I am not an expert, but the way that I understand self-driving systems is that there are multiple models running, and then those outputs are fused into yet another model which outputs the raw controls/actuations. In other words, I see this model/trainer as the "conductor", telling the car how it should approach an intersection, enter a highway, deal with merging traffic or construction zones, etc.
There is another model which interprets visual data to assist with lane-keeping, slow down or stop for pedestrians, inform the conductor of road signs... The final model combines all these inputs and incorporates the user preferences and then decides whether to brake or accelerate, how much to rotate the steering wheel.
Idk heh. The point of the high performance training is you can train the "conductor" role faster, and run inference faster. Assuming the car has limited compute/gpu resources, if you have a very high performance conductor function, you can dedicate that much more budget to visual/sensor inference and or any other models like the Trolley Problem decider (jk).
I am "the react guy" -- but before that I built knockout applications with requirejs & angular applications with coffeescript and gulp, and before that with a 2000 line long script.js & jquery ;)
I don't really disagree with anything you said, except to point out that a lot of the current crop of web devs have never experienced what it is to build an app without any abstraction such as React, and quite understandably have no idea what problems it is doing for them.
I do know that since hopping onto react with all of that ~baggage~ context, I have never wanted to program UIs with a different model. It is true that hooks introduce a layer of abstraction that is sometimes difficult to reason about, but IMO they boil down the problems we faced with class components/lifecycle/server rendering gotchas, and put them front and center - forcing you to confront and fix them rather than settling for a solution that works 99% of the time.
> the current crop of web devs have never experienced what it is to build an app without any abstraction such as React, and quite understandably have no idea what problems it is doing for them.
This is true. A lot of those struggles that the earlier people established, the patterns and best uses, those are lessons learned the hard way, and the impact on generation 2 is often much less because that wasn't a problem that needed to be overcome.
We bundle most, and ship npm-shrinkwrap.json for these ones we don't. The shrinkwrap is pruned (via a a custom script, not npm's prune command, because of reasons ) at publish time, as npm installs all dev dependencies if they exist in the shrinkwrap file (despite being marked as "dev": true in said shrinkwrap).
I thought the same thing - reminded me of birds. (I was casually aware that we knew they had some sort of magnetism sensing, but hadn't caught up with the fact that we think it is a visual thing)
We currently don't support it out-of-the-box, but a community member is building a custom multi-tenant solution on top of Medusa essentially using the engine as building blocks.
Our architecture allows you to create custom entities, services and endpoints in your own Node.js project. Additionally, we use Avilix for dependency injection which allows you to use all services across all parts of the system; core, plugins, your custom project. In combination, you are provided with the primitives that can handle commerce business logic in almost any type of project.
There is another model which interprets visual data to assist with lane-keeping, slow down or stop for pedestrians, inform the conductor of road signs... The final model combines all these inputs and incorporates the user preferences and then decides whether to brake or accelerate, how much to rotate the steering wheel.
Idk heh. The point of the high performance training is you can train the "conductor" role faster, and run inference faster. Assuming the car has limited compute/gpu resources, if you have a very high performance conductor function, you can dedicate that much more budget to visual/sensor inference and or any other models like the Trolley Problem decider (jk).
edit: grammar/details