You can also upgrade to the g2.8xlarge instance ( $2.60 per hour) to obtain four K520 GPUs (for a grand total of 16GB of memory).įor most of us, the g2.8xlarge is a bit expensive, especially if you’re only doing deep learning as a hobby. The GPU included on the system is a K520 with 4GB of memory and 1,536 cores. This instance is named the g2.2xlarge instance and costs approximately $0.65 per hour. How to install CUDA Toolkit and cuDNN for deep learningĪs I mentioned in an earlier blog post, Amazon offers an EC2 instance that provides access to the GPU for computation purposes. Feel free to spin up an instance of your own and follow along.īy the time you’re finished this tutorial, you’ll have a brand new system ready for deep learning. Specifically, I’ll be using an Amazon EC2 g2.2xlarge machine running Ubuntu 14.04. In the remainder of this blog post, I’ll demonstrate how to install both the NVIDIA CUDA Toolkit and the cuDNN library for deep learning. Using the cuDNN package, you can increase training speeds by upwards of 44%, with over 6x speedups in Torch and Caffe. The cuDNN library: A GPU-accelerated library of primitives for deep neural networks.This toolkit includes a compiler specifically designed for NVIDIA GPUs and associated math libraries + optimization routines. The NVIDIA CUDA Toolkit: A development environment for building GPU-accelerated applications.If you already have an NVIDIA supported GPU, then the next logical step is to install two important libraries: And the more GPUs you have, the better off you are. If you’re serious about doing any type of deep learning, you should be utilizing your GPU rather than your CPU. In both cases with a bit of reading of the documents, you should be able to make the reinstall process easier by skipping the driver install (very straightforward with runfile installer, with package manager would involve a force reinstall of cuda-10-0 rather than cuda).Click here to download the source code to this post However the runfile installer should not require any removal/uninstall. However with a bit of googling I believe you should be able to discover commands to make the package manager for your linux distro do a force reinstall. To get the package manager method to cooperate, it may be simplest to uninstall. If you have a missing component (not sure how that came about) you should be able to simply re-install the same CUDA version (10.0). I suggest following the linux install guide. The install package used was almost certainly “cuda”: cuda-10-0.x86_64 10.0.130-1 cuda It is almost certainly not:īecause there are no instructions for CUDA install anywhere that I am aware of, that refer to such a package name. Is intended to refer to the same that was used for install. The command you have used for uninstall is not contained anywhere in the linux install guide. The output of the tools you have used suggest to me that you did not use a runfile install method but actually used a package manager install method. if there is a way of restoring the file without having to reinstall cuda or how to uninstall cuda-10.0 properly, please let me know. The reason I am trying to uninstall is I am missing this file libcusolver.so.10.0ĭirectory. So I am not sure how to uninstall my cuda-10.0. I get error: No Match for argument: cuda-10-0.x86_64 Sudo /usr/local/cuda-X.Y/bin/uninstall_cuda_X.Y.plīut when I try to uninstall with command: sudo yum remove cuda-10-0.x86_64 Nontheless, I have tried all 3 methods suggested by I am confident that I have used run file to install my cuda-10.0.
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