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Deploy Docker Desktop on Windows with WSL2

Before you can install and run Eggplant Generator in Docker Desktop on a Windows system (or systems), you need to verify your system meets the following prerequisites and perform the following pre-deployment steps.

Docker-Specific Software Requirements for Eggplant Generator on Windows​

note

See the Prerequisites for information about the required hardware and memory for Eggplant Generator.

SoftwareVersion
Microsoft Windows (64-bit)
  • Server 2022 (Build 20348.740 and higher) / 2023
  • 10 (version 2004 and higher, Build 19043 and higher) / 11
    • Docker DesktopDesktop with WSL 2 enabled, up-to-date. See Install Docker Desktop on Windows for more information.
    Docker ComposeCompose 2.23.0 or higher (included in the Docker Desktop bundle)
    NVIDIA CUDA DriverCUDA Driver for WSL, 12.x / up-to-date. See GPU in Windows Subsystem for Linux (WSL) for information about installing the CUDA driver.

    Installation and Pre-Deployment Checklist​

    Installing Docker Desktop​

    Install Docker Desktop on the Windows system where you plan to run the Docker container that will host Eggplant Generator.

    1. Download the required software. Refer to the Software Requirements table above for the list of what you need.

    2. Install Docker Desktop with the default settings that have Use WSL 2 instead of Hyper-V (recommended) enabled as shown in the sample Docker Desktop installation screen below. For more information, see Install Docker Desktop on Windows in the Docker documentation.

    3. Proceed through the installation. The Docker installation progress displays in the installer.

    Verifying the Docker Desktop Installation​

    1. Open a shell on your Windows system, if you do not already have one open.

    2. Verify that the Docker Compose version is 2.23.0 or higher by running the following command in your shell. If the output from the following command shows an older version, delete the cache folder at C:\Users\<username>\.docker and restart Docker Desktop.

      docker compose version

    3. In the Docker Desktop app, verify that WSL 2 is enabled in the Settings, under Resources > Advanced as shown in the following sample Docker Desktop screen.

    tip

    If you do not have access to a GPU skip the rest of this section and continue to Deploying Eggplant Generator with Docker to install and deploy Eggplant Generator.

    Pre-deployment Checklist​

    Before you install Eggplant Generator, please verify the items on this pre-deployment checklist:

    1. Verify the NVIDIA CUDA GPU is Installed and Configured​

    Verify the NVIDIA CUDA GPU is installed and configured on the system where you plan to run Eggplant Generator in a container:

    1. Run the following command to verify the GPU is installed and configured:

      nvidia-smi

    2. Compare the expected output below with the output on your system. The output below (from the command in the previous step) shows a successful GPU installation and configuration.

      Mon Jan 20 05:48:32 2025
      +-----------------------------------------------------------------------------------------+
      | NVIDIA-SMI 553.62                 Driver Version: 553.62         CUDA Version: 12.4     |
      |-----------------------------------------+------------------------+----------------------+
      | GPU  Name                     TCC/WDDM  | Bus-Id          Disp.A | Volatile Uncorr. ECC |
      | Fan  Temp   Perf          Pwr:Usage/Cap |           Memory-Usage | GPU-Util  Compute M. |
      |                                         |                        |               MIG M. |
      |=========================================+========================+======================|
      |   0  NVIDIA RTX A2000             WDDM  |   00000000:21:00.0 Off |                  Off |
      | 30%   29C    P8              4W /   70W |     375MiB /   6138MiB |      0%      Default |
      |                                         |                        |                  N/A |
      +-----------------------------------------+------------------------+----------------------+

      +-----------------------------------------------------------------------------------------+
      | Processes:                                                                              |
      |  GPU   GI   CI        PID   Type   Process name                              GPU Memory |
      |        ID   ID                                                               Usage      |
      |=========================================================================================|
      |    0   N/A  N/A      8424    C+G   C:\Windows\explorer.exe                     N/A      |
      |    0   N/A  N/A     13548    C+G   ...crosoft\Edge\Application\msedge.exe      N/A      |
      |    0   N/A  N/A     14320    C+G   ...n\132.0.2957.115\msedgewebview2.exe      N/A      |
      |    0   N/A  N/A     14780    C+G   ...5n1h2txyewy\ShellExperienceHost.exe      N/A      |
      |    0   N/A  N/A     16236    C+G   ...\Docker\frontend\Docker Desktop.exe      N/A      |
      |    0   N/A  N/A     16480    C+G   ....Search_cw5n1h2txyewy\SearchApp.exe      N/A      |
      |    0   N/A  N/A     17660    C+G   ...ekyb3d8bbwe\PhoneExperienceHost.exe      N/A      |
      |    0   N/A  N/A     17972    C+G   ...n\132.0.2957.115\msedgewebview2.exe      N/A      |
      |    0   N/A  N/A     19020    C+G   ...CBS_cw5n1h2txyewy\TextInputHost.exe      N/A      |
      |    0   N/A  N/A     19660    C+G   ....Search_cw5n1h2txyewy\SearchApp.exe      N/A      |
      +-----------------------------------------------------------------------------------------+

    2. Verify the NVIDIA CUDA GPU is Installed and Configured on Container Environments​

    Verify the NVIDIA CUDA GPU is installed and configured on the system where you plan to run Eggplant Generator on container environments:

    1. Run the following command to verify the CUDA GPU is installed and configured:

      docker run --rm -it --gpus=all nvcr.io/nvidia/k8s/cuda-sample:nbody nbody -gpu -benchmark

    2. Compare the expected output below with the output on your system. The output below (from the command in the previous step) shows a successful GPU installation and configuration.

      Run "nbody -benchmark [-numbodies=<numBodies>]" 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=<N>    (number of bodies (>= 1) to run in simulation)
              -device=<d>       (where d=0,1,2.... for the CUDA device to use)
              -numdevices=<i>   (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)
              -cpu              (run n-body simulation on the CPU)
              -tipsy=<file.bin> (load a tipsy model file for simulation)

      NOTE: The CUDA Samples are not meant for performance measurements. Results may vary when GPU Boost is enabled.

      > Windowed mode
      > Simulation data stored in video memory
      > Single precision floating point simulation
      > 1 Devices used for simulation
      GPU Device 0: "Ampere" with compute capability 8.6

      > Compute 8.6 CUDA device: [NVIDIA RTX A2000]
      26624 bodies, total time for 10 iterations: 31.963 ms
      = 221.767 billion interactions per second
      = 4435.343 single-precision GFLOP/s at 20 flops per interaction

    Next Step: Deploying Eggplant Generator with Docker​

    After you install and verify your Docker Desktop and NVIDIA GPU CUDA configuration, and complete the pre-deployment checklist, continue to Deploying Eggplant Generator with Docker to install and deploy Eggplant Generator.

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