{"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":"2023-04-20T14:24:52","modified_gmt":"2023-04-20T18:24:52","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":"<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<p><\/p>\n<blockquote><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><\/blockquote>\n<p><\/p>\n<h3>VM Image Information<\/h3>\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<p><\/p>\n<h3><a id=\"account\"><\/a>Account<\/h3>\n<p><em>Username:Password<\/em><\/p>\n<p>student:student<\/p>\n<blockquote><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><\/blockquote>\n<p><\/p>\n<h3><a id=\"access\"><\/a>Access<\/h3>\n<p>From outside of Carleton you will need to VPN to Carleton in order to access the VM<\/p>\n<ul>\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<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<li><strong>ssh<\/strong>: use a terminal window to gain ssh access<\/li>\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<blockquote><p>Recommended usage is to VPN to Carleton and then use the x2go client to connect to your VM<\/p><\/blockquote>\n<p><\/p>\n<h3><a id=\"software\"><\/a>Software<\/h3>\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<h3><a id=\"cuda\"><\/a>CUDA Toolkit 10.1<\/h3>\n<p>The CUDA toolkit is installed with samples.<\/p>\n<blockquote><p>Samples are installed here \/home\/student\/NVIDIA_CUDA-10.1_Samples as well as in \/usr\/local\/cuda-10.1\/samples<\/p><\/blockquote>\n<p>Run this sample to test CUDA and probe your GPU:<\/p>\n<p><code><br \/>\nstudent@scs-tensorflow$ \/usr\/local\/cuda-10.1\/samples\/1_Utilities\/deviceQuery\/deviceQuery<\/code><\/p>\n<p><code>CUDA Device Query (Runtime API) version (CUDART static linking)<\/code><\/p>\n<p><code>Detected 1 CUDA Capable device(s)<\/code><\/p>\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<p>deviceQuery, CUDA Driver = CUDART, CUDA Driver Version = 11.0, CUDA Runtime Version = 10.1, NumDevs = 1<br \/>\nResult = PASS<\/p>\n<blockquote><p>You can run nvcc -V to check your CUDA version. clinfo also gives you a lot of info about your GPU and\u00a0 NVIDIA software installed.<\/p><\/blockquote>\n<p><\/p>\n<h3><a id=\"cudnn\"><\/a>cuDNN<\/h3>\n<p>You can test the cuDNN package by running the sample code here:<\/p>\n<p><code>\/usr\/src\/cudnn_samples_v8\/conv_sample<\/code><\/p>\n<p><strong>Note<\/strong>: You need to make and run the sample code<\/p>\n<h3><a id=\"tensorflow\"><\/a>Tensorflow<\/h3>\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<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:~$<br \/>\n<\/code><\/p>\n<p><\/p>\n<h3><a id=\"keras\"><\/a>Keras<\/h3>\n<p>Keras is a python deep learning API. Keras was installed using pip3 on python3 using the venv environment.<\/p>\n<p>As a requirement pyyaml, h5py, numpy, and scipy were installed.<\/p>\n<p>You can check the Keras version as follows:<\/p>\n<p><code>student@scs-gpu-tensorflow:~$ 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:~$<\/code><\/p>\n<p><\/p>\n<p><strong><a id=\"ai\"><\/a>ai-benchmark<\/strong><\/p>\n<p>The AI benchmark for Linux is installed. You can run the benchmark and then probe the GPU to see if the job is running. So it&#8217;s a two step procedure:<\/p>\n<ol>\n<li>run the benchmark (ai-benchmark)<\/li>\n<li>check the GPU for jobs (nvidia-smi -l)<\/li>\n<\/ol>\n<p>After the benchmarking software completes it will list a score. The score can be looked up on their website<\/p>\n<p><a href=\"http:\/\/ai-benchmark.com\/ranking_deeplearning.html\" target=\"_blank\" rel=\"noopener noreferrer\">http:\/\/ai-benchmark.com\/ranking_deeplearning.html<\/a><\/p>\n<p>and you can verify that your number is similar to their benchmark test for your GPU.<\/p>\n<p>Run the following python script to run your benchmark (takes about 20 minutes to complete):<\/p>\n<p><code>from ai_benchmark import AIBenchmark<br \/>\nbenchmark = AIBenchmark()<br \/>\nresults = benchmark.run()<br \/>\n<\/code><\/p>\n<p><\/p>\n<p>Test run looks something like:<\/p>\n<p>(venv) student@scs-tensorflow:~$ 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<p>&#8230;&#8230;..<\/p>\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 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. VM Image Information Image Name: scs-gpu-TENSORFLOW-2021 Creation Date: January 26, 2021 Operating System: Ubuntu 20.04 Window Manager: XFCE Intended usage: [&hellip;]<\/p>\n","protected":false},"author":7,"featured_media":0,"parent":6535,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_relevanssi_hide_post":"","_relevanssi_hide_content":"","_relevanssi_pin_for_all":"","_relevanssi_pin_keywords":"","_relevanssi_unpin_keywords":"","_relevanssi_related_keywords":"","_relevanssi_related_include_ids":"","_relevanssi_related_exclude_ids":"","_relevanssi_related_no_append":"","_relevanssi_related_not_related":"","_relevanssi_related_posts":"","_relevanssi_noindex_reason":"","_mi_skip_tracking":false,"_exactmetrics_sitenote_active":false,"_exactmetrics_sitenote_note":"","_exactmetrics_sitenote_category":0,"footnotes":"","_links_to":"","_links_to_target":""},"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v21.2 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>DEPRECATED - SCS Tensorflow GPU VM 2021 - School of Computer Science<\/title>\n<meta name=\"description\" content=\"Account Access Software CUDA Toolkit cuDNN Tensorflow Keras ai benchmark &nbsp; 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