Clearer interaction of TensorFlowModel with new framework versions #1444

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Clearer interaction of TensorFlowModel with new framework versions#1444
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nargokul
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Tensorflowtype: feature request

Description

@athewsey
athewsey
opened on Apr 28, 2020

Is your feature request related to a problem? Please describe.
We can create a sagemaker.tensorflow.model.TensorFlowModel for new versions of TensorFlow (e.g. 2.0, 2.1), but get a "container not found" error when trying to deploy() it.

Presumably this is because newer framework versions should make use of sagemaker.tensorflow.serving.Model instead, for the new-style TFServing based container instead of the old-style inference container?

Describe the solution you'd like
For these new TF versions where the old-style container isn't supported and there's no "choice", it would be best to make the core TensorFlowModel class produce a TFServing-based model.

Describe alternatives you've considered
Alternatively could raise errors on TensorFlowModel init with a new/unsupported framework version, and consider adding docs deprecation warnings to the old class suggesting the new serving-based class instead for modern framework versions.

Additional context
Clear, centralized documentation of SageMaker-provided framework container image URIs would also help, as it might be clearer what the SDK is trying to do wrong.

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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
    Clearer interaction of TensorFlowModel with new framework versions #1444
    Closed
    Closed
    Clearer interaction of TensorFlowModel with new framework versions#1444
    Assignees
    nargokul
    Labels
    Tensorflowtype: feature request
    Description
    @athewsey
    athewsey
    opened on Apr 28, 2020
    Is your feature request related to a problem? Please describe.
    
We can create a sagemaker.tensorflow.model.TensorFlowModel for new versions of TensorFlow (e.g. 2.0, 2.1), but get a "container not found" error when trying to deploy() it.
    Presumably this is because newer framework versions should make use of sagemaker.tensorflow.serving.Model instead, for the new-style TFServing based container instead of the old-style inference container?
    Describe the solution you'd like
    
For these new TF versions where the old-style container isn't supported and there's no "choice", it would be best to make the core TensorFlowModel class produce a TFServing-based model.
    Describe alternatives you've considered
    
Alternatively could raise errors on TensorFlowModel init with a new/unsupported framework version, and consider adding docs deprecation warnings to the old class suggesting the new serving-based class instead for modern framework versions.
    Additional context
    
Clear, centralized documentation of SageMaker-provided framework container image URIs would also help, as it might be clearer what the SDK is trying to do wrong.
    Metadata
    Metadata
    Assignees
    Labels
    Tensorflowtype: feature request
    Type
    No type
    Projects
    No projects
      Milestone
      No milestone
      Relationships
      None yet
      Development
      No branches or pull requests
      Issue actions
      
, '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
      Clearer interaction of TensorFlowModel with new framework versions #1444
      Closed
      Closed
      Clearer interaction of TensorFlowModel with new framework versions#1444
      Assignees
      nargokul
      Labels
      Tensorflowtype: feature request
      Description
      @athewsey
      athewsey
      opened on Apr 28, 2020
      Is your feature request related to a problem? Please describe.
      
We can create a sagemaker.tensorflow.model.TensorFlowModel for new versions of TensorFlow (e.g. 2.0, 2.1), but get a "container not found" error when trying to deploy() it.
      Presumably this is because newer framework versions should make use of sagemaker.tensorflow.serving.Model instead, for the new-style TFServing based container instead of the old-style inference container?
      Describe the solution you'd like
      
For these new TF versions where the old-style container isn't supported and there's no "choice", it would be best to make the core TensorFlowModel class produce a TFServing-based model.
      Describe alternatives you've considered
      
Alternatively could raise errors on TensorFlowModel init with a new/unsupported framework version, and consider adding docs deprecation warnings to the old class suggesting the new serving-based class instead for modern framework versions.
      Additional context
      
Clear, centralized documentation of SageMaker-provided framework container image URIs would also help, as it might be clearer what the SDK is trying to do wrong.
      Metadata
      Metadata
      Assignees
      Labels
      Tensorflowtype: feature request
      Type
      No type
      Projects
      No projects
        Milestone
        No milestone
        Relationships
        None yet
        Development
        No branches or pull requests
        Issue actions
        
, '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
        Clearer interaction of TensorFlowModel with new framework versions #1444
        Closed
        Closed
        Clearer interaction of TensorFlowModel with new framework versions#1444
        Assignees
        nargokul
        Labels
        Tensorflowtype: feature request
        Description
        @athewsey
        athewsey
        opened on Apr 28, 2020
        Is your feature request related to a problem? Please describe.
        
We can create a sagemaker.tensorflow.model.TensorFlowModel for new versions of TensorFlow (e.g. 2.0, 2.1), but get a "container not found" error when trying to deploy() it.
        Presumably this is because newer framework versions should make use of sagemaker.tensorflow.serving.Model instead, for the new-style TFServing based container instead of the old-style inference container?
        Describe the solution you'd like
        
For these new TF versions where the old-style container isn't supported and there's no "choice", it would be best to make the core TensorFlowModel class produce a TFServing-based model.
        Describe alternatives you've considered
        
Alternatively could raise errors on TensorFlowModel init with a new/unsupported framework version, and consider adding docs deprecation warnings to the old class suggesting the new serving-based class instead for modern framework versions.
        Additional context
        
Clear, centralized documentation of SageMaker-provided framework container image URIs would also help, as it might be clearer what the SDK is trying to do wrong.
        Metadata
        Metadata
        Assignees
        Labels
        Tensorflowtype: feature request
        Type
        No type
        Projects
        No projects
          Milestone
          No milestone
          Relationships
          None yet
          Development
          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
          Clearer interaction of TensorFlowModel with new framework versions #1444
          Closed
          Closed
          Clearer interaction of TensorFlowModel with new framework versions#1444
          Assignees
          nargokul
          Labels
          Tensorflowtype: feature request
          Description
          @athewsey
          athewsey
          opened on Apr 28, 2020
          Is your feature request related to a problem? Please describe.
          
We can create a sagemaker.tensorflow.model.TensorFlowModel for new versions of TensorFlow (e.g. 2.0, 2.1), but get a "container not found" error when trying to deploy() it.
          Presumably this is because newer framework versions should make use of sagemaker.tensorflow.serving.Model instead, for the new-style TFServing based container instead of the old-style inference container?
          Describe the solution you'd like
          
For these new TF versions where the old-style container isn't supported and there's no "choice", it would be best to make the core TensorFlowModel class produce a TFServing-based model.
          Describe alternatives you've considered
          
Alternatively could raise errors on TensorFlowModel init with a new/unsupported framework version, and consider adding docs deprecation warnings to the old class suggesting the new serving-based class instead for modern framework versions.
          Additional context
          
Clear, centralized documentation of SageMaker-provided framework container image URIs would also help, as it might be clearer what the SDK is trying to do wrong.
          Metadata
          Metadata
          Assignees
          Labels
          Tensorflowtype: feature request
          Type
          No type
          Projects
          No projects
            Milestone
            No 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
            Clearer interaction of TensorFlowModel with new framework versions #1444
            Closed
            Closed
            Clearer interaction of TensorFlowModel with new framework versions#1444
            Assignees
            nargokul
            Labels
            Tensorflowtype: feature request
            Description
            @athewsey
            athewsey
            opened on Apr 28, 2020
            Is your feature request related to a problem? Please describe.
            
We can create a sagemaker.tensorflow.model.TensorFlowModel for new versions of TensorFlow (e.g. 2.0, 2.1), but get a "container not found" error when trying to deploy() it.
            Presumably this is because newer framework versions should make use of sagemaker.tensorflow.serving.Model instead, for the new-style TFServing based container instead of the old-style inference container?
            Describe the solution you'd like
            
For these new TF versions where the old-style container isn't supported and there's no "choice", it would be best to make the core TensorFlowModel class produce a TFServing-based model.
            Describe alternatives you've considered
            
Alternatively could raise errors on TensorFlowModel init with a new/unsupported framework version, and consider adding docs deprecation warnings to the old class suggesting the new serving-based class instead for modern framework versions.
            Additional context
            
Clear, centralized documentation of SageMaker-provided framework container image URIs would also help, as it might be clearer what the SDK is trying to do wrong.
            Metadata
            Metadata
            Assignees
            Labels
            Tensorflowtype: feature request
            Type
            No type
            Projects
            No projects
              Milestone
              No 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
              Clearer interaction of TensorFlowModel with new framework versions #1444
              Closed
              Closed
              Clearer interaction of TensorFlowModel with new framework versions#1444
              Assignees
              nargokul
              Labels
              Tensorflowtype: feature request
              Description
              @athewsey
              athewsey
              opened on Apr 28, 2020
              Is your feature request related to a problem? Please describe.
              
We can create a sagemaker.tensorflow.model.TensorFlowModel for new versions of TensorFlow (e.g. 2.0, 2.1), but get a "container not found" error when trying to deploy() it.
              Presumably this is because newer framework versions should make use of sagemaker.tensorflow.serving.Model instead, for the new-style TFServing based container instead of the old-style inference container?
              Describe the solution you'd like
              
For these new TF versions where the old-style container isn't supported and there's no "choice", it would be best to make the core TensorFlowModel class produce a TFServing-based model.
              Describe alternatives you've considered
              
Alternatively could raise errors on TensorFlowModel init with a new/unsupported framework version, and consider adding docs deprecation warnings to the old class suggesting the new serving-based class instead for modern framework versions.
              Additional context
              
Clear, centralized documentation of SageMaker-provided framework container image URIs would also help, as it might be clearer what the SDK is trying to do wrong.
              Metadata
              Metadata
              Assignees
              Labels
              Tensorflowtype: feature request
              Type
              No type
              Projects
              No projects
                Milestone
                No milestone
                Relationships
                None yet
                Development
                No branches or pull requests
                Issue actions
                
, '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
                Clearer interaction of TensorFlowModel with new framework versions #1444
                Closed
                Closed
                Clearer interaction of TensorFlowModel with new framework versions#1444
                Assignees
                nargokul
                Labels
                Tensorflowtype: feature request
                Description
                @athewsey
                athewsey
                opened on Apr 28, 2020
                Is your feature request related to a problem? Please describe.
                
We can create a sagemaker.tensorflow.model.TensorFlowModel for new versions of TensorFlow (e.g. 2.0, 2.1), but get a "container not found" error when trying to deploy() it.
                Presumably this is because newer framework versions should make use of sagemaker.tensorflow.serving.Model instead, for the new-style TFServing based container instead of the old-style inference container?
                Describe the solution you'd like
                
For these new TF versions where the old-style container isn't supported and there's no "choice", it would be best to make the core TensorFlowModel class produce a TFServing-based model.
                Describe alternatives you've considered
                
Alternatively could raise errors on TensorFlowModel init with a new/unsupported framework version, and consider adding docs deprecation warnings to the old class suggesting the new serving-based class instead for modern framework versions.
                Additional context
                
Clear, centralized documentation of SageMaker-provided framework container image URIs would also help, as it might be clearer what the SDK is trying to do wrong.
                Metadata
                Metadata
                Assignees
                Labels
                Tensorflowtype: feature request
                Type
                No type
                Projects
                No projects
                  Milestone
                  No milestone
                  Relationships
                  None yet
                  Development
                  No branches or pull requests
                  Issue actions