Kubeflow is your perfect Machine Learning workstation

It's (mostly) true that Data Scientists do not care about infrastructure. Indeed, even though DevOps is a very interesting field, most of them are not exactly eager to start a VM, allocate the needed resources, configure the network, ssh into the machine, build a docker image and launch a Jupyter Notebook server. To cut to the chase, in this story, we create a ready to use, GPU accelerated Deep Learning environment, that has already TensorFlow and PyTorch installed. To do that we need to create the Dockerfile that describes the environment, build it and use it as the image of the Notebook server inside a Kubeflow instance. So, without further ado let's see the Dockerfile and walk through it step by step.