commit | f044eefd0e55529db17ee1134c962070de4bc058 | [log] [tgz] |
---|---|---|
author | Eric Lunderberg <Lunderberg@users.noreply.github.com> | Wed May 15 08:16:15 2024 -0500 |
committer | GitHub <noreply@github.com> | Wed May 15 08:16:15 2024 -0500 |
tree | 7ddc3f0ecef3b01072aa3ad12fc7912144721b13 | |
parent | b49468ddf11a1103d82f11009a0b3253a49705aa [diff] |
[Runtime][Disco] Restore checks for hangup of disco pipe (#16997) This resolves a conflict between two recent changes. In https://github.com/apache/tvm/pull/16989, reads of size zero are used to identify hangups in `ProcessSession`. In https://github.com/apache/tvm/pull/16992, reads of size zero are treated as an error to avoid infinite loops while waiting for data to be ready. For a long-term resolution, the `dmlc::Stream` interface will need to be updated, so that the `Write` method returns the number of bytes written, just as the `Read` method currently does. This will allow the calling scope to verify the number of bytes received.
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.