Kineto
A CPU+GPU Profiling library that provides access to timeline traces and hardware performance counters.
Install / Use
/learn @pytorch/KinetoREADME
Kineto
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
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.
PyTorch TensorBoard Profiler (Deprecated)
[!WARNING] The TensorBoard integration with PyTorch profiler (<code>tb_plugin</code> submodule) is deprecated and scheduled for permanent removal on 03/05/2026. If you rely on <code>tb_plugin</code>, please comment on the <a href="https://github.com/pytorch/kineto/issues/1248">RFC issue</a> and consider migrating your workflow. The code will be deleted after the feedback period.
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.
Holistic Trace Analsysis
In order to compare Kineto traces across ranks, we reccomend using the Holistic Trace Analysis tool.
Releases and Contributing
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.
License
Kineto has a BSD-style license, as found in the LICENSE file.
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