Multiple Methods for Using PyTorch with CUDA 11.3 and Existing CUDA 11.2

• PyTorch relies on the CUDA toolkit for GPU acceleration.
• Each PyTorch version is built to work with a specific CUDA version.
• In this case, PyTorch compiled for CUDA 11.3 won't work seamlessly with CUDA 11.2 on your system. There might be compatibility issues.

Possible solutions:

# Replace "1.x.x+cu112" with the specific PyTorch version for CUDA 11.2
pip install torch==1.x.x+cu112 torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cu112

Explanation:

• pip install: This command is used to install Python packages.
• torch==1.x.x+cu112: This specifies the PyTorch package with the == ensuring a specific version and +cu112 indicating compatibility with CUDA 11.2.
• torchvision: This installs the companion library for computer vision tasks.
• torchaudio: This installs the library for audio processing tasks (both are part of the PyTorch ecosystem)
• --extra-index-url https://download.pytorch.org/whl/cu112: This points the installer to the specific PyTorch repository containing wheels (pre-built packages) compatible with CUDA 11.2.

Remember:

• Replace "1.x.x+cu112" with the actual PyTorch version confirmed to work with CUDA 11.2. You can find compatible versions on the PyTorch website [pytorch.org].
• This approach assumes a pre-built PyTorch version for CUDA 11.2 exists.

1. Install CUDA 11.3 alongside CUDA 11.2:

This approach requires managing your CUDA environment variables to point to the desired version during PyTorch installation. Here's a general outline:

• Set environment variables like PATH, LD_LIBRARY_PATH (Linux) or CUDA_PATH (Windows) to point to the CUDA 11.3 toolkit directories during PyTorch installation. You can achieve this temporarily using the terminal or permanently through system settings.
• Install PyTorch using pip or conda specifying the compatibility with CUDA 11.3 (exact syntax may vary depending on the chosen method).

Here's an example using conda (assuming a pre-built PyTorch with CUDA 11.3 exists):

# Set environment variables (replace paths accordingly)
export PATH=/usr/local/cuda-11.3/bin:$PATH
export LD_LIBRARY_PATH=/usr/local/cuda-11.3/lib64:$LD_LIBRARY_PATH

# Install PyTorch with CUDA 11.3 compatibility
conda install pytorch torchvision torchaudio cudatoolkit=11.3 -c pytorch

• Replace the paths in the environment variable commands with the actual installation directories of CUDA 11.3 on your system.
• This approach requires switching environment variables depending on whether you want to use CUDA 11.2 or 11.3 with PyTorch.

1. Build PyTorch from source:

Building PyTorch from source gives you the most control but requires more technical expertise. Here's a general idea:

• Follow the build instructions for your operating system, specifying CUDA 11.2 during the configuration process. This typically involves editing the setup.py file or using build flags.
• Compile and install the custom built PyTorch library.

Here are some resources to get you started:

• Building from source requires familiarity with compiling code and managing dependencies.
• This method offers more flexibility but can be time-consuming compared to using pre-built versions.