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![]() Best Video Converters Web Development Install The LinuxDownload and install the Linux kernel update package.To get the best out of the file system performance when bind-mounting files, we recommend storing source code and other data that is bind-mounted into Linux containers (i.e., with docker run -v :) in the Linux file system, rather than the Windows file system. For detailed instructions, refer to the Microsoft documentation. Enable WSL 2 feature on Windows. Install Windows 10, version 1903 or higher. PrerequisitesBefore you install the Docker Desktop WSL 2 backend, you must complete the following steps: Download mac emulator sheepshaverInstead, from a Linux shell use a command like docker run -v ~/my-project:/sources Where ~ is expanded by the Linux shell to $HOME. Therefore avoidDocker run -v /mnt/c/users:/users (where /mnt/c is mounted from Windows). Performance is much higher when files are bind-mounted from the LinuxFilesystem, rather than remoted from the Windows host. For example, some web development workflows rely on inotify events for automatic reloading when files have changed. Linux containers only receive file change events (“inotify events”) if theOriginal files are stored in the Linux filesystem. Follow the usual installation instructions to install Docker Desktop. InstallEnsure you have completed the steps described in the Prerequisites section before installing the Docker Desktop Stable 2.3.0.2 release. To avoid any potential conflicts with using WSL 2 on Docker Desktop, you must uninstall any previous versions of Docker Engine and CLI installed directly through Linux distributions before installing Docker Desktop.Download Docker Desktop Stable 2.3.0.2 or a later release. If you have concerns about CPU or memory usage, you can configure limits on the memory, CPU, Swap size allocated to the WSL 2 utility VM. To change your default WSL distro, run wsl -set-default. WSL can run distributions in both v1 or v2 mode.To upgrade your existing Linux distro to v2, run:To set v2 as the default version for future installations, run:When Docker Desktop restarts, go to Settings > Resources > WSL Integration.The Docker-WSL integration will be enabled on your default WSL distribution. Start Docker Desktop from the Windows Start menu.From the Docker menu, select Settings > General.Select the Use WSL 2 based engine check box.If you have installed Docker Desktop on a system that supports WSL 2, this option will be enabled by default.Ensure the distribution runs in WSL 2 mode. Read the information displayed on the screen and enable WSL 2 to continue. Open VSCode and install the Remote - WSL extension. This workflow can be pretty straightforward if you are using VSCode. After you have enabled WSL 2 on Docker Desktop, you can start working with your code inside the Linux distro and ideally with your IDE still in Windows. We recommend that you have your code in your default Linux distribution for the best development experience using Docker and WSL 2. Alpine users can use the alpine-pkg-glibc package to deploy glibc alongside musl to run the integration.The following section describes how to start developing your applications using Docker and WSL 2. This can cause issues when running musl-based distros such as Alpine Linux. ![]() Please let us know your feedback by creating an issue in the Docker Desktop for Windows GitHub repository and adding the WSL 2 label. Default to use 64 Cores/SMGPU Device 0: "GeForce RTX 2060 with Max-Q Design" with compute capability 7.5> Compute 7.5 CUDA device: 30720 bodies, total time for 10 iterations: 69.280 ms= 136.219 billion interactions per second= 2724.379 single-precision GFLOP/s at 20 flops per interactionYour feedback is very important to us. Results may vary when GPU Boost is enabled.> Single precision floating point simulationMapSMtoCores for SM 7.5 is undefined. For the CUDA device to use ) -numdevices= (where i =(number of CUDA devices > 0 ) to use for simulation ) -compare (compares simulation results running once on the default GPU and once on the CPU)-tipsy= (load a tipsy model file for simulation ) > NOTE: The CUDA Samples are not meant for performance measurements. Make sure the WSL 2 backend is enabled in Docker DesktopTo validate that everything works as expected, run the following command to run a short benchmark on your GPU:$ docker run -rm -it -gpus =all nvcr.io/nvidia/k8s/cuda-sample:nbody nbody -gpu -benchmark Run "nbody -benchmark " to measure performance.-fullscreen (run n-body simulation in fullscreen mode)-fp64 (use double precision floating point values for simulation)-hostmem (stores simulation data in host memory)-benchmark (run benchmark to measure performance)-numbodies= (number of bodies (>= 1 ) to run in simulation ) -device= (where d =0,1,2.
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