There is an update for XXXX, should I get it? So if you don’t know, go for the latest stable release compatible over your software stack. And these options bleed down the dependency list (also known as software stack). Which version(s) to use?ĭepends what features you need if you need the latest then go with that.ĭifferent cards have different compute capability and different CUDA requirements.

I will be using Python 3.5 - further setup instructions are given in the Python example page. I will be using Matlab 2018a locally and on HPC. But don’t despair you can do the Artemis examples! And there are still plenty of high-level libraries that can work with OpenCL (as opposed to CUDA), see an example of Keras/TensorFlow here Other Software Matlab Nevertheless, there are still drivers from NVIDA, and a few options with external GPUs. This was done by Apple for various reasons. If you have Mac product newer than about 2014 you probably don’t have a CUDA-capable GPU card. Gcc (Ubuntu 6.5.0-2ubuntu1~18.04) 6.5.0 20181026Īnd the Nvidia cuda compiler (installed with the CUDA toolkit), nvcc Then installing the CUDA drivers for the driver/GPU combo. Probably as simple as selecting the NVIDIA driver. The way I achieved this was by launching the 圆4 Native Tools Command Prompt from the Developer command prompt shortcuts as listed here You may need to link the correct cl.exe and nvcc somehow. Nvcc: NVIDIA (R) Cuda compiler driver Cuda compilation tools, release 10.0, V10.0.130 Nvidia cuda compiler (installed with the CUDA toolkit), nvcc

Microsoft (R) C/C++ Optimizing Compiler Version 7.1 for 圆4 Specific versions of tools working together for me are:Ĭ compiler, installed with Visual Studio 2017, cl.exe Install the NVIDIA graphics driver and CUDA drivers.ĭownload both from the NVIDIA download page. NVIDIA driver and CUDA local installation instructions Windows 10 So be sure to know what kind of GPU card you are testing and deploying on. Generally code is forwards compatible, but not backwards compatible. The older generation on the training nodes are Tesla K40s with compute capability of 3.5. Artemis has NVIDA Tesla V100s with a compute capability of 7.0.

We will be using the Artemis Training accounts today (instructions detailed below.)Īll the data/scripts used in this course is location on Artemis at /project/Training/GPUtraining (which you can access through the training accounts) or you can download the data directly from here GPU RequirementsĪn CUDA capable GPU card. If you only want to follow along using Artemis, all you need is an SSH client, as described below. If you want to follow along and complete the exercises on your local machine use these setup instructions. Introduction to GPUs on Argus (Win10): GPU and SSH setup

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