Nvidia Gpu Keras

From keras import backend as k k tensorflow backend get available gpus you need to a d d the following block after importing keras if you are working on a machine for example which have 56 core cpu and a gpu.

Nvidia gpu keras. I have tested that the nightly build for the windows gpu version of tensorflow 1 6 works with cuda 9 0 and cudnn 7. Updated some steps for newer tensorflow versions. Keras is a high level neural. The nvidia deep learning institute dli offers hands on training in ai accelerated computing and accelerated data science.

2x intel xeon cpu e5 2698 v4 2 20ghz 20 cores per socket 40 cores total. See the list of cuda enabled gpu cards. In this post i will outline how to configure install the drivers and packages needed to set up keras deep learning framework on windows 10 on both gpu cpu systems. Nvidia gpu card with cuda compute capability 3 5 or higher.

Developers data scientists researchers and students can get practical experience powered by gpus in the cloud. These results were obtained on an nvidia dgx 1 system with 8 pascal gpus and the following system details. For comparison an nvidia tesla k80 is reaching 43us step but is 10x more expensive. Library keras install keras tensorflow gpu note that on all platforms you must be running an nvidia gpu with cuda compute capability 3 5 or higher in order to run the gpu version of tensorflow.

Configure an install tensorflow 2 0 gpu cuda keras python 3 7 in windows 10. For resnet 50 keras s multi gpu performance on an nvidia dgx 1 is even competitive with training this model using some other frameworks native apis.