commit | 2e5230bdad0410f392849e2c591da42365341977 | [log] [tgz] |
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author | Liam Sturge <liam.sturge@arm.com> | Fri Mar 01 17:36:59 2024 +0000 |
committer | Liam Sturge <liam.sturge@arm.com> | Tue Mar 19 08:55:25 2024 +0000 |
tree | df2d4aaddc480f7aaee5ed049fbdb6ba30c0ba80 | |
parent | fbfa92658568428b27c6ee5762ab7fe2f7c0b415 [diff] |
Enable ONNX and PyTorch tests on AArch64 This makes use of work done in enabling PyTorch and ONNX tests to be run on AArch64 to actually enable the tests to run as part of CI. Co-Authored-By: Nicola Lancellotti <Nicola.Lancellotti@arm.com> Co-Authored-By: Leandro Nunes <Leandro.Nunes@arm.com>
Documentation | Contributors | Community | Release Notes
Apache TVM 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.
TVM is licensed under the Apache-2.0 license.
Check out the TVM Documentation site for installation instructions, tutorials, examples, and more. The Getting Started with TVM tutorial is a great place to start.
TVM adopts apache committer model, we aim to create an open source project that is maintained and owned by the community. Check out the Contributor Guide.
We learned a lot from the following projects when building TVM.