Again, I'm curious what you want to learn from the training code?
Chengyao's medium post is great, but it is only possible because the models, the code that runs them and the code that parses the outputs is fully open source.
Chengyao's Medium post is advanced reverse-engineering work requiring a detailed knowledge of the appearance of specific ML algorithms saved in a binary format. And even with this knowledge, Chengyao was only able to _speculate_ about the behavior of the model and the desired response to certain inputs.
What would satisfy me from Comma, if they were aspiring to some kind of "open" label, would be a detailed document explaining each layer of the ML system and what its goals are - like Chengyao's Medium post, but without the need to reverse-engineer the system and attempt to infer its behavior!
Now, maybe Comma don't aspire to be truly open, in which case, that's fine - In that case, Comma is a closed model with an open-source CAN interceptor on top. So essentially, crowd-sourcing the tedious and high-liability parts (vehicle integration, driving video) while owning the valuable parts (training data and model architecture). Very cool!
It's not like a PE format which is compiled from something else higher level.