If you want to perform deep learning or GPU-based computational processing on a PC equipped with NVIDIA GPUs, it is recommended to install CUDA (Compute Unified Device Architecture) CUDA is NVIDIA’s parallel computing platform and set of APIs to facilitate parallel processing on GPUs.
I recently purchased a PC with an NVIDIA graphics card, but CUDA was not installed. The following is a way to check.
Open a command prompt, type nvcc -V and press enter. This command displays the version of the CUDA compiler. If CUDA is installed, you should see output similar to the following
nvcc: NVIDIA (R) Cuda compiler driver
Copyright (c) 2005-2020 NVIDIA Corporation
Built on Mon_Nov_30_19:15:10_Pacific_Standard_Time_2020
Cuda compilation tools, release 11.2, V11.2.67
Build cuda_11.2.r11.2/compiler.29373293_0
The following message was displayed in this environment.
nvcc: The term ‘nvcc’ is not recognized as a name of a cmdlet, function, script file, or executable program.
Check the spelling of the name, or if a path was included, verify that the path is correct and try again.
Open Explorer and locate the folder C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA This folder is the default location of the CUDA installation. If CUDA is installed, there will be subfolders for each version within this folder.
Open the NVIDIA Control Panel and select “System Information” from the menu on the left side. If the “Components” section in the right window contains “CUDA,” then CUDA is installed.
On the other hand, CUDA is not required for games or general tasks. In games, the GPU on the graphics board is responsible for depicting images and video, and CUDA is not required for that. In general tasks, the CPU performs tasks continuously and works with a wide range of processing, but there is no need to use CUDA for those tasks. The source is the following schematics.
So, ultimately, consider installing CUDA depending on your use and needs. It is necessary for image generation such as STABLE diffusion, which is a hot topic right now. Also, neutrino diffusion is a neural network based singing voice synthesizer that can make AI sing. VOICEVOX, which I use extensively, is a free, medium-quality text-to-speech software.
PowerDirector does not require CUDA, but using it can speed up video encoding and effects rendering.
There are many open source and free software programs that support CUDA. Here is a partial list
- Deep Learning Frameworks:.
- TensorFlow: A deep learning framework developed by Google. It is widely used and is used in many applications and research.
- PyTorch: A deep learning framework developed by Facebook. Very popular in the research community.
- Keras: A high-level neural network API that runs on top of TensorFlow and is easy to use for beginners.
- Caffe: A framework developed by Berkeley Vision and Learning Center (BVLC).
- Neural Network Con sole: Neural Network Console is a deep learning development tool from Sony. With a graphical user interface, this tool allows users to design, train, and evaluate deep learning models in an intuitive manner.
1, Drag-and-drop design: intuitively design the architecture of your network.
2, Automated Learning: Easily configure and optimize the learning process.
3、Visual evaluation: Visualize learning progress and results in real-time.
4, CUDA support: Leverage NVIDIA GPUs for fast learning and evaluation.
- GPU computation libraries:.
- cuDNN: GPU-accelerated library for deep learning provided by NVIDIA.
- cuBLAS: A CUDA-compatible BLAS library that supports fast linear algebra computations.
- cuFFT: A CUDA-compatible fast Fourier transform library.
- GPU Programming: GPU programming
- Numba: Python just-in-time compiler, making it easy to write CUDA code from Python.
- Thrust: C template library to implement parallel algorithms using backends such as CUDA and OpenMP.
- Simulation & Rendering: A C library for simulation and rendering.
- Blender: 3D content creation suite with a renderer called Cycles that supports CUDA and allows GPU rendering.
- Other: Blender.
- Darknet: A framework for object detection algorithms called YOLO (You Only Look Once) that supports CUDA.