Benchmarks we ran ourselves show that we're faster than TensorFlow using multi-GPUs for a non-trivial image processing task: https://github.com/deeplearning4j/dl4j-benchmark. That's the best apples to apples we have for the moment.
When you're aiming to put deep learning into production, a bunch of other things are important too, notably integrations. DL4J comes with integrations for Hadoop, Kafka and ElasticSearch as well as Spark. In the inference stage, we autoscale elastically as a micro-service using Lagom and a REST API. Most frameworks are just libs that don't solve problems deeper in the workflow. Our tools include data pipelines with DataVec (reusable data preprocessing), to model evaluation with Arbiter and a GUI for heuristics during training.
I have to correct chris here. He is talking about a lot of features that are in our enterprise version SKIL.
We will offer a limited developer version of SKIL for free.
Think of SKIL as similar to gitlab or github enterprise.
In SKIL we also have auto provisioning of a cluster and a higher level interface for running deep learning workloads. It auto configures most of the parameters like the spark worker native library path and setting up things like a training UI as well as installation of the mkl and cudnn libraries.
Optionally, you can also run a version of this with DC/OS and co where there is a packaged spark.
What we do have in dl4j is the raw components you can use to create these things such as datavec and dl4j-streaming which covers our integration with kafka.
so dl4j works with spark ? https://deeplearning4j.org/spark#how
is it because spark does "distributed computing" very efficiently ? In that case, would the apples-to-apples comparison be versus spark+tensorflow ? https://databricks.com/blog/2016/12/21/deep-learning-on-data...