TensorflowModel & gRPC #1472

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TensorflowModel & gRPC#1472
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@ratulray
ratulray
opened on May 7, 2020

With reference to this comment explaining the difference between TensorflowModel and Model, I can not find the implementation of the server side code for TensorflowModel (the github links given are broken).
I see that tf_container/serve.py was removed in the following change:
aws/sagemaker-tensorflow-training-toolkit@12fd7ef
And I can not find where the latest container-side implementation lies in github.

Similarly it'll be useful to be able to check out container-side implementation corresponding to older container/sagemaker/serve.py.

The reason I'm asking is:
I want to be able to export a model with build_parsing_serving_input_receiver_fn (which expects a serialized tf.Example while serving) and this doesn't work well with TF REST api as it's not possible to put raw bytes into JSON payload. So, it works with TensorflowModel and not with Model (example). But I don't understand why we can not use py3 with TensorflowModel as TensorFlow serving api library supports both py2 & py3.
Ideally I would like to do something like what this example does, sending a serialized gRPC request (e.g. PredictionRequest proto) directly from client, but can not figure out how to do that cleanly (and hopefully without involving py2).

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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
      TensorflowModel & gRPC #1472
      Closed
      Closed
      TensorflowModel & gRPC#1472
      Labels
      type: question
      Description
      @ratulray
      ratulray
      opened on May 7, 2020
      With reference to this comment explaining the difference between TensorflowModel and Model, I can not find the implementation of the server side code for TensorflowModel (the github links given are broken).
      
I see that tf_container/serve.py was removed in the following change:
      aws/sagemaker-tensorflow-training-toolkit@12fd7ef
      
And I can not find where the latest container-side implementation lies in github.
      Similarly it'll be useful to be able to check out container-side implementation corresponding to older container/sagemaker/serve.py.
      The reason I'm asking is:
      
I want to be able to export a model with build_parsing_serving_input_receiver_fn (which expects a serialized tf.Example while serving) and this doesn't work well with TF REST api as it's not possible to put raw bytes into JSON payload. So, it works with TensorflowModel and not with Model (example). But I don't understand why we can not use py3 with TensorflowModel as TensorFlow serving api library supports both py2 & py3.
      
Ideally I would like to do something like what this example does, sending a serialized gRPC request (e.g. PredictionRequest proto) directly from client, but can not figure out how to do that cleanly (and hopefully without involving py2).
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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
          TensorflowModel & gRPC #1472
          Closed
          Closed
          TensorflowModel & gRPC#1472
          Labels
          type: question
          Description
          @ratulray
          ratulray
          opened on May 7, 2020
          With reference to this comment explaining the difference between TensorflowModel and Model, I can not find the implementation of the server side code for TensorflowModel (the github links given are broken).
          
I see that tf_container/serve.py was removed in the following change:
          aws/sagemaker-tensorflow-training-toolkit@12fd7ef
          
And I can not find where the latest container-side implementation lies in github.
          Similarly it'll be useful to be able to check out container-side implementation corresponding to older container/sagemaker/serve.py.
          The reason I'm asking is:
          
I want to be able to export a model with build_parsing_serving_input_receiver_fn (which expects a serialized tf.Example while serving) and this doesn't work well with TF REST api as it's not possible to put raw bytes into JSON payload. So, it works with TensorflowModel and not with Model (example). But I don't understand why we can not use py3 with TensorflowModel as TensorFlow serving api library supports both py2 & py3.
          
Ideally I would like to do something like what this example does, sending a serialized gRPC request (e.g. PredictionRequest proto) directly from client, but can not figure out how to do that cleanly (and hopefully without involving py2).
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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
              TensorflowModel & gRPC #1472
              Closed
              Closed
              TensorflowModel & gRPC#1472
              Labels
              type: question
              Description
              @ratulray
              ratulray
              opened on May 7, 2020
              With reference to this comment explaining the difference between TensorflowModel and Model, I can not find the implementation of the server side code for TensorflowModel (the github links given are broken).
              
I see that tf_container/serve.py was removed in the following change:
              aws/sagemaker-tensorflow-training-toolkit@12fd7ef
              
And I can not find where the latest container-side implementation lies in github.
              Similarly it'll be useful to be able to check out container-side implementation corresponding to older container/sagemaker/serve.py.
              The reason I'm asking is:
              
I want to be able to export a model with build_parsing_serving_input_receiver_fn (which expects a serialized tf.Example while serving) and this doesn't work well with TF REST api as it's not possible to put raw bytes into JSON payload. So, it works with TensorflowModel and not with Model (example). But I don't understand why we can not use py3 with TensorflowModel as TensorFlow serving api library supports both py2 & py3.
              
Ideally I would like to do something like what this example does, sending a serialized gRPC request (e.g. PredictionRequest proto) directly from client, but can not figure out how to do that cleanly (and hopefully without involving py2).
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, '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
                  TensorflowModel & gRPC #1472
                  Closed
                  Closed
                  TensorflowModel & gRPC#1472
                  Labels
                  type: question
                  Description
                  @ratulray
                  ratulray
                  opened on May 7, 2020
                  With reference to this comment explaining the difference between TensorflowModel and Model, I can not find the implementation of the server side code for TensorflowModel (the github links given are broken).
                  
I see that tf_container/serve.py was removed in the following change:
                  aws/sagemaker-tensorflow-training-toolkit@12fd7ef
                  
And I can not find where the latest container-side implementation lies in github.
                  Similarly it'll be useful to be able to check out container-side implementation corresponding to older container/sagemaker/serve.py.
                  The reason I'm asking is:
                  
I want to be able to export a model with build_parsing_serving_input_receiver_fn (which expects a serialized tf.Example while serving) and this doesn't work well with TF REST api as it's not possible to put raw bytes into JSON payload. So, it works with TensorflowModel and not with Model (example). But I don't understand why we can not use py3 with TensorflowModel as TensorFlow serving api library supports both py2 & py3.
                  
Ideally I would like to do something like what this example does, sending a serialized gRPC request (e.g. PredictionRequest proto) directly from client, but can not figure out how to do that cleanly (and hopefully without involving py2).
                  Metadata
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                  No one assigned
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                      No milestone
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                      None yet
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, '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
                      TensorflowModel & gRPC #1472
                      Closed
                      Closed
                      TensorflowModel & gRPC#1472
                      Labels
                      type: question
                      Description
                      @ratulray
                      ratulray
                      opened on May 7, 2020
                      With reference to this comment explaining the difference between TensorflowModel and Model, I can not find the implementation of the server side code for TensorflowModel (the github links given are broken).
                      
I see that tf_container/serve.py was removed in the following change:
                      aws/sagemaker-tensorflow-training-toolkit@12fd7ef
                      
And I can not find where the latest container-side implementation lies in github.
                      Similarly it'll be useful to be able to check out container-side implementation corresponding to older container/sagemaker/serve.py.
                      The reason I'm asking is:
                      
I want to be able to export a model with build_parsing_serving_input_receiver_fn (which expects a serialized tf.Example while serving) and this doesn't work well with TF REST api as it's not possible to put raw bytes into JSON payload. So, it works with TensorflowModel and not with Model (example). But I don't understand why we can not use py3 with TensorflowModel as TensorFlow serving api library supports both py2 & py3.
                      
Ideally I would like to do something like what this example does, sending a serialized gRPC request (e.g. PredictionRequest proto) directly from client, but can not figure out how to do that cleanly (and hopefully without involving py2).
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, '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
                          TensorflowModel & gRPC #1472
                          Closed
                          Closed
                          TensorflowModel & gRPC#1472
                          Labels
                          type: question
                          Description
                          @ratulray
                          ratulray
                          opened on May 7, 2020
                          With reference to this comment explaining the difference between TensorflowModel and Model, I can not find the implementation of the server side code for TensorflowModel (the github links given are broken).
                          
I see that tf_container/serve.py was removed in the following change:
                          aws/sagemaker-tensorflow-training-toolkit@12fd7ef
                          
And I can not find where the latest container-side implementation lies in github.
                          Similarly it'll be useful to be able to check out container-side implementation corresponding to older container/sagemaker/serve.py.
                          The reason I'm asking is:
                          
I want to be able to export a model with build_parsing_serving_input_receiver_fn (which expects a serialized tf.Example while serving) and this doesn't work well with TF REST api as it's not possible to put raw bytes into JSON payload. So, it works with TensorflowModel and not with Model (example). But I don't understand why we can not use py3 with TensorflowModel as TensorFlow serving api library supports both py2 & py3.
                          
Ideally I would like to do something like what this example does, sending a serialized gRPC request (e.g. PredictionRequest proto) directly from client, but can not figure out how to do that cleanly (and hopefully without involving py2).
                          Metadata
                          Metadata
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                          No one assigned
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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
                              TensorflowModel & gRPC #1472
                              Closed
                              Closed
                              TensorflowModel & gRPC#1472
                              Labels
                              type: question
                              Description
                              @ratulray
                              ratulray
                              opened on May 7, 2020
                              With reference to this comment explaining the difference between TensorflowModel and Model, I can not find the implementation of the server side code for TensorflowModel (the github links given are broken).
                              
I see that tf_container/serve.py was removed in the following change:
                              aws/sagemaker-tensorflow-training-toolkit@12fd7ef
                              
And I can not find where the latest container-side implementation lies in github.
                              Similarly it'll be useful to be able to check out container-side implementation corresponding to older container/sagemaker/serve.py.
                              The reason I'm asking is:
                              
I want to be able to export a model with build_parsing_serving_input_receiver_fn (which expects a serialized tf.Example while serving) and this doesn't work well with TF REST api as it's not possible to put raw bytes into JSON payload. So, it works with TensorflowModel and not with Model (example). But I don't understand why we can not use py3 with TensorflowModel as TensorFlow serving api library supports both py2 & py3.
                              
Ideally I would like to do something like what this example does, sending a serialized gRPC request (e.g. PredictionRequest proto) directly from client, but can not figure out how to do that cleanly (and hopefully without involving py2).
                              Metadata
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