Torq_boi

Our preference for training in our own datacenter has nothing to do with wokeness. Did you read the blog post? The reasons are clearly explained.

The climate in Wyoming and Montana are actually worse in terms of climate. San Diego's climate extremes are less extreme than those places. Though moving out of CA is a good idea for power cost reasons, also addressed in the blog.

Yes, it's easy to destroy the servers with a lot of dust and/or high humidity. But with filtering and ensuring humidity never exceeds 45% we've had pretty good results.

Yes, we still use the azure for user-facing services and the website. They don't need GPUs and don't need expensive resources, so it's not as worth it to bring those in-house.

We also rely on github. It has historically been good a service, but getting worth it.

We actually do have an in-house chef lol.

The models are now saved in ONNX format. Which is the most readable format available. You can view the architecture of the model with a basic neural network viewer.

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.

When you say not everything is open source. I assume you mean the training code is not open source? I'm curious what you would want to learn from that? You wouldn't be able to actually train a model since you wouldn't have access to the data.

The end-user can inspect, audit and understand the decisions their vehicle is making. All you have to do is see how the neural network behaves for different inputs. That's the correct approach, whether you have access to the training code or not.