Kineto is a library used in the PyTorch Profiler.

The Kineto project enables:

• performance observability and diagnostics across common ML bottleneck components
• actionable recommendations for common issues
• integration of external system-level profiling tools
• integration with popular visualization platforms and analysis pipelines

The central component of Kineto is Libkineto, a profiling library with special focus on low-overhead GPU timeline tracing.

Libkineto is an in-process profiling library integrated with the PyTorch Profiler. Please refer to the README file in the libkineto folder as well as documentation on the new PyTorch Profiler API.

The goal of the PyTorch TensorBoard Profiler is to provide a seamless and intuitive end-to-end profiling experience, including straightforward collection from PyTorch and insightful visualizations and recommendations in the TensorBoard UI. Please refer to the README file in the tb_plugin folder.

In order to compare Kineto traces across ranks, we reccomend using the Holistic Trace Analysis tool.

We will follow the PyTorch release schedule which roughly happens on a 3 month basis.

We appreciate all contributions. If you are planning to contribute back bug-fixes, please do so without any further discussion.

If you plan to contribute new features, please first open an issue and discuss the feature with us. Sending a PR without discussion might end up resulting in a rejected PR because we might be taking the infrastructure in a different direction than you might be aware of. We expect the architecture to keep evolving.

Kineto has a BSD-style license, as found in the LICENSE file.