fvncc

You can also check https://github.com/sequenceplanner/r2r for another example of ROS2 bindings (using in production at work for a couple of ROS nodes).

But TBH, in a Rust world, it’s worth revisiting the assumptions behind the ROS node architecture, since Rust is so strong at scaling to large monolithic applications (due to the strict hierarchical code patterns it encourages).

A transitional Rust approach, that doesn't try to reimplement everything from scratch, could do something like a strangler pattern: Take each ROS node, run them separately in “jails” with a Rust API around each one, then implement the plumbing/management logic in pure Rust.

Comment · 3 years ago

Well each time you use the await keyword you are saying its a safe point to exit, which is more predictable than killing at random points. Holding locks across await points is an anti-pattern, and Rust at least can give a hint if you try to do that. Async/await implementations will also generally allow you to run cleanup code on cancellation (but the exact mechanism depends on the language).

In the end, its about expressing a state machine in a more concise implicit way, which is a suitable level of abstraction for most use cases.

Comment · 3 years ago

One advantage of async/await is that its easier to cancel things. For example, this leads to the design pattern where you have multiple futures and you want to select the one that finishes first and cancel the rest.

In regular threaded programming, cancellation is a bit more painful as you need to have some type of cancellation token used each time the thread waits for something. This a) is more verbose and b) can lead to bugs where you forget to implement the cancellation logic.

Comment · 3 years ago

There are a few options I’ve seen for “liveview” type approaches in Python, see e.g. reactpy and streamlit. (Assuming you are not looking specifically for something that transpiles to JS.)

Comment · 3 years ago

That would work nicely :)

Comment · 3 years ago

Hi is there any plan to have high level model training tools (one example is things like Pytorch dataset loaders), or is the focus more on inference/deployment use cases?

Comment · 4 years ago

Actually chemical weapons don’t do well on “cost effectiveness”, apparently:

https://acoup.blog/2020/03/20/collections-why-dont-we-use-ch...

Comment · 6 years ago

Maybe variational inference is a possibility? You can try searching for black box stochastic gradient variational inference, easy to use and scales well.

Comment · 6 years ago

Another example is RealNVP, which has the same structure to enable inversion (partition, process half, then use the result to mutate the other half in an invertible way).

Sadly, I gather the computational performance Is pretty bad ..

https://arxiv.org/abs/1605.08803