{"id":18955,"date":"2024-07-29T10:29:57","date_gmt":"2024-07-29T14:29:57","guid":{"rendered":"https:\/\/carleton.ca\/scs\/?page_id=18955"},"modified":"2025-05-09T14:39:43","modified_gmt":"2025-05-09T18:39:43","slug":"scs-gpu-vm-2024-5","status":"publish","type":"page","link":"https:\/\/carleton.ca\/scs\/tech-support\/cuda-gpu-computing\/scs-gpu-vm-2024-5\/","title":{"rendered":"SCS GPU VM 2024-5"},"content":{"rendered":"<p><a href=\"#details\">GPU VM Account Details<\/a><br \/>\n<a href=\"#testing\">Testing the GPU VM software<\/a><br \/>\n<a href=\"#troubleshooting\">Troubleshooting<\/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. Even though Ubuntu 24.04 is the current long release version we chose Ubuntu 22.04 because some of the dependent libraries were not available for 24.04 at the time of this installation.<\/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-tensorflow-2024-08-19<br \/>\n<strong>Creation Date<\/strong>: August 19, 2024 (TBD)<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 Machine. You 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>TurboVNC <\/strong>(graphical desktop):<a href=\"https:\/\/github.com\/TurboVNC\/turbovnc\/releases\" target=\"_blank\" rel=\"noopener\"> download the TurboVNC client<\/a> and start it and enter &#8216;&#x73;&#x74;&#x75;&#x64;&#x65;&#x6e;&#x74;&#x40;&#49;&#51;&#52;&#46;&#49;&#49;&#55;&#46;26&#46;&#x58;&#x58;&#x58;&#8217; where XXX is the IP that was assigned to your VM. <a href=\"https:\/\/carleton.ca\/scs\/2024\/ssh-connection-with-turbovnc\/\">Detailed TurboVNC instructions are here<\/a><br \/>\n<a href=\"https:\/\/carleton.ca\/scs\/wp-content\/uploads\/turboVNC-login.jpg\"><img decoding=\"async\" loading=\"lazy\" class=\"size-full wp-image-19069 alignnone\" src=\"https:\/\/carleton.ca\/scs\/wp-content\/uploads\/turboVNC-login.jpg\" alt=\"TurboVNC Login\" width=\"486\" height=\"131\" srcset=\"https:\/\/carleton.ca\/scs\/wp-content\/uploads\/turboVNC-login.jpg 486w, https:\/\/carleton.ca\/scs\/wp-content\/uploads\/turboVNC-login-240x65.jpg 240w, https:\/\/carleton.ca\/scs\/wp-content\/uploads\/turboVNC-login-400x108.jpg 400w, https:\/\/carleton.ca\/scs\/wp-content\/uploads\/turboVNC-login-160x43.jpg 160w, https:\/\/carleton.ca\/scs\/wp-content\/uploads\/turboVNC-login-360x97.jpg 360w\" sizes=\"(max-width: 486px) 100vw, 486px\" \/><\/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 TurboVNC 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<li>run the command &#8216;passwd&#8217; to change your local Ubuntu password<\/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<thead>\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<\/thead>\n<tbody>\n<tr>\n<td style=\"width: 39.9824%;\">NVIDIA Driver Ver.<\/td>\n<td style=\"width: 101.276%;\">550.90.07<\/td>\n<\/tr>\n<tr>\n<td style=\"width: 39.9824%;\">CUDA Driver Version<\/td>\n<td style=\"width: 101.276%;\">12.4<\/td>\n<\/tr>\n<tr>\n<td style=\"width: 39.9824%;\">CUDA Runtime Version<\/td>\n<td style=\"width: 101.276%;\">12.3<\/td>\n<\/tr>\n<tr>\n<td style=\"width: 39.9824%;\">cuDNN<\/td>\n<td style=\"width: 101.276%;\">8.9.7<\/td>\n<\/tr>\n<tr>\n<td style=\"width: 39.9824%;\">GCC<\/td>\n<td style=\"width: 101.276%;\">9.5.0 \/ 11.4.0 \/ 12.3.0<\/td>\n<\/tr>\n<tr>\n<td style=\"width: 39.9824%;\">Bazel<\/td>\n<td style=\"width: 101.276%;\">6.5.0<\/td>\n<\/tr>\n<tr>\n<td style=\"width: 39.9824%;\">OpenCV<\/td>\n<td style=\"width: 101.276%;\">4.5.4<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>Python software:<\/p>\n<table style=\"width: 45.4126%; height: 203px;\" border=\"1\">\n<thead>\n<tr>\n<td style=\"width: 40.2032%;\"><strong>Software<\/strong><\/td>\n<td style=\"width: 101.055%;\"><strong>Version<\/strong><\/td>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td style=\"width: 40.2032%;\">Python<\/td>\n<td style=\"width: 101.055%;\">3.10.12<\/td>\n<\/tr>\n<tr>\n<td style=\"width: 40.2032%;\">Pip3<\/td>\n<td style=\"width: 101.055%;\">24.2<\/td>\n<\/tr>\n<tr>\n<td style=\"width: 40.2032%;\">Tensorflow<\/td>\n<td style=\"width: 101.055%;\">2.16.1<\/td>\n<\/tr>\n<tr>\n<td style=\"width: 40.2032%;\">Python-torch<\/td>\n<td style=\"width: 101.055%;\">2.4.0+cu118<\/td>\n<\/tr>\n<tr>\n<td style=\"width: 40.2032%;\">Keras<\/td>\n<td style=\"width: 101.055%;\">3.4.1<\/td>\n<\/tr>\n<tr>\n<td style=\"width: 40.2032%;\">Pandas<\/td>\n<td style=\"width: 101.055%;\">2.2.2<\/td>\n<\/tr>\n<tr>\n<td style=\"width: 40.2032%;\">Numpy<\/td>\n<td style=\"width: 101.055%;\">1.26.4<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p><\/p>\n<p><\/p><\/dd><dl><\/div>\n<p><\/p>\n<h2><a id=\"testing\"><\/a>Testing the GPU VM software<\/h2>\n<p>Here are some helpful tests to see if your software is running correctly.<\/p>\n<p>This image has a script that tests all the AI software:<\/p>\n<p><code>python3 \/home\/student\/os-scripts\/ai-software-test.bash<\/code><\/p>\n<div class=\"slideme\"><dl class=\"slideme__list\"><dt class=\"slideme__term\"><a href=\"#slideme-probing-your-gpu\" aria-expanded=\"false\" aria-controls=\"slideme-probing-your-gpu\" class=\"slideme__heading slideme__trigger\">Probing your GPU<\/a><\/dt><dd class=\"slideme__description\" id=\"slideme-probing-your-gpu\" aria-hidden=\"true\"><p><\/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>nvidia-smi -l<\/p>\n<p>You should be able to view a realtime command prompt window that shows:<\/p>\n<p>CUDA version<br \/>\nGPU name<br \/>\nWhat job is running on your GPU<br \/>\nStats about your GPU: realtime heat, load and memory usage<\/p>\n<p><\/p><\/dd><dl><\/div>\n<div class=\"slideme\"><dl class=\"slideme__list\"><dt class=\"slideme__term\"><a href=\"#slideme-testing-cuda\" aria-expanded=\"false\" aria-controls=\"slideme-testing-cuda\" class=\"slideme__heading slideme__trigger\">Testing CUDA<\/a><\/dt><dd class=\"slideme__description\" id=\"slideme-testing-cuda\" aria-hidden=\"true\"><p><\/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 the student account&#8217;s xterm shell:<\/p>\n<p><code>\/home\/student\/cuda-samples\/Samples\/1_Utilities\/deviceQuery\/deviceQuery<\/code><\/p>\n<p>This is an excellent test to check:<\/p>\n<ol>\n<li>if your GPU is detected<\/li>\n<li>if CUDA is installed correctly<\/li>\n<li>the hardware and software details of your GPU<\/li>\n<\/ol>\n<p>The output for in this instance looks like:<\/p>\n<p><code><br \/>\nCUDA Device Query (Runtime API) version (CUDART static linking)<\/code><\/p>\n<p>Detected 1 CUDA Capable device(s)<\/p>\n<p>Device 0: &#8220;NVIDIA A16&#8221;<br \/>\nCUDA Driver Version \/ Runtime Version 12.4 \/ 12.3<br \/>\nCUDA Capability Major\/Minor version number: 8.6<br \/>\nTotal amount of global memory: 14878 MBytes (15600910336 bytes)<br \/>\n(010) Multiprocessors, (128) CUDA Cores\/MP: 1280 CUDA Cores<br \/>\nGPU Max Clock rate: 1755 MHz (1.75 GHz)<br \/>\nMemory Clock rate: 6251 Mhz<br \/>\nMemory Bus Width: 128-bit<br \/>\nL2 Cache Size: 2097152 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: 102400 bytes<br \/>\nTotal number of registers available per block: 65536<br \/>\nWarp size: 32<br \/>\nMaximum number of threads per multiprocessor: 1536<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 2 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: Enabled<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 = 12.4, CUDA Runtime Version = 12.3, NumDevs = 1<br \/>\nResult = PASS<\/p>\n<p><\/p>\n<p><\/p><\/dd><dl><\/div>\n<p><\/p>\n<div class=\"slideme\"><dl class=\"slideme__list\"><dt class=\"slideme__term\"><a href=\"#slideme-test-cudnn\" aria-expanded=\"false\" aria-controls=\"slideme-test-cudnn\" class=\"slideme__heading slideme__trigger\">Test CuDNN <\/a><\/dt><dd class=\"slideme__description\" id=\"slideme-test-cudnn\" aria-hidden=\"true\"><p><\/p>\n<p>The following will test:<\/p>\n<ul>\n<li>If your NVIDIA driver and CUDA install is working<\/li>\n<li>that your CUDNN libraries are working<\/li>\n<\/ul>\n<p>To verify you have the CuDNN installed run:<\/p>\n<p><code>sudo apt search cudnn | grep installed<\/code><\/p>\n<p>It also requires environment variables that are already set for the student user in this default VM. See the CuDNN installation guide how to set the environment variables for CuDNN.<\/p>\n<p>The sample code is already compiled in the &#8216;student&#8217; user account. Run the sample code as follows in the shell:<\/p>\n<p><\/p>\n<p><code>\u200b\/home\/student\/cudnn-samples\/mnistCUDNN\/mnistCUDNN<\/code><\/p>\n<p><\/p>\n<p><strong>CuDNN Sample code output should look like this:<\/strong><\/p>\n<p><code><br \/>\nExecuting: mnistCUDNN<br \/>\ncudnnGetVersion() : 8907 , CUDNN_VERSION from cudnn.h : 8907 (8.9.7)<br \/>\nHost compiler version : GCC 11.4.0<\/code><\/p>\n<p>There are 1 CUDA capable devices on your machine :<br \/>\ndevice 0 : sms 10 Capabilities 8.6, SmClock 1755.0 Mhz, MemSize (Mb) 14878, MemClock 6251.0 Mhz, Ecc=1, 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 0 memory<\/p>\n<p>\/\/\/\/\/\/\/ more output cut for brevity \/\/\/\/\/\/\/<\/p>\n<p>Resulting 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<div class=\"slideme\"><dl class=\"slideme__list\"><dt class=\"slideme__term\"><a href=\"#slideme-check-tensorflow\" aria-expanded=\"false\" aria-controls=\"slideme-check-tensorflow\" class=\"slideme__heading slideme__trigger\">Check Tensorflow<\/a><\/dt><dd class=\"slideme__description\" id=\"slideme-check-tensorflow\" aria-hidden=\"true\"><p><\/p>\n<p>Tensorflow version:<\/p>\n<p><code>python3 -c 'import tensorflow as tf; print(tf.__version__)'<\/code><code dir=\"ltr\"><\/code><\/p>\n<p>Test tensorflow GPU support:<\/p>\n<p><code dir=\"ltr\"><span class=\"pln\">python3 -c \"import tensorflow as tf; print(tf.config.list_physical_devices('GPU'))\"<\/span><\/code><\/p>\n<p>Tensorflow benchmark test that tests GPU and performance:<\/p>\n<p><code>python3 \/home\/student\/os-scripts\/tensor-bench.py<\/code><\/p>\n<p><\/p><\/dd><dl><\/div>\n<div class=\"slideme\"><dl class=\"slideme__list\"><dt class=\"slideme__term\"><a href=\"#slideme-test-pytorch-keras-and-pandas\" aria-expanded=\"false\" aria-controls=\"slideme-test-pytorch-keras-and-pandas\" class=\"slideme__heading slideme__trigger\">Test pyTorch, keras and pandas <\/a><\/dt><dd class=\"slideme__description\" id=\"slideme-test-pytorch-keras-and-pandas\" aria-hidden=\"true\"><p><\/p>\n<p>The following programs will test if the software is installed correctly on your VM<\/p>\n<p>pyTorch<\/p>\n<p><code>python3 \/home\/student\/os-scripts\/torch-test.py<\/code><\/p>\n<p>keras<\/p>\n<p><code>python3 \/home\/student\/os-scripts\/keras-test.py<\/code><\/p>\n<p>pandas<\/p>\n<p><code>python3 \/home\/student\/os-scripts\/pandas-test.py<\/code><\/p>\n<p><\/p><\/dd><dl><\/div>\n<div class=\"slideme\"><dl class=\"slideme__list\"><dt class=\"slideme__term\"><a href=\"#slideme-tensorrt\" aria-expanded=\"false\" aria-controls=\"slideme-tensorrt\" class=\"slideme__heading slideme__trigger\">TensorRT<\/a><\/dt><dd class=\"slideme__description\" id=\"slideme-tensorrt\" aria-hidden=\"true\"><p><\/p>\n<p>NVIDIA TensorRT is a high-performance inference optimizer and runtime that delivers low latency and high throughput for deep learning inference applications.<\/p>\n<p>TensorRT is installed on this image and you can check the samples here:<\/p>\n<p>\/usr\/src\/tensorrt\/samples<\/p>\n<p>Unfortunately the tensorflow integration is not working. Most likely this version of tensorflow is not enabled for TensorRT support. Probably the easiest way to run tensorflow with tensorRT support is to load the docker image. Note: you need to change the default docker networking, conflicts with the Carleton wireless network.<\/p>\n<p><\/p>\n<p><\/p>\n<p><\/p><\/dd><dl><\/div>\n<p><\/p>\n<h2><a id=\"troubleshooting\"><\/a>Troubleshooting<\/h2>\n<div class=\"slideme\"><dl class=\"slideme__list\"><dt class=\"slideme__term\"><a href=\"#slideme-extend-the-lvm-filesystem\" aria-expanded=\"false\" aria-controls=\"slideme-extend-the-lvm-filesystem\" class=\"slideme__heading slideme__trigger\">Extend the LVM filesystem <\/a><\/dt><dd class=\"slideme__description\" id=\"slideme-extend-the-lvm-filesystem\" aria-hidden=\"true\"><p><\/p>\n<p>Usually the file system is using all the allocated space assigned to it.\u00a0 When Openstack assigns disk space it will allocate the same amount as your disk mage and sometimes that disk image will have far less space than what is allocated for you. To expand your disk space to the total amount that was allocated to you can run the following command:<\/p>\n<p><code>sudo \/home\/student\/os-scripts\/extend-lvm.sh \/dev\/vda<\/code><\/p>\n<p>LVM is the logical volume manager that allows &#8216;dynamic&#8217; allocation of disk space.<\/p>\n<p><\/p><\/dd><dl><\/div>\n<div class=\"slideme\"><dl class=\"slideme__list\"><dt class=\"slideme__term\"><a href=\"#slideme-numa-node-read-from-sysfs\" aria-expanded=\"false\" aria-controls=\"slideme-numa-node-read-from-sysfs\" class=\"slideme__heading slideme__trigger\">NUMA node read from SysFS...<\/a><\/dt><dd class=\"slideme__description\" id=\"slideme-numa-node-read-from-sysfs\" aria-hidden=\"true\"><p><\/p>\n<p>Because this is &#8216;bleeding&#8217; edge software you can expect some software issues. This one in particular is concerning NUMA node error reading from memory. If you want to fix this particular &#8216;warning&#8217; there is a bash script you can run:<\/p>\n<p><code>sudo \/home\/student\/os-scripts\/numa-node-fix.bash<\/code><\/p>\n<p><strong>Reference<\/strong>: <a href=\"https:\/\/gist.github.com\/zrruziev\/b93e1292bf2ee39284f834ec7397ee9f\" target=\"_blank\" rel=\"noopener noreferrer\">https:\/\/gist.github.com\/zrruziev\/b93e1292bf2ee39284f834ec7397ee9f<\/a><\/p>\n<p><\/p><\/dd><dl><\/div>\n<div class=\"slideme\"><dl class=\"slideme__list\"><dt class=\"slideme__term\"><a href=\"#slideme-need-to-use-another-version-of-gcc\" aria-expanded=\"false\" aria-controls=\"slideme-need-to-use-another-version-of-gcc\" class=\"slideme__heading slideme__trigger\">Need to use another version of gcc?<\/a><\/dt><dd class=\"slideme__description\" id=\"slideme-need-to-use-another-version-of-gcc\" aria-hidden=\"true\"><p><\/p>\n<p>There are two versions of gcc installed on this VM. You can use the update-alternatives to switch between the versions:<\/p>\n<pre>  sudo update-alternatives --config gcc<\/pre>\n<p><\/p><\/dd><dl><\/div>\n","protected":false},"excerpt":{"rendered":"<p>GPU VM Account Details Testing the GPU VM software Troubleshooting 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. Even though Ubuntu 24.04 is the current long release version we chose Ubuntu 22.04 because some of the dependent libraries were not [&hellip;]<\/p>\n","protected":false},"author":7,"featured_media":0,"parent":808,"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>SCS GPU VM 2024-5 - School of Computer Science<\/title>\n<meta name=\"description\" content=\"GPU VM Account Details Testing the GPU VM software Troubleshooting This page will show you how to test your virtual machine&#039;s GPU and access and test the\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/carleton.ca\/scs\/tech-support\/cuda-gpu-computing\/scs-gpu-vm-2024-5\/\" \/>\n<meta name=\"twitter:label1\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data1\" content=\"7 minutes\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"WebPage\",\"@id\":\"https:\/\/carleton.ca\/scs\/tech-support\/cuda-gpu-computing\/scs-gpu-vm-2024-5\/\",\"url\":\"https:\/\/carleton.ca\/scs\/tech-support\/cuda-gpu-computing\/scs-gpu-vm-2024-5\/\",\"name\":\"SCS GPU VM 2024-5 - School of Computer Science\",\"isPartOf\":{\"@id\":\"https:\/\/carleton.ca\/scs\/#website\"},\"datePublished\":\"2024-07-29T14:29:57+00:00\",\"dateModified\":\"2025-05-09T18:39:43+00:00\",\"description\":\"GPU VM Account Details Testing the GPU VM software Troubleshooting This page will show you how to test your virtual machine's GPU and access and test the\",\"breadcrumb\":{\"@id\":\"https:\/\/carleton.ca\/scs\/tech-support\/cuda-gpu-computing\/scs-gpu-vm-2024-5\/#breadcrumb\"},\"inLanguage\":\"en-US\",\"potentialAction\":[{\"@type\":\"ReadAction\",\"target\":[\"https:\/\/carleton.ca\/scs\/tech-support\/cuda-gpu-computing\/scs-gpu-vm-2024-5\/\"]}]},{\"@type\":\"BreadcrumbList\",\"@id\":\"https:\/\/carleton.ca\/scs\/tech-support\/cuda-gpu-computing\/scs-gpu-vm-2024-5\/#breadcrumb\",\"itemListElement\":[{\"@type\":\"ListItem\",\"position\":1,\"name\":\"Home\",\"item\":\"https:\/\/carleton.ca\/scs\/\"},{\"@type\":\"ListItem\",\"position\":2,\"name\":\"Technical Support\",\"item\":\"https:\/\/carleton.ca\/scs\/tech-support\/\"},{\"@type\":\"ListItem\",\"position\":3,\"name\":\"SCS GPU Computing with Openstack\",\"item\":\"https:\/\/carleton.ca\/scs\/tech-support\/cuda-gpu-computing\/\"},{\"@type\":\"ListItem\",\"position\":4,\"name\":\"SCS GPU VM 2024-5\"}]},{\"@type\":\"WebSite\",\"@id\":\"https:\/\/carleton.ca\/scs\/#website\",\"url\":\"https:\/\/carleton.ca\/scs\/\",\"name\":\"School of Computer Science\",\"description\":\"Carleton University\",\"potentialAction\":[{\"@type\":\"SearchAction\",\"target\":{\"@type\":\"EntryPoint\",\"urlTemplate\":\"https:\/\/carleton.ca\/scs\/?s={search_term_string}\"},\"query-input\":\"required name=search_term_string\"}],\"inLanguage\":\"en-US\"}]}<\/script>\n<!-- \/ Yoast SEO plugin. -->","yoast_head_json":{"title":"SCS GPU VM 2024-5 - School of Computer Science","description":"GPU VM Account Details Testing the GPU VM software Troubleshooting This page will show you how to test your virtual machine's GPU and access and test the","robots":{"index":"index","follow":"follow","max-snippet":"max-snippet:-1","max-image-preview":"max-image-preview:large","max-video-preview":"max-video-preview:-1"},"canonical":"https:\/\/carleton.ca\/scs\/tech-support\/cuda-gpu-computing\/scs-gpu-vm-2024-5\/","twitter_misc":{"Est. reading time":"7 minutes"},"schema":{"@context":"https:\/\/schema.org","@graph":[{"@type":"WebPage","@id":"https:\/\/carleton.ca\/scs\/tech-support\/cuda-gpu-computing\/scs-gpu-vm-2024-5\/","url":"https:\/\/carleton.ca\/scs\/tech-support\/cuda-gpu-computing\/scs-gpu-vm-2024-5\/","name":"SCS GPU VM 2024-5 - School of Computer Science","isPartOf":{"@id":"https:\/\/carleton.ca\/scs\/#website"},"datePublished":"2024-07-29T14:29:57+00:00","dateModified":"2025-05-09T18:39:43+00:00","description":"GPU VM Account Details Testing the GPU VM software Troubleshooting This page will show you how to test your virtual machine's GPU and access and test the","breadcrumb":{"@id":"https:\/\/carleton.ca\/scs\/tech-support\/cuda-gpu-computing\/scs-gpu-vm-2024-5\/#breadcrumb"},"inLanguage":"en-US","potentialAction":[{"@type":"ReadAction","target":["https:\/\/carleton.ca\/scs\/tech-support\/cuda-gpu-computing\/scs-gpu-vm-2024-5\/"]}]},{"@type":"BreadcrumbList","@id":"https:\/\/carleton.ca\/scs\/tech-support\/cuda-gpu-computing\/scs-gpu-vm-2024-5\/#breadcrumb","itemListElement":[{"@type":"ListItem","position":1,"name":"Home","item":"https:\/\/carleton.ca\/scs\/"},{"@type":"ListItem","position":2,"name":"Technical Support","item":"https:\/\/carleton.ca\/scs\/tech-support\/"},{"@type":"ListItem","position":3,"name":"SCS GPU Computing with Openstack","item":"https:\/\/carleton.ca\/scs\/tech-support\/cuda-gpu-computing\/"},{"@type":"ListItem","position":4,"name":"SCS GPU VM 2024-5"}]},{"@type":"WebSite","@id":"https:\/\/carleton.ca\/scs\/#website","url":"https:\/\/carleton.ca\/scs\/","name":"School of Computer Science","description":"Carleton University","potentialAction":[{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https:\/\/carleton.ca\/scs\/?s={search_term_string}"},"query-input":"required name=search_term_string"}],"inLanguage":"en-US"}]}},"acf":{"banner_image_type":"upload","banner_button":"no","banner_uploaded_image":{"ID":19072,"id":19072,"title":"NVIDIA 1080 Ti","filename":"GPU3-1600x700-1.jpg","filesize":549344,"url":"https:\/\/carleton.ca\/scs\/wp-content\/uploads\/GPU3-1600x700-1.jpg","link":"https:\/\/carleton.ca\/scs\/tech-support\/cuda-gpu-computing\/scs-gpu-vm-2024-5\/gpu3-1600x700\/","alt":"NVIDIA 1080 Ti","author":"7","description":"NVIDIA 1080 Ti","caption":"","name":"gpu3-1600x700","status":"inherit","uploaded_to":18955,"date":"2024-08-20 13:28:11","modified":"2024-08-20 13:28:32","menu_order":0,"mime_type":"image\/jpeg","type":"image","subtype":"jpeg","icon":"https:\/\/carleton.ca\/scs\/wp\/wp-includes\/images\/media\/default.png","width":1600,"height":700,"sizes":{"thumbnail":"https:\/\/carleton.ca\/scs\/wp-content\/uploads\/GPU3-1600x700-1-160x70.jpg","thumbnail-width":160,"thumbnail-height":70,"medium":"https:\/\/carleton.ca\/scs\/wp-content\/uploads\/GPU3-1600x700-1-240x105.jpg","medium-width":240,"medium-height":105,"medium_large":"https:\/\/carleton.ca\/scs\/wp-content\/uploads\/GPU3-1600x700-1-768x336.jpg","medium_large-width":768,"medium_large-height":336,"large":"https:\/\/carleton.ca\/scs\/wp-content\/uploads\/GPU3-1600x700-1-400x175.jpg","large-width":400,"large-height":175,"gallery-thumb":"https:\/\/carleton.ca\/scs\/wp-content\/uploads\/GPU3-1600x700-1-300x230.jpg","gallery-thumb-width":300,"gallery-thumb-height":230,"1536x1536":"https:\/\/carleton.ca\/scs\/wp-content\/uploads\/GPU3-1600x700-1-1536x672.jpg","1536x1536-width":1536,"1536x1536-height":672,"2048x2048":"https:\/\/carleton.ca\/scs\/wp-content\/uploads\/GPU3-1600x700-1.jpg","2048x2048-width":1600,"2048x2048-height":700,"banner":"https:\/\/carleton.ca\/scs\/wp-content\/uploads\/GPU3-1600x700-1.jpg","banner-width":1600,"banner-height":700,"people":"https:\/\/carleton.ca\/scs\/wp-content\/uploads\/GPU3-1600x700-1-200x200.jpg","people-width":200,"people-height":200,"post-thumb":"https:\/\/carleton.ca\/scs\/wp-content\/uploads\/GPU3-1600x700-1-300x230.jpg","post-thumb-width":300,"post-thumb-height":230,"rotator-image":"https:\/\/carleton.ca\/scs\/wp-content\/uploads\/GPU3-1600x700-1-800x600.jpg","rotator-image-width":800,"rotator-image-height":600,"video-thumb":"https:\/\/carleton.ca\/scs\/wp-content\/uploads\/GPU3-1600x700-1-360x158.jpg","video-thumb-width":360,"video-thumb-height":158}},"banner_opacity":"dark"},"_links":{"self":[{"href":"https:\/\/carleton.ca\/scs\/wp-json\/wp\/v2\/pages\/18955"}],"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\/7"}],"replies":[{"embeddable":true,"href":"https:\/\/carleton.ca\/scs\/wp-json\/wp\/v2\/comments?post=18955"}],"version-history":[{"count":4,"href":"https:\/\/carleton.ca\/scs\/wp-json\/wp\/v2\/pages\/18955\/revisions"}],"predecessor-version":[{"id":21086,"href":"https:\/\/carleton.ca\/scs\/wp-json\/wp\/v2\/pages\/18955\/revisions\/21086"}],"up":[{"embeddable":true,"href":"https:\/\/carleton.ca\/scs\/wp-json\/wp\/v2\/pages\/808"}],"wp:attachment":[{"href":"https:\/\/carleton.ca\/scs\/wp-json\/wp\/v2\/media?parent=18955"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}