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IMPORTANT: This repository is being deprecated. Please migrate or onboard your ML tests to our new repository here.

ML Testing Accelerators

A set of tools and examples to run machine learning tests on ML hardware accelerators (TPUs or GPUs) using Google Cloud Platform.

This is not an officially supported Google product.

Getting Started

In this mode, your tests and/or models run on an automated schedule in GKE. Results are collected by the "Metrics Handler" and written to BigQuery.

  1. Install all of our development prerequisites.
  2. Follow instructions in the deployments directory to set up a Kubernetes Cluster.
  3. Follow instructions in the images directory to set up the Docker image that your tests will run.
  4. Deploy the metrics handler to Google Cloud Functions.
  5. Deploy the event publisher to you GKE cluster.
  6. See templates directory for a JSonnet template library to generate test config files.
  7. (Optional) Set up a dashboard to view test results. See dashboard directory for instructions.

Are you interested in using ML Testing Accelerators? E-mail ml-testing-accelerators-users@googlegroups.com and tell us about your use-case. We're happy to help you get started.

About

Testing framework for Deep Learning models (Tensorflow and PyTorch) on Google Cloud hardware accelerators (TPU and GPU)

Topics

gpumachine-learningtesting-acceleratorstpu

Resources

Readme
Apache-2.0 license

Contributing

Contributing
Activity
Custom properties

Stars

64 stars

Watchers

34 watching

Forks

59 forks
Report repository

Releases

Packages

Used by

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" + '

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IMPORTANT: This repository is being deprecated. Please migrate or onboard your ML tests to our new repository here.
ML Testing Accelerators
A set of tools and examples to run machine learning tests on ML hardware
accelerators (TPUs or GPUs) using Google Cloud Platform.
This is not an officially supported Google product.
Getting Started
In this mode, your tests and/or models run on an automated schedule in GKE.
Results are collected by the "Metrics Handler" and written to BigQuery.
  1. Install all of our development prerequisites.
  2. Follow instructions in the deployments directory to set up a Kubernetes Cluster.
  3. Follow instructions in the images directory to set up the Docker image that your tests will run.
  4. Deploy the metrics handler to Google Cloud Functions.
  5. Deploy the event publisher to you GKE cluster.
  6. See templates directory for a JSonnet template library to generate test config files.
  7. (Optional) Set up a dashboard to view test results. See  dashboard  directory for instructions.
Are you interested in using ML Testing Accelerators? E-mail ml-testing-accelerators-users@googlegroups.com and tell us about your use-case. We're happy to help you get started.
About
Testing framework for Deep Learning models (Tensorflow and PyTorch) on Google Cloud hardware accelerators (TPU and GPU)
Topics
gpumachine-learningtesting-acceleratorstpu
Resources
Readme
Apache-2.0 license
Contributing
Contributing
Activity
Custom properties
Stars
64 stars
Watchers
34 watching
Forks
59 forks
Report repository
Releases
Packages
Used by
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
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IMPORTANT: This repository is being deprecated. Please migrate or onboard your ML tests to our new repository here.
ML Testing Accelerators
A set of tools and examples to run machine learning tests on ML hardware
accelerators (TPUs or GPUs) using Google Cloud Platform.
This is not an officially supported Google product.
Getting Started
In this mode, your tests and/or models run on an automated schedule in GKE.
Results are collected by the "Metrics Handler" and written to BigQuery.
  1. Install all of our development prerequisites.
  2. Follow instructions in the deployments directory to set up a Kubernetes Cluster.
  3. Follow instructions in the images directory to set up the Docker image that your tests will run.
  4. Deploy the metrics handler to Google Cloud Functions.
  5. Deploy the event publisher to you GKE cluster.
  6. See templates directory for a JSonnet template library to generate test config files.
  7. (Optional) Set up a dashboard to view test results. See  dashboard  directory for instructions.
Are you interested in using ML Testing Accelerators? E-mail ml-testing-accelerators-users@googlegroups.com and tell us about your use-case. We're happy to help you get started.
About
Testing framework for Deep Learning models (Tensorflow and PyTorch) on Google Cloud hardware accelerators (TPU and GPU)
Topics
gpumachine-learningtesting-acceleratorstpu
Resources
Readme
Apache-2.0 license
Contributing
Contributing
Activity
Custom properties
Stars
64 stars
Watchers
34 watching
Forks
59 forks
Report repository
Releases
Packages
Used by
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
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IMPORTANT: This repository is being deprecated. Please migrate or onboard your ML tests to our new repository here.
ML Testing Accelerators
A set of tools and examples to run machine learning tests on ML hardware
accelerators (TPUs or GPUs) using Google Cloud Platform.
This is not an officially supported Google product.
Getting Started
In this mode, your tests and/or models run on an automated schedule in GKE.
Results are collected by the "Metrics Handler" and written to BigQuery.
  1. Install all of our development prerequisites.
  2. Follow instructions in the deployments directory to set up a Kubernetes Cluster.
  3. Follow instructions in the images directory to set up the Docker image that your tests will run.
  4. Deploy the metrics handler to Google Cloud Functions.
  5. Deploy the event publisher to you GKE cluster.
  6. See templates directory for a JSonnet template library to generate test config files.
  7. (Optional) Set up a dashboard to view test results. See  dashboard  directory for instructions.
Are you interested in using ML Testing Accelerators? E-mail ml-testing-accelerators-users@googlegroups.com and tell us about your use-case. We're happy to help you get started.
About
Testing framework for Deep Learning models (Tensorflow and PyTorch) on Google Cloud hardware accelerators (TPU and GPU)
Topics
gpumachine-learningtesting-acceleratorstpu
Resources
Readme
Apache-2.0 license
Contributing
Contributing
Activity
Custom properties
Stars
64 stars
Watchers
34 watching
Forks
59 forks
Report repository
Releases
Packages
Used by
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
IMPORTANT: This repository is being deprecated. Please migrate or onboard your ML tests to our new repository here.
ML Testing Accelerators
A set of tools and examples to run machine learning tests on ML hardware
accelerators (TPUs or GPUs) using Google Cloud Platform.
This is not an officially supported Google product.
Getting Started
In this mode, your tests and/or models run on an automated schedule in GKE.
Results are collected by the "Metrics Handler" and written to BigQuery.
  1. Install all of our development prerequisites.
  2. Follow instructions in the deployments directory to set up a Kubernetes Cluster.
  3. Follow instructions in the images directory to set up the Docker image that your tests will run.
  4. Deploy the metrics handler to Google Cloud Functions.
  5. Deploy the event publisher to you GKE cluster.
  6. See templates directory for a JSonnet template library to generate test config files.
  7. (Optional) Set up a dashboard to view test results. See  dashboard  directory for instructions.
Are you interested in using ML Testing Accelerators? E-mail ml-testing-accelerators-users@googlegroups.com and tell us about your use-case. We're happy to help you get started.
About
Testing framework for Deep Learning models (Tensorflow and PyTorch) on Google Cloud hardware accelerators (TPU and GPU)
Topics
gpumachine-learningtesting-acceleratorstpu
Resources
Readme
Apache-2.0 license
Contributing
Contributing
Activity
Custom properties
Stars
64 stars
Watchers
34 watching
Forks
59 forks
Report repository
Releases
Packages
Used by
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
IMPORTANT: This repository is being deprecated. Please migrate or onboard your ML tests to our new repository here.
ML Testing Accelerators
A set of tools and examples to run machine learning tests on ML hardware
accelerators (TPUs or GPUs) using Google Cloud Platform.
This is not an officially supported Google product.
Getting Started
In this mode, your tests and/or models run on an automated schedule in GKE.
Results are collected by the "Metrics Handler" and written to BigQuery.
  1. Install all of our development prerequisites.
  2. Follow instructions in the deployments directory to set up a Kubernetes Cluster.
  3. Follow instructions in the images directory to set up the Docker image that your tests will run.
  4. Deploy the metrics handler to Google Cloud Functions.
  5. Deploy the event publisher to you GKE cluster.
  6. See templates directory for a JSonnet template library to generate test config files.
  7. (Optional) Set up a dashboard to view test results. See  dashboard  directory for instructions.
Are you interested in using ML Testing Accelerators? E-mail ml-testing-accelerators-users@googlegroups.com and tell us about your use-case. We're happy to help you get started.
About
Testing framework for Deep Learning models (Tensorflow and PyTorch) on Google Cloud hardware accelerators (TPU and GPU)
Topics
gpumachine-learningtesting-acceleratorstpu
Resources
Readme
Apache-2.0 license
Contributing
Contributing
Activity
Custom properties
Stars
64 stars
Watchers
34 watching
Forks
59 forks
Report repository
Releases
Packages
Used by
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
IMPORTANT: This repository is being deprecated. Please migrate or onboard your ML tests to our new repository here.
ML Testing Accelerators
A set of tools and examples to run machine learning tests on ML hardware
accelerators (TPUs or GPUs) using Google Cloud Platform.
This is not an officially supported Google product.
Getting Started
In this mode, your tests and/or models run on an automated schedule in GKE.
Results are collected by the "Metrics Handler" and written to BigQuery.
  1. Install all of our development prerequisites.
  2. Follow instructions in the deployments directory to set up a Kubernetes Cluster.
  3. Follow instructions in the images directory to set up the Docker image that your tests will run.
  4. Deploy the metrics handler to Google Cloud Functions.
  5. Deploy the event publisher to you GKE cluster.
  6. See templates directory for a JSonnet template library to generate test config files.
  7. (Optional) Set up a dashboard to view test results. See  dashboard  directory for instructions.
Are you interested in using ML Testing Accelerators? E-mail ml-testing-accelerators-users@googlegroups.com and tell us about your use-case. We're happy to help you get started.
About
Testing framework for Deep Learning models (Tensorflow and PyTorch) on Google Cloud hardware accelerators (TPU and GPU)
Topics
gpumachine-learningtesting-acceleratorstpu
Resources
Readme
Apache-2.0 license
Contributing
Contributing
Activity
Custom properties
Stars
64 stars
Watchers
34 watching
Forks
59 forks
Report repository
Releases
Packages
Used by
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
IMPORTANT: This repository is being deprecated. Please migrate or onboard your ML tests to our new repository here.
ML Testing Accelerators
A set of tools and examples to run machine learning tests on ML hardware
accelerators (TPUs or GPUs) using Google Cloud Platform.
This is not an officially supported Google product.
Getting Started
In this mode, your tests and/or models run on an automated schedule in GKE.
Results are collected by the "Metrics Handler" and written to BigQuery.
  1. Install all of our development prerequisites.
  2. Follow instructions in the deployments directory to set up a Kubernetes Cluster.
  3. Follow instructions in the images directory to set up the Docker image that your tests will run.
  4. Deploy the metrics handler to Google Cloud Functions.
  5. Deploy the event publisher to you GKE cluster.
  6. See templates directory for a JSonnet template library to generate test config files.
  7. (Optional) Set up a dashboard to view test results. See  dashboard  directory for instructions.
Are you interested in using ML Testing Accelerators? E-mail ml-testing-accelerators-users@googlegroups.com and tell us about your use-case. We're happy to help you get started.
About
Testing framework for Deep Learning models (Tensorflow and PyTorch) on Google Cloud hardware accelerators (TPU and GPU)
Topics
gpumachine-learningtesting-acceleratorstpu
Resources
Readme
Apache-2.0 license
Contributing
Contributing
Activity
Custom properties
Stars
64 stars
Watchers
34 watching
Forks
59 forks
Report repository
Releases
Packages
Used by
Contributors
Languages