EVO X

EvoX Installation Guide

EvoX Installation Guide

Install EvoX

EvoX is available at PyPI and can be installed via:

# install pytorch first
# for example:
pip install torch

# then install EvoX
pip install "evox[default]"

You can also assign extra options during the installation, currently available extras are vis, neuroevolution, test, docs, default. For example, to install EvoX with all features, run the following command:

pip install "evox[vis,neuroevolution]"

Install PyTorch with accelerator support

evox relies on torch to provide hardware acceleration. The overall architecture of these Python packages looks like this:

stateDiagram-v2
    torch : torch
    nv_gpu : NVIDIA GPU
    amd_gpu : AMD GPU
    cpu : CPU

    direction LR

    evox --> torch
    torch --> nv_gpu
    torch --> amd_gpu
    torch --> cpu

To summarize, whether evox has CPU support or Nvidia GPU support (CUDA) or AMD GPU support (ROCm) depends on the installed PyTorch version. Please refer to the PyTorch official website for more installation help: torch

Nvidia GPU support on Windows

EvoX support GPU acceleration through PyTorch. There are two ways to use PyTorch with GPU acceleration on Windows:

  1. Using WSL 2 (Windows Subsystem for Linux) and install PyTorch on the Linux side.
  2. Directly install PyTorch on Windows.

For option 2, we provide a one-click script for fast deployment on fresh installed windows 10/11 64bit with Nvidia GPUs. The script will not use WSL 2 and will install the native Pytorch version on Windows. It will automatically install related applications like VSCode, Git and MiniForge3.

  • Ensure the Nvidia driver is properly installed first. Otherwise the script will fall back to cpu mode.
  • When running the script, ensure a stable network (accessible to github.com etc.).
  • If the script is failed due to network failure, close and reopen it to continue the installation.

Manual installation on Windows

If you prefer to install PyTorch directly on Windows manually, you can follow the steps below:

  1. Install Nvidia driver as mentioned above.
  2. Install Python 3.10 or above from python.org.
  3. Install PyTorch.
  4. (Optional) Install triton-windows for torch.compile support on Windows.
  5. Install EvoX.

Windows WSL 2

Download the latest NVIDIA Windows GPU Driver and install it. Then your WSL 2 will support Nvidia GPUs in its Linux environments.

Warning: Do NOT install any NVIDIA GPU Linux driver within WSL 2. Install the driver on the Windows side.

NVIDIA has a detailed [CUDA on WSL User Guide](https://docs.nvidia.com/cuda/wsl-user-guide/index.html)

AMD GPU (ROCm) support

We recommend using a Docker container from rocm/pytorch.

docker run -it --network=host --device=/dev/kfd --device=/dev/dri --group-add=video --ipc=host --cap-add=SYS_PTRACE --security-opt seccomp=unconfined --shm-size 8G -v $HOME/dockerx:/dockerx -w /dockerx rocm/pytorch​:latest

Verify the installation

Open a Python terminal, and run the following:

from torch.utils.collect_env import get_pretty_env_info
import evox

print(get_pretty_env_info())
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