Here we will use Python 3 and TensorFlow 1.9, but there are dockers with Pytorch, Caffe, trained model, and more.ĭocker run -rm -it -runtime=nvidia -net=host -v : įor example, for a local docker, use: docker run -rm -it -runtime=nvidia -net=host -v /my_server_dir/:/naomi 989143febb16 An interesting advantage that I am exploring now is to enrich your environment with git repositories, manuals, etc. You prepare your working environment in normal surroundings then you/system admin, migrate it to the server. When you must work on a server without the internet (yes, this is painful not to have StackOverflow) a docker is your savior :).

Since my main work is in Deep Learning on medical (highly secured) data, I use dockers a lot.

All you need is to download a docker from NGC cloud that contains the environment you need with all the Cuda code.Īctually, after writing this post, I find myself using it more and more. All you need is a Unix system with updated Cuda drivers. No more fighting with Cuda versions and GCC compilers. No more sweating over the installation of Deep Learning environment.

Tensorboard docker on mac