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Sure, here's a step-by-step tutorial on how to install PyTorch with CUDA support on Ubuntu. CUDA is a parallel computing platform and application programming interface model created by Nvidia.
Step 1: Install NVIDIA GPU Drivers
First, you need to ensure that you have the appropriate NVIDIA GPU drivers installed. You can install them using the following commands:
Reboot your system after the installation is complete:
Step 2: Install CUDA Toolkit
Next, you need to install the CUDA Toolkit. Check the NVIDIA CUDA Toolkit download page for the latest version. As of my last update, you can install CUDA 11.1 with the following commands:
Step 3: Install cuDNN
After installing CUDA, you also need to install cuDNN, which is a GPU-accelerated library for deep neural networks. You can download cuDNN from the NVIDIA cuDNN download page (you may need to create a free account to access the download). Once downloaded, extract the archive and copy the files to the CUDA toolkit directory:
Step 4: Install PyTorch
Now that you have CUDA and cuDNN installed, you can install PyTorch with GPU support. You can use pip to install the latest version of PyTorch:
This command will install the CPU version of PyTorch. If you want the GPU version, make sure to use a version that specifies CUDA support. For example:
Replace 1.10.0+cu111, 0.11.1+cu111, and 0.10.0+cu111 with the versions you want to install. You can find the correct version on the PyTorch website.
Step 5: Verify Installation
You can verify the installation by launching a Python interpreter and importing PyTorch:
If the installation is successful, it should print True, indicating that PyTorch can access your GPU.
That's it! You've successfully installed PyTorch with CUDA support on Ubuntu. Now you can use the power of your GPU to accelerate deep learning tasks.
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