No more sweating over the installation of Deep Learning environment, the new approach is to work with dockers from Nvidia cloud. I find myself using it more and more. Since my main work is in Deep Learning on medical (highly secured) data, I use dockers a lot.
No more sweating over the installation of Deep Learning environment. No more fighting with Cuda versions and GCC compilers. Welcome to the docker era. All you need is a Unix system with updated Cuda drivers. All you need is to download a docker from NGC cloud that contains the environment you need with all the Cuda code.
Actually, after writing this post, I find myself using it more and more. Since my main work is in Deep Learning on medical (highly secured) data, I use dockers a lot. When you must work on a server without the internet (yes, this is painful not to have StackOverflow) a docker is your savior :). You prepare your working environment in normal surroundings then you/system admin, migrate it to the server. An interesting advantage that I am exploring now is to enrich your environment with git repositories, manuals, etc. I will update here the methods I find.
Here we will use Python 3 and TensorFlow 1.9, but there are dockers with Pytorch, Caffe, trained model, and more.
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