commit | 0e23122846aa3b7a5350102d8c06fa21695d34be | [log] [tgz] |
---|---|---|
author | Xiyou Zhou <xiyou@octoml.ai> | Tue Jun 28 11:04:13 2022 -0700 |
committer | GitHub <noreply@github.com> | Tue Jun 28 11:04:13 2022 -0700 |
tree | f707eeb631085b5023ad12ee0ef2db6ac510b231 | |
parent | 97b3076c3532f73a9d9eeba26a3f329f8e0f803d [diff] |
[MetaSchedule] Enable Adapative Training For XGBoost Cost Model (#11892) CostModel retraining is a time consuming part for MetaSchedule tuning, similar to AutoScheduler, we can alleviate it with an adapative way of increasing waiting period between each retraining. This PR introduced an argument called `adpative_training` in `TuneConfig` and the constructor of `XGBoostModel` to enable the capability. Testing tuning scripts are also updated.
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.