Add option to use precomputed templates

[GMNGeoffrey]

My experience is that template processing takes a huge fraction of the overall runtime. It's also all on CPU, which is a waste if you're using GPU machines.

Note that this uses the features.pkl which is already saved by the existing code. For more stability across numpy versions, etc. we could instead use npz files with numpy.save/numpy.load as features.pkl just holds a dictionary of numpy arrays. I wanted to first gauge the reaction to this general idea though.

See discussion in #895

[google-cla]

Thanks for your pull request! It looks like this may be your first contribution to a Google open source project. Before we can look at your pull request, you'll need to sign a Contributor License Agreement (CLA).

View this failed invocation of the CLA check for more information.

For the most up to date status, view the checks section at the bottom of the pull request.

[GMNGeoffrey]

I also have additional changes to use an npz file instead, enable just running the featurization code (e.g. so you can do that on a CPU-only machine first), and break the run with precomputed features out into a separate script so it doesn't try to look for all the db files. Happy to upstream those, but as I noted I wanted to get some feedback on the general idea.

I'll sort out the CLA on our side

[GMNGeoffrey]

Alright CLA check is passing. @Augustin-Zidek could you PTAL?