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Predictor

Introduction

  • Predictor is an Online Inference Server for Machine/Deep Learning models.
  • It is designed to aim for Low Latency and High Throughput.
  • It is designed to aim for Distrubuted and Concurrent real-time system.

Get Started - Build

1. clone the repo

$ git clone http://github.com/algo-data-platform/PredictorService.git
$ cd PredictorService/

2. build

(assuming you are at the repo base dir: PredictorService/)

$ sh ./build.sh [release/debug]

Get Started - Run Server

1. start predictor server

(assuming you are at the repo base dir: PredictorService/)

$ cd runtime/
$ sh ./start_predictor.sh

it should print out a message with an url to see the server status, such as:

check predictor status on http://local_host:10048/server/status

2. load model into the predictor service

(assuming you are at the repo base dir: PredictorService/)

$ cd runtime/
$ sh ./load_model.sh

it should print out a message with an url to see the model status, such as:

check model status on http://local_host:10048/get_service_model_info

Done! Now you have a predictor server running (with a model loaded into the memory) and ready to inference model requests!

Get Started - Run Client

1. build sdk

(assuming you are at the repo base dir: PredictorService/)

$ sh sdk/build-predictor-sdk.sh release

2. build example (client)

(assuming you are at the repo base dir: PredictorService/)

$ cd sdk/sdk_package/latest/example
$ sh ./build-predictor-example.sh

This should build an executable binary such as predictor_example_calculate_vector and predictor_example_predict, you can run them as regular binary programs:

$ ./predictor_example_predict

And if you have your server up in previous step, this example sends requests to your server and gets back predict results.

About

No description, website, or topics provided.

Resources

Readme
Apache-2.0 license
Activity
Custom properties

Stars

3 stars

Watchers

0 watching

Forks

5 forks
Report repository

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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
BranchesTags
Repository files navigation
Predictor
Introduction
  • Predictor is an Online Inference Server for Machine/Deep Learning models.
  • It is designed to aim for Low Latency and High Throughput.
  • It is designed to aim for Distrubuted and Concurrent real-time system.
Get Started - Build
1. clone the repo
$ git clone http://github.com/algo-data-platform/PredictorService.git
$ cd PredictorService/
2. build
(assuming you are at the repo base dir: PredictorService/)
$ sh ./build.sh [release/debug]
Get Started - Run Server
1. start predictor server
(assuming you are at the repo base dir: PredictorService/)
$ cd runtime/
$ sh ./start_predictor.sh
it should print out a message with an url to see the server status, such as:
check predictor status on http://local_host:10048/server/status
2. load model into the predictor service
(assuming you are at the repo base dir: PredictorService/)
$ cd runtime/
$ sh ./load_model.sh
it should print out a message with an url to see the model status, such as:
check model status on http://local_host:10048/get_service_model_info
Done! Now you have a predictor server running (with a model loaded into the memory) and ready to inference model requests!
Get Started - Run Client
1. build sdk
(assuming you are at the repo base dir: PredictorService/)
$ sh sdk/build-predictor-sdk.sh release
2. build example (client)
(assuming you are at the repo base dir: PredictorService/)
$ cd sdk/sdk_package/latest/example
$ sh ./build-predictor-example.sh
This should build an executable binary such as predictor_example_calculate_vector and predictor_example_predict, you can run them as regular binary programs:
$ ./predictor_example_predict

And if you have your server up in previous step, this example sends requests to your server and gets back predict results.
About
No description, website, or topics provided.
Resources
Readme
Apache-2.0 license
Activity
Custom properties
Stars
3 stars
Watchers
0 watching
Forks
5 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
Repository files navigation
Predictor
Introduction
  • Predictor is an Online Inference Server for Machine/Deep Learning models.
  • It is designed to aim for Low Latency and High Throughput.
  • It is designed to aim for Distrubuted and Concurrent real-time system.
Get Started - Build
1. clone the repo
$ git clone http://github.com/algo-data-platform/PredictorService.git
$ cd PredictorService/
2. build
(assuming you are at the repo base dir: PredictorService/)
$ sh ./build.sh [release/debug]
Get Started - Run Server
1. start predictor server
(assuming you are at the repo base dir: PredictorService/)
$ cd runtime/
$ sh ./start_predictor.sh
it should print out a message with an url to see the server status, such as:
check predictor status on http://local_host:10048/server/status
2. load model into the predictor service
(assuming you are at the repo base dir: PredictorService/)
$ cd runtime/
$ sh ./load_model.sh
it should print out a message with an url to see the model status, such as:
check model status on http://local_host:10048/get_service_model_info
Done! Now you have a predictor server running (with a model loaded into the memory) and ready to inference model requests!
Get Started - Run Client
1. build sdk
(assuming you are at the repo base dir: PredictorService/)
$ sh sdk/build-predictor-sdk.sh release
2. build example (client)
(assuming you are at the repo base dir: PredictorService/)
$ cd sdk/sdk_package/latest/example
$ sh ./build-predictor-example.sh
This should build an executable binary such as predictor_example_calculate_vector and predictor_example_predict, you can run them as regular binary programs:
$ ./predictor_example_predict

And if you have your server up in previous step, this example sends requests to your server and gets back predict results.
About
No description, website, or topics provided.
Resources
Readme
Apache-2.0 license
Activity
Custom properties
Stars
3 stars
Watchers
0 watching
Forks
5 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
Repository files navigation
Predictor
Introduction
  • Predictor is an Online Inference Server for Machine/Deep Learning models.
  • It is designed to aim for Low Latency and High Throughput.
  • It is designed to aim for Distrubuted and Concurrent real-time system.
Get Started - Build
1. clone the repo
$ git clone http://github.com/algo-data-platform/PredictorService.git
$ cd PredictorService/
2. build
(assuming you are at the repo base dir: PredictorService/)
$ sh ./build.sh [release/debug]
Get Started - Run Server
1. start predictor server
(assuming you are at the repo base dir: PredictorService/)
$ cd runtime/
$ sh ./start_predictor.sh
it should print out a message with an url to see the server status, such as:
check predictor status on http://local_host:10048/server/status
2. load model into the predictor service
(assuming you are at the repo base dir: PredictorService/)
$ cd runtime/
$ sh ./load_model.sh
it should print out a message with an url to see the model status, such as:
check model status on http://local_host:10048/get_service_model_info
Done! Now you have a predictor server running (with a model loaded into the memory) and ready to inference model requests!
Get Started - Run Client
1. build sdk
(assuming you are at the repo base dir: PredictorService/)
$ sh sdk/build-predictor-sdk.sh release
2. build example (client)
(assuming you are at the repo base dir: PredictorService/)
$ cd sdk/sdk_package/latest/example
$ sh ./build-predictor-example.sh
This should build an executable binary such as predictor_example_calculate_vector and predictor_example_predict, you can run them as regular binary programs:
$ ./predictor_example_predict

And if you have your server up in previous step, this example sends requests to your server and gets back predict results.
About
No description, website, or topics provided.
Resources
Readme
Apache-2.0 license
Activity
Custom properties
Stars
3 stars
Watchers
0 watching
Forks
5 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
Predictor
Introduction
  • Predictor is an Online Inference Server for Machine/Deep Learning models.
  • It is designed to aim for Low Latency and High Throughput.
  • It is designed to aim for Distrubuted and Concurrent real-time system.
Get Started - Build
1. clone the repo
$ git clone http://github.com/algo-data-platform/PredictorService.git
$ cd PredictorService/
2. build
(assuming you are at the repo base dir: PredictorService/)
$ sh ./build.sh [release/debug]
Get Started - Run Server
1. start predictor server
(assuming you are at the repo base dir: PredictorService/)
$ cd runtime/
$ sh ./start_predictor.sh
it should print out a message with an url to see the server status, such as:
check predictor status on http://local_host:10048/server/status
2. load model into the predictor service
(assuming you are at the repo base dir: PredictorService/)
$ cd runtime/
$ sh ./load_model.sh
it should print out a message with an url to see the model status, such as:
check model status on http://local_host:10048/get_service_model_info
Done! Now you have a predictor server running (with a model loaded into the memory) and ready to inference model requests!
Get Started - Run Client
1. build sdk
(assuming you are at the repo base dir: PredictorService/)
$ sh sdk/build-predictor-sdk.sh release
2. build example (client)
(assuming you are at the repo base dir: PredictorService/)
$ cd sdk/sdk_package/latest/example
$ sh ./build-predictor-example.sh
This should build an executable binary such as predictor_example_calculate_vector and predictor_example_predict, you can run them as regular binary programs:
$ ./predictor_example_predict

And if you have your server up in previous step, this example sends requests to your server and gets back predict results.
About
No description, website, or topics provided.
Resources
Readme
Apache-2.0 license
Activity
Custom properties
Stars
3 stars
Watchers
0 watching
Forks
5 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
Predictor
Introduction
  • Predictor is an Online Inference Server for Machine/Deep Learning models.
  • It is designed to aim for Low Latency and High Throughput.
  • It is designed to aim for Distrubuted and Concurrent real-time system.
Get Started - Build
1. clone the repo
$ git clone http://github.com/algo-data-platform/PredictorService.git
$ cd PredictorService/
2. build
(assuming you are at the repo base dir: PredictorService/)
$ sh ./build.sh [release/debug]
Get Started - Run Server
1. start predictor server
(assuming you are at the repo base dir: PredictorService/)
$ cd runtime/
$ sh ./start_predictor.sh
it should print out a message with an url to see the server status, such as:
check predictor status on http://local_host:10048/server/status
2. load model into the predictor service
(assuming you are at the repo base dir: PredictorService/)
$ cd runtime/
$ sh ./load_model.sh
it should print out a message with an url to see the model status, such as:
check model status on http://local_host:10048/get_service_model_info
Done! Now you have a predictor server running (with a model loaded into the memory) and ready to inference model requests!
Get Started - Run Client
1. build sdk
(assuming you are at the repo base dir: PredictorService/)
$ sh sdk/build-predictor-sdk.sh release
2. build example (client)
(assuming you are at the repo base dir: PredictorService/)
$ cd sdk/sdk_package/latest/example
$ sh ./build-predictor-example.sh
This should build an executable binary such as predictor_example_calculate_vector and predictor_example_predict, you can run them as regular binary programs:
$ ./predictor_example_predict

And if you have your server up in previous step, this example sends requests to your server and gets back predict results.
About
No description, website, or topics provided.
Resources
Readme
Apache-2.0 license
Activity
Custom properties
Stars
3 stars
Watchers
0 watching
Forks
5 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
Predictor
Introduction
  • Predictor is an Online Inference Server for Machine/Deep Learning models.
  • It is designed to aim for Low Latency and High Throughput.
  • It is designed to aim for Distrubuted and Concurrent real-time system.
Get Started - Build
1. clone the repo
$ git clone http://github.com/algo-data-platform/PredictorService.git
$ cd PredictorService/
2. build
(assuming you are at the repo base dir: PredictorService/)
$ sh ./build.sh [release/debug]
Get Started - Run Server
1. start predictor server
(assuming you are at the repo base dir: PredictorService/)
$ cd runtime/
$ sh ./start_predictor.sh
it should print out a message with an url to see the server status, such as:
check predictor status on http://local_host:10048/server/status
2. load model into the predictor service
(assuming you are at the repo base dir: PredictorService/)
$ cd runtime/
$ sh ./load_model.sh
it should print out a message with an url to see the model status, such as:
check model status on http://local_host:10048/get_service_model_info
Done! Now you have a predictor server running (with a model loaded into the memory) and ready to inference model requests!
Get Started - Run Client
1. build sdk
(assuming you are at the repo base dir: PredictorService/)
$ sh sdk/build-predictor-sdk.sh release
2. build example (client)
(assuming you are at the repo base dir: PredictorService/)
$ cd sdk/sdk_package/latest/example
$ sh ./build-predictor-example.sh
This should build an executable binary such as predictor_example_calculate_vector and predictor_example_predict, you can run them as regular binary programs:
$ ./predictor_example_predict

And if you have your server up in previous step, this example sends requests to your server and gets back predict results.
About
No description, website, or topics provided.
Resources
Readme
Apache-2.0 license
Activity
Custom properties
Stars
3 stars
Watchers
0 watching
Forks
5 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); }
})();
})();

Skip to content
BranchesTags
Repository files navigation
Predictor
Introduction
  • Predictor is an Online Inference Server for Machine/Deep Learning models.
  • It is designed to aim for Low Latency and High Throughput.
  • It is designed to aim for Distrubuted and Concurrent real-time system.
Get Started - Build
1. clone the repo
$ git clone http://github.com/algo-data-platform/PredictorService.git
$ cd PredictorService/
2. build
(assuming you are at the repo base dir: PredictorService/)
$ sh ./build.sh [release/debug]
Get Started - Run Server
1. start predictor server
(assuming you are at the repo base dir: PredictorService/)
$ cd runtime/
$ sh ./start_predictor.sh
it should print out a message with an url to see the server status, such as:
check predictor status on http://local_host:10048/server/status
2. load model into the predictor service
(assuming you are at the repo base dir: PredictorService/)
$ cd runtime/
$ sh ./load_model.sh
it should print out a message with an url to see the model status, such as:
check model status on http://local_host:10048/get_service_model_info
Done! Now you have a predictor server running (with a model loaded into the memory) and ready to inference model requests!
Get Started - Run Client
1. build sdk
(assuming you are at the repo base dir: PredictorService/)
$ sh sdk/build-predictor-sdk.sh release
2. build example (client)
(assuming you are at the repo base dir: PredictorService/)
$ cd sdk/sdk_package/latest/example
$ sh ./build-predictor-example.sh
This should build an executable binary such as predictor_example_calculate_vector and predictor_example_predict, you can run them as regular binary programs:
$ ./predictor_example_predict

And if you have your server up in previous step, this example sends requests to your server and gets back predict results.
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