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Collective Knowledge repository for collaboratively benchmarking and optimising embedded deep vision runtime library for Jetson TX1

All CK components can be found at cKnowledge.io and in one GitHub repository!

This project is hosted by the cTuning foundation.

compatibilityLicense

Introduction

CK-TensorRT is an open framework for collaborative and reproducible optimisation of convolutional neural networks for Jetson TX1 based on the Collective Knowledge framework. It's based on the Deep Inference framework from Dustin Franklin (a Jetson developer @ NVIDIA). In essence, CK-TensorRT is simply a suite of convenient wrappers with unified JSON API for customizable building, evaluating and multi-objective optimisation of Jetson Inference runtime library for Jetson TX1.

Authors/contributors

Quick installation on Ubuntu

TBD

Installing general dependencies

$ sudo apt install coreutils \
build-essential \
make \
cmake \
wget \
git \
python \
python-pip

Installing CK-TensorRT dependencies

$ sudo apt install libqt4-dev \
libglew-dev \
libgstreamer1.0-dev

Installing CK

$ sudo pip install ck
$ ck version

Installing CK-TensorRT repository

$ ck pull repo:ck-tensorrt

Building CK-TensorRT and all dependencies via CK

The first time you run a TensorRT program (e.g. tensorrt-test), CK will build and install all missing dependencies on your machine, download the required data sets and start the benchmark:

$ ck run program:tensorrt-test

Related projects and initiatives

We are working with the community to unify and crowdsource performance analysis and tuning of various DNN frameworks (or any realistic workload) using the Collective Knowledge Technology:

About

Collective Knowledge repository for NVIDIA's TensorRT

Resources

Readme
License
Activity
Custom properties

Stars

37 stars

Watchers

11 watching

Forks

16 forks
Report repository

Releases

Packages

Contributors

Languages

, '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
Collective Knowledge repository for collaboratively benchmarking and optimising embedded deep vision runtime library for Jetson TX1
All CK components can be found at cKnowledge.io and in one GitHub repository!
This project is hosted by the cTuning foundation.
compatibilityLicense
Introduction
CK-TensorRT is an open framework for
collaborative and reproducible optimisation of convolutional neural networks for Jetson TX1
based on the Collective Knowledge framework.
It's based on the Deep Inference framework from
Dustin Franklin (a Jetson developer @ NVIDIA).
In essence, CK-TensorRT is simply a suite of convenient wrappers with unified JSON API
for customizable building, evaluating and multi-objective optimisation
of Jetson Inference runtime library for Jetson TX1.
Authors/contributors
Quick installation on Ubuntu
TBD
Installing general dependencies
$ sudo apt install coreutils \
build-essential \
make \
cmake \
wget \
git \
python \
python-pip

Installing CK-TensorRT dependencies
$ sudo apt install libqt4-dev \
libglew-dev \
libgstreamer1.0-dev

Installing CK
$ sudo pip install ck
$ ck version

Installing CK-TensorRT repository
$ ck pull repo:ck-tensorrt

Building CK-TensorRT and all dependencies via CK
The first time you run a TensorRT program (e.g. tensorrt-test), CK will
build and install all missing dependencies on your machine,
download the required data sets and start the benchmark:
$ ck run program:tensorrt-test

Related projects and initiatives
We are working with the community to unify and crowdsource performance analysis
and tuning of various DNN frameworks (or any realistic workload)
using the Collective Knowledge Technology:
About
Collective Knowledge repository for NVIDIA's TensorRT
Resources
Readme
License
Activity
Custom properties
Stars
37 stars
Watchers
11 watching
Forks
16 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
Collective Knowledge repository for collaboratively benchmarking and optimising embedded deep vision runtime library for Jetson TX1
All CK components can be found at cKnowledge.io and in one GitHub repository!
This project is hosted by the cTuning foundation.
compatibilityLicense
Introduction
CK-TensorRT is an open framework for
collaborative and reproducible optimisation of convolutional neural networks for Jetson TX1
based on the Collective Knowledge framework.
It's based on the Deep Inference framework from
Dustin Franklin (a Jetson developer @ NVIDIA).
In essence, CK-TensorRT is simply a suite of convenient wrappers with unified JSON API
for customizable building, evaluating and multi-objective optimisation
of Jetson Inference runtime library for Jetson TX1.
Authors/contributors
Quick installation on Ubuntu
TBD
Installing general dependencies
$ sudo apt install coreutils \
build-essential \
make \
cmake \
wget \
git \
python \
python-pip

Installing CK-TensorRT dependencies
$ sudo apt install libqt4-dev \
libglew-dev \
libgstreamer1.0-dev

Installing CK
$ sudo pip install ck
$ ck version

Installing CK-TensorRT repository
$ ck pull repo:ck-tensorrt

Building CK-TensorRT and all dependencies via CK
The first time you run a TensorRT program (e.g. tensorrt-test), CK will
build and install all missing dependencies on your machine,
download the required data sets and start the benchmark:
$ ck run program:tensorrt-test

Related projects and initiatives
We are working with the community to unify and crowdsource performance analysis
and tuning of various DNN frameworks (or any realistic workload)
using the Collective Knowledge Technology:
About
Collective Knowledge repository for NVIDIA's TensorRT
Resources
Readme
License
Activity
Custom properties
Stars
37 stars
Watchers
11 watching
Forks
16 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
Collective Knowledge repository for collaboratively benchmarking and optimising embedded deep vision runtime library for Jetson TX1
All CK components can be found at cKnowledge.io and in one GitHub repository!
This project is hosted by the cTuning foundation.
compatibilityLicense
Introduction
CK-TensorRT is an open framework for
collaborative and reproducible optimisation of convolutional neural networks for Jetson TX1
based on the Collective Knowledge framework.
It's based on the Deep Inference framework from
Dustin Franklin (a Jetson developer @ NVIDIA).
In essence, CK-TensorRT is simply a suite of convenient wrappers with unified JSON API
for customizable building, evaluating and multi-objective optimisation
of Jetson Inference runtime library for Jetson TX1.
Authors/contributors
Quick installation on Ubuntu
TBD
Installing general dependencies
$ sudo apt install coreutils \
build-essential \
make \
cmake \
wget \
git \
python \
python-pip

Installing CK-TensorRT dependencies
$ sudo apt install libqt4-dev \
libglew-dev \
libgstreamer1.0-dev

Installing CK
$ sudo pip install ck
$ ck version

Installing CK-TensorRT repository
$ ck pull repo:ck-tensorrt

Building CK-TensorRT and all dependencies via CK
The first time you run a TensorRT program (e.g. tensorrt-test), CK will
build and install all missing dependencies on your machine,
download the required data sets and start the benchmark:
$ ck run program:tensorrt-test

Related projects and initiatives
We are working with the community to unify and crowdsource performance analysis
and tuning of various DNN frameworks (or any realistic workload)
using the Collective Knowledge Technology:
About
Collective Knowledge repository for NVIDIA's TensorRT
Resources
Readme
License
Activity
Custom properties
Stars
37 stars
Watchers
11 watching
Forks
16 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
Collective Knowledge repository for collaboratively benchmarking and optimising embedded deep vision runtime library for Jetson TX1
All CK components can be found at cKnowledge.io and in one GitHub repository!
This project is hosted by the cTuning foundation.
compatibilityLicense
Introduction
CK-TensorRT is an open framework for
collaborative and reproducible optimisation of convolutional neural networks for Jetson TX1
based on the Collective Knowledge framework.
It's based on the Deep Inference framework from
Dustin Franklin (a Jetson developer @ NVIDIA).
In essence, CK-TensorRT is simply a suite of convenient wrappers with unified JSON API
for customizable building, evaluating and multi-objective optimisation
of Jetson Inference runtime library for Jetson TX1.
Authors/contributors
Quick installation on Ubuntu
TBD
Installing general dependencies
$ sudo apt install coreutils \
build-essential \
make \
cmake \
wget \
git \
python \
python-pip

Installing CK-TensorRT dependencies
$ sudo apt install libqt4-dev \
libglew-dev \
libgstreamer1.0-dev

Installing CK
$ sudo pip install ck
$ ck version

Installing CK-TensorRT repository
$ ck pull repo:ck-tensorrt

Building CK-TensorRT and all dependencies via CK
The first time you run a TensorRT program (e.g. tensorrt-test), CK will
build and install all missing dependencies on your machine,
download the required data sets and start the benchmark:
$ ck run program:tensorrt-test

Related projects and initiatives
We are working with the community to unify and crowdsource performance analysis
and tuning of various DNN frameworks (or any realistic workload)
using the Collective Knowledge Technology:
About
Collective Knowledge repository for NVIDIA's TensorRT
Resources
Readme
License
Activity
Custom properties
Stars
37 stars
Watchers
11 watching
Forks
16 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
Collective Knowledge repository for collaboratively benchmarking and optimising embedded deep vision runtime library for Jetson TX1
All CK components can be found at cKnowledge.io and in one GitHub repository!
This project is hosted by the cTuning foundation.
compatibilityLicense
Introduction
CK-TensorRT is an open framework for
collaborative and reproducible optimisation of convolutional neural networks for Jetson TX1
based on the Collective Knowledge framework.
It's based on the Deep Inference framework from
Dustin Franklin (a Jetson developer @ NVIDIA).
In essence, CK-TensorRT is simply a suite of convenient wrappers with unified JSON API
for customizable building, evaluating and multi-objective optimisation
of Jetson Inference runtime library for Jetson TX1.
Authors/contributors
Quick installation on Ubuntu
TBD
Installing general dependencies
$ sudo apt install coreutils \
build-essential \
make \
cmake \
wget \
git \
python \
python-pip

Installing CK-TensorRT dependencies
$ sudo apt install libqt4-dev \
libglew-dev \
libgstreamer1.0-dev

Installing CK
$ sudo pip install ck
$ ck version

Installing CK-TensorRT repository
$ ck pull repo:ck-tensorrt

Building CK-TensorRT and all dependencies via CK
The first time you run a TensorRT program (e.g. tensorrt-test), CK will
build and install all missing dependencies on your machine,
download the required data sets and start the benchmark:
$ ck run program:tensorrt-test

Related projects and initiatives
We are working with the community to unify and crowdsource performance analysis
and tuning of various DNN frameworks (or any realistic workload)
using the Collective Knowledge Technology:
About
Collective Knowledge repository for NVIDIA's TensorRT
Resources
Readme
License
Activity
Custom properties
Stars
37 stars
Watchers
11 watching
Forks
16 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
Collective Knowledge repository for collaboratively benchmarking and optimising embedded deep vision runtime library for Jetson TX1
All CK components can be found at cKnowledge.io and in one GitHub repository!
This project is hosted by the cTuning foundation.
compatibilityLicense
Introduction
CK-TensorRT is an open framework for
collaborative and reproducible optimisation of convolutional neural networks for Jetson TX1
based on the Collective Knowledge framework.
It's based on the Deep Inference framework from
Dustin Franklin (a Jetson developer @ NVIDIA).
In essence, CK-TensorRT is simply a suite of convenient wrappers with unified JSON API
for customizable building, evaluating and multi-objective optimisation
of Jetson Inference runtime library for Jetson TX1.
Authors/contributors
Quick installation on Ubuntu
TBD
Installing general dependencies
$ sudo apt install coreutils \
build-essential \
make \
cmake \
wget \
git \
python \
python-pip

Installing CK-TensorRT dependencies
$ sudo apt install libqt4-dev \
libglew-dev \
libgstreamer1.0-dev

Installing CK
$ sudo pip install ck
$ ck version

Installing CK-TensorRT repository
$ ck pull repo:ck-tensorrt

Building CK-TensorRT and all dependencies via CK
The first time you run a TensorRT program (e.g. tensorrt-test), CK will
build and install all missing dependencies on your machine,
download the required data sets and start the benchmark:
$ ck run program:tensorrt-test

Related projects and initiatives
We are working with the community to unify and crowdsource performance analysis
and tuning of various DNN frameworks (or any realistic workload)
using the Collective Knowledge Technology:
About
Collective Knowledge repository for NVIDIA's TensorRT
Resources
Readme
License
Activity
Custom properties
Stars
37 stars
Watchers
11 watching
Forks
16 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
Collective Knowledge repository for collaboratively benchmarking and optimising embedded deep vision runtime library for Jetson TX1
All CK components can be found at cKnowledge.io and in one GitHub repository!
This project is hosted by the cTuning foundation.
compatibilityLicense
Introduction
CK-TensorRT is an open framework for
collaborative and reproducible optimisation of convolutional neural networks for Jetson TX1
based on the Collective Knowledge framework.
It's based on the Deep Inference framework from
Dustin Franklin (a Jetson developer @ NVIDIA).
In essence, CK-TensorRT is simply a suite of convenient wrappers with unified JSON API
for customizable building, evaluating and multi-objective optimisation
of Jetson Inference runtime library for Jetson TX1.
Authors/contributors
Quick installation on Ubuntu
TBD
Installing general dependencies
$ sudo apt install coreutils \
build-essential \
make \
cmake \
wget \
git \
python \
python-pip

Installing CK-TensorRT dependencies
$ sudo apt install libqt4-dev \
libglew-dev \
libgstreamer1.0-dev

Installing CK
$ sudo pip install ck
$ ck version

Installing CK-TensorRT repository
$ ck pull repo:ck-tensorrt

Building CK-TensorRT and all dependencies via CK
The first time you run a TensorRT program (e.g. tensorrt-test), CK will
build and install all missing dependencies on your machine,
download the required data sets and start the benchmark:
$ ck run program:tensorrt-test

Related projects and initiatives
We are working with the community to unify and crowdsource performance analysis
and tuning of various DNN frameworks (or any realistic workload)
using the Collective Knowledge Technology:
About
Collective Knowledge repository for NVIDIA's TensorRT
Resources
Readme
License
Activity
Custom properties
Stars
37 stars
Watchers
11 watching
Forks
16 forks
Report repository
Releases
Packages
Contributors
Languages