Serious number crunching on Google Cloud
2017-03-14 — 2018-07-30
computers are awful
concurrency hell
premature optimization
Wherein the use of Google Cloud’s CloudML is examined and its reliance on Python 2.7 and Google Storage APIs is noted, while integration points with TensorFlow, Dataflow, and Datalab are outlined.
I want to do cloud machine learning using Google’s CloudML offering.
Google cloud might interoperate well with a bunch of Google products, such as TensorFlow. I use that. Let’s see how it works.
1 Notes
Obviously, this will work better if you commit to Google storage APIs, etc.
Note (2017-02-14): Google supports Python 2.7 only — so be prepared to party like it’s 2010. (Is this still so?)
Seems to also support VMs?
There is the usual thicket of weird service names.
Basic workflow bits are
- cloud storage stores data
- dataflow supplies data
- model fitting done using CloudML
- control this from Datalab
Also good to know:
2 Painstaking run through
See How is Google number crunching awful?.
Google now has their own instruction manual for one common use case, TensorFlow. TensorFlow without a PhD.