Because it seemed on the surface that you guys replaced tf. If you guys offer a better ecosystem where tf and spark work together (but don't really rip out tf), then it is even more awesome.
Granted, a ton of it is TF. Google is doing an amazing job now.
We're trying to be a moderate middle ground.
TF and pytorch and anything in python tends to be an interface.
Core logic still operates in C/C++.
That's great when you want speed, but you end up missing the benefits of the JVM (the tooling is great for monitoring and things folks need at scale).
So we do everything via JNI where we push down the math one block with minimal overhead if any.
That and if you want to write production code, you don't need to push logic down to c. You can do something JVM based instead, which gives you kotlin,scala,clojure,..
Because right now, it looks like it is tensorflow vs dl4j.
We play up what's unique about Dl4j anyways. It's right in the name. We're square focused on the JVM.
We also hedge our bets against spark. Most of our logic doesn't even run on spark.
Spark is just a facilitator..it's not where anything that matters runs. We could just as easily use flink or apex here too. Those are also JVM based streaming engines.
We can't overplay spark because most of our deployments won't even be with spark. We don't even use spark in our own production inference tools. We just deploy as a microservice.
I have personally been campaigning for ONNX to merge/leverage Apache Arrow (https://news.ycombinator.com/item?id=15195658). It probably makes sense for the efforts to build on top of each other.
YMMV ;)
I feel like you're trying to cross 2 worlds that shouldn't be for arrow/ONNX though. You're trying to map a neural net description language to a columnar format..that doesn't make any sense to me. In our case, our runtime will understand both but for different reasons though.
We will import the format for our neural nets and integrate with arrow for our ETL -> tensor pipelines.
None of these frameworks you're mentioning integrate properly, are really supported by an actual community, and don't get meaningful updates.
The biggest problem you run in to pretty quickly is maintenance. TF and spark both update quickly.
Every time someone has attempted to do a "TF on spark" they don't add ETL, proper JVM integrations (proper control of cuda and memory management from the JVM), there are also usually strange interactions with JVM/python run times.
Also, please don't ignore the rest of dl4j. We have a whole suite of tools in there. It's not just a matrix lib with autodiff like TF and co is.
TF in its own way is adding some of this stuff like TF records and some of their readers, but it's not going to add a lot in the way of things like connectors to kafka and a lot of the big data ecosystem.
They've basically added HDFS..that was about it.
Dl4j is also significantly easier to deploy. It's just a jar file or zip file..you don't need a blob of c++ just to run a model.