#NVIDIA CUDA DRIVER WINDOWS INSTALL#

#NVIDIA CUDA DRIVER WINDOWS INSTALL#

Install the latest version of the Nvidia CUDA Toolkit from here.

This software prepares your GPU for deep learning computations. This application is the most significant software that helps your GPU interact with the deep learning programs that you will write in your Anaconda prompt. During the installation of the Nvidia CUDA software, it will check for any supported versions of Studio code installed on your machine. Installation of the Microsoft visual studio is a necessary step for the installation of the Nvidia CUDA software. Screenshot of Anaconda Navigator after installation:Д¬lick here for the detailed installation documents. Download your Anaconda installation setup from here. It comes with the Jupyter Notebook and Spyder console for Python. Be very careful during the following installation processes: Install AnacondaДЄnaconda will be our main coding terminal for this setup of the deep learning environment. Thus, you cannot go any further if you do not have CUDA cores in your dedicated GPU.

The software installation path is a bit finicky, since it is both hardware and software specific.

RAM: 8GB Dual channel memory or higher(16GB recommended).

You can measure your hardware compute score and compatibility from the NVIDIA developer website. Tensorflow requires a minimum CUDA compute specification score of 3.0. NOTE: Your hardware has to support GPU accelerated deep learning. Nevertheless, you must have a CPU with at least four cores and eight threads (hyperthreading/simultaneous multi-threading enabled), because this process requires heavy parallel processing resources.

In this article, we will show the detailed process for setting up the deep learning environment using CUDA GPU, Anaconda Jupyter, Keras, and Tensorflow (for windows)Д®2E GPU machines provide better performance and cost-efficiency, in comparison to standalone service providers.ДЇor deep learning, the CUDA cores of Nvidia, graphics drivers are preferred in comparison to CPUs, because those cores are specifically designed for tasks like parallel processing, real-time image upscaling, doing petaflops of calculations each second, high-definition video rendering, encoding, decoding.

Nvidia cuda driver windows