{"id":14701,"date":"2022-05-25T13:48:10","date_gmt":"2022-05-25T17:48:10","guid":{"rendered":"https:\/\/carleton.ca\/scs\/?page_id=14701"},"modified":"2023-01-04T11:30:39","modified_gmt":"2023-01-04T16:30:39","slug":"scs-gpu-vm-fall-2022","status":"publish","type":"page","link":"https:\/\/carleton.ca\/scs\/tech-support\/scs-gpu-vm-fall-2022\/","title":{"rendered":"SCS GPU VM Fall 2022"},"content":{"rendered":"<p><a href=\"#details\">GPU VM Account Details<\/a><br \/>\n<a href=\"#testing\">Testing the GPU VM software<\/a><\/p>\n<p>This page will show you how to test your virtual machine&#8217;s GPU and access and test the software that is pre-installed on it.<\/p>\n<h2><a id=\"details\"><\/a>GPU VM Account Details<\/h2>\n<div class=\"slideme\"><dl class=\"slideme__list\"><dt class=\"slideme__term\"><a href=\"#slideme-image-information\" aria-expanded=\"false\" aria-controls=\"slideme-image-information\" class=\"slideme__heading slideme__trigger\">Image Information<\/a><\/dt><dd class=\"slideme__description\" id=\"slideme-image-information\" aria-hidden=\"true\"><p><\/p>\n<p><strong>Image Name<\/strong>: scs-gpu-fall-2022<br \/>\n<strong>Creation Date<\/strong>: June 8, 2022<br \/>\n<strong>Operating System<\/strong>: Ubuntu 22.04<br \/>\n<strong>Window Manager<\/strong>: XFCE<br \/>\n<strong>Intended usage<\/strong>: Openstack GPU virtual machine with AI programming support<\/p>\n<p><\/p><\/dd><dl><\/div>\n<div class=\"slideme\"><dl class=\"slideme__list\"><dt class=\"slideme__term\"><a href=\"#slideme-account\" aria-expanded=\"false\" aria-controls=\"slideme-account\" class=\"slideme__heading slideme__trigger\">Account<\/a><\/dt><dd class=\"slideme__description\" id=\"slideme-account\" aria-hidden=\"true\"><p><\/p>\n<p>Accessing your GPU Virtual MachineYou will be given a username and password once your VM is ready.<\/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 \u2018passwd\u2019.<\/p><\/blockquote>\n<p>Please note:<\/p>\n<ul>\n<li>Change the default password for your account!<\/li>\n<li>Your account does not have Openstack dashboard access. Access is by IP address only.<\/li>\n<li>There are no system backups for your VM, that is, you are responsible for your own backups!<\/li>\n<\/ul>\n<p><\/p><\/dd><dl><\/div>\n<div class=\"slideme\"><dl class=\"slideme__list\"><dt class=\"slideme__term\"><a href=\"#slideme-accessing-your-gpu-virtual-machine\" aria-expanded=\"false\" aria-controls=\"slideme-accessing-your-gpu-virtual-machine\" class=\"slideme__heading slideme__trigger\">Accessing your GPU Virtual Machine<\/a><\/dt><dd class=\"slideme__description\" id=\"slideme-accessing-your-gpu-virtual-machine\" aria-hidden=\"true\"><p><\/p>\n<p>1. 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>:\u00a0<a href=\"https:\/\/carleton.ca\/its\/help-centre\/remote-access\/\" target=\"_blank\" rel=\"noopener noreferrer\">Carleton VPN<\/a>\u00a0when connecting from outside of the campus<\/li>\n<\/ul>\n<p>2. Listed are the ways you can connect to your VM:<\/p>\n<ul>\n<li><strong>x2go <\/strong>(graphical desktop): download\u00a0<a href=\"https:\/\/wiki.x2go.org\/doku.php\/download:start\" target=\"_blank\" rel=\"noopener noreferrer\">x2go client<\/a>\u00a0to get a\u00a0 full graphical desktop (use\u00a0<strong>Session Type:<\/strong> XFCE). More info: <a href=\"https:\/\/carleton.ca\/scs\/2021\/ssh-connection-with-x2go-remote-desktop-client\/\" target=\"_blank\" rel=\"noopener noreferrer\">SSH Connection with x2go Remote Desktop Client<\/a><\/li>\n<li><strong>ssh<\/strong> (command line): <a href=\"https:\/\/carleton.ca\/scs\/2021\/connecting-to-an-openstack-instance-using-ssh-putty\/\" target=\"_blank\" rel=\"noopener noreferrer\">use a terminal window to gain ssh access<\/a><\/li>\n<\/ul>\n<p><\/p><\/dd><dl><\/div>\n<div class=\"slideme\"><dl class=\"slideme__list\"><dt class=\"slideme__term\"><a href=\"#slideme-system-administration\" aria-expanded=\"false\" aria-controls=\"slideme-system-administration\" class=\"slideme__heading slideme__trigger\">System Administration<\/a><\/dt><dd class=\"slideme__description\" id=\"slideme-system-administration\" aria-hidden=\"true\"><p><\/p>\n<ul>\n<li>You have full root\/sudo privileges on your GPU VM.<\/li>\n<li>You can restart your VM using the &#8216;reboot&#8217; command<\/li>\n<li>If you shutdown your VM then you need to contact an SCS Sysadmin to Start the VM for you<\/li>\n<li>If your VM is in an unusable state or it is difficult to fix errors then you have the option to re-launch your VM. Please contact the SCS System Administrator to re-launch the VM for you. Re-launching it means terminating the VM (you lose all local data) and launching a new instance.<\/li>\n<li>Sometimes the VM is not accessible via <a href=\"https:\/\/carleton.ca\/scs\/2021\/ssh-connection-with-x2go-remote-desktop-client\/\">x2go<\/a> but you can access it using an <a href=\"https:\/\/carleton.ca\/scs\/2021\/connecting-to-an-openstack-instance-using-ssh-putty\/\">ssh terminal like putty<\/a><\/li>\n<\/ul>\n<p><\/p><\/dd><dl><\/div>\n<div class=\"slideme\"><dl class=\"slideme__list\"><dt class=\"slideme__term\"><a href=\"#slideme-software\" aria-expanded=\"false\" aria-controls=\"slideme-software\" class=\"slideme__heading slideme__trigger\">Software<\/a><\/dt><dd class=\"slideme__description\" id=\"slideme-software\" aria-hidden=\"true\"><p><\/p>\n<p>This is the tested software installed on this virtual machine.<\/p>\n<table style=\"width: 45.4126%; height: 203px;\" border=\"1\">\n<tbody>\n<tr>\n<td style=\"width: 39.9824%;\"><strong>Software<\/strong><\/td>\n<td style=\"width: 101.276%;\"><strong>Version<\/strong><\/td>\n<\/tr>\n<tr>\n<td style=\"width: 39.9824%;\">NVIDIA Driver Ver.<\/td>\n<td style=\"width: 101.276%;\">11.6<\/td>\n<\/tr>\n<tr>\n<td style=\"width: 39.9824%;\">CUDA Runtime<\/td>\n<td style=\"width: 101.276%;\">11.7<\/td>\n<\/tr>\n<tr>\n<td style=\"width: 39.9824%;\">cuDNN<\/td>\n<td style=\"width: 101.276%;\">8.4.0<\/td>\n<\/tr>\n<tr>\n<td style=\"width: 39.9824%;\">Python<\/td>\n<td style=\"width: 101.276%;\">3.10.4<\/td>\n<\/tr>\n<tr>\n<td style=\"width: 39.9824%;\">Pip3<\/td>\n<td style=\"width: 101.276%;\">22.1.1<\/td>\n<\/tr>\n<tr>\n<td style=\"width: 39.9824%;\">GCC<\/td>\n<td style=\"width: 101.276%;\">11.2.0<\/td>\n<\/tr>\n<tr>\n<td style=\"width: 39.9824%;\">Tensorflow<\/td>\n<td style=\"width: 101.276%;\">2.9.1<\/td>\n<\/tr>\n<tr>\n<td style=\"width: 39.9824%;\">Python-torch<\/td>\n<td style=\"width: 101.276%;\">1.8.0<\/td>\n<\/tr>\n<tr>\n<td style=\"width: 39.9824%;\">Keras<\/td>\n<td style=\"width: 101.276%;\">2.9.0<\/td>\n<\/tr>\n<tr>\n<td style=\"width: 39.9824%;\">Keras-processing<\/td>\n<td style=\"width: 101.276%;\">1.1.2<\/td>\n<\/tr>\n<tr>\n<td style=\"width: 39.9824%;\">Pandas<\/td>\n<td style=\"width: 101.276%;\">1.4.2<\/td>\n<\/tr>\n<tr>\n<td style=\"width: 39.9824%;\">Numpy<\/td>\n<td style=\"width: 101.276%;\">1.22.4<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p><\/p><\/dd><dl><\/div>\n<h2><a id=\"testing\"><\/a>Testing the GPU VM software<\/h2>\n<p><strong>Probing your GPU<\/strong><\/p>\n<p>This command allows you test if the GPU is being detected, identifies the GPU and shows any running jobs, utilisation, and memory usage in real time:<\/p>\n<p><code>nvidia-smi -l<\/code><\/p>\n<p><strong>Testing CUDA<\/strong><\/p>\n<p>There are CUDA samples that you can download, compile and run for your version of CUDA. These samples have been compiled for you in your account. One of the samples probes your GPU and gives you detailed spec&#8217;s about it. You can try to run this sample code in your account (long output):<\/p>\n<p><code>cuda-samples\/Samples\/1_Utilities\/deviceQuery\/deviceQuery<\/code><\/p>\n<div class=\"slideme\"><dl class=\"slideme__list\"><dt class=\"slideme__term\"><a href=\"#slideme-cuda-sample-code-output\" aria-expanded=\"false\" aria-controls=\"slideme-cuda-sample-code-output\" class=\"slideme__heading slideme__trigger\">CUDA Sample code output<\/a><\/dt><dd class=\"slideme__description\" id=\"slideme-cuda-sample-code-output\" aria-hidden=\"true\"><p><\/p>\n<p>CUDA Device Query (Runtime API) version (CUDART static linking)<\/p>\n<p>Detected 1 CUDA Capable device(s)<\/p>\n<p>Device 0: &#8220;NVIDIA GeForce RTX 2080 SUPER&#8221;<br \/>\nCUDA Driver Version \/ Runtime Version 11.6 \/ 11.7<br \/>\nCUDA Capability Major\/Minor version number: 7.5<br \/>\nTotal amount of global memory: 7982 MBytes (8369930240 bytes)<br \/>\n(048) Multiprocessors, (064) CUDA Cores\/MP: 3072 CUDA Cores<br \/>\nGPU Max Clock rate: 1815 MHz (1.81 GHz)<br \/>\nMemory Clock rate: 7751 Mhz<br \/>\nMemory Bus Width: 256-bit<br \/>\nL2 Cache Size: 4194304 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 shared memory per multiprocessor: 65536 bytes<br \/>\nTotal number of registers available per block: 65536<br \/>\nWarp size: 32<br \/>\nMaximum number of threads per multiprocessor: 1024<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 3 copy engine(s)<br \/>\nRun time limit on kernels: No<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 Managed Memory: 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;<\/p>\n<p>deviceQuery, CUDA Driver = CUDART, CUDA Driver Version = 11.6, CUDA Runtime Version = 11.7, NumDevs = 1<br \/>\nResult = PASS<\/p>\n<p><\/p><\/dd><dl><\/div>\n<p><strong>Check CuDNN Installation<\/strong><\/p>\n<p>If the file exists then you will have CuDNN installed:<\/p>\n<p><code>cat \/usr\/local\/cuda\/include\/cudnn_version.h | grep CUDNN_MAJOR -A 2<\/code><\/p>\n<p>You can also run sample code:<\/p>\n<p><code>cd \/usr\/src\/cudnn_samples_v8\/mnistCUDNN<br \/>\n.\/mnistCUDNN<\/code><\/p>\n<div class=\"slideme\"><dl class=\"slideme__list\"><dt class=\"slideme__term\"><a href=\"#slideme-cudnn-sample-code-output\" aria-expanded=\"false\" aria-controls=\"slideme-cudnn-sample-code-output\" class=\"slideme__heading slideme__trigger\">CuDNN Sample code output<\/a><\/dt><dd class=\"slideme__description\" id=\"slideme-cudnn-sample-code-output\" aria-hidden=\"true\"><p><\/p>\n<p>Executing: mnistCUDNN<br \/>\ncudnnGetVersion() : 8303 , CUDNN_VERSION from cudnn.h : 8303 (8.3.3)<br \/>\nHost compiler version : GCC 11.2.0<\/p>\n<p>There are 1 CUDA capable devices on your machine :<br \/>\ndevice 0 : sms 48 Capabilities 7.5, SmClock 1815.0 Mhz, MemSize (Mb) 7982, MemClock 7751.0 Mhz, Ecc=0, boardGroupID=0<br \/>\nUsing device 0<\/p>\n<p>Testing single precision<br \/>\nLoading binary file data\/conv1.bin<br \/>\nLoading binary file data\/conv1.bias.bin<br \/>\nLoading binary file data\/conv2.bin<br \/>\nLoading binary file data\/conv2.bias.bin<br \/>\nLoading binary file data\/ip1.bin<br \/>\nLoading binary file data\/ip1.bias.bin<br \/>\nLoading binary file data\/ip2.bin<br \/>\nLoading binary file data\/ip2.bias.bin<br \/>\nLoading image data\/one_28x28.pgm<br \/>\nPerforming forward propagation &#8230;<br \/>\nTesting cudnnGetConvolutionForwardAlgorithm_v7 &#8230;<br \/>\n^^^^ CUDNN_STATUS_SUCCESS for Algo 1: -1.000000 time requiring 0 memory<br \/>\n^^^^ CUDNN_STATUS_SUCCESS for Algo 0: -1.000000 time requiring 0 memory<br \/>\n^^^^ CUDNN_STATUS_SUCCESS for Algo 2: -1.000000 time requiring 57600 memory<br \/>\n^^^^ CUDNN_STATUS_SUCCESS for Algo 5: -1.000000 time requiring 178432 memory<br \/>\n^^^^ CUDNN_STATUS_SUCCESS for Algo 4: -1.000000 time requiring 184784 memory<br \/>\n^^^^ CUDNN_STATUS_SUCCESS for Algo 7: -1.000000 time requiring 2057744 memory<br \/>\n^^^^ CUDNN_STATUS_NOT_SUPPORTED for Algo 6: -1.000000 time requiring 0 memory<br \/>\n^^^^ CUDNN_STATUS_NOT_SUPPORTED for Algo 3: -1.000000 time requiring 0 memory<br \/>\nTesting cudnnFindConvolutionForwardAlgorithm &#8230;<br \/>\n^^^^ CUDNN_STATUS_SUCCESS for Algo 1: 0.045056 time requiring 0 memory<br \/>\n^^^^ CUDNN_STATUS_SUCCESS for Algo 0: 0.045792 time requiring 0 memory<br \/>\n^^^^ CUDNN_STATUS_SUCCESS for Algo 2: 0.103648 time requiring 57600 memory<br \/>\n^^^^ CUDNN_STATUS_SUCCESS for Algo 5: 0.155648 time requiring 178432 memory<br \/>\n^^^^ CUDNN_STATUS_SUCCESS for Algo 7: 0.222272 time requiring 2057744 memory<br \/>\n^^^^ CUDNN_STATUS_SUCCESS for Algo 4: 0.407360 time requiring 184784 memory<br \/>\n^^^^ CUDNN_STATUS_NOT_SUPPORTED for Algo 6: -1.000000 time requiring 0 memory<br \/>\n^^^^ CUDNN_STATUS_NOT_SUPPORTED for Algo 3: -1.000000 time requiring 0 memory<br \/>\nTesting cudnnGetConvolutionForwardAlgorithm_v7 &#8230;<br \/>\n^^^^ CUDNN_STATUS_SUCCESS for Algo 4: -1.000000 time requiring 2450080 memory<br \/>\n^^^^ CUDNN_STATUS_SUCCESS for Algo 5: -1.000000 time requiring 4656640 memory<br \/>\n^^^^ CUDNN_STATUS_SUCCESS for Algo 1: -1.000000 time requiring 2000 memory<br \/>\n^^^^ CUDNN_STATUS_SUCCESS for Algo 0: -1.000000 time requiring 0 memory<br \/>\n^^^^ CUDNN_STATUS_SUCCESS for Algo 7: -1.000000 time requiring 1433120 memory<br \/>\n^^^^ CUDNN_STATUS_SUCCESS for Algo 2: -1.000000 time requiring 128000 memory<br \/>\n^^^^ CUDNN_STATUS_NOT_SUPPORTED for Algo 6: -1.000000 time requiring 0 memory<br \/>\n^^^^ CUDNN_STATUS_NOT_SUPPORTED for Algo 3: -1.000000 time requiring 0 memory<br \/>\nTesting cudnnFindConvolutionForwardAlgorithm &#8230;<br \/>\n^^^^ CUDNN_STATUS_SUCCESS for Algo 0: 0.065536 time requiring 0 memory<br \/>\n^^^^ CUDNN_STATUS_SUCCESS for Algo 1: 0.086368 time requiring 2000 memory<br \/>\n^^^^ CUDNN_STATUS_SUCCESS for Algo 2: 0.087712 time requiring 128000 memory<br \/>\n^^^^ CUDNN_STATUS_SUCCESS for Algo 7: 0.141056 time requiring 1433120 memory<br \/>\n^^^^ CUDNN_STATUS_SUCCESS for Algo 5: 0.143360 time requiring 4656640 memory<br \/>\n^^^^ CUDNN_STATUS_SUCCESS for Algo 4: 0.191616 time requiring 2450080 memory<br \/>\n^^^^ CUDNN_STATUS_NOT_SUPPORTED for Algo 6: -1.000000 time requiring 0 memory<br \/>\n^^^^ CUDNN_STATUS_NOT_SUPPORTED for Algo 3: -1.000000 time requiring 0 memory<br \/>\nResulting weights from Softmax:<br \/>\n0.0000000 0.9999399 0.0000000 0.0000000 0.0000561 0.0000000 0.0000012 0.0000017 0.0000010 0.0000000<br \/>\nLoading image data\/three_28x28.pgm<br \/>\nPerforming forward propagation &#8230;<br \/>\nTesting cudnnGetConvolutionForwardAlgorithm_v7 &#8230;<br \/>\n^^^^ CUDNN_STATUS_SUCCESS for Algo 1: -1.000000 time requiring 0 memory<br \/>\n^^^^ CUDNN_STATUS_SUCCESS for Algo 0: -1.000000 time requiring 0 memory<br \/>\n^^^^ CUDNN_STATUS_SUCCESS for Algo 2: -1.000000 time requiring 57600 memory<br \/>\n^^^^ CUDNN_STATUS_SUCCESS for Algo 5: -1.000000 time requiring 178432 memory<br \/>\n^^^^ CUDNN_STATUS_SUCCESS for Algo 4: -1.000000 time requiring 184784 memory<br \/>\n^^^^ CUDNN_STATUS_SUCCESS for Algo 7: -1.000000 time requiring 2057744 memory<br \/>\n^^^^ CUDNN_STATUS_NOT_SUPPORTED for Algo 6: -1.000000 time requiring 0 memory<br \/>\n^^^^ CUDNN_STATUS_NOT_SUPPORTED for Algo 3: -1.000000 time requiring 0 memory<br \/>\nTesting cudnnFindConvolutionForwardAlgorithm &#8230;<br \/>\n^^^^ CUDNN_STATUS_SUCCESS for Algo 0: 0.040960 time requiring 0 memory<br \/>\n^^^^ CUDNN_STATUS_SUCCESS for Algo 1: 0.043232 time requiring 0 memory<br \/>\n^^^^ CUDNN_STATUS_SUCCESS for Algo 2: 0.076064 time requiring 57600 memory<br \/>\n^^^^ CUDNN_STATUS_SUCCESS for Algo 7: 0.104064 time requiring 2057744 memory<br \/>\n^^^^ CUDNN_STATUS_SUCCESS for Algo 4: 0.135072 time requiring 184784 memory<br \/>\n^^^^ CUDNN_STATUS_SUCCESS for Algo 5: 0.136448 time requiring 178432 memory<br \/>\n^^^^ CUDNN_STATUS_NOT_SUPPORTED for Algo 6: -1.000000 time requiring 0 memory<br \/>\n^^^^ CUDNN_STATUS_NOT_SUPPORTED for Algo 3: -1.000000 time requiring 0 memory<br \/>\nTesting cudnnGetConvolutionForwardAlgorithm_v7 &#8230;<br \/>\n^^^^ CUDNN_STATUS_SUCCESS for Algo 4: -1.000000 time requiring 2450080 memory<br \/>\n^^^^ CUDNN_STATUS_SUCCESS for Algo 5: -1.000000 time requiring 4656640 memory<br \/>\n^^^^ CUDNN_STATUS_SUCCESS for Algo 1: -1.000000 time requiring 2000 memory<br \/>\n^^^^ CUDNN_STATUS_SUCCESS for Algo 0: -1.000000 time requiring 0 memory<br \/>\n^^^^ CUDNN_STATUS_SUCCESS for Algo 7: -1.000000 time requiring 1433120 memory<br \/>\n^^^^ CUDNN_STATUS_SUCCESS for Algo 2: -1.000000 time requiring 128000 memory<br \/>\n^^^^ CUDNN_STATUS_NOT_SUPPORTED for Algo 6: -1.000000 time requiring 0 memory<br \/>\n^^^^ CUDNN_STATUS_NOT_SUPPORTED for Algo 3: -1.000000 time requiring 0 memory<br \/>\nTesting cudnnFindConvolutionForwardAlgorithm &#8230;<br \/>\n^^^^ CUDNN_STATUS_SUCCESS for Algo 0: 0.061952 time requiring 0 memory<br \/>\n^^^^ CUDNN_STATUS_SUCCESS for Algo 1: 0.079872 time requiring 2000 memory<br \/>\n^^^^ CUDNN_STATUS_SUCCESS for Algo 2: 0.088064 time requiring 128000 memory<br \/>\n^^^^ CUDNN_STATUS_SUCCESS for Algo 7: 0.116736 time requiring 1433120 memory<br \/>\n^^^^ CUDNN_STATUS_SUCCESS for Algo 4: 0.141312 time requiring 2450080 memory<br \/>\n^^^^ CUDNN_STATUS_SUCCESS for Algo 5: 0.156608 time requiring 4656640 memory<br \/>\n^^^^ CUDNN_STATUS_NOT_SUPPORTED for Algo 6: -1.000000 time requiring 0 memory<br \/>\n^^^^ CUDNN_STATUS_NOT_SUPPORTED for Algo 3: -1.000000 time requiring 0 memory<br \/>\nResulting weights from Softmax:<br \/>\n0.0000000 0.0000000 0.0000000 0.9999288 0.0000000 0.0000711 0.0000000 0.0000000 0.0000000 0.0000000<br \/>\nLoading image data\/five_28x28.pgm<br \/>\nPerforming forward propagation &#8230;<br \/>\nResulting weights from Softmax:<br \/>\n0.0000000 0.0000008 0.0000000 0.0000002 0.0000000 0.9999820 0.0000154 0.0000000 0.0000012 0.0000006<\/p>\n<p>Result of classification: 1 3 5<\/p>\n<p>Test passed!<\/p>\n<p>Testing half precision (math in single precision)<br \/>\nLoading binary file data\/conv1.bin<br \/>\nLoading binary file data\/conv1.bias.bin<br \/>\nLoading binary file data\/conv2.bin<br \/>\nLoading binary file data\/conv2.bias.bin<br \/>\nLoading binary file data\/ip1.bin<br \/>\nLoading binary file data\/ip1.bias.bin<br \/>\nLoading binary file data\/ip2.bin<br \/>\nLoading binary file data\/ip2.bias.bin<br \/>\nLoading image data\/one_28x28.pgm<br \/>\nPerforming forward propagation &#8230;<br \/>\nTesting cudnnGetConvolutionForwardAlgorithm_v7 &#8230;<br \/>\n^^^^ CUDNN_STATUS_SUCCESS for Algo 1: -1.000000 time requiring 0 memory<br \/>\n^^^^ CUDNN_STATUS_SUCCESS for Algo 0: -1.000000 time requiring 0 memory<br \/>\n^^^^ CUDNN_STATUS_SUCCESS for Algo 2: -1.000000 time requiring 0 memory<br \/>\n^^^^ CUDNN_STATUS_SUCCESS for Algo 5: -1.000000 time requiring 178432 memory<br \/>\n^^^^ CUDNN_STATUS_SUCCESS for Algo 4: -1.000000 time requiring 184784 memory<br \/>\n^^^^ CUDNN_STATUS_SUCCESS for Algo 7: -1.000000 time requiring 2057744 memory<br \/>\n^^^^ CUDNN_STATUS_NOT_SUPPORTED for Algo 6: -1.000000 time requiring 0 memory<br \/>\n^^^^ CUDNN_STATUS_NOT_SUPPORTED for Algo 3: -1.000000 time requiring 0 memory<br \/>\nTesting cudnnFindConvolutionForwardAlgorithm &#8230;<br \/>\n^^^^ CUDNN_STATUS_SUCCESS for Algo 0: 0.041760 time requiring 0 memory<br \/>\n^^^^ CUDNN_STATUS_SUCCESS for Algo 1: 0.043552 time requiring 0 memory<br \/>\n^^^^ CUDNN_STATUS_SUCCESS for Algo 2: 0.045792 time requiring 0 memory<br \/>\n^^^^ CUDNN_STATUS_SUCCESS for Algo 7: 0.110592 time requiring 2057744 memory<br \/>\n^^^^ CUDNN_STATUS_SUCCESS for Algo 5: 0.129056 time requiring 178432 memory<br \/>\n^^^^ CUDNN_STATUS_SUCCESS for Algo 4: 0.155648 time requiring 184784 memory<br \/>\n^^^^ CUDNN_STATUS_NOT_SUPPORTED for Algo 6: -1.000000 time requiring 0 memory<br \/>\n^^^^ CUDNN_STATUS_NOT_SUPPORTED for Algo 3: -1.000000 time requiring 0 memory<br \/>\nTesting cudnnGetConvolutionForwardAlgorithm_v7 &#8230;<br \/>\n^^^^ CUDNN_STATUS_SUCCESS for Algo 1: -1.000000 time requiring 0 memory<br \/>\n^^^^ CUDNN_STATUS_SUCCESS for Algo 0: -1.000000 time requiring 0 memory<br \/>\n^^^^ CUDNN_STATUS_SUCCESS for Algo 2: -1.000000 time requiring 64000 memory<br \/>\n^^^^ CUDNN_STATUS_SUCCESS for Algo 5: -1.000000 time requiring 4656640 memory<br \/>\n^^^^ CUDNN_STATUS_SUCCESS for Algo 4: -1.000000 time requiring 2450080 memory<br \/>\n^^^^ CUDNN_STATUS_SUCCESS for Algo 7: -1.000000 time requiring 1433120 memory<br \/>\n^^^^ CUDNN_STATUS_NOT_SUPPORTED for Algo 6: -1.000000 time requiring 0 memory<br \/>\n^^^^ CUDNN_STATUS_NOT_SUPPORTED for Algo 3: -1.000000 time requiring 0 memory<br \/>\nTesting cudnnFindConvolutionForwardAlgorithm &#8230;<br \/>\n^^^^ CUDNN_STATUS_SUCCESS for Algo 0: 0.086784 time requiring 0 memory<br \/>\n^^^^ CUDNN_STATUS_SUCCESS for Algo 2: 0.091136 time requiring 64000 memory<br \/>\n^^^^ CUDNN_STATUS_SUCCESS for Algo 1: 0.100032 time requiring 0 memory<br \/>\n^^^^ CUDNN_STATUS_SUCCESS for Algo 7: 0.151584 time requiring 1433120 memory<br \/>\n^^^^ CUDNN_STATUS_SUCCESS for Algo 5: 0.164160 time requiring 4656640 memory<br \/>\n^^^^ CUDNN_STATUS_SUCCESS for Algo 4: 0.167936 time requiring 2450080 memory<br \/>\n^^^^ CUDNN_STATUS_NOT_SUPPORTED for Algo 6: -1.000000 time requiring 0 memory<br \/>\n^^^^ CUDNN_STATUS_NOT_SUPPORTED for Algo 3: -1.000000 time requiring 0 memory<br \/>\nResulting weights from Softmax:<br \/>\n0.0000001 1.0000000 0.0000001 0.0000000 0.0000563 0.0000001 0.0000012 0.0000017 0.0000010 0.0000001<br \/>\nLoading image data\/three_28x28.pgm<br \/>\nPerforming forward propagation &#8230;<br \/>\nTesting cudnnGetConvolutionForwardAlgorithm_v7 &#8230;<br \/>\n^^^^ CUDNN_STATUS_SUCCESS for Algo 1: -1.000000 time requiring 0 memory<br \/>\n^^^^ CUDNN_STATUS_SUCCESS for Algo 0: -1.000000 time requiring 0 memory<br \/>\n^^^^ CUDNN_STATUS_SUCCESS for Algo 2: -1.000000 time requiring 0 memory<br \/>\n^^^^ CUDNN_STATUS_SUCCESS for Algo 5: -1.000000 time requiring 178432 memory<br \/>\n^^^^ CUDNN_STATUS_SUCCESS for Algo 4: -1.000000 time requiring 184784 memory<br \/>\n^^^^ CUDNN_STATUS_SUCCESS for Algo 7: -1.000000 time requiring 2057744 memory<br \/>\n^^^^ CUDNN_STATUS_NOT_SUPPORTED for Algo 6: -1.000000 time requiring 0 memory<br \/>\n^^^^ CUDNN_STATUS_NOT_SUPPORTED for Algo 3: -1.000000 time requiring 0 memory<br \/>\nTesting cudnnFindConvolutionForwardAlgorithm &#8230;<br \/>\n^^^^ CUDNN_STATUS_SUCCESS for Algo 2: 0.032544 time requiring 0 memory<br \/>\n^^^^ CUDNN_STATUS_SUCCESS for Algo 0: 0.040544 time requiring 0 memory<br \/>\n^^^^ CUDNN_STATUS_SUCCESS for Algo 1: 0.045824 time requiring 0 memory<br \/>\n^^^^ CUDNN_STATUS_SUCCESS for Algo 5: 0.112160 time requiring 178432 memory<br \/>\n^^^^ CUDNN_STATUS_SUCCESS for Algo 4: 0.118624 time requiring 184784 memory<br \/>\n^^^^ CUDNN_STATUS_SUCCESS for Algo 7: 0.143264 time requiring 2057744 memory<br \/>\n^^^^ CUDNN_STATUS_NOT_SUPPORTED for Algo 6: -1.000000 time requiring 0 memory<br \/>\n^^^^ CUDNN_STATUS_NOT_SUPPORTED for Algo 3: -1.000000 time requiring 0 memory<br \/>\nTesting cudnnGetConvolutionForwardAlgorithm_v7 &#8230;<br \/>\n^^^^ CUDNN_STATUS_SUCCESS for Algo 1: -1.000000 time requiring 0 memory<br \/>\n^^^^ CUDNN_STATUS_SUCCESS for Algo 0: -1.000000 time requiring 0 memory<br \/>\n^^^^ CUDNN_STATUS_SUCCESS for Algo 2: -1.000000 time requiring 64000 memory<br \/>\n^^^^ CUDNN_STATUS_SUCCESS for Algo 5: -1.000000 time requiring 4656640 memory<br \/>\n^^^^ CUDNN_STATUS_SUCCESS for Algo 4: -1.000000 time requiring 2450080 memory<br \/>\n^^^^ CUDNN_STATUS_SUCCESS for Algo 7: -1.000000 time requiring 1433120 memory<br \/>\n^^^^ CUDNN_STATUS_NOT_SUPPORTED for Algo 6: -1.000000 time requiring 0 memory<br \/>\n^^^^ CUDNN_STATUS_NOT_SUPPORTED for Algo 3: -1.000000 time requiring 0 memory<br \/>\nTesting cudnnFindConvolutionForwardAlgorithm &#8230;<br \/>\n^^^^ CUDNN_STATUS_SUCCESS for Algo 0: 0.085632 time requiring 0 memory<br \/>\n^^^^ CUDNN_STATUS_SUCCESS for Algo 2: 0.089536 time requiring 64000 memory<br \/>\n^^^^ CUDNN_STATUS_SUCCESS for Algo 1: 0.097856 time requiring 0 memory<br \/>\n^^^^ CUDNN_STATUS_SUCCESS for Algo 5: 0.165888 time requiring 4656640 memory<br \/>\n^^^^ CUDNN_STATUS_SUCCESS for Algo 7: 0.174080 time requiring 1433120 memory<br \/>\n^^^^ CUDNN_STATUS_SUCCESS for Algo 4: 0.176128 time requiring 2450080 memory<br \/>\n^^^^ CUDNN_STATUS_NOT_SUPPORTED for Algo 6: -1.000000 time requiring 0 memory<br \/>\n^^^^ CUDNN_STATUS_NOT_SUPPORTED for Algo 3: -1.000000 time requiring 0 memory<br \/>\nResulting weights from Softmax:<br \/>\n0.0000000 0.0000000 0.0000000 1.0000000 0.0000000 0.0000714 0.0000000 0.0000000 0.0000000 0.0000000<br \/>\nLoading image data\/five_28x28.pgm<br \/>\nPerforming forward propagation &#8230;<br \/>\nResulting weights from Softmax:<br \/>\n0.0000000 0.0000008 0.0000000 0.0000002 0.0000000 1.0000000 0.0000154 0.0000000 0.0000012 0.0000006<\/p>\n<p>Result of classification: 1 3 5<\/p>\n<p>Test passed!<\/p>\n<p><\/p><\/dd><dl><\/div>\n<p><\/p>\n<p><\/p>\n<p><strong>Check Tensorflow<\/strong><\/p>\n<p>Version:<\/p>\n<p><code>python3 -c 'import tensorflow as tf; print(tf.__version__)'<\/code><\/p>\n<p><strong>AI Benchmark<\/strong><\/p>\n<p>Test using Ai benchmark (may take 20 minutes):<\/p>\n<p><code>python3 -c 'from ai_benchmark import AIBenchmark;benchmark = AIBenchmark();results = benchmark.run()'<\/code><\/p>\n<p>Then lookup the results. The chart will tell you how well your GPU is performing. If its not performing well then you may not be using GPU!<\/p>\n<div class=\"slideme\"><dl class=\"slideme__list\"><dt class=\"slideme__term\"><a href=\"#slideme-successful-aibenchmark-output\" aria-expanded=\"false\" aria-controls=\"slideme-successful-aibenchmark-output\" class=\"slideme__heading slideme__trigger\">Successful AIBenchmark output<\/a><\/dt><dd class=\"slideme__description\" id=\"slideme-successful-aibenchmark-output\" aria-hidden=\"true\"><p><\/p>\n<p>&gt;&gt; AI-Benchmark-v.0.1.2<br \/>\n&gt;&gt; Let the AI Games begin..<\/p>\n<p>* TF Version: 2.9.1<br \/>\n* Platform: Linux-5.15.0-37-generic-x86_64-with-glibc2.35<br \/>\n* CPU: N\/A<br \/>\n* CPU RAM: 47 GB<br \/>\n* GPU\/0: NVIDIA GeForce RTX 2080 SUPER<br \/>\n* GPU RAM: 6.5 GB<br \/>\n* CUDA Version: 11.7<br \/>\n* CUDA Build: V11.7.64<\/p>\n<p>The benchmark is running&#8230;<br \/>\nThe tests might take up to 20 minutes<br \/>\nPlease don&#8217;t interrupt the script<\/p>\n<p>1\/19. MobileNet-V2<\/p>\n<p>1.1 &#8211; inference | batch=50, size=224&#215;224: 60.8 \u00b1 12.9 ms<br \/>\n1.2 &#8211; training | batch=50, size=224&#215;224: 203 \u00b1 2 ms<\/p>\n<p>2\/19. Inception-V3<\/p>\n<p>2.1 &#8211; inference | batch=20, size=346&#215;346: 63.6 \u00b1 2.6 ms<br \/>\n2.2 &#8211; training | batch=20, size=346&#215;346: 199 \u00b1 4 ms<\/p>\n<p>3\/19. Inception-V4<\/p>\n<p>3.1 &#8211; inference | batch=10, size=346&#215;346: 62.2 \u00b1 3.8 ms<br \/>\n3.2 &#8211; training | batch=10, size=346&#215;346: 216 \u00b1 1 ms<\/p>\n<p>4\/19. Inception-ResNet-V2<\/p>\n<p>4.1 &#8211; inference | batch=10, size=346&#215;346: 85.2 \u00b1 6.2 ms<br \/>\n4.2 &#8211; training | batch=8, size=346&#215;346: 232 \u00b1 8 ms<\/p>\n<p>5\/19. ResNet-V2-50<\/p>\n<p>5.1 &#8211; inference | batch=10, size=346&#215;346: 44.0 \u00b1 2.3 ms<br \/>\n5.2 &#8211; training | batch=10, size=346&#215;346: 127 \u00b1 1 ms<\/p>\n<p>6\/19. ResNet-V2-152<\/p>\n<p>6.1 &#8211; inference | batch=10, size=256&#215;256: 55.9 \u00b1 2.3 ms<br \/>\n6.2 &#8211; training | batch=10, size=256&#215;256: 178 \u00b1 3 ms<\/p>\n<p>7\/19. VGG-16<\/p>\n<p>7.1 &#8211; inference | batch=20, size=224&#215;224: 102 \u00b1 1 ms<br \/>\n7.2 &#8211; training | batch=2, size=224&#215;224: 166 \u00b1 1 ms<\/p>\n<p>8\/19. SRCNN 9-5-5<\/p>\n<p>8.1 &#8211; inference | batch=10, size=512&#215;512: 77.3 \u00b1 1.9 ms<br \/>\n8.2 &#8211; inference | batch=1, size=1536&#215;1536: 90.8 \u00b1 3.5 ms<br \/>\n8.3 &#8211; training | batch=10, size=512&#215;512: 253 \u00b1 4 ms<\/p>\n<p>9\/19. VGG-19 Super-Res<\/p>\n<p>9.1 &#8211; inference | batch=10, size=256&#215;256: 112 \u00b1 3 ms<br \/>\n9.2 &#8211; inference | batch=1, size=1024&#215;1024: 178 \u00b1 1 ms<br \/>\n9.3 &#8211; training | batch=10, size=224&#215;224: 266.1 \u00b1 0.8 ms<\/p>\n<p>10\/19. ResNet-SRGAN<\/p>\n<p>10.1 &#8211; inference | batch=10, size=512&#215;512: 112 \u00b1 1 ms<br \/>\n10.2 &#8211; inference | batch=1, size=1536&#215;1536: 99.7 \u00b1 1.9 ms<br \/>\n10.3 &#8211; training | batch=5, size=512&#215;512: 166 \u00b1 3 ms<\/p>\n<p>11\/19. ResNet-DPED<\/p>\n<p>11.1 &#8211; inference | batch=10, size=256&#215;256: 115.0 \u00b1 0.8 ms<br \/>\n11.2 &#8211; inference | batch=1, size=1024&#215;1024: 185 \u00b1 4 ms<br \/>\n11.3 &#8211; training | batch=15, size=128&#215;128: 185.6 \u00b1 0.7 ms<\/p>\n<p>12\/19. U-Net<\/p>\n<p>12.1 &#8211; inference | batch=4, size=512&#215;512: 216 \u00b1 1 ms<br \/>\n12.2 &#8211; inference | batch=1, size=1024&#215;1024: 209.7 \u00b1 1.0 ms<br \/>\n12.3 &#8211; training | batch=4, size=256&#215;256: 220 \u00b1 1 ms<\/p>\n<p>13\/19. Nvidia-SPADE<\/p>\n<p>13.1 &#8211; inference | batch=5, size=128&#215;128: 95.4 \u00b1 1.0 ms<br \/>\n13.2 &#8211; training | batch=1, size=128&#215;128: 175 \u00b1 1 ms<\/p>\n<p>14\/19. ICNet<\/p>\n<p>14.1 &#8211; inference | batch=5, size=1024&#215;1536: 233 \u00b1 16 ms<br \/>\n14.2 &#8211; training | batch=10, size=1024&#215;1536: 776 \u00b1 35 ms<\/p>\n<p>15\/19. PSPNet<\/p>\n<p>15.1 &#8211; inference | batch=5, size=720&#215;720: 415 \u00b1 7 ms<br \/>\n15.2 &#8211; training | batch=1, size=512&#215;512: 156 \u00b1 2 ms<\/p>\n<p>16\/19. DeepLab<\/p>\n<p>16.1 &#8211; inference | batch=2, size=512&#215;512: 114 \u00b1 2 ms<br \/>\n16.2 &#8211; training | batch=1, size=384&#215;384: 127 \u00b1 4 ms<\/p>\n<p>17\/19. Pixel-RNN<\/p>\n<p>17.1 &#8211; inference | batch=50, size=64&#215;64: 1019 \u00b1 33 ms<br \/>\n17.2 &#8211; training | batch=10, size=64&#215;64: 5049 \u00b1 216 ms<\/p>\n<p>18\/19. LSTM-Sentiment<\/p>\n<p>18.1 &#8211; inference | batch=100, size=1024&#215;300: 763 \u00b1 46 ms<br \/>\n18.2 &#8211; training | batch=10, size=1024&#215;300: 2314 \u00b1 142 ms<\/p>\n<p>19\/19. GNMT-Translation<\/p>\n<p>19.1 &#8211; inference | batch=1, size=1&#215;20: 271 \u00b1 14 ms<\/p>\n<p>Device Inference Score: 10318<br \/>\nDevice Training Score: 10631<br \/>\n<strong>Device AI Score: 20949<\/strong><\/p>\n<p>For more information and results, please visit http:\/\/ai-benchmark.com\/alpha<\/p>\n<p><\/p><\/dd><dl><\/div>\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><\/p>\n<blockquote><p>Please note that is you want to install a different version of tensorflow it can be tricky because it has specific CUDA and CuDNN dependencies. Re-installing them can be problematic!<\/p><\/blockquote>\n<p><strong>Check Pytorch<\/strong><\/p>\n<p>Version:<\/p>\n<p><code>python3 -c \"import torch; print(torch.__version__)\"<\/code><\/p>\n<p>Test code:<\/p>\n<p><code>python3 -c 'import torch;x=torch.rand(5,3);print(x)'<\/code><\/p>\n<p><\/p>\n<p><\/p>\n<p><\/p>\n<p><\/p>\n<p><\/p>\n<p><\/p>\n<p><\/p>\n","protected":false},"excerpt":{"rendered":"<p>GPU VM Account Details Testing the GPU VM software This page will show you how to test your virtual machine&#8217;s GPU and access and test the software that is pre-installed on it. GPU VM Account Details Testing the GPU VM software Probing your GPU This command allows you test if the GPU is being detected, [&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\/ 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