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Kineto

Kineto is part of the PyTorch Profiler.

The Kineto project was started to help enable

  • 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

A central component is libkineto, a profiling library with special focus on low-overhead GPU timeline tracing.

The PyTorch Profiler Tensorboard plugin provides powerful and intuitive visualizations of profiling results, as well asactionable recommendations, and is the best way to experience the new PyTorch Profiler.

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

The goal of the PyTorch 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.

Future development direction:

Some areas we're currently working on:

  • 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

Releases and Contributing

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.

License

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

About

A CPU+GPU Profiling library that provides access to timeline traces and hardware performance counters.

Resources

Readme
License

Code of conduct

Code of conduct

Contributing

Contributing
Activity

Stars

0 stars

Watchers

0 watching

Forks

0 forks
Report repository

Releases

Packages

Contributors

Languages

, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Add copy buttons to all 
 blocks\n(function() {\n function addCopyButtons() {\n document.querySelectorAll('pre code').forEach(function(codeBlock) {\n if (codeBlock.parentElement.hasAttribute('data-copy-added')) return;\n codeBlock.parentElement.setAttribute('data-copy-added', 'true');\n \n var btn = document.createElement('button');\n btn.textContent = 'Copy';\n btn.style.cssText = 'position:absolute;top:4px;right:4px;padding:2px 8px;font-size:11px;background:#4ecdc4;border:none;border-radius:4px;color:#1a1a2e;cursor:pointer;opacity:0.7;transition:opacity 0.2s;';\n btn.onmouseover = function() { this.style.opacity = '1'; };\n btn.onmouseout = function() { this.style.opacity = '0.7'; };\n btn.onclick = function() {\n navigator.clipboard.writeText(codeBlock.textContent).then(function() {\n btn.textContent = 'Copied!';\n setTimeout(function() { btn.textContent = 'Copy'; }, 1500);\n });\n };\n codeBlock.parentElement.style.position = 'relative';\n codeBlock.parentElement.appendChild(btn);\n });\n }\n \n addCopyButtons();\n \n // Re-run on dynamic content\n var observer = new MutationObserver(addCopyButtons);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Add Copy Buttons to Code Blocks");
}
} catch(__e) { console.warn('[Userscript:Add Copy Buttons to Code Blocks]', __e); }
})();
(function(){
try {
var __m = "github.com";
var __re = new RegExp('^' + "github\\.com" + '

Skip to content
BranchesTags
Repository files navigation
Kineto
Kineto is part of the PyTorch Profiler.
The Kineto project was started to help enable
  • 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
A central component is libkineto, a profiling library with special focus on low-overhead GPU timeline tracing.
The PyTorch Profiler Tensorboard plugin provides powerful and intuitive visualizations of profiling results, as well asactionable recommendations, and is the best way to experience the new PyTorch Profiler.
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
The goal of the PyTorch 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.
Future development direction:
Some areas we're currently working on:
  • 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
Releases and Contributing
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.
License
Kineto has a BSD-style license, as found in the LICENSE file.
About
A CPU+GPU Profiling library that provides access to timeline traces and hardware performance counters. 
Resources
Readme
License
Code of conduct
Code of conduct
Contributing
Contributing
Activity
Stars
0 stars
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Force GitHub README to respect dark mode\n(function() {\n var style = document.createElement('style');\n style.textContent = '\n .markdown-body {\n color-scheme: dark light;\n }\n .markdown-body pre { background: #161b22 !important; }\n .markdown-body code { background: rgba(110, 118, 129, 0.4) !important; }\n .markdown-body table th, .markdown-body table td { border-color: #30363d !important; }\n .markdown-body img { background: #0d1117; }\n .markdown-body blockquote { border-left-color: #8b949e; }\n .markdown-body hr { border-color: #30363d; }\n ';\n document.head.appendChild(style);\n})();", "GitHub Dark Mode README Fix");
}
} catch(__e) { console.warn('[Userscript:GitHub Dark Mode README Fix]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
BranchesTags
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Kineto
Kineto is part of the PyTorch Profiler.
The Kineto project was started to help enable
  • 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
A central component is libkineto, a profiling library with special focus on low-overhead GPU timeline tracing.
The PyTorch Profiler Tensorboard plugin provides powerful and intuitive visualizations of profiling results, as well asactionable recommendations, and is the best way to experience the new PyTorch Profiler.
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
The goal of the PyTorch 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.
Future development direction:
Some areas we're currently working on:
  • 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
Releases and Contributing
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.
License
Kineto has a BSD-style license, as found in the LICENSE file.
About
A CPU+GPU Profiling library that provides access to timeline traces and hardware performance counters. 
Resources
Readme
License
Code of conduct
Code of conduct
Contributing
Contributing
Activity
Stars
0 stars
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Highlight search terms from Google/DuckDuckGo/Bing referrer\n(function() {\n var ref = document.referrer;\n var terms = [];\n \n if (ref.includes('google.com') || ref.includes('duckduckgo.com') || ref.includes('bing.com')) {\n var url = new URL(ref);\n var q = url.searchParams.get('q') || url.searchParams.get('p');\n if (q) {\n terms = q.split(/\\s+/).filter(function(t) { return t.length > 2; });\n }\n }\n \n if (terms.length === 0) return;\n \n var style = document.createElement('style');\n style.textContent = '.userscript-highlight { background: #fbbf24; color: #1a1a2e; padding: 1px 3px; border-radius: 2px; }';\n document.head.appendChild(style);\n \n function highlight(node) {\n if (node.nodeType === 3) { // text node\n var text = node.textContent;\n var found = false;\n terms.forEach(function(term) {\n var regex = new RegExp('(' + term.replace(/[.*+?^${}()|[\\]\\\\]/g, '\\\\') + ')', 'gi');\n if (regex.test(text)) {\n found = true;\n var frag = document.createDocumentFragment();\n var parts = text.split(regex);\n parts.forEach(function(part, i) {\n if (i % 2 === 0) {\n frag.appendChild(document.createTextNode(part));\n } else {\n var span = document.createElement('span');\n span.className = 'userscript-highlight';\n span.textContent = part;\n frag.appendChild(span);\n }\n });\n node.parentNode.replaceChild(frag, node);\n }\n });\n } else if (node.nodeType === 1 && node.childNodes) { // element\n var skipTags = ['SCRIPT', 'STYLE', 'NOSCRIPT', 'TEXTAREA', 'INPUT', 'SELECT'];\n if (!skipTags.includes(node.tagName)) {\n Array.from(node.childNodes).forEach(highlight);\n }\n }\n }\n \n highlight(document.body);\n \n // Re-highlight on dynamic content\n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1 || node.nodeType === 3) highlight(node);\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Highlight Search Terms");
}
} catch(__e) { console.warn('[Userscript:Highlight Search Terms]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
BranchesTags
Repository files navigation
Kineto
Kineto is part of the PyTorch Profiler.
The Kineto project was started to help enable
  • 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
A central component is libkineto, a profiling library with special focus on low-overhead GPU timeline tracing.
The PyTorch Profiler Tensorboard plugin provides powerful and intuitive visualizations of profiling results, as well asactionable recommendations, and is the best way to experience the new PyTorch Profiler.
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
The goal of the PyTorch 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.
Future development direction:
Some areas we're currently working on:
  • 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
Releases and Contributing
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.
License
Kineto has a BSD-style license, as found in the LICENSE file.
About
A CPU+GPU Profiling library that provides access to timeline traces and hardware performance counters. 
Resources
Readme
License
Code of conduct
Code of conduct
Contributing
Contributing
Activity
Stars
0 stars
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Strip utm_, fbclid, gclid, etc. from all links on page\n(function() {\n var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content',\n 'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid',\n 'ref', 'ref_src', 'source', 'medium', 'campaign'];\n \n function cleanUrl(url) {\n try {\n var u = new URL(url, window.location.origin);\n var changed = false;\n trackingParams.forEach(function(p) {\n if (u.searchParams.has(p)) {\n u.searchParams.delete(p);\n changed = true;\n }\n });\n return changed ? u.toString() : url;\n } catch (e) {\n return url;\n }\n }\n \n function cleanLinks() {\n document.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n \n cleanLinks();\n \n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1) {\n if (node.tagName === 'A') cleanLinks();\n node.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Remove Tracking Parameters from Links");
}
} catch(__e) { console.warn('[Userscript:Remove Tracking Parameters from Links]', __e); }
})();
(function(){
try {
var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '

Skip to content
BranchesTags
Repository files navigation
Kineto
Kineto is part of the PyTorch Profiler.
The Kineto project was started to help enable
  • 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
A central component is libkineto, a profiling library with special focus on low-overhead GPU timeline tracing.
The PyTorch Profiler Tensorboard plugin provides powerful and intuitive visualizations of profiling results, as well asactionable recommendations, and is the best way to experience the new PyTorch Profiler.
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
The goal of the PyTorch 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.
Future development direction:
Some areas we're currently working on:
  • 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
Releases and Contributing
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.
License
Kineto has a BSD-style license, as found in the LICENSE file.
About
A CPU+GPU Profiling library that provides access to timeline traces and hardware performance counters. 
Resources
Readme
License
Code of conduct
Code of conduct
Contributing
Contributing
Activity
Stars
0 stars
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Auto-enable theater mode on YouTube\n(function() {\n function tryTheater() {\n var btn = document.querySelector('button[aria-label=\"Theater mode\"], ytd-player #player button[title=\"Theater mode\"]');\n if (btn && !btn.classList.contains('activated')) {\n btn.click();\n }\n }\n \n // Try immediately\n tryTheater();\n \n // Try after navigation (SPA)\n var lastUrl = location.href;\n setInterval(function() {\n if (location.href !== lastUrl) {\n lastUrl = location.href;\n setTimeout(tryTheater, 500);\n }\n }, 1000);\n \n // Also try on player load\n var observer = new MutationObserver(tryTheater);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "YouTube Theater Mode Default");
}
} catch(__e) { console.warn('[Userscript:YouTube Theater Mode Default]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
BranchesTags
Repository files navigation
Kineto
Kineto is part of the PyTorch Profiler.
The Kineto project was started to help enable
  • 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
A central component is libkineto, a profiling library with special focus on low-overhead GPU timeline tracing.
The PyTorch Profiler Tensorboard plugin provides powerful and intuitive visualizations of profiling results, as well asactionable recommendations, and is the best way to experience the new PyTorch Profiler.
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
The goal of the PyTorch 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.
Future development direction:
Some areas we're currently working on:
  • 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
Releases and Contributing
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.
License
Kineto has a BSD-style license, as found in the LICENSE file.
About
A CPU+GPU Profiling library that provides access to timeline traces and hardware performance counters. 
Resources
Readme
License
Code of conduct
Code of conduct
Contributing
Contributing
Activity
Stars
0 stars
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Remove or un-stick sticky/fixed headers that block content\n(function() {\n function unstick() {\n document.querySelectorAll('header, nav, [role=\"banner\"], .header, .navbar, .sticky, .fixed-top, [style*=\"position: fixed\"], [style*=\"position:sticky\"]').forEach(function(el) {\n if (el.style.position === 'fixed' || el.style.position === 'sticky' || \n getComputedStyle(el).position === 'fixed' || getComputedStyle(el).position === 'sticky') {\n el.style.position = 'static';\n el.style.top = 'auto';\n el.style.zIndex = 'auto';\n }\n });\n }\n \n unstick();\n \n var observer = new MutationObserver(unstick);\n observer.observe(document.body, { childList: true, subtree: true, attributes: true, attributeFilter: ['style', 'class'] });\n})();", "Kill Sticky Headers");
}
} catch(__e) { console.warn('[Userscript:Kill Sticky Headers]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
BranchesTags
Repository files navigation
Kineto
Kineto is part of the PyTorch Profiler.
The Kineto project was started to help enable
  • 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
A central component is libkineto, a profiling library with special focus on low-overhead GPU timeline tracing.
The PyTorch Profiler Tensorboard plugin provides powerful and intuitive visualizations of profiling results, as well asactionable recommendations, and is the best way to experience the new PyTorch Profiler.
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
The goal of the PyTorch 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.
Future development direction:
Some areas we're currently working on:
  • 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
Releases and Contributing
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.
License
Kineto has a BSD-style license, as found in the LICENSE file.
About
A CPU+GPU Profiling library that provides access to timeline traces and hardware performance counters. 
Resources
Readme
License
Code of conduct
Code of conduct
Contributing
Contributing
Activity
Stars
0 stars
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Universal Dark Mode - works on any site\n(function() {\n var enabled = true;\n \n function applyDarkMode() {\n if (!enabled) return;\n \n // Create style element if it doesn't exist\n var style = document.getElementById('universal-dark-mode-style');\n if (!style) {\n style = document.createElement('style');\n style.id = 'universal-dark-mode-style';\n document.head.appendChild(style);\n }\n \n // Dark mode CSS - inverts colors but preserves images/video\n style.textContent = '\n /* Invert everything except media */\n html {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #1a1a2e !important;\n }\n \n /* Restore images, videos, iframes, canvas */\n img, video, iframe, canvas, svg, picture, [style*=\"background-image\"] {\n filter: invert(1) hue-rotate(180deg) !important;\n }\n \n /* Preserve specific elements that should not be inverted */\n .no-dark-mode, .no-dark-mode *,\n [data-theme=\"light\"], [data-theme=\"light\"],\n .ace_editor, .ace_editor *,\n .CodeMirror, .CodeMirror *,\n .monaco-editor, .monaco-editor *,\n .markdown-body pre, .markdown-body pre *,\n .highlight, .highlight *,\n pre code, pre code * {\n filter: none !important;\n }\n \n /* Fix common UI elements */\n .modal, .popup, .dropdown-menu, .tooltip, .popover {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #2d2d44 !important;\n border-color: #444 !important;\n }\n \n /* Scrollbars */\n ::-webkit-scrollbar { background: #1a1a2e !important; }\n ::-webkit-scrollbar-thumb { background: #444 !important; }\n ::-webkit-scrollbar-thumb:hover { background: #555 !important; }\n \n /* Selection */\n ::selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ::-moz-selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ';\n }\n \n function removeDarkMode() {\n var style = document.getElementById('universal-dark-mode-style');\n if (style) style.remove();\n }\n \n // Toggle with Alt+Shift+D\n document.addEventListener('keydown', function(e) {\n if (e.altKey && e.shiftKey && e.key === 'D') {\n e.preventDefault();\n enabled = !enabled;\n if (enabled) {\n applyDarkMode();\n console.log('[Universal Dark Mode] Enabled');\n } else {\n removeDarkMode();\n console.log('[Universal Dark Mode] Disabled');\n }\n }\n });\n \n // Apply on load\n applyDarkMode();\n \n // Re-apply on dynamic content\n var observer = new MutationObserver(function(mutations) {\n if (enabled && !document.getElementById('universal-dark-mode-style')) {\n applyDarkMode();\n }\n });\n observer.observe(document.head, { childList: true });\n \n console.log('[Universal Dark Mode] Loaded - Press Alt+Shift+D to toggle');\n})();", "Universal Dark Mode");
}
} catch(__e) { console.warn('[Userscript:Universal Dark Mode]', __e); }
})();
})();

Skip to content
BranchesTags
Repository files navigation
Kineto
Kineto is part of the PyTorch Profiler.
The Kineto project was started to help enable
  • 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
A central component is libkineto, a profiling library with special focus on low-overhead GPU timeline tracing.
The PyTorch Profiler Tensorboard plugin provides powerful and intuitive visualizations of profiling results, as well asactionable recommendations, and is the best way to experience the new PyTorch Profiler.
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
The goal of the PyTorch 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.
Future development direction:
Some areas we're currently working on:
  • 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
Releases and Contributing
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.
License
Kineto has a BSD-style license, as found in the LICENSE file.
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A CPU+GPU Profiling library that provides access to timeline traces and hardware performance counters. 
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