Hi Dan,

The way I am storing the ints should only require 1 byte per feature, uint8,

During training, the features are stored in memory.  This internal storage representation depends of the feature semantic, but not how the features are feed. For example, numerical features are stored as float (4 bytes), boolean as stored as bytes (1 byte) and pre-discretized numerical features are stored as int16 (2 bytes).

NameError: ‘IN_NODE’ is not defined

This looks like a python argument error :).
Make sure to call:
model = tfdf.keras.RandomForestModel(sorting_strategy="IN_NODE")

instead of
model = tfdf.keras.RandomForestModel(sorting_strategy=IN_NODE)


I was also wondering about the TF graph, which I don’t completely understand, but then wouldn’t many layer DNNs blow up similarly?

This is a limitation of the current TF-DF code (which call  Yggdrasil DF  underneath). I am creating this feature request 3 for it.