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Chris my cofounder forgot to disclose he works on the project :). I"ll do it for him. I'd just like to say that as far as this niche is concerned. This is bas

Chris my cofounder forgot to disclose he works on the project :). I"ll do it for him. I'd just like to say that as far as this niche is concerned. This is basically an attempt at "non gpus on spark". We are heavily biased towards cuda and distributed gpu applications: https://blogs.nvidia.com/blog/2016/10/06/how-skymind-nvidia-deep-learning/ https://blogs.nvidia.com/blog/2016/10/06/how-skymind-nvidia-... I respect what intel is trying to do here, but it's going to take a lot more than "we built stuff" to get anyone to switch let alone build a community around. To be fair to intel, I can't wait to see what they do with accelerators and phi, but I need to see more results first. Competition in the space is definitely needed :D. We have yet to see fpgas and nervana acquisition really play out as well. It will take them a while to catch up either way.

sandGorgon 10y ago
that sounds interesting. so dl4j works with spark ? https://deeplearning4j.org/spark#how 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-databricks.html https://databricks.com/blog/2016/12/21/deep-learning-on-data...
- vonnik 10y ago
  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 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. https://github.com/deeplearning4j/DataVec https://github.com/deeplearning4j/DataVec https://github.com/deeplearning4j/Arbiter https://github.com/deeplearning4j/Arbiter https://deeplearning4j.org/visualization https://deeplearning4j.org/visualization
  - agibsonccc 10y ago
    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.
deepnotderp 10y ago
Nothing to be "biased" about, Cuda is the industry standard.
- agibsonccc 10y ago
  Sure :D. It's still in my interest to disclose we partner with nvidia pretty closely though. I would hope to see competition here but we have a large vested interest in gpus succeeding. Thanks for the sentiment though!
  - deepnotderp 10y ago
    Oh I don't like the situation either, I'm just saying there's no need to be guilty! :)

sandGorgon 10y ago

that sounds interesting. so dl4j works with spark ? https://deeplearning4j.org/spark#how 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-databricks.html https://databricks.com/blog/2016/12/21/deep-learning-on-data...

vonnik 10y ago
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 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. https://github.com/deeplearning4j/DataVec https://github.com/deeplearning4j/DataVec https://github.com/deeplearning4j/Arbiter https://github.com/deeplearning4j/Arbiter https://deeplearning4j.org/visualization https://deeplearning4j.org/visualization
- agibsonccc 10y ago
  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.

deepnotderp 10y ago

Nothing to be "biased" about, Cuda is the industry standard.

agibsonccc 10y ago
Sure :D. It's still in my interest to disclose we partner with nvidia pretty closely though. I would hope to see competition here but we have a large vested interest in gpus succeeding. Thanks for the sentiment though!
- deepnotderp 10y ago
  Oh I don't like the situation either, I'm just saying there's no need to be guilty! :)