How is Google Cloud ML awful this week?

tl;dr Google Cloud ML is probably excellent if you design your algorithm from the ground up for it, but if you have something that runs perfectly well on your laptop and you wrote it to use, e.g. local files, or modern versions of Python, or custom compiled code, then you are going to need to substantially rewrite. NB this all is outdated now.

I want to do cloud machine learning using Google’s CloudML offering.

The goal: Working through getting and analysing the magnatagatune dataset in Google Cloud on my macOS laptop using TensorFlow.

I will follow approximately the least-nervy HOWTO, which sadly conveniently sidesteps many of my difficulties by having the input data be magically good.

There are too many ways to get the damn thing going.

There is much documentation for all these things, but it’s often unclear from page to page what the hell is happening, since it’s not clear whether you are spinning up VMs locally or in Google’s cloud.

One way is the purely cloud-based cloud shell, but this is clearly too fragile and restrictive for real usage unless you are in Mountain View.

Offline there is a Docker-based thing, called cloud datalab, which AFAICT is a somewhat monolithic machine image that approximates the online APIs or something. Here is a surly Google help page which implies as much.

Or you can install a bunch of Python packages from the command line.

Anyway, the terminology page is the clearest explanation of all terms before they get washed out in a combinatorial explosion. Lin Yi-Jhen does the best disambiguation, though.

Do I need this datalab nonsense? I can’t tell. I just want to run TensorFlow. Idea: proceed installing stuff randomly until eventually I have finished a deep-learning-based paper.

gcloud config set core/project learning-gamelan
gcloud config set compute/zone asia-east1-a

datalab create learning_gamelan

datalab delete learning_gamelan