Home: https://tvm.apache.org/

Package license: Apache-2.0

Feedstock license: BSD-3-Clause

Summary: Open Deep Learning Compiler Stack

Development: https://github.com/apache/incubator-tvm

Documentation: https://tvm.apache.org/docs/

Apache TVM (incubating) is a compiler stack for deep learning systems. It is designed to close the gap between the productivity-focused deep learning frameworks, and the performance- and efficiency-focused hardware backends. TVM works with deep learning frameworks to provide end to end compilation to different backends.

Azure

Variant Status linux_64_c_compiler_version10cuda_compiler_version11.2cxx_compiler_version10numpy1.19python3.7.____cpython linux_64_c_compiler_version10cuda_compiler_version11.2cxx_compiler_version10numpy1.19python3.8.____cpython linux_64_c_compiler_version10cuda_compiler_version11.2cxx_compiler_version10numpy1.19python3.9.____cpython linux_64_c_compiler_version10cuda_compiler_version11.2cxx_compiler_version10numpy1.21python3.10.____cpython linux_64_c_compiler_version10cuda_compiler_versionNonecxx_compiler_version10numpy1.19python3.7.____cpython linux_64_c_compiler_version10cuda_compiler_versionNonecxx_compiler_version10numpy1.19python3.8.____cpython linux_64_c_compiler_version10cuda_compiler_versionNonecxx_compiler_version10numpy1.19python3.9.____cpython linux_64_c_compiler_version10cuda_compiler_versionNonecxx_compiler_version10numpy1.21python3.10.____cpython osx_64_numpy1.19python3.7.____cpython osx_64_numpy1.19python3.8.____cpython osx_64_numpy1.19python3.9.____cpython osx_64_numpy1.21python3.10.____cpython osx_arm64_numpy1.19python3.8.____cpython osx_arm64_numpy1.19python3.9.____cpython osx_arm64_numpy1.21python3.10.____cpython

Name	Downloads	Version	Platforms

Installing tvm-py 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, tvm-py can be installed with:

conda install tvm-py

It is possible to list all of the versions of tvm-py available on your platform with:

conda search tvm-py --channel 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 CircleCI, AppVeyor and TravisCI it is possible to build and upload installable packages to the conda-forge Anaconda-Cloud 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.

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)

If you would like to improve the tvm-py 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/tvm-py-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.

• @marcelotrevisani
• @ngam