Kineto is a PyTorch performance profiling library (libkineto) focused on providing low-overhead full-system instrumentation for production workloads. Libkineto is fully integrated with the PyToch Profiler, providing GPU profiling capabilities and in the future other system-level profiling. This repo also includes the PyTorch Profiler Tensorboard plugin, providing an easy-to-use end-to-end profiling experience.
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 plugin 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.
- Support for tracing distributed workloads
- Trace processing, analysis and recommendation engine
- System-level activities, multiple tracing sources
- Profiling and monitoring daemon for larger scale deployments
We will follow the PyTorch release schedule which roughly happens on an every 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.