How severe does this issue affect your experience of using Ray?
- Low: It annoys or frustrates me for a moment.
we are currently using
ray.utils.ActorPool as a flexible way to build up a queue to stream data into our Deep-Learning pipeline. We did initially (a few months ago) try to do the same thing with rays Datasets, but were unable to get a “smooth” experience and instead had issues with resource utilization peaks and idleness.
ActorPool is a strong and simple utility, that enabled us to build exactly what we needed (full and direct control over the elements that we put into queue, no need to materialize the data into some predefined format; currently we load the data from an https endpoint within the actor, not before) without much hassle. What I get from the deprecation message now is that we will be forced to use Datasets again in the future with
ActorPoolStrategy, which looking at the code seems to be somewhat hidden and not easily adaptable to what we are doing right now (also it’s not a public API, so I don’t think it’s intended to be customized).
In our opinion, one of the advantages of ray are strong primitives like
ActorPool and we are sad to see it go.
I understand that ray is moving in the direction of strong and stable Machine Learning APIs with AIR (which we are also using and mostly happy about), but we view those utilities that are a more low level and customizable as just as valuable.
Is there any chance the
ActorPool will get a full replacement or even stay? I don’t see why it is necessary to remove it, except for strategical reasons pushing towards Datasets.
Please let me know if there is a better place to voice such feedback.