Go to cloud.io.net
Click on "Connect New Device"
Choose the supplier you wish your hardware be grouped on
Add a unique name for your device, Ideal format would be similar to the following: My-Test-Device
Click on the "Windows" field
If you choose GPU Worker and your device doesn't have GPU the setup will fail
Follow the steps on our documentation for Docker, Cuda and Nvidia drivers installtion
Run this command in terminal and make sure docker desktop is running on the background
Keep hitting refresh while you wait for your new device to connect.
To check if it's enabled, go to task manager performance so you see here :
If it's not enabled, follow these steps:
- To enable virtualization technology in your BIOS or UEFI settings, you need to access your computer's BIOS or UEFI configuration menu during the boot process. The specific steps can vary depending on your computer's manufacturer and model, but here are the general steps to enable virtualization.
- install WSL 2 by opening the PowerShell as an Administrator. To do this, search for "PowerShell" in the Start menu, right-click on "Windows PowerShell," and select "Run as administrator."
- Run the following command to enable the WSL feature in Windows 10/11:
dism.exe /online /enable-feature /featurename:Microsoft-Windows-Subsystem-Linux /all /norestart
- Then Enable the Virtual Machine Platform Feature while still in the same PowerShell window by running the following command:
dism.exe /online /enable-feature /featurename:VirtualMachinePlatform /all /norestart
- Then set WSL 2 as the Default Version (you might be required to restart your machine sometimes):
wsl --set-default-version 2
Go to the docker website: https://www.docker.com/products/docker-desktop/ and click on download for Windows:
Run the installation process and after the installation is done reboot the machine:
- Start the docker desktop and in docker, select wsl2 to integrate:
- Verify the installation by opening CMD and typing:
- You will then receive the following output:
Docker version 24.0.6, build ed223bc
That's it. You have docker installed and ready.
- To check that you have the correct drivers, open a command line on your Windows PC (Windows key + R, type cmd) and type into it the following:nvidia-smi . If you encounter the following error message:
C:\Users>nvidia-smi 'nvidia-smi' is not recognized as an internal or external command, operable program or batch file.
It means that you do not have NVIDIA drivers installed. To install them, follow the steps below:
- Go to the Nvidia website https://www.nvidia.com/download/index.aspx and type in your GPU's name, then click on search:
- Click on the "Download" button for the NVIDIA driver appropriate for your GPU and Windows version.
- Once the download is complete, start the installation, select the first option and click on "Agree and Continue".
- Once the installation is done, it's essential to reboot your computer. Restart your machine to ensure that the new NVIDIA driver is fully integrated into your system.
- After your computer has rebooted, open a Command Prompt (Windows key + R, type cmd) and type the following command:
- You should see this outcome:
That's it. You have the correct NVIDIA drivers installed and ready.
- Visit the NVIDIA CUDA Toolkit download page: https://developer.nvidia.com/cuda-downloads
- Select your operating system (e.g., Windows).
- Choose your architecture (usually x86_64 for 64-bit Windows).
- Download the exe local Installer. After downloading the file, run the installer :
- And follow the installation process.
- Then, verify the installation process. Open a Command Prompt (Windows key + R, type cmd) and type the following command:
- You should get the following answer:
nvcc: NVIDIA (R) Cuda compiler driver Copyright (c) 2005-2022 NVIDIA Corporation Built on Wed_Sep_21_10:41:10_Pacific_Daylight_Time_2022 Cuda compilation tools, release 11.8, V11.8.89 Build cuda_11.8.r11.8/compiler.31833905_0
That's it. You have the CUDA toolkit installed and ready.
Be aware that we are now installing a 20GB size container it contains all the packages needed for the ML apps, everything happens inside the container, nothing gets out from the container to your filesystem
Updated 5 days ago