 conda-forge
gfortran_linux-64 7.5.0 h09487f9_20 conda-forge
gxx_impl_linux-64 7.5.0 hdf63c60_6 conda-forge
gxx_linux-64 7.5.0 h09487f9_20 conda-forge
icu 67.1 he1b5a44_0 conda-forge
ld_impl_linux-64 2.34 h53a641e_5 conda-forge
libblas 3.8.0 11_openblas conda-forge
libcblas 3.8.0 11_openblas conda-forge
libffi 3.2.1 he1b5a44_1007 conda-forge
libgcc-ng 9.2.0 h24d8f2e_2 conda-forge
libgfortran-ng 7.5.0 hdf63c60_6 conda-forge
libgomp 9.2.0 h24d8f2e_2 conda-forge
liblapack 3.8.0 11_openblas conda-forge
libopenblas 0.3.6 h6e990d7_6 conda-forge
libstdcxx-ng 9.2.0 hdf63c60_2 conda-forge
llvm-openmp 8.0.1 hc9558a2_0 conda-forge
lz4-c 1.9.2 he1b5a44_1 conda-forge
mlpack 3.3.1 py38h5a835a2_0 conda-forge
ncurses 6.1 hf484d3e_1002 conda-forge
numpy 1.18.5 py38h8854b6b_0 conda-forge
openblas 0.3.6 h6e990d7_6 conda-forge
openmp 8.0.1 0 conda-forge
openssl 1.1.1g h516909a_0 conda-forge
pandas 1.0.4 py38hcb8c335_0 conda-forge
pip 20.1.1 py_1 conda-forge
python 3.8.3 cpython_he5300dc_0 conda-forge
python-dateutil 2.8.1 py_0 conda-forge
python_abi 3.8 1_cp38 conda-forge
pytz 2020.1 pyh9f0ad1d_0 conda-forge
readline 8.0 hf8c457e_0 conda-forge
setuptools 47.3.1 py38h32f6830_0 conda-forge
six 1.15.0 pyh9f0ad1d_0 conda-forge
sqlite 3.30.1 hcee41ef_0 conda-forge
superlu 5.2.1 hfe2efc7_1207 conda-forge
tk 8.6.10 hed695b0_0 conda-forge
wheel 0.34.2 py_1 conda-forge
xz 5.2.5 h516909a_0 conda-forge
zlib 1.2.11 h516909a_1006 conda-forge
zstd 1.4.4 h6597ccf_3 conda-forge

Details about conda and system ( conda info ):

$ conda info

active environment : mlpack
active env location : /home/pesoke/miniconda3/envs/mlpack
shell level : 1
user config file : /home/pesoke/.condarc
populated config files :
conda version : 4.8.3
conda-build version : not installed
python version : 3.7.6.final.0
virtual packages : __glibc=2.12
base environment : /home/pesoke/miniconda3 (writable)
channel URLs : https://repo.anaconda.com/pkgs/main/linux-64
https://repo.anaconda.com/pkgs/main/noarch
https://repo.anaconda.com/pkgs/r/linux-64
https://repo.anaconda.com/pkgs/r/noarch
package cache : /home/pesoke/miniconda3/pkgs
/home/pesoke/.conda/pkgs
envs directories : /home/pesoke/miniconda3/envs
/home/pesoke/.conda/envs
platform : linux-64
user-agent : conda/4.8.3 requests/2.23.0 CPython/3.7.6 Linux/2.6.32-642.el6.x86_64 rhel/6.8 glibc/2.12
UID:GID : 34683:300
netrc file : None
offline mode : Falsecon

Would it be possible to provide an MKL-based build?

Comment:

At the moment, this package requires openblas. Would it be possible to provide an MKL-based build of this package in addition?

conda-forge / mlpack-feedstock Goto Github PK

mlpack-feedstock's Introduction

About mlpack-feedstock

Current build status

Current release info

Installing mlpack

About conda-forge

Terminology

Updating mlpack-feedstock

Feedstock Maintainers

mlpack-feedstock's People

Contributors

Watchers

Forkers

mlpack-feedstock's Issues

Solution to issue cannot be found in the documentation.

Issue

Installed packages

Environment info

Name Version Build Channel

Comment:

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