{"id":8578,"date":"2021-01-26T16:25:16","date_gmt":"2021-01-26T21:25:16","guid":{"rendered":"https:\/\/carleton.ca\/scs\/?page_id=8578"},"modified":"2026-06-02T14:59:28","modified_gmt":"2026-06-02T18:59:28","slug":"scs-tensorflow-gpu-vm-2021","status":"publish","type":"page","link":"https:\/\/carleton.ca\/scs\/tech-support\/scs-tensorflow-gpu-vm-2021\/","title":{"rendered":"DEPRECATED &#8211; SCS Tensorflow GPU VM 2021"},"content":{"rendered":"\n<section class=\"w-screen px-6 cu-section cu-section--white ml-offset-center md:px-8 lg:px-14\">\n    <div class=\"space-y-6 cu-max-w-child-5xl  md:space-y-10 cu-prose-first-last\">\n\n            <div class=\"cu-textmedia flex flex-col lg:flex-row mx-auto gap-6 md:gap-10 my-6 md:my-12 first:mt-0 max-w-5xl\">\n        <div class=\"justify-start cu-textmedia-content cu-prose-first-last\" style=\"flex: 0 0 100%;\">\n            <header class=\"font-light prose-xl cu-pageheader md:prose-2xl cu-component-updated cu-prose-first-last\">\n                                    <h1 class=\"cu-prose-first-last font-semibold !mt-2 mb-4 md:mb-6 relative after:absolute after:h-px after:bottom-0 after:bg-cu-red after:left-px text-3xl md:text-4xl lg:text-5xl lg:leading-[3.5rem] pb-5 after:w-10 text-cu-black-700 not-prose\">\n                        DEPRECATED &#8211; SCS Tensorflow GPU VM 2021\n                    <\/h1>\n                \n                                \n                            <\/header>\n\n                    <\/div>\n\n            <\/div>\n\n    <\/div>\n<\/section>\n\n<p><a href=\"#account\">Account<\/a><br>\n<a href=\"#access\">Access<\/a><br>\n<a href=\"#software\">Software<\/a><br>\n<a href=\"#cuda\">CUDA Toolkit<\/a><br>\n<a href=\"#cudnn\">cuDNN<\/a><br>\n<a href=\"#tensorflow\">Tensorflow<\/a><br>\n<a href=\"#keras\">Keras<\/a><br>\n<a href=\"#ai\">ai benchmark<\/a><\/p>\n\n\n\n<p>&nbsp;<\/p>\n\n\n<div class=\"not-prose cu-quote cu-component-spacing\">\n<blockquote class=\"wp-block-quote is-layout-flow wp-block-quote-is-layout-flow\">\n<p>This page will show you how to test your VM&#8217;s GPU and access and test the software that is pre-installed on the scs-gpu-TENSORFLOW-2021 virtual machine.<\/p>\n<\/blockquote>\n<\/div>\n\n\n<p>&nbsp;<\/p>\n\n\n\n<h3 id=\"vm-image-information\" class=\"wp-block-heading\">VM Image Information<\/h3>\n\n\n\n<p><strong>Image Name<\/strong>: scs-gpu-TENSORFLOW-2021<br>\n<strong>Creation Date<\/strong>: January 26, 2021<br>\n<strong>Operating System<\/strong>: Ubuntu 20.04<br>\n<strong>Window Manager<\/strong>: XFCE<br>\n<strong>Intended usage<\/strong>: This Openstack GPU virtual machine comes pre-installed with tensorflow install for python3 configured for this GPU.<\/p>\n\n\n\n<p>&nbsp;<\/p>\n\n\n\n<h3 id=\"account\" class=\"wp-block-heading\"><a id=\"account\"><\/a>Account<\/h3>\n\n\n\n<p><em>Username:Password<\/em><\/p>\n\n\n\n<p>student:student<\/p>\n\n\n<div class=\"not-prose cu-quote cu-component-spacing\">\n<blockquote class=\"wp-block-quote is-layout-flow wp-block-quote-is-layout-flow\">\n<p>Please change your password as soon as the VM is provisioned for you. This can be done by logging into your VM and then opening a terminal window and typing &#8216;passwd&#8217;.<\/p>\n<\/blockquote>\n<\/div>\n\n\n<p>&nbsp;<\/p>\n\n\n\n<h3 id=\"access\" class=\"wp-block-heading\"><a id=\"access\"><\/a>Access<\/h3>\n\n\n\n<p>From outside of Carleton you will need to VPN to Carleton in order to access the VM<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>VPN<\/strong>: <a href=\"https:\/\/carleton.ca\/its\/help-centre\/remote-access\/\" target=\"_blank\" rel=\"noopener noreferrer\">Carleton VPN<\/a> when connecting from outside of the campus<\/li>\n\n\n\n<li><strong>x2go<\/strong>: download <a href=\"https:\/\/wiki.x2go.org\/doku.php\/download:start\" target=\"_blank\" rel=\"noopener noreferrer\">x2go client<\/a> to get a\u00a0 full graphical desktop (use <strong>Session Type:<\/strong> XFCE)<\/li>\n\n\n\n<li><strong>ssh<\/strong>: use a terminal window to gain ssh access<\/li>\n\n\n\n<li><strong>console<\/strong>: <a href=\"https:\/\/openstack.scs.carleton.ca\" target=\"_blank\" rel=\"noopener noreferrer\">SCS Open Stack horizon<\/a> web interface (not recommended)<\/li>\n<\/ul>\n\n\n<div class=\"not-prose cu-quote cu-component-spacing\">\n<blockquote class=\"wp-block-quote is-layout-flow wp-block-quote-is-layout-flow\">\n<p>Recommended usage is to VPN to Carleton and then use the x2go client to connect to your VM<\/p>\n<\/blockquote>\n<\/div>\n\n\n<p>&nbsp;<\/p>\n\n\n\n<h3 id=\"software\" class=\"wp-block-heading\"><a id=\"software\"><\/a>Software<\/h3>\n\n\n\n<div class=\"table-wrapper\">\n<table style=\"width: 30.9438%; height: 203px;\" border=\"1\">\n<tbody>\n<tr>\n<td style=\"width: 51.25%;\"><strong>Software<\/strong><\/td>\n<td style=\"width: 46.5625%;\"><strong>Version<\/strong><\/td>\n<\/tr>\n<tr>\n<td style=\"width: 51.25%;\">NVIDIA Driver<\/td>\n<td style=\"width: 46.5625%;\">450.66<\/td>\n<\/tr>\n<tr>\n<td style=\"width: 51.25%;\">CUDA Toolkit<\/td>\n<td style=\"width: 46.5625%;\">10.1<\/td>\n<\/tr>\n<tr>\n<td style=\"width: 51.25%;\">CUDA Driver<\/td>\n<td style=\"width: 46.5625%;\">11<\/td>\n<\/tr>\n<tr>\n<td style=\"width: 51.25%;\">CUDA Runtime<\/td>\n<td style=\"width: 46.5625%;\">10.1<\/td>\n<\/tr>\n<tr>\n<td style=\"width: 51.25%;\">cuDNN<\/td>\n<td style=\"width: 46.5625%;\">8.0.2.39<\/td>\n<\/tr>\n<tr>\n<td style=\"width: 51.25%;\">NCCL<\/td>\n<td style=\"width: 46.5625%;\">2.7.8<\/td>\n<\/tr>\n<tr>\n<td style=\"width: 51.25%;\">Tensorflow<\/td>\n<td style=\"width: 46.5625%;\">2.3.0<\/td>\n<\/tr>\n<tr>\n<td style=\"width: 51.25%;\">Keras<\/td>\n<td style=\"width: 46.5625%;\">2.4.3<\/td>\n<\/tr>\n<tr>\n<td style=\"width: 51.25%;\">Pandas<\/td>\n<td style=\"width: 46.5625%;\">0.25.3<\/td>\n<\/tr>\n<tr>\n<td style=\"width: 51.25%;\">Numpy<\/td>\n<td style=\"width: 46.5625%;\">1.18.5<\/td>\n<\/tr>\n<tr>\n<td style=\"width: 51.25%;\">Anaconda<\/td>\n<td style=\"width: 46.5625%;\">2020.11<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n\n\n\n<h3 id=\"cuda-toolkit-10-1\" class=\"wp-block-heading\"><a id=\"cuda\"><\/a>CUDA Toolkit 10.1<\/h3>\n\n\n\n<p>The CUDA toolkit is installed with samples.<\/p>\n\n\n<div class=\"not-prose cu-quote cu-component-spacing\">\n<blockquote class=\"wp-block-quote is-layout-flow wp-block-quote-is-layout-flow\">\n<p>Samples are installed here \/home\/student\/NVIDIA_CUDA-10.1_Samples as well as in \/usr\/local\/cuda-10.1\/samples<\/p>\n<\/blockquote>\n<\/div>\n\n\n<p>Run this sample to test CUDA and probe your GPU:<\/p>\n\n\n\n<p><code><br>\nstudent@scs-tensorflow$ \/usr\/local\/cuda-10.1\/samples\/1_Utilities\/deviceQuery\/deviceQuery<\/code><\/p>\n\n\n\n<p><code>CUDA Device Query (Runtime API) version (CUDART static linking)<\/code><\/p>\n\n\n\n<p><code>Detected 1 CUDA Capable device(s)<\/code><\/p>\n\n\n\n<p><code>Device 0: \"TITAN V\"<br>\nCUDA Driver Version \/ Runtime Version 11.0 \/ 10.1<br>\nCUDA Capability Major\/Minor version number: 7.0<br>\nTotal amount of global memory: 12067 MBytes (12652838912 bytes)<br>\n(80) Multiprocessors, ( 64) CUDA Cores\/MP: 5120 CUDA Cores<br>\nGPU Max Clock rate: 1455 MHz (1.46 GHz)<br>\nMemory Clock rate: 850 Mhz<br>\nMemory Bus Width: 3072-bit<br>\nL2 Cache Size: 4718592 bytes<br>\nMaximum Texture Dimension Size (x,y,z) 1D=(131072), 2D=(131072, 65536), 3D=(16384, 16384, 16384)<br>\nMaximum Layered 1D Texture Size, (num) layers 1D=(32768), 2048 layers<br>\nMaximum Layered 2D Texture Size, (num) layers 2D=(32768, 32768), 2048 layers<br>\nTotal amount of constant memory: 65536 bytes<br>\nTotal amount of shared memory per block: 49152 bytes<br>\nTotal number of registers available per block: 65536<br>\nWarp size: 32<br>\nMaximum number of threads per multiprocessor: 2048<br>\nMaximum number of threads per block: 1024<br>\nMax dimension size of a thread block (x,y,z): (1024, 1024, 64)<br>\nMax dimension size of a grid size (x,y,z): (2147483647, 65535, 65535)<br>\nMaximum memory pitch: 2147483647 bytes<br>\nTexture alignment: 512 bytes<br>\nConcurrent copy and kernel execution: Yes with 7 copy engine(s)<br>\nRun time limit on kernels: Yes<br>\nIntegrated GPU sharing Host Memory: No<br>\nSupport host page-locked memory mapping: Yes<br>\nAlignment requirement for Surfaces: Yes<br>\nDevice has ECC support: Disabled<br>\nDevice supports Unified Addressing (UVA): Yes<br>\nDevice supports Compute Preemption: Yes<br>\nSupports Cooperative Kernel Launch: Yes<br>\nSupports MultiDevice Co-op Kernel Launch: Yes<br>\nDevice PCI Domain ID \/ Bus ID \/ location ID: 0 \/ 0 \/ 5<br>\nCompute Mode:<br>\n&lt; Default (multiple host threads can use ::cudaSetDevice() with device simultaneously) &gt;<\/code><\/p>\n\n\n\n<p>deviceQuery, CUDA Driver = CUDART, CUDA Driver Version = 11.0, CUDA Runtime Version = 10.1, NumDevs = 1<br>\nResult = PASS<\/p>\n\n\n<div class=\"not-prose cu-quote cu-component-spacing\">\n<blockquote class=\"wp-block-quote is-layout-flow wp-block-quote-is-layout-flow\">\n<p>You can run nvcc -V to check your CUDA version. clinfo also gives you a lot of info about your GPU and&nbsp; NVIDIA software installed.<\/p>\n<\/blockquote>\n<\/div>\n\n\n<p>&nbsp;<\/p>\n\n\n\n<h3 id=\"cudnn\" class=\"wp-block-heading\"><a id=\"cudnn\"><\/a>cuDNN<\/h3>\n\n\n\n<p>You can test the cuDNN package by running the sample code here:<\/p>\n\n\n\n<p><code>\/usr\/src\/cudnn_samples_v8\/conv_sample<\/code><\/p>\n\n\n\n<p><strong>Note<\/strong>: You need to make and run the sample code<\/p>\n\n\n\n<h3 id=\"tensorflow\" class=\"wp-block-heading\"><a id=\"tensorflow\"><\/a>Tensorflow<\/h3>\n\n\n\n<p>Tensorflow was installed using pip3 on python3 using the <strong>venv<\/strong> environment. You can check the tensforflow version as follows:<\/p>\n\n\n\n<p><code><br>\nstudent@scs-gpu-tensorflow:~$ source .\/venv\/bin\/activate<br>\n(venv) student@scs-gpu-tensorflow:~$ pip3 list | grep tensorflow<br>\ntensorflow 2.3.0<br>\ntensorflow-estimator 2.3.0<br>\n(venv) student@scs-gpu-tensorflow:~$ python -c 'import tensorflow as tf; print(tf.__version__)'<br>\n2020-09-14 15:44:10.391460: I tensorflow\/stream_executor\/platform\/default\/dso_loader.cc:48] Successfully opened dynamic library libcudart.so.10.1<br>\n2.3.0<br>\n(venv) student@scs-gpu-tensorflow:~$ deactivate<br>\nstudent@scs-gpu-tensorflow:~$<br>\n<\/code><\/p>\n\n\n\n<p>&nbsp;<\/p>\n\n\n\n<h3 id=\"keras\" class=\"wp-block-heading\"><a id=\"keras\"><\/a>Keras<\/h3>\n\n\n\n<p>Keras is a python deep learning API. Keras was installed using pip3 on python3 using the venv environment.<\/p>\n\n\n\n<p>As a requirement pyyaml, h5py, numpy, and scipy were installed.<\/p>\n\n\n\n<p>You can check the Keras version as follows:<\/p>\n\n\n\n<p><code>student@scs-gpu-tensorflow:~$ source .\/venv\/bin\/activate<br>\n(venv) student@scs-gpu-tensorflow:~$ pip3 show keras<br>\nName: Keras<br>\nVersion: 2.4.3<br>\nSummary: Deep Learning for humans<br>\n(venv) student@scs-gpu-tensorflow:~$ deactivate<br>\nstudent@scs-gpu-tensorflow:~$<\/code><\/p>\n\n\n\n<p>&nbsp;<\/p>\n\n\n\n<p><strong><a id=\"ai\"><\/a>ai-benchmark<\/strong><\/p>\n\n\n\n<p>The AI benchmark for Linux is installed. You can run the benchmark and then probe the GPU to see if the job is running. So it&#8217;s a two step procedure:<\/p>\n\n\n\n<ol class=\"wp-block-list\">\n<li>run the benchmark (ai-benchmark)<\/li>\n\n\n\n<li>check the GPU for jobs (nvidia-smi -l)<\/li>\n<\/ol>\n\n\n\n<p>After the benchmarking software completes it will list a score. The score can be looked up on their website<\/p>\n\n\n\n<p><a href=\"http:\/\/ai-benchmark.com\/ranking_deeplearning.html\" target=\"_blank\" rel=\"noopener noreferrer\">http:\/\/ai-benchmark.com\/ranking_deeplearning.html<\/a><\/p>\n\n\n\n<p>and you can verify that your number is similar to their benchmark test for your GPU.<\/p>\n\n\n\n<p>Run the following python script to run your benchmark (takes about 20 minutes to complete):<\/p>\n\n\n\n<p><code>from ai_benchmark import AIBenchmark<br>\nbenchmark = AIBenchmark()<br>\nresults = benchmark.run()<br>\n<\/code><\/p>\n\n\n\n<p>&nbsp;<\/p>\n\n\n\n<p>Test run looks something like:<\/p>\n\n\n\n<p>(venv) student@scs-tensorflow:~$ python<br>\nPython 3.8.2 (default, Jul 16 2020, 14:00:26)<br>\n[GCC 9.3.0] on linux<br>\nType &#8220;help&#8221;, &#8220;copyright&#8221;, &#8220;credits&#8221; or &#8220;license&#8221; for more information.<br>\n&gt;&gt;&gt; from ai_benchmark import AIBenchmark<br>\n2020-09-16 16:00:15.485714: I tensorflow\/stream_executor\/platform\/default\/dso_loader.cc:48] Successfully opened dynamic library libcudart.so.10.1<br>\n&gt;&gt;&gt; benchmark = AIBenchmark()&gt;&gt; AI-Benchmark-v.0.1.2<br>\n&gt;&gt; Let the AI Games begin..&gt;&gt;&gt; results = benchmark.run()<\/p>\n\n\n\n<p>&#8230;&#8230;..<\/p>\n\n\n\n<p>Device Inference Score: 14198<br>\nDevice Training Score: 14056<br>\nDevice AI Score: 28254<br>\nFor more information and results, please visit http:\/\/ai-benchmark.com\/alpha<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Account Access Software CUDA Toolkit cuDNN Tensorflow Keras ai benchmark &nbsp; This page will show you how to test your VM&#8217;s GPU and access and test the software that is pre-installed on the scs-gpu-TENSORFLOW-2021 virtual machine. &nbsp; VM Image Information Image Name: scs-gpu-TENSORFLOW-2021 Creation Date: January 26, 2021 Operating System: Ubuntu 20.04 Window Manager: XFCE [&hellip;]<\/p>\n","protected":false},"author":2,"featured_media":0,"parent":6535,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_acf_changed":false,"_cu_dining_location_slug":"","footnotes":"","_links_to":"","_links_to_target":""},"cu_page_type":[],"class_list":["post-8578","page","type-page","status-publish","hentry"],"acf":{"cu_post_thumbnail":false},"_links":{"self":[{"href":"https:\/\/carleton.ca\/scs\/wp-json\/wp\/v2\/pages\/8578","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/carleton.ca\/scs\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/carleton.ca\/scs\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/carleton.ca\/scs\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/carleton.ca\/scs\/wp-json\/wp\/v2\/comments?post=8578"}],"version-history":[{"count":3,"href":"https:\/\/carleton.ca\/scs\/wp-json\/wp\/v2\/pages\/8578\/revisions"}],"predecessor-version":[{"id":16706,"href":"https:\/\/carleton.ca\/scs\/wp-json\/wp\/v2\/pages\/8578\/revisions\/16706"}],"up":[{"embeddable":true,"href":"https:\/\/carleton.ca\/scs\/wp-json\/wp\/v2\/pages\/6535"}],"wp:attachment":[{"href":"https:\/\/carleton.ca\/scs\/wp-json\/wp\/v2\/media?parent=8578"}],"wp:term":[{"taxonomy":"cu_page_type","embeddable":true,"href":"https:\/\/carleton.ca\/scs\/wp-json\/wp\/v2\/cu_page_type?post=8578"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}