GPU Setup#

NeurEco can leverage NVIDIA GPUs for accelerated training. This page explains how to install the required GPU drivers.

On this page

Requirements#

To use GPU acceleration with NeurEco, you need:

  • An NVIDIA GPU with compute capability 7.5 or higher

  • An NVIDIA GPU driver at least as recent as the minimum version in NVIDIA Driver Version; we recommend the version given there or a later one

  • For the Python library, a GPU edition of NeurEco and the CUDA build of PyTorch (see below)

Note

To check your GPU’s compute capability, visit the NVIDIA CUDA GPUs page.

No CUDA toolkit is needed: the desktop app and the neureco command include the CUDA libraries they use, and the Python library uses the CUDA libraries that come with PyTorch.

The CPU-only edition of the neureco package does not use the GPU. A GPU edition comes from the Adagos package index for your PyTorch version, and pip takes the CUDA build of PyTorch from the PyTorch package index (on Windows, PyTorch from PyPI is CPU-only). For PyTorch 2.9, on Windows and Linux:

pip install "neureco~=5.0" --extra-index-url https://download.pytorch.org/whl/cu128 --extra-index-url https://adagos.com/downloads/neureco5/whl/torch2.9/

If you installed the CPU-only edition before, run pip uninstall neureco torch first. The version of a GPU edition ends with its PyTorch and CUDA build, for example 5.0.1974+torch2.9.cu128 on Windows; on Linux, the PyTorch 2.9 and 2.8 editions end in cu129 and run with the CUDA 12.8 build of PyTorch. For PyTorch 2.8 or 2.7, see Installation.

NVIDIA Driver Version#

NeurEco 5.0 uses CUDA 12: the desktop app and the neureco command include CUDA 12.9, and the GPU editions of the Python library use the CUDA 12 libraries of the PyTorch build installed with them.

Driver version

Linux

Windows

Minimum

525.60.13

527.41

Recommended

575.51.03 or newer

576.02 or newer

The minimum versions run CUDA 12 through NVIDIA’s CUDA minor version compatibility. The recommended versions are the drivers of CUDA 12.9, the version included with NeurEco. Use them in particular with GeForce RTX 50 series GPUs and other GPUs of compute capability 9.0 or higher.

To see the installed driver version, run nvidia-smi: it prints the version in its first lines.

Windows Installation#

Step 1: Download the Driver#

Download the latest NVIDIA driver for your GPU from the NVIDIA Driver Downloads page.

Step 2: Install#

  1. Launch the downloaded installer

  2. Follow the on-screen instructions

  3. Restart your computer when prompted

Step 3: Verify#

Open a command prompt and run:

nvidia-smi

You should see information about your GPU and driver version.

Linux Installation (Debian/Ubuntu)#

Steps 1 to 3 are for Ubuntu. On Debian, enable the contrib, non-free and non-free-firmware components in your APT sources (/etc/apt/sources.list or /etc/apt/sources.list.d/debian.sources), run sudo apt update and sudo apt install linux-headers-amd64 nvidia-driver (see the Debian wiki), then continue with Step 4.

Step 1: Add the Repository#

Open a terminal and add the NVIDIA driver repository:

sudo add-apt-repository ppa:graphics-drivers/ppa
sudo apt update

Step 2: Find Available Drivers#

List available driver versions:

ubuntu-drivers list

Or check the NVIDIA Unix Drivers page for the latest version.

Step 3: Install the Driver#

Install the latest driver (replace VERSION_NUMBER with the highest version from the list):

sudo apt install nvidia-driver-VERSION_NUMBER

For example:

sudo apt install nvidia-driver-580

Step 4: Reboot#

Restart your computer to load the new driver:

sudo reboot

Step 5: Verify#

After rebooting, verify the installation:

nvidia-smi

Linux Installation (Red Hat)#

These steps are for Red Hat Enterprise Linux 9 and compatible distributions. For other releases, see the NVIDIA Driver Installation Guide.

Step 1: Enable EPEL Repository#

sudo dnf install https://dl.fedoraproject.org/pub/epel/epel-release-latest-9.noarch.rpm

Step 2: Install the Driver#

Add the NVIDIA repository and install the driver:

sudo dnf config-manager --add-repo https://developer.download.nvidia.com/compute/cuda/repos/rhel9/x86_64/cuda-rhel9.repo
sudo dnf module enable nvidia-driver:open-dkms
sudo dnf install nvidia-open

Or download directly from the NVIDIA Driver Downloads page.

Step 3: Reboot and Verify#

sudo reboot
nvidia-smi

Verifying GPU Usage in NeurEco#

In Python, check that PyTorch can see your GPU (the NeurEco Python library uses the PyTorch installed in your environment):

import torch

# Check if CUDA is available
print(f"CUDA available: {torch.cuda.is_available()}")

# Check GPU name
if torch.cuda.is_available():
    print(f"GPU: {torch.cuda.get_device_name(0)}")

During training, you can monitor GPU usage with nvidia-smi:

# Run in a separate terminal during training
nvidia-smi -l 1

Troubleshooting#

“CUDA not available” in Python

  • Ensure the NVIDIA driver is installed (nvidia-smi should work)

  • Reinstall NeurEco with the CUDA build of PyTorch (on Windows, PyTorch from PyPI is CPU-only). For PyTorch 2.9:

    pip uninstall neureco torch
pip install "neureco~=5.0" --extra-index-url https://download.pytorch.org/whl/cu128 --extra-index-url https://adagos.com/downloads/neureco5/whl/torch2.9/

    For PyTorch 2.8 or 2.7, use the package indexes of your edition, given in Installation.

“CUDA driver version is insufficient for CUDA runtime version” or “the provided PTX was compiled with an unsupported toolchain”

  • The NVIDIA driver is older than NeurEco needs: install at least the recommended version in NVIDIA Driver Version, then restart the computer

“Driver/library version mismatch”

  • Reboot your computer

  • If the issue persists, reinstall the NVIDIA driver

“Compute capability too low”

  • Your GPU is too old for NeurEco. GPUs with compute capability below 7.5 are not supported.

  • Consider upgrading your GPU or using CPU-only mode

GPU not detected

  • Ensure the GPU is properly seated in the PCIe slot

  • Check that your power supply provides adequate power

  • On laptops, ensure you’re using the discrete GPU (not integrated graphics)