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Setup and installation of NVIDIA CUDA Suit on GCP

This section describes how to provision and connect to NVIDIA CUDA Suit VM solution on GCP.

  1. Open NVIDIA CUDA Suit listing on GCP Marketplace
  2. Click Get Started.

/img/gcp/nvidia-ubuntu/marketplace.png

It will ask you to enable the API’s if they are not enabled already for your account. Please click on enable as shown in the screenshot.

/img/gcp/nvidia-ubuntu/enable-api.png

  • It will take you to the agreement page. On this page, you can change the project from the project selector on top navigator bar as shown in the below screenshot.

  • Accept the Terms and agreements by ticking the checkbox and clicking on the AGREE button. /img/common/gcp_agreement_page.png

  • It will show you the successfully agreed popup page. Click on Deploy. /img/common/gcp_agreement_accept_page.png

  • On deployment page, give a name to your deployment.

  • In Deployment Service Account section, click on Existing radio button and Choose a service account from the Select a Service Account dropdown.
  • If you don't see any service account in dropdown, then change the radio button to New Account and create the new service account here.
  • If after selecting New Account option, you get below permission error message then please reach out to your GCP admin to create service account by following Step by step guide to create GCP Service Account and then refresh this deployment page once the service account is created, it should be available in the dropdown.

  • You are missing resourcemanager.projects.setIamPolicy permission, which is needed to set the required roles on the created Service Account
  • Select a zone where you want to launch the VM(such as us-east1-a)
  • Optionally change the number of cores and amount of memory. ( This defaults to 2 vCPUs and 7.5 GB ram)
  • The VM can only be deployed with the NVIDIA GPU instance. You can change the Type and number of GPUs from the dropdown. (This defaults to 1 NVIDIA T4 GPU )

Note: GPU availability is limited to certain zones.

NOTE: While deploying the instance on GPU, if you encounter the quota exhaust error or you are unable to deploy the instance on GPU VM then please refer to our Request Quota on Google Cloud Platform

  • Optionally change the boot disk type and size. (This defaults to ‘Standard Persistent Disk’ and 45 GB respectively)
  • Optionally change the network name and subnetwork names. Be sure that whichever network you specify has ports 22 (for ssh) and 3389 (for RDP) exposed.
  • Click Deploy when you are done.
  • NVIDIA CUDA Suit will begin deploying.

/img/gcp/nvidia-ubuntu/deployed.png

  1. A summary page displays when the compute engine is successfully deployed. Click on the Instance link to go to the instance page .

  2. On the instance page, click on the “SSH” button, select “Open in browser window”.

/img/gcp/jupyter-python-notebook/ssh-option.png

  1. This will open SSH window in a browser.
  2. Run below command to set the password for “ubuntu” user
sudo passwd ubuntu

/img/gcp/jupyter-python-notebook/ssh-passwd.png

  1. Now the password for ubuntu user is set, you can connect to the VM’s desktop environment from any local windows machine using RDP or linux machine using Remmina.

  2. To connect using RDP via Windows machine, first note the external IP of the VM from VM details page as highlighted below

/img/gcp/jupyter-python-notebook/external-ip.png

  1. Then From your local windows machine, goto “start” menu, in the search box type and select “Remote desktop connection”

  2. In the “Remote Desktop connection” wizard, paste the external ip and click connect

/img/gcp/jupyter-python-notebook/rdp.png

  1. This will connect you to the VM’s desktop environment. Provide “ubuntu” as the userid and the password set in step 6 to authenticate. Click OK

/img/gcp/jupyter-python-notebook/rdp-login.png

  1. Now you are connected to out of box NVIDIA CUDA Suit environment via Windows machines.

/img/aws/nvidia-ubuntu/rdp-desktop.png

  1. To connect using RDP via Linux machine, first note the external IP of the VM from VM details page,then from your local Linux machine, goto menu, in the search box type and select “Remmina”.

    Note: If you don’t have Remmina installed on your Linux machine, firstInstall Remmina as per your linux distribution.

/img/gcp/common/remmina-search.png

  1. In the “Remmina Remote Desktop Client” wizard, select the RDP option from dropdown and paste the external ip and click enter.

/img/gcp/common/remmina-external-ip.png

  1. This will connect you to the VM’s desktop environment. Provide “ubuntu” as the userid and the password set in step 6 to authenticate. Click OK

/img/gcp/common/remmina-rdp-login.png

  1. Now you are connected to out of box NVIDIA CUDA Suit environment via Linux machine.

/img/aws/nvidia-ubuntu/rdp-desktop.png

  1. The VM comes with various NVIDIA utilities and Cuda utilities installed out of the box. To access the Nvidia Nsight Compute , open the terminal from the RDP desktop window and run below command. This command will launch the Nsight Compute user interface.
ncu-ui

/img/aws/nvidia-ubuntu/ncu-ui-command.png

/img/aws/nvidia-ubuntu/ncu-ui.png

  1. To access the Nvidia Nsight System ,open the terminal from the RDP desktop window and run below command. This command will launch the Nsight System user interface.
nsys-ui

/img/aws/nvidia-ubuntu/nsys-ui-command.png

/img/aws/nvidia-ubuntu/nsys-ui-black-theme.png

  1. You can also monitor the GPU utilization of these utilites. To do so, open the new terminal window and run
watch -n 1 nvidia-smi

/img/aws/nvidia-ubuntu/ncu-ui-watch-output.png

/img/aws/nvidia-ubuntu/nsys-ui-watch-output.png

  1. You can find the nsight compute and nsight system setup under /opt/nvidia/ directory.

/img/aws/nvidia-ubuntu/installation-directory.png

/img/aws/nvidia-ubuntu/installation-compute-directory.png

/img/aws/nvidia-ubuntu/installation-system-directory.png

  1. The VM comes with various Nvidia utilies installed out of the box.

/img/aws/nvidia-ubuntu/nvidia-utilies.png

  1. The VM also comes with various CUDA utilies installed out of the box.

/img/aws/nvidia-ubuntu/cuda-utilies.png

  1. Below is the CUDA installation directory
/usr/local/cuda-12.4/

/img/aws/nvidia-ubuntu/cuda-installation.png

  1. All cuda utilities are installed under
/usr/local/cuda-12.4/bin

/img/aws/nvidia-ubuntu/cuda-utilies-installation.png

  1. The VM also comes with the “Nsight compute visual studio code Edition” extension installed out of the box.

/img/aws/nvidia-ubuntu/nsight-vscode-edition.png

Please refer to NVIDIA official documentation for more details on how to use these tools.

NVIDIA Official Documentation

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