The nvidia-cuda-toolkit software package provides a set of tools and libraries for developing and running CUDA (Compute Unified Device Architecture) applications on NVIDIA GPUs. CUDA is a parallel computing platform and programming model developed by NVIDIA, which allows developers to write high-performance code that can execute on NVIDIA GPUs.
The nvidia-cuda-toolkit package includes the following components:
- CUDA runtime libraries, which provide low-level access to the GPU hardware and allow you to launch parallel kernels and manage data transfers between the CPU and GPU.
- CUDA development tools, including the CUDA compiler (nvcc), CUDA debugger (cuda-gdb), and CUDA profiler (nvprof), which allow you to develop and debug CUDA applications.
- CUDA math libraries, such as cuBLAS, cuFFT, and cuRAND, which provide optimized implementations of common mathematical operations for use in CUDA applications.
- CUDA code samples and documentation, which provide examples and guidance for developing CUDA applications.
Uninstalling nvidia-cuda-toolkit software package from Ubuntu
You can uninstall nvidia-cuda-toolkit package from Ubuntu using the below command:
$ sudo apt-get remove nvidia-cuda-toolkit
Uninstall nvidia-cuda-toolkit including dependent package
If you would like to remove nvidia-cuda-toolkit and it’s dependent packages which are no longer needed from Ubuntu:
$ sudo apt-get remove --auto-remove nvidia-cuda-toolkit
Use Purging nvidia-cuda-toolkit
If you use with purge options to nvidia-cuda-toolkit package all the configuration and dependent packages will be removed.
$ sudo apt-get purge nvidia-cuda-toolkit
If you use purge options along with auto remove, will be removed everything regarding the package, It’s really useful when you want to reinstall again.
$ sudo apt-get purge --auto-remove nvidia-cuda-toolkit
In summary, the nvidia-cuda-toolkit software package provides a set of tools and libraries for developing and running CUDA applications on NVIDIA GPUs. It includes CUDA runtime libraries, development tools, math libraries, code samples, and documentation. To use it, you will need an NVIDIA GPU that supports CUDA, as well as the appropriate NVIDIA graphics driver for your system.