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tensorflask

These are example of hosting a TensorFlow models as Flask services for inference.

I provide ansible configuration for provisioning services in aws. I'm working through configuring everything to work through ansible and docker containers for easy of deployment and modularity.

Features thus far are the installation of multiple hosts, the installation of docker dependencies, large file upload support, some tensorflow models, and basic flask site hosting

Setup

  1. You need Python 3.x with Flask and TensorFlow installed. You can download ActivePython 3.5 which has all the required dependencies already pre-installed.
  2. Clone to repository by clicking the clone button above.
  3. Run python app.py.

Usage

start in ansible/ansible-aws directory and run the playbooks from there

License

Licensed under the Apache 2.0 license. See LICENSE file for details.

About

Simple example hosting TensorFlow model as Flask service

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Readme
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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" + '

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tensorflask
These are example of hosting a TensorFlow models as Flask services for inference.
I provide ansible configuration for provisioning services in aws. I'm working through configuring everything to work through ansible and docker containers for easy of deployment and modularity.
Features thus far are the installation of multiple hosts, the installation of docker dependencies, large file upload support, some tensorflow models, and basic flask site hosting
Setup
  1. You need Python 3.x with Flask and TensorFlow installed. You can download ActivePython 3.5 which has all the required dependencies already pre-installed.
  2. Clone to repository by clicking the clone button above.
  3. Run python app.py.
Usage
start in ansible/ansible-aws directory and run the playbooks from there
License
Licensed under the Apache 2.0 license. See LICENSE file for details.
About
Simple example hosting TensorFlow model as Flask service
Resources
Readme
Apache-2.0 license
Activity
Custom properties
Stars
0 stars
Watchers
1 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages

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

Skip to content
BranchesTags
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tensorflask
These are example of hosting a TensorFlow models as Flask services for inference.
I provide ansible configuration for provisioning services in aws. I'm working through configuring everything to work through ansible and docker containers for easy of deployment and modularity.
Features thus far are the installation of multiple hosts, the installation of docker dependencies, large file upload support, some tensorflow models, and basic flask site hosting
Setup
  1. You need Python 3.x with Flask and TensorFlow installed. You can download ActivePython 3.5 which has all the required dependencies already pre-installed.
  2. Clone to repository by clicking the clone button above.
  3. Run python app.py.
Usage
start in ansible/ansible-aws directory and run the playbooks from there
License
Licensed under the Apache 2.0 license. See LICENSE file for details.
About
Simple example hosting TensorFlow model as Flask service
Resources
Readme
Apache-2.0 license
Activity
Custom properties
Stars
0 stars
Watchers
1 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages

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

Skip to content
BranchesTags
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tensorflask
These are example of hosting a TensorFlow models as Flask services for inference.
I provide ansible configuration for provisioning services in aws. I'm working through configuring everything to work through ansible and docker containers for easy of deployment and modularity.
Features thus far are the installation of multiple hosts, the installation of docker dependencies, large file upload support, some tensorflow models, and basic flask site hosting
Setup
  1. You need Python 3.x with Flask and TensorFlow installed. You can download ActivePython 3.5 which has all the required dependencies already pre-installed.
  2. Clone to repository by clicking the clone button above.
  3. Run python app.py.
Usage
start in ansible/ansible-aws directory and run the playbooks from there
License
Licensed under the Apache 2.0 license. See LICENSE file for details.
About
Simple example hosting TensorFlow model as Flask service
Resources
Readme
Apache-2.0 license
Activity
Custom properties
Stars
0 stars
Watchers
1 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages

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

Skip to content
BranchesTags
Repository files navigation
tensorflask
These are example of hosting a TensorFlow models as Flask services for inference.
I provide ansible configuration for provisioning services in aws. I'm working through configuring everything to work through ansible and docker containers for easy of deployment and modularity.
Features thus far are the installation of multiple hosts, the installation of docker dependencies, large file upload support, some tensorflow models, and basic flask site hosting
Setup
  1. You need Python 3.x with Flask and TensorFlow installed. You can download ActivePython 3.5 which has all the required dependencies already pre-installed.
  2. Clone to repository by clicking the clone button above.
  3. Run python app.py.
Usage
start in ansible/ansible-aws directory and run the playbooks from there
License
Licensed under the Apache 2.0 license. See LICENSE file for details.
About
Simple example hosting TensorFlow model as Flask service
Resources
Readme
Apache-2.0 license
Activity
Custom properties
Stars
0 stars
Watchers
1 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages

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

Skip to content
BranchesTags
Repository files navigation
tensorflask
These are example of hosting a TensorFlow models as Flask services for inference.
I provide ansible configuration for provisioning services in aws. I'm working through configuring everything to work through ansible and docker containers for easy of deployment and modularity.
Features thus far are the installation of multiple hosts, the installation of docker dependencies, large file upload support, some tensorflow models, and basic flask site hosting
Setup
  1. You need Python 3.x with Flask and TensorFlow installed. You can download ActivePython 3.5 which has all the required dependencies already pre-installed.
  2. Clone to repository by clicking the clone button above.
  3. Run python app.py.
Usage
start in ansible/ansible-aws directory and run the playbooks from there
License
Licensed under the Apache 2.0 license. See LICENSE file for details.
About
Simple example hosting TensorFlow model as Flask service
Resources
Readme
Apache-2.0 license
Activity
Custom properties
Stars
0 stars
Watchers
1 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages

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

Skip to content
BranchesTags
Repository files navigation
tensorflask
These are example of hosting a TensorFlow models as Flask services for inference.
I provide ansible configuration for provisioning services in aws. I'm working through configuring everything to work through ansible and docker containers for easy of deployment and modularity.
Features thus far are the installation of multiple hosts, the installation of docker dependencies, large file upload support, some tensorflow models, and basic flask site hosting
Setup
  1. You need Python 3.x with Flask and TensorFlow installed. You can download ActivePython 3.5 which has all the required dependencies already pre-installed.
  2. Clone to repository by clicking the clone button above.
  3. Run python app.py.
Usage
start in ansible/ansible-aws directory and run the playbooks from there
License
Licensed under the Apache 2.0 license. See LICENSE file for details.
About
Simple example hosting TensorFlow model as Flask service
Resources
Readme
Apache-2.0 license
Activity
Custom properties
Stars
0 stars
Watchers
1 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages

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

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BranchesTags
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tensorflask
These are example of hosting a TensorFlow models as Flask services for inference.
I provide ansible configuration for provisioning services in aws. I'm working through configuring everything to work through ansible and docker containers for easy of deployment and modularity.
Features thus far are the installation of multiple hosts, the installation of docker dependencies, large file upload support, some tensorflow models, and basic flask site hosting
Setup
  1. You need Python 3.x with Flask and TensorFlow installed. You can download ActivePython 3.5 which has all the required dependencies already pre-installed.
  2. Clone to repository by clicking the clone button above.
  3. Run python app.py.
Usage
start in ansible/ansible-aws directory and run the playbooks from there
License
Licensed under the Apache 2.0 license. See LICENSE file for details.
About
Simple example hosting TensorFlow model as Flask service
Resources
Readme
Apache-2.0 license
Activity
Custom properties
Stars
0 stars
Watchers
1 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages