About pytorch-cpu-feedstock

Feedstock license: BSD-3-Clause

Package license: BSD-3-Clause

Summary: PyTorch is an optimized tensor library for deep learning using GPUs and CPUs.

Development: https://github.com/pytorch/pytorch

Current build status

Azure

Variant	Status
linux_64_blas_implgenericc_compiler_version11c_stdlib_version2.17cuda_compilernvcccuda_compiler_version11.8cxx_compiler_version11
linux_64_blas_implgenericc_compiler_version12c_stdlib_version2.17cuda_compilercuda-nvcccuda_compiler_version12.0cxx_compiler_version12
linux_64_blas_implgenericc_compiler_version12c_stdlib_version2.28cuda_compilercuda-nvcccuda_compiler_version12.6cxx_compiler_version12
linux_64_blas_implgenericc_compiler_version13c_stdlib_version2.17cuda_compilerNonecuda_compiler_versionNonecxx_compiler_version13
linux_64_blas_implmklc_compiler_version11c_stdlib_version2.17cuda_compilernvcccuda_compiler_version11.8cxx_compiler_version11
linux_64_blas_implmklc_compiler_version12c_stdlib_version2.17cuda_compilercuda-nvcccuda_compiler_version12.0cxx_compiler_version12
linux_64_blas_implmklc_compiler_version12c_stdlib_version2.28cuda_compilercuda-nvcccuda_compiler_version12.6cxx_compiler_version12
linux_64_blas_implmklc_compiler_version13c_stdlib_version2.17cuda_compilerNonecuda_compiler_versionNonecxx_compiler_version13
linux_aarch64_c_compiler_version12c_stdlib_version2.28cuda_compilercuda-nvcccuda_compiler_version12.0cxx_compiler_version12
linux_aarch64_c_compiler_version12c_stdlib_version2.28cuda_compilercuda-nvcccuda_compiler_version12.6cxx_compiler_version12
linux_aarch64_c_compiler_version13c_stdlib_version2.17cuda_compilerNonecuda_compiler_versionNonecxx_compiler_version13
osx_64_blas_implgenericnumpy2.0python3.10.____cpython
osx_64_blas_implgenericnumpy2.0python3.11.____cpython
osx_64_blas_implgenericnumpy2.0python3.12.____cpython
osx_64_blas_implgenericnumpy2.0python3.9.____cpython
osx_64_blas_implgenericnumpy2python3.13.____cp313
osx_64_blas_implmklnumpy2.0python3.10.____cpython
osx_64_blas_implmklnumpy2.0python3.11.____cpython
osx_64_blas_implmklnumpy2.0python3.12.____cpython
osx_64_blas_implmklnumpy2.0python3.9.____cpython
osx_64_blas_implmklnumpy2python3.13.____cp313
osx_arm64_numpy2.0python3.10.____cpython
osx_arm64_numpy2.0python3.11.____cpython
osx_arm64_numpy2.0python3.12.____cpython
osx_arm64_numpy2.0python3.9.____cpython
osx_arm64_numpy2python3.13.____cp313

Current release info

Name	Downloads	Version	Platforms

Installing pytorch-cpu

Installing pytorch-cpu 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, libtorch, pytorch, pytorch-cpu, pytorch-gpu can be installed with conda:

conda install libtorch pytorch pytorch-cpu pytorch-gpu

or with mamba:

mamba install libtorch pytorch pytorch-cpu pytorch-gpu

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

conda search libtorch --channel conda-forge

or with mamba:

mamba search libtorch --channel conda-forge

Alternatively, mamba repoquery may provide more information:

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

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

# List dependencies of `libtorch`:
mamba repoquery depends libtorch --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 pytorch-cpu-feedstock

If you would like to improve the pytorch-cpu 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/pytorch-cpu-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.

conda-forge / pytorch-cpu-feedstock

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