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Anyscale, from the creators of the Ray distributed computing project, launches
- thinkingkong 7y agoProject Ray is specifically more focused on scaling AI systems it seems. That is sort of important but also missing from the article. https://bair.berkeley.edu/blog/2018/01/09/ray/ https://bair.berkeley.edu/blog/2018/01/09/ray/
- ignoramous 7y agoPrevious discussions on the Ray project: https://news.ycombinator.com/item?id=15481169 https://news.ycombinator.com/item?id=15481169 https://news.ycombinator.com/item?id=16510610 https://news.ycombinator.com/item?id=16510610 https://news.ycombinator.com/item?id=20064241 https://news.ycombinator.com/item?id=20064241
- one_electron 7y agoanother one! berkeley rise lab is such a powerhouse - spark, mesos, etc. just in the past few years. it really puts some of these big tech companies to shame.
- zamadatix 7y agoI think spark and mesos are about a decade old at this point. I'm not really sure anyone is being put to shame either, there are wildly successful alternatives to all of these.
- vonnik 7y agoRay user here. Their adoption is exponential. Much faster than Spark when it was "the same age." It's not limited to AI workloads, that's just its flagship use case with RLlib.
- choppaface 7y agoHas Ray grown beyond reinforcement learning? Ray appears to lack a datastore or locality mechanism for at-rest distributed data, which is really important for ETL and SQL workloads. So far Ray looks like it has some nice features that are missing from the Spark RDD API, but for ETL / SQL it looks like you’d only choose Ray there for your own entertainment. It would be compelling if Ray were to provide Horovod support versus the authors re-applying some of same research in their own thing. Ray programming API + code distribution + Horovod performance primitives is what the community probably wants.
- namibj 7y agoCan you compare these to https://github.com/TimelyDataflow/timely-dataflow https://github.com/TimelyDataflow/timely-dataflow ? It seems Spark is rolling on inertia more than being a modular system for low-latency cluster computing.
- agibsonccc 7y agoIt's still pretty early days for ray yet. That being said, spark never really got the hang of doing machine learning properly. It "works" but not for newer workloads which ray is trying to support. It's good someone is building a company around it. I could see them building services on top of it and build a SAAS like databricks did with spark. I'll be curious to see how ray matures.
- choppaface 7y agoI agree that ML on Spark was only a limited hit—- iterative jobs would actually be feasible versus Hadoop—- I still have yet to find a better ETL and SQL tool, and that’s a big part of most ML projects. I’m worried about Ray as a SAAS Co because so far it looks to me like they’re riding reinforcement learning hype. They’d need to really penetrate the users of Horovod and Tensorflow Distributed to get beyond a beach head. And what if TPUs and Cerebras become more common? Because then the maker for multi-machine workloads becomes smaller (definitely not zero though).