In this tutorial, we will go through the step-by-step process of installing CUDA and PyTorch on your system. CUDA is a parallel computing platform and application programming interface (API) model created by NVIDIA for running computations on NVIDIA GPUs. PyTorch is an open-source machine learning library based on the Torch library and is widely used for deep learning tasks.
Before we begin, please make sure you have a compatible NVIDIA GPU and the latest NVIDIA driver installed on your system. You can check the compatibility of your GPU with CUDA and PyTorch on their respective websites.
Step 1: Install CUDA
1.1 Download the CUDA Toolkit from the NVIDIA website. Make sure to select the version that is compatible with your operating system and GPU.
1.2 Run the installer and follow the on-screen instructions. Make sure to install all the components including the CUDA Toolkit, CUDA Samples, and CUDA Visual Studio Integration (if applicable).
1.3 After the installation is complete, add the CUDA Toolkit to your system PATH environment variable. This is usually done by adding the following directories to your PATH: C:Program FilesNVIDIA GPU Computing ToolkitCUDAvxx.xbin and C:Program FilesNVIDIA GPU Computing ToolkitCUDAvxx.xextrasCUPTIlib64.
1.4 Finally, verify the installation by opening a command prompt and running the following command: nvcc –version. You should see the version of the CUDA Toolkit installed on your system.
Step 2: Install PyTorch
2.1 You can install PyTorch using pip, Anaconda, or from source. In this tutorial, we will use pip to install PyTorch.
2.2 Open a command prompt or terminal and run the following command to install PyTorch with CUDA support: pip install torch torchvision torchaudio
2.3 PyTorch will be downloaded and installed along with any necessary dependencies. This may take some time depending on your internet connection and system specifications.
2.4 Once the installation is complete, you can verify the installation by opening a Python interpreter and importing the torch module. If there are no errors, PyTorch has been successfully installed on your system.
Step 3: Verify the Installation
3.1 To verify that PyTorch is using CUDA for GPU acceleration, you can run the following code snippet in a Python interpreter:
import torch
print(torch.cuda.is_available()) # should return True if CUDA is enabled
print(torch.cuda.get_device_name()) # should return the name of your GPUIf the above code returns True and the name of your GPU, then PyTorch is successfully using CUDA for GPU acceleration.
Congratulations! You have successfully installed CUDA and PyTorch on your system. You are now ready to start building and training deep learning models using PyTorch with GPU acceleration.
Hi, I am new in this CUDA Pytorch Deep Learning study,
I have GeForce 930M in my laptop. According to link you gave, it is still support CUDA (compute compatibility = 5)
What I want to ask:
How to determine what is the "highest" pytorch version and "cuda toolkit" version that can we use for above "compute compatibility = 5" (or above GeForce 930M) ?
Do you have any reference/link that list/explain above?
Best,
GPU Compatibility: https://developer.nvidia.com/cuda-gpus
PyTorch Installation: https://pytorch.org/get-started/locally/
#Test Code
import torch
cuda_available = torch.cuda.is_available()
print("CUDA?", cuda_available)
if cuda_available:
print("DEVICE:", torch.cuda.get_device_name(0))
print("Version:", torch.version.cuda)