How do I run PyTorch on NVIDIA RTX 4090 GPUs?

Running PyTorch on NVIDIA RTX 4090 GPUs: Step-by-Step Guide

The NVIDIA RTX 4090 GPU is a powerful graphics card ideal for deep learning workloads, and PyTorch supports it through CUDA. To run PyTorch successfully on an RTX 4090, you'll need to ensure compatible drivers, CUDA toolkit, and PyTorch installation.

Below, we'll outline the steps clearly to set up PyTorch for RTX 4090 GPUs.

1. System Requirements and Compatibility

Before starting, ensure your system meets these requirements:

• Operating System: Linux (Ubuntu recommended), Windows 10 or 11, or WSL2 (Windows Subsystem for Linux)
• Driver: Latest NVIDIA GPU drivers compatible with RTX 4090
• CUDA Toolkit: CUDA 11.8 or newer recommended for RTX 4090 GPUs
• PyTorch Version: PyTorch 2.0 or newer recommended for optimal performance and compatibility

2. Install NVIDIA Drivers

Visit the NVIDIA Driver Downloads page and select your GPU (RTX 4090) model, OS, and download the latest driver.

On Ubuntu, you can use:

sudo apt update
sudo apt install nvidia-driver-535  # Replace '535' with latest driver number
sudo reboot

Check your driver installation with:

nvidia-smi

A successful installation will display GPU details clearly, including the RTX 4090 GPU.

3. Install CUDA Toolkit

PyTorch now includes CUDA binaries, but installing the CUDA toolkit separately can be beneficial for developing custom CUDA extensions or debugging purposes.

CUDA 11.8 or newer is recommended for RTX 4090 GPUs.

Visit CUDA Toolkit Downloads and select your operating system. Alternatively, on Ubuntu, use command-line installation:

wget https://developer.download.nvidia.com/compute/cuda/12.2.0/local_installers/cuda_12.2.0_535.54.03_linux.run
sudo sh cuda_12.2.0_535.54.03_linux.run

Make sure to add CUDA to your PATH (~/.bashrc):

export PATH=/usr/local/cuda/bin${PATH:+:${PATH}}
export LD_LIBRARY_PATH=/usr/local/cuda/lib64${LD_LIBRARY_PATH:+:${LD_LIBRARY_PATH}}

Verify CUDA installation with:

nvcc --version

4. Install PyTorch with CUDA Support

PyTorch supports RTX 4090 GPUs starting from CUDA 11.8 and PyTorch 2.0. Install directly from PyTorch’s official website:

Recommended command (PyTorch 2.1 with CUDA 12.1):

pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu121

(To check the latest version and CUDA version support, visit PyTorch Get Started page.)

5. Verify PyTorch and GPU Compatibility

Confirm PyTorch correctly detects your RTX 4090 GPU and CUDA installation:

import torch

print(torch.__version__)
print(torch.cuda.is_available())
print(torch.cuda.get_device_name(0))

Expected output:

2.1.0
True
NVIDIA GeForce RTX 4090

6. Optimizing PyTorch for RTX 4090 GPUs

To maximize performance, consider the following optimizations:

• Use Automatic Mixed Precision (AMP): Enables faster training with reduced memory usage.

from torch.cuda.amp import autocast

with autocast():
    outputs = model(inputs)

• Leverage Tensor Cores: RTX 4090 GPUs perform exceptionally well with mixed-precision (FP16/BF16) computations.
• Adjust batch size and hyperparameters: RTX 4090 GPUs have substantial VRAM (24 GB), allowing larger batch sizes and better parallelization.

7. Troubleshooting Common Issues

If you encounter issues, try:

• Check CUDA and PyTorch compatibility: Ensure both versions match.
• Driver issues: Reinstall or upgrade NVIDIA drivers if GPU detection fails.
• CUDA Out-of-Memory (OOM): Reduce batch size or use mixed precision training.

Conclusion

By following the above steps, you can easily run PyTorch on NVIDIA RTX 4090 GPUs, leveraging the full capabilities of this powerful GPU for your deep learning workloads.