agibsonccc

The "eclipse foundation" is actually not just sponsored by IBM. Nor is the IDE. As for what support it provides, that's actually spot on and is under a similar idea to what apache and linux provide. There are of course differences: but for most people the vague idea that open source foundations exist and provide some similar functions as a way to host projects is enough for this discussion

A lot of eclipse's revenue actually comes from working groups with Jakarta EE being the biggest one. There are also IOT, Automotive and many other working groups + projects under the eclipse umbrella.

Comment · 6 years ago

If something were to be more "neutral" what would you hope to see exactly? Something performant is typically going to be framework/hardware specific.

Comment · 6 years ago

Hi Fareesh, I'd love to hear more about your use case. Email's in my profile.

Comment · 6 years ago

Hi, could you describe your use case a bit? Just an alarm trigger for deer in the backyard?

Comment · 6 years ago

Could you elaborate on some of the problems you had overall?

Comment · 6 years ago

Few questions:

1. Did you build this for your own use cases? Interesting side project?

2. How do you feel about the need for base64 being a requirement on the endpoints? Isn't GRPC the wrong medium for this? Also, what do you see as the main limitations right now? The models?

Comment · 6 years ago

How did you set it up on the software side though? How flexible/customizable was it?

Comment · 6 years ago

Your concerns are right on point. I agree that spark is a great sql/etl tool. My thinking was on the "math execution" part. Ray is able to doa bit more there. I do feel like there is a bit of hype riding going on here as well.

One interesting thing that could happen is the hardware gets better, and then these distributed schedulers might not be able to keep up with all the different options on the market.

There is also the tension of the hardware vendors wanting to give away things that only run on their chips vs the software makers who want things to run on every chip. It seems like there will be a lot of competition among the various infra players in the next few years now that nvidia is starting to have real competition now (even if it's not big yet)

Comment · 6 years ago

It'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.

Comment · 7 years ago

Graal itself still has limitations with many libraries yet, the biggest one being having any library that heavily uses reflection. I'd look at some of the work red hat is doing with some of its java libraries and quarkus: https://developers.redhat.com/blog/2019/03/07/quarkus-next-g...

Comment · 7 years ago

"Analytics" generally means "cython only". The numpy dep ends up being a problem.

Generally if you want python/java interaction, we maintain a tool called javacpp that handles this, we even bundle cpython: https://github.com/bytedeco/javacpp-presets

GraalVM itself also depends on javacpp for a portion of its features (LLVM wrapper): https://github.com/oracle/graal/blob/315f5dcf69c2e73fd13a5f8...

I'd be happy to answer questions about the overlap of the 2. I can say I happily execute python scripts from our embedded python and even point the python execution at an anaconda distribution.

Comment · 7 years ago

Tech side here if you have any other questions!

Comment · 7 years ago

Thanks Nikk!

Comment · 7 years ago

We control the whole framework down to the bare metal. We also have our own built in memory allocators.

There's a bit more to jvm than just training models. Java based application servers are still widely deployed for example.

There's a whole data engineering niche we target here as well (for the spark pipelines) where python isn't a good fit for that team.

Then there's the fact we're still easier to run keras on spark than any other framework thanks to our model import.

That doesn't account for what we're doing with inference. Many vendors in this space are just running kubernetes bundling other tools they don't control. We actually engage various large companies in custom chip development running other DL frameworks due to this low level control.

Depending on what you're looking to do, we're still the standard for pre compiled binaries compiled as jar files: https://repo1.maven.org/maven2/org/nd4j/nd4j-native/1.0.0-be...

You won't find any other framework running pre cooked avx binaries and IBM power at the same time.

Happy to talk about more depending on what your focus is.