[pcmoritz]

For Ray, the main use case at the moment is parallel/distributing machine learning algorithms, people have been using it for parallelizing MC(MC) style applications, doing hyperparameter search, (pre-)process data, we are using it for reinforcement learning (and have a library for that, see http://ray.readthedocs.io/en/latest/rllib.html)

More broadly it is useful for many parallel/distributed Python applications where low latency (~1ms) and high throughput of tasks are a requirement.

[Eridrus]

Python is almost synonymous with shitty performance in my mind; am I just wrong about typical python performance, or are you doing something special to make this of a non-issue (e.g. the way numpy essentially shoves all the work into C), or is the flexibility Ray affords worth the performance penalty for your users?

[pcmoritz]

There are two considerations here.

(1) Python single threaded performance: Here, most of the libraries we are using are implemented in C++ (like numpy, TensorFlow, Cython to speed up the code, etc.). Ray is orthogonal to that.

(2) Python parallel performance: Here Python is mostly problematic because of its lack of support for threading (the GIL is one problem here); we handle this problem by using multiple processes and shared memory throughout. Efficient serialization makes this feasible.

The core of Ray is implemented in C++, so performance is not an issue for that; also all of the serialization is implemented in C++.