Comment by Tenoke
4 years ago
I've used Ray for about a year (typically for thousands of ML tasks, spread across ~48-120 cores simultaneously) and it's a pleasure to use at least using the basic API. Admittedly, I had problems when trying to use some of the more advanced approaches but I didn't really need them and I can definitely recommend it since the performance is great.
Just out of curiosity, what kind of work requires thousands of ML tasks? (Assuming you're talking about training and not inference?)
The thousands of tasks are inference but I also use ray to train/update a double digit models simultaneously (~1 per user).