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Documentation for TensorRT in TensorFlow (TF-TRT)

The documentaion on how to accelerate inference in TensorFlow with TensorRT (TF-TRT) is here: https://docs.nvidia.com/deeplearning/dgx/tf-trt-user-guide/index.html

Examples for TensorRT in TensorFlow (TF-TRT)

This repository contains a number of different examples that show how to use TF-TRT. TF-TRT is a part of TensorFlow that optimizes TensorFlow graphs using TensorRT. We have used these examples to verify the accuracy and performance of TF-TRT. For more information see Verified Models.

Examples

Using TensorRT in TensorFlow (TF-TRT)

This module provides necessary bindings and introduces TRTEngineOp operator that wraps a subgraph in TensorRT. This module is under active development.

Installing TF-TRT

Currently Tensorflow nightly builds include TF-TRT by default, which means you don't need to install TF-TRT separately. You can pull the latest TF containers from docker hub or install the latest TF pip package to get access to the latest TF-TRT.

If you want to use TF-TRT on NVIDIA Jetson platform, you can find the download links for the relevant Tensorflow pip packages here: https://docs.nvidia.com/deeplearning/dgx/index.html#installing-frameworks-for-jetson

Installing TensorRT

In order to make use of TF-TRT, you will need a local installation of TensorRT from the NVIDIA Developer website. Installation instructions for compatibility with TensorFlow are provided on the TensorFlow GPU support guide.

Documentation

TF-TRT documentaion gives an overview of the supported functionalities, provides tutorials and verified models, explains best practices with troubleshooting guides.

Tests

TF-TRT includes both Python tests and C++ unit tests. Most of Python tests are located in the test directory and they can be executed uring bazel test or directly with the Python command. Most of the C++ unit tests are used to test the conversion functions that convert each TF op to a number of TensorRT layers.

Compilation

In order to compile the module, you need to have a local TensorRT installation (libnvinfer.so and respective include files). During the configuration step, TensorRT should be enabled and installation path should be set. If installed through package managers (deb,rpm), configure script should find the necessary components from the system automatically. If installed from tar packages, user has to set path to location where the library is installed during configuration.

bazel build --config=cuda --config=opt //tensorflow/tools/pip_package:build_pip_package
bazel-bin/tensorflow/tools/pip_package/build_pip_package /tmp/

License

Apache License 2.0

About

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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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Repository files navigation
Documentation for TensorRT in TensorFlow (TF-TRT)
The documentaion on how to accelerate inference in TensorFlow with TensorRT (TF-TRT) is here: https://docs.nvidia.com/deeplearning/dgx/tf-trt-user-guide/index.html
Examples for TensorRT in TensorFlow (TF-TRT)
This repository contains a number of different examples
that show how to use
TF-TRT.
TF-TRT is a part of TensorFlow
that optimizes TensorFlow graphs using
TensorRT.
We have used these examples to verify the accuracy and
performance of TF-TRT. For more information see
Verified Models.
Examples
Using TensorRT in TensorFlow (TF-TRT)
This module provides necessary bindings and introduces
TRTEngineOp operator that wraps a subgraph in TensorRT.
This module is under active development.
Installing TF-TRT
Currently Tensorflow nightly builds include TF-TRT by default,
which means you don't need to install TF-TRT separately.
You can pull the latest TF containers from docker hub or
install the latest TF pip package to get access to the latest TF-TRT.
If you want to use TF-TRT on NVIDIA Jetson platform, you can find
the download links for the relevant Tensorflow pip packages here:
https://docs.nvidia.com/deeplearning/dgx/index.html#installing-frameworks-for-jetson
Installing TensorRT
In order to make use of TF-TRT, you will need a local installation
of TensorRT from the
NVIDIA Developer website.
Installation instructions for compatibility with TensorFlow are provided on the
TensorFlow GPU support guide.
Documentation
TF-TRT documentaion
gives an overview of the supported functionalities, provides tutorials
and verified models, explains best practices with troubleshooting guides.
Tests
TF-TRT includes both Python tests and C++ unit tests.
Most of Python tests are located in the test directory
and they can be executed uring bazel test or directly
with the Python command. Most of the C++ unit tests are
used to test the conversion functions that convert each TF op to
a number of TensorRT layers.
Compilation
In order to compile the module, you need to have a local TensorRT installation
(libnvinfer.so and respective include files). During the configuration step,
TensorRT should be enabled and installation path should be set. If installed
through package managers (deb,rpm), configure script should find the necessary
components from the system automatically. If installed from tar packages, user
has to set path to location where the library is installed during configuration.
bazel build --config=cuda --config=opt //tensorflow/tools/pip_package:build_pip_package
bazel-bin/tensorflow/tools/pip_package/build_pip_package /tmp/
License
Apache License 2.0
About
TensorFlow/TensorRT integration
Resources
Readme
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0 forks
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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('^' + ".*" + '

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Repository files navigation
Documentation for TensorRT in TensorFlow (TF-TRT)
The documentaion on how to accelerate inference in TensorFlow with TensorRT (TF-TRT) is here: https://docs.nvidia.com/deeplearning/dgx/tf-trt-user-guide/index.html
Examples for TensorRT in TensorFlow (TF-TRT)
This repository contains a number of different examples
that show how to use
TF-TRT.
TF-TRT is a part of TensorFlow
that optimizes TensorFlow graphs using
TensorRT.
We have used these examples to verify the accuracy and
performance of TF-TRT. For more information see
Verified Models.
Examples
Using TensorRT in TensorFlow (TF-TRT)
This module provides necessary bindings and introduces
TRTEngineOp operator that wraps a subgraph in TensorRT.
This module is under active development.
Installing TF-TRT
Currently Tensorflow nightly builds include TF-TRT by default,
which means you don't need to install TF-TRT separately.
You can pull the latest TF containers from docker hub or
install the latest TF pip package to get access to the latest TF-TRT.
If you want to use TF-TRT on NVIDIA Jetson platform, you can find
the download links for the relevant Tensorflow pip packages here:
https://docs.nvidia.com/deeplearning/dgx/index.html#installing-frameworks-for-jetson
Installing TensorRT
In order to make use of TF-TRT, you will need a local installation
of TensorRT from the
NVIDIA Developer website.
Installation instructions for compatibility with TensorFlow are provided on the
TensorFlow GPU support guide.
Documentation
TF-TRT documentaion
gives an overview of the supported functionalities, provides tutorials
and verified models, explains best practices with troubleshooting guides.
Tests
TF-TRT includes both Python tests and C++ unit tests.
Most of Python tests are located in the test directory
and they can be executed uring bazel test or directly
with the Python command. Most of the C++ unit tests are
used to test the conversion functions that convert each TF op to
a number of TensorRT layers.
Compilation
In order to compile the module, you need to have a local TensorRT installation
(libnvinfer.so and respective include files). During the configuration step,
TensorRT should be enabled and installation path should be set. If installed
through package managers (deb,rpm), configure script should find the necessary
components from the system automatically. If installed from tar packages, user
has to set path to location where the library is installed during configuration.
bazel build --config=cuda --config=opt //tensorflow/tools/pip_package:build_pip_package
bazel-bin/tensorflow/tools/pip_package/build_pip_package /tmp/
License
Apache License 2.0
About
TensorFlow/TensorRT integration
Resources
Readme
Apache-2.0 license
Activity
Stars
0 stars
Watchers
0 watching
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0 forks
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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('^' + ".*" + '

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Repository files navigation
Documentation for TensorRT in TensorFlow (TF-TRT)
The documentaion on how to accelerate inference in TensorFlow with TensorRT (TF-TRT) is here: https://docs.nvidia.com/deeplearning/dgx/tf-trt-user-guide/index.html
Examples for TensorRT in TensorFlow (TF-TRT)
This repository contains a number of different examples
that show how to use
TF-TRT.
TF-TRT is a part of TensorFlow
that optimizes TensorFlow graphs using
TensorRT.
We have used these examples to verify the accuracy and
performance of TF-TRT. For more information see
Verified Models.
Examples
Using TensorRT in TensorFlow (TF-TRT)
This module provides necessary bindings and introduces
TRTEngineOp operator that wraps a subgraph in TensorRT.
This module is under active development.
Installing TF-TRT
Currently Tensorflow nightly builds include TF-TRT by default,
which means you don't need to install TF-TRT separately.
You can pull the latest TF containers from docker hub or
install the latest TF pip package to get access to the latest TF-TRT.
If you want to use TF-TRT on NVIDIA Jetson platform, you can find
the download links for the relevant Tensorflow pip packages here:
https://docs.nvidia.com/deeplearning/dgx/index.html#installing-frameworks-for-jetson
Installing TensorRT
In order to make use of TF-TRT, you will need a local installation
of TensorRT from the
NVIDIA Developer website.
Installation instructions for compatibility with TensorFlow are provided on the
TensorFlow GPU support guide.
Documentation
TF-TRT documentaion
gives an overview of the supported functionalities, provides tutorials
and verified models, explains best practices with troubleshooting guides.
Tests
TF-TRT includes both Python tests and C++ unit tests.
Most of Python tests are located in the test directory
and they can be executed uring bazel test or directly
with the Python command. Most of the C++ unit tests are
used to test the conversion functions that convert each TF op to
a number of TensorRT layers.
Compilation
In order to compile the module, you need to have a local TensorRT installation
(libnvinfer.so and respective include files). During the configuration step,
TensorRT should be enabled and installation path should be set. If installed
through package managers (deb,rpm), configure script should find the necessary
components from the system automatically. If installed from tar packages, user
has to set path to location where the library is installed during configuration.
bazel build --config=cuda --config=opt //tensorflow/tools/pip_package:build_pip_package
bazel-bin/tensorflow/tools/pip_package/build_pip_package /tmp/
License
Apache License 2.0
About
TensorFlow/TensorRT integration
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Readme
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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" + '

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Repository files navigation
Documentation for TensorRT in TensorFlow (TF-TRT)
The documentaion on how to accelerate inference in TensorFlow with TensorRT (TF-TRT) is here: https://docs.nvidia.com/deeplearning/dgx/tf-trt-user-guide/index.html
Examples for TensorRT in TensorFlow (TF-TRT)
This repository contains a number of different examples
that show how to use
TF-TRT.
TF-TRT is a part of TensorFlow
that optimizes TensorFlow graphs using
TensorRT.
We have used these examples to verify the accuracy and
performance of TF-TRT. For more information see
Verified Models.
Examples
Using TensorRT in TensorFlow (TF-TRT)
This module provides necessary bindings and introduces
TRTEngineOp operator that wraps a subgraph in TensorRT.
This module is under active development.
Installing TF-TRT
Currently Tensorflow nightly builds include TF-TRT by default,
which means you don't need to install TF-TRT separately.
You can pull the latest TF containers from docker hub or
install the latest TF pip package to get access to the latest TF-TRT.
If you want to use TF-TRT on NVIDIA Jetson platform, you can find
the download links for the relevant Tensorflow pip packages here:
https://docs.nvidia.com/deeplearning/dgx/index.html#installing-frameworks-for-jetson
Installing TensorRT
In order to make use of TF-TRT, you will need a local installation
of TensorRT from the
NVIDIA Developer website.
Installation instructions for compatibility with TensorFlow are provided on the
TensorFlow GPU support guide.
Documentation
TF-TRT documentaion
gives an overview of the supported functionalities, provides tutorials
and verified models, explains best practices with troubleshooting guides.
Tests
TF-TRT includes both Python tests and C++ unit tests.
Most of Python tests are located in the test directory
and they can be executed uring bazel test or directly
with the Python command. Most of the C++ unit tests are
used to test the conversion functions that convert each TF op to
a number of TensorRT layers.
Compilation
In order to compile the module, you need to have a local TensorRT installation
(libnvinfer.so and respective include files). During the configuration step,
TensorRT should be enabled and installation path should be set. If installed
through package managers (deb,rpm), configure script should find the necessary
components from the system automatically. If installed from tar packages, user
has to set path to location where the library is installed during configuration.
bazel build --config=cuda --config=opt //tensorflow/tools/pip_package:build_pip_package
bazel-bin/tensorflow/tools/pip_package/build_pip_package /tmp/
License
Apache License 2.0
About
TensorFlow/TensorRT integration
Resources
Readme
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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('^' + ".*" + '

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tftrt
tftrt
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README.md
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Repository files navigation
Documentation for TensorRT in TensorFlow (TF-TRT)
The documentaion on how to accelerate inference in TensorFlow with TensorRT (TF-TRT) is here: https://docs.nvidia.com/deeplearning/dgx/tf-trt-user-guide/index.html
Examples for TensorRT in TensorFlow (TF-TRT)
This repository contains a number of different examples
that show how to use
TF-TRT.
TF-TRT is a part of TensorFlow
that optimizes TensorFlow graphs using
TensorRT.
We have used these examples to verify the accuracy and
performance of TF-TRT. For more information see
Verified Models.
Examples
Using TensorRT in TensorFlow (TF-TRT)
This module provides necessary bindings and introduces
TRTEngineOp operator that wraps a subgraph in TensorRT.
This module is under active development.
Installing TF-TRT
Currently Tensorflow nightly builds include TF-TRT by default,
which means you don't need to install TF-TRT separately.
You can pull the latest TF containers from docker hub or
install the latest TF pip package to get access to the latest TF-TRT.
If you want to use TF-TRT on NVIDIA Jetson platform, you can find
the download links for the relevant Tensorflow pip packages here:
https://docs.nvidia.com/deeplearning/dgx/index.html#installing-frameworks-for-jetson
Installing TensorRT
In order to make use of TF-TRT, you will need a local installation
of TensorRT from the
NVIDIA Developer website.
Installation instructions for compatibility with TensorFlow are provided on the
TensorFlow GPU support guide.
Documentation
TF-TRT documentaion
gives an overview of the supported functionalities, provides tutorials
and verified models, explains best practices with troubleshooting guides.
Tests
TF-TRT includes both Python tests and C++ unit tests.
Most of Python tests are located in the test directory
and they can be executed uring bazel test or directly
with the Python command. Most of the C++ unit tests are
used to test the conversion functions that convert each TF op to
a number of TensorRT layers.
Compilation
In order to compile the module, you need to have a local TensorRT installation
(libnvinfer.so and respective include files). During the configuration step,
TensorRT should be enabled and installation path should be set. If installed
through package managers (deb,rpm), configure script should find the necessary
components from the system automatically. If installed from tar packages, user
has to set path to location where the library is installed during configuration.
bazel build --config=cuda --config=opt //tensorflow/tools/pip_package:build_pip_package
bazel-bin/tensorflow/tools/pip_package/build_pip_package /tmp/
License
Apache License 2.0
About
TensorFlow/TensorRT integration
Resources
Readme
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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('^' + ".*" + '

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Repository files navigation
Documentation for TensorRT in TensorFlow (TF-TRT)
The documentaion on how to accelerate inference in TensorFlow with TensorRT (TF-TRT) is here: https://docs.nvidia.com/deeplearning/dgx/tf-trt-user-guide/index.html
Examples for TensorRT in TensorFlow (TF-TRT)
This repository contains a number of different examples
that show how to use
TF-TRT.
TF-TRT is a part of TensorFlow
that optimizes TensorFlow graphs using
TensorRT.
We have used these examples to verify the accuracy and
performance of TF-TRT. For more information see
Verified Models.
Examples
Using TensorRT in TensorFlow (TF-TRT)
This module provides necessary bindings and introduces
TRTEngineOp operator that wraps a subgraph in TensorRT.
This module is under active development.
Installing TF-TRT
Currently Tensorflow nightly builds include TF-TRT by default,
which means you don't need to install TF-TRT separately.
You can pull the latest TF containers from docker hub or
install the latest TF pip package to get access to the latest TF-TRT.
If you want to use TF-TRT on NVIDIA Jetson platform, you can find
the download links for the relevant Tensorflow pip packages here:
https://docs.nvidia.com/deeplearning/dgx/index.html#installing-frameworks-for-jetson
Installing TensorRT
In order to make use of TF-TRT, you will need a local installation
of TensorRT from the
NVIDIA Developer website.
Installation instructions for compatibility with TensorFlow are provided on the
TensorFlow GPU support guide.
Documentation
TF-TRT documentaion
gives an overview of the supported functionalities, provides tutorials
and verified models, explains best practices with troubleshooting guides.
Tests
TF-TRT includes both Python tests and C++ unit tests.
Most of Python tests are located in the test directory
and they can be executed uring bazel test or directly
with the Python command. Most of the C++ unit tests are
used to test the conversion functions that convert each TF op to
a number of TensorRT layers.
Compilation
In order to compile the module, you need to have a local TensorRT installation
(libnvinfer.so and respective include files). During the configuration step,
TensorRT should be enabled and installation path should be set. If installed
through package managers (deb,rpm), configure script should find the necessary
components from the system automatically. If installed from tar packages, user
has to set path to location where the library is installed during configuration.
bazel build --config=cuda --config=opt //tensorflow/tools/pip_package:build_pip_package
bazel-bin/tensorflow/tools/pip_package/build_pip_package /tmp/
License
Apache License 2.0
About
TensorFlow/TensorRT integration
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Documentation for TensorRT in TensorFlow (TF-TRT)
The documentaion on how to accelerate inference in TensorFlow with TensorRT (TF-TRT) is here: https://docs.nvidia.com/deeplearning/dgx/tf-trt-user-guide/index.html
Examples for TensorRT in TensorFlow (TF-TRT)
This repository contains a number of different examples
that show how to use
TF-TRT.
TF-TRT is a part of TensorFlow
that optimizes TensorFlow graphs using
TensorRT.
We have used these examples to verify the accuracy and
performance of TF-TRT. For more information see
Verified Models.
Examples
Using TensorRT in TensorFlow (TF-TRT)
This module provides necessary bindings and introduces
TRTEngineOp operator that wraps a subgraph in TensorRT.
This module is under active development.
Installing TF-TRT
Currently Tensorflow nightly builds include TF-TRT by default,
which means you don't need to install TF-TRT separately.
You can pull the latest TF containers from docker hub or
install the latest TF pip package to get access to the latest TF-TRT.
If you want to use TF-TRT on NVIDIA Jetson platform, you can find
the download links for the relevant Tensorflow pip packages here:
https://docs.nvidia.com/deeplearning/dgx/index.html#installing-frameworks-for-jetson
Installing TensorRT
In order to make use of TF-TRT, you will need a local installation
of TensorRT from the
NVIDIA Developer website.
Installation instructions for compatibility with TensorFlow are provided on the
TensorFlow GPU support guide.
Documentation
TF-TRT documentaion
gives an overview of the supported functionalities, provides tutorials
and verified models, explains best practices with troubleshooting guides.
Tests
TF-TRT includes both Python tests and C++ unit tests.
Most of Python tests are located in the test directory
and they can be executed uring bazel test or directly
with the Python command. Most of the C++ unit tests are
used to test the conversion functions that convert each TF op to
a number of TensorRT layers.
Compilation
In order to compile the module, you need to have a local TensorRT installation
(libnvinfer.so and respective include files). During the configuration step,
TensorRT should be enabled and installation path should be set. If installed
through package managers (deb,rpm), configure script should find the necessary
components from the system automatically. If installed from tar packages, user
has to set path to location where the library is installed during configuration.
bazel build --config=cuda --config=opt //tensorflow/tools/pip_package:build_pip_package
bazel-bin/tensorflow/tools/pip_package/build_pip_package /tmp/
License
Apache License 2.0
About
TensorFlow/TensorRT integration
Resources
Readme
Apache-2.0 license
Activity
Stars
0 stars
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages