A CPU+GPU Profiling library that provides access to timeline traces and hardware performance counters.
OTHER License
Kineto is part of the PyTorch Profiler.
The Kineto project enables:
A central component 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.
Holistic Trace Analysis (HTA) is an open source performance debugging library aimed at distributed workloads. HTA takes as input PyTorch Profiler traces and elevates the performance bottlenecks to enable faster debugging. Here's a partial list of features in HTA:
For a complete list see here.
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.
Some areas we're currently working on:
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.