Ability to limit AutoML resource using (amount of parallel threads)  #6061

Open
Open
Ability to limit AutoML resource using (amount of parallel threads) #6061
Labels
AutoML.NETAutomating various steps of the machine learning processenhancementNew feature or request
Milestone
ML.NET Future

Description

@80LevelElf
80LevelElf
opened on Jan 28, 2022

The current AutoML is not good to use when you have trained a lot of models at the same time in the cloud.
If you have some amount of pods in your Kubernetes cluster it doesn't matter how many AutoML experiments you execute at the same time. 4 experiments or 1 experiment at the same time use 100% of CPU (It brokes health checks and so on)

Low-level API of trainers (like FastForestBinaryTrainer) has options like NumberOfThreads and some other trainer-specific options you can use to handle the workload.

Is it able to add something like this to AutoML API?

But the most brilliant solution is some sort of smart property like ResourceUsingRatio which can be from 0.0 to 1.0
ResourceUsingRatio = 1.0 means the experiment use the maximum of potential resources (mainly CPU) it needs or the machine has.

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      , '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
      Ability to limit AutoML resource using (amount of parallel threads)  #6061
      Open
      Open
      Ability to limit AutoML resource using (amount of parallel threads) #6061
      Labels
      AutoML.NETAutomating various steps of the machine learning processenhancementNew feature or request
      Milestone
      ML.NET Future
      Description
      @80LevelElf
      80LevelElf
      opened on Jan 28, 2022
      The current AutoML is not good to use when you have trained a lot of models at the same time in the cloud.
      
If you have some amount of pods in your Kubernetes cluster it doesn't matter how many AutoML experiments you execute at the same time. 4 experiments or 1 experiment at the same time use 100% of CPU (It brokes health checks and so on)
      Low-level API of trainers (like FastForestBinaryTrainer) has options like NumberOfThreads and some other trainer-specific options you can use to handle the workload.
      Is it able to add something like this to AutoML API?
      But the most brilliant solution is some sort of smart property like ResourceUsingRatio which can be from 0.0 to 1.0
      
ResourceUsingRatio = 1.0 means the experiment use the maximum of potential resources (mainly CPU) it needs or the machine has.
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        AutoML.NETAutomating various steps of the machine learning processenhancementNew feature or request
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, '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
          Ability to limit AutoML resource using (amount of parallel threads)  #6061
          Open
          Open
          Ability to limit AutoML resource using (amount of parallel threads) #6061
          Labels
          AutoML.NETAutomating various steps of the machine learning processenhancementNew feature or request
          Milestone
          ML.NET Future
          Description
          @80LevelElf
          80LevelElf
          opened on Jan 28, 2022
          The current AutoML is not good to use when you have trained a lot of models at the same time in the cloud.
          
If you have some amount of pods in your Kubernetes cluster it doesn't matter how many AutoML experiments you execute at the same time. 4 experiments or 1 experiment at the same time use 100% of CPU (It brokes health checks and so on)
          Low-level API of trainers (like FastForestBinaryTrainer) has options like NumberOfThreads and some other trainer-specific options you can use to handle the workload.
          Is it able to add something like this to AutoML API?
          But the most brilliant solution is some sort of smart property like ResourceUsingRatio which can be from 0.0 to 1.0
          
ResourceUsingRatio = 1.0 means the experiment use the maximum of potential resources (mainly CPU) it needs or the machine has.
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          No one assigned
            Labels
            AutoML.NETAutomating various steps of the machine learning processenhancementNew feature or request
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            No projects
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              None yet
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, '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
              Ability to limit AutoML resource using (amount of parallel threads)  #6061
              Open
              Open
              Ability to limit AutoML resource using (amount of parallel threads) #6061
              Labels
              AutoML.NETAutomating various steps of the machine learning processenhancementNew feature or request
              Milestone
              ML.NET Future
              Description
              @80LevelElf
              80LevelElf
              opened on Jan 28, 2022
              The current AutoML is not good to use when you have trained a lot of models at the same time in the cloud.
              
If you have some amount of pods in your Kubernetes cluster it doesn't matter how many AutoML experiments you execute at the same time. 4 experiments or 1 experiment at the same time use 100% of CPU (It brokes health checks and so on)
              Low-level API of trainers (like FastForestBinaryTrainer) has options like NumberOfThreads and some other trainer-specific options you can use to handle the workload.
              Is it able to add something like this to AutoML API?
              But the most brilliant solution is some sort of smart property like ResourceUsingRatio which can be from 0.0 to 1.0
              
ResourceUsingRatio = 1.0 means the experiment use the maximum of potential resources (mainly CPU) it needs or the machine has.
              Metadata
              Metadata
              Assignees
              No one assigned
                Labels
                AutoML.NETAutomating various steps of the machine learning processenhancementNew feature or request
                Type
                No type
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                No projects
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                  None yet
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                  No branches or pull requests
                  Issue actions
                  
, '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
                  Ability to limit AutoML resource using (amount of parallel threads)  #6061
                  Open
                  Open
                  Ability to limit AutoML resource using (amount of parallel threads) #6061
                  Labels
                  AutoML.NETAutomating various steps of the machine learning processenhancementNew feature or request
                  Milestone
                  ML.NET Future
                  Description
                  @80LevelElf
                  80LevelElf
                  opened on Jan 28, 2022
                  The current AutoML is not good to use when you have trained a lot of models at the same time in the cloud.
                  
If you have some amount of pods in your Kubernetes cluster it doesn't matter how many AutoML experiments you execute at the same time. 4 experiments or 1 experiment at the same time use 100% of CPU (It brokes health checks and so on)
                  Low-level API of trainers (like FastForestBinaryTrainer) has options like NumberOfThreads and some other trainer-specific options you can use to handle the workload.
                  Is it able to add something like this to AutoML API?
                  But the most brilliant solution is some sort of smart property like ResourceUsingRatio which can be from 0.0 to 1.0
                  
ResourceUsingRatio = 1.0 means the experiment use the maximum of potential resources (mainly CPU) it needs or the machine has.
                  Metadata
                  Metadata
                  Assignees
                  No one assigned
                    Labels
                    AutoML.NETAutomating various steps of the machine learning processenhancementNew feature or request
                    Type
                    No type
                    Projects
                    No projects
                      Milestone
                      Relationships
                      None yet
                      Development
                      No branches or pull requests
                      Issue actions
                      
, '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
                      Ability to limit AutoML resource using (amount of parallel threads)  #6061
                      Open
                      Open
                      Ability to limit AutoML resource using (amount of parallel threads) #6061
                      Labels
                      AutoML.NETAutomating various steps of the machine learning processenhancementNew feature or request
                      Milestone
                      ML.NET Future
                      Description
                      @80LevelElf
                      80LevelElf
                      opened on Jan 28, 2022
                      The current AutoML is not good to use when you have trained a lot of models at the same time in the cloud.
                      
If you have some amount of pods in your Kubernetes cluster it doesn't matter how many AutoML experiments you execute at the same time. 4 experiments or 1 experiment at the same time use 100% of CPU (It brokes health checks and so on)
                      Low-level API of trainers (like FastForestBinaryTrainer) has options like NumberOfThreads and some other trainer-specific options you can use to handle the workload.
                      Is it able to add something like this to AutoML API?
                      But the most brilliant solution is some sort of smart property like ResourceUsingRatio which can be from 0.0 to 1.0
                      
ResourceUsingRatio = 1.0 means the experiment use the maximum of potential resources (mainly CPU) it needs or the machine has.
                      Metadata
                      Metadata
                      Assignees
                      No one assigned
                        Labels
                        AutoML.NETAutomating various steps of the machine learning processenhancementNew feature or request
                        Type
                        No type
                        Projects
                        No projects
                          Milestone
                          Relationships
                          None yet
                          Development
                          No branches or pull requests
                          Issue actions
                          
, '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
                          Ability to limit AutoML resource using (amount of parallel threads)  #6061
                          Open
                          Open
                          Ability to limit AutoML resource using (amount of parallel threads) #6061
                          Labels
                          AutoML.NETAutomating various steps of the machine learning processenhancementNew feature or request
                          Milestone
                          ML.NET Future
                          Description
                          @80LevelElf
                          80LevelElf
                          opened on Jan 28, 2022
                          The current AutoML is not good to use when you have trained a lot of models at the same time in the cloud.
                          
If you have some amount of pods in your Kubernetes cluster it doesn't matter how many AutoML experiments you execute at the same time. 4 experiments or 1 experiment at the same time use 100% of CPU (It brokes health checks and so on)
                          Low-level API of trainers (like FastForestBinaryTrainer) has options like NumberOfThreads and some other trainer-specific options you can use to handle the workload.
                          Is it able to add something like this to AutoML API?
                          But the most brilliant solution is some sort of smart property like ResourceUsingRatio which can be from 0.0 to 1.0
                          
ResourceUsingRatio = 1.0 means the experiment use the maximum of potential resources (mainly CPU) it needs or the machine has.
                          Metadata
                          Metadata
                          Assignees
                          No one assigned
                            Labels
                            AutoML.NETAutomating various steps of the machine learning processenhancementNew feature or request
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                            No type
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                              None yet
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                              No branches or pull requests
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, '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
                              Ability to limit AutoML resource using (amount of parallel threads)  #6061
                              Open
                              Open
                              Ability to limit AutoML resource using (amount of parallel threads) #6061
                              Labels
                              AutoML.NETAutomating various steps of the machine learning processenhancementNew feature or request
                              Milestone
                              ML.NET Future
                              Description
                              @80LevelElf
                              80LevelElf
                              opened on Jan 28, 2022
                              The current AutoML is not good to use when you have trained a lot of models at the same time in the cloud.
                              
If you have some amount of pods in your Kubernetes cluster it doesn't matter how many AutoML experiments you execute at the same time. 4 experiments or 1 experiment at the same time use 100% of CPU (It brokes health checks and so on)
                              Low-level API of trainers (like FastForestBinaryTrainer) has options like NumberOfThreads and some other trainer-specific options you can use to handle the workload.
                              Is it able to add something like this to AutoML API?
                              But the most brilliant solution is some sort of smart property like ResourceUsingRatio which can be from 0.0 to 1.0
                              
ResourceUsingRatio = 1.0 means the experiment use the maximum of potential resources (mainly CPU) it needs or the machine has.
                              Metadata
                              Metadata
                              Assignees
                              No one assigned
                                Labels
                                AutoML.NETAutomating various steps of the machine learning processenhancementNew feature or request
                                Type
                                No type
                                Projects
                                No projects
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                                  None yet
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                                  No branches or pull requests
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