Sudo usermod -aG docker $USER Step 9) test docker. Sudo apt install -y docker.io Step 8) give user permissions to use docker without sudo Sudo apt install -y nvidia-driver nvidia-cuda-toolkit Step 6) test nvidia driver Sudo apt update -y & sudo apt upgrade -y & sudo apt dist-upgrade -y Step 5) install nvidia gpu drivers and CUDA software See Install following software within Kali Linux Step 4) upgrade current distro See: 2.1.6) If missing Dlls are reported: See: tensorflow/tensorflow#44291 2.2) Install Docker for Desktop from Step 3) Install one of following presentation managers: 3.1) GWSL for Windows (XWindows manager) from Microsoft Store or 3.2) vcxsrv for Windows (XWindows manager) from 3.3) apt install xrdp (on Linux) to use Windows RDP instead of XWindows manager. See: 2.1.4) Verify GPU is accessible from Tensorflow: python -c "import tensorflow as tf tf.config.list_physical_devices('GPU')" 2.1.5) If cudart64_101.dll is missing. Using Tensorflow to verify access to GPU. Only needed if CUDA and CUDNN are wanted. Wsl -install -d kali-linux Step 2) On Windows: 2.1) Install NVidia for WSL Driver: Following steps (2.1.x) are optional. Installation takes 30 minutes on AMD 4800H notebook Step 1) On Windows: install Kali Linux in WSL2. Installation of other GUIs may be possible using similar methods (KeX, XFCE) but others don't presently work (Gnome and Cinnamon). Seems stable but some things aren't working in GUI such as audio and network management. Notes on installing Kali Linux in WSL2 including KDE Plasma GUI, Docker, Nvidia GPU and CUDA Use case is for exploring WSL2 GUI capabilities for data science.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |