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.
Because right now, it looks like it is tensorflow vs dl4j.