5 ms·

I feel at this point this is not a sensible thing to do any more unfortunately. I totally get the impulse though. For instance, there is still nothing great on

by mks40 9y ago

I feel at this point this is not a sensible thing to do any more unfortunately. I totally get the impulse though. For instance, there is still nothing great on the JVM for deep learning with symbolic differentiation (deeplearning4j does not have this, correct me if this has changed). On the other hand, I realize that between writing native interfaces, symbolic differentiation (e.g. writing a port of autograd), network optimisers, custom layers, parameter servers, multi-GPU scheduling and so forth, I'd spend years before getting to do what I wanted to implement in the first place.

agibsonccc 9y ago
We are adding this now to our tensor library: https://github.com/deeplearning4j/nd4j/pull/1750 https://github.com/deeplearning4j/nd4j/pull/1750 I've also added numpy interop via our new python interface jumpy: https://github.com/deeplearning4j/jumpy https://github.com/deeplearning4j/jumpy We are doing a lot more than autograd though, this is going to support dynamic computation graphs, give you direct access to a graph data structure and will later be usable from nd4s (our scala wrapper) Rather than spending time going back and forth implementing all of those things you could just pitch in with our existing efforts (hint: you'd actually be getting something done rather than debating ;)) We have a parameter server for word2vec, various kinds of optimizers and the like:https://github.com/deeplearning4j/nd4j/tree/master/nd4j-parameter-server-parent https://github.com/deeplearning4j/nd4j/tree/master/nd4j-para... I'd also just like to note for anyone else reading this: Mulling over doing something helps no one. If you see something that's open source that's close to what you want try engaging the authors to see what they have to say. Maybe they will guide you. We've done that recently for our lapack integration with cpu and gpu as well as various neural net implementations. No offense but it kills me to see comments like this. I see tons of people complaining about features yet doing nothing to add them let alone engaging open source authors. It's kinda funny - every time someone has actually did that I've hired them. The developers that actually take action when engaging open source are amazing people.I have a feeling it's because they take the time to learn and get their feet wet even if it's initimidating. Other neat community initiatives include flink: https://issues.apache.org/jira/browse/FLINK-5782 https://issues.apache.org/jira/browse/FLINK-5782 Nasa (Apache Tika): https://github.com/apache/tika/pull/165 https://github.com/apache/tika/pull/165 A language for our ETL library DataVec (supports binary vectorization AND sql like transformations!): https://github.com/deeplearning4j/DataVec/issues/224 https://github.com/deeplearning4j/DataVec/issues/224 A scala lib like tensorflow built on top of nd4j: https://github.com/ThoughtWorksInc/DeepLearning.scala https://github.com/ThoughtWorksInc/DeepLearning.scala Our spark ml integration: https://github.com/deeplearning4j/deeplearning4j/tree/master/deeplearning4j-scaleout/spark/dl4j-spark-ml https://github.com/deeplearning4j/deeplearning4j/tree/master... The community is very active. We have 4200 people in a gitter room alone: http://gitter.im/deeplearning4j/deeplearning4j http://gitter.im/deeplearning4j/deeplearning4j
- mks40 9y ago
  I have contributed to dl4j though ;) I did not mean not criticise dl4j at all, I was simply pointing out an example of a feature I know I was missing at a point, I think we are actually agreeing. It does not always make sense to start something from scratch even though it's fun and a great learning experience. The ramp-up to something really useful in deep learning is simply very high. Further, few people can be an expert on the whole stack and I have no problem admitting to myself that even if I spent 2 years writing something from scratch, many parts would simply not be as good as something I could copy from an existing open source library. That's why contributing to open source also makes more sense to me - you get to work on a part that you can be good at. Also should point out that when I was having problems with custom loss functions a year ago you guys were extremely helpful on Gitter in discussing issues.
  - agibsonccc 9y ago
    Hard to tell from an HN user name :D. That's great to hear! I get how hard it can be - what you get out of it is learning though. We have some seriously cool examples that are just weekend projects for folks right now with javafx for example: https://github.com/deeplearning4j/dl4j-examples/pull/421 https://github.com/deeplearning4j/dl4j-examples/pull/421 A lot of community contributions are in the examples now, if you haven't used dl4j in a while maybe take a look.
- RandyRanderson 9y ago
  You need to have some demos that one can download and get to work easily. Before you say "we do - look at this link..." see sentence 1.
  - agibsonccc 9y ago
    https://deeplearning4j.org/quickstart https://deeplearning4j.org/quickstart I agree with you if my aim were to mainly promote new users here - I was more targeting someone who knew what dl4j was already and had maybe used it. I usually don't comment unless someone mentions the library by name. 99.99999% of the people who comment on here are going to likely be more interested in python in which I usually point them at keras. Thanks for the feedback though! I'm not sure what to do beyond "git clone and import into intellij". If you'd like feel free to file an issue on I'm guessing? the nd4j repo you were looking at? We always take feedback seriously if people take 5 seconds to post problems they've found. My head of training does our docs and videos and updates the site when he can. Here are some of our youtube videos: https://www.youtube.com/channel/UCa-HKBJwkfzs4AgZtdUuBXQ https://www.youtube.com/channel/UCa-HKBJwkfzs4AgZtdUuBXQ

by mks40 9y ago

agibsonccc 9y ago

We are adding this now to our tensor library: https://github.com/deeplearning4j/nd4j/pull/1750 https://github.com/deeplearning4j/nd4j/pull/1750 I've also added numpy interop via our new python interface jumpy: https://github.com/deeplearning4j/jumpy https://github.com/deeplearning4j/jumpy We are doing a lot more than autograd though, this is going to support dynamic computation graphs, give you direct access to a graph data structure and will later be usable from nd4s (our scala wrapper) Rather than spending time going back and forth implementing all of those things you could just pitch in with our existing efforts (hint: you'd actually be getting something done rather than debating ;)) We have a parameter server for word2vec, various kinds of optimizers and the like:https://github.com/deeplearning4j/nd4j/tree/master/nd4j-parameter-server-parent https://github.com/deeplearning4j/nd4j/tree/master/nd4j-para... I'd also just like to note for anyone else reading this: Mulling over doing something helps no one. If you see something that's open source that's close to what you want try engaging the authors to see what they have to say. Maybe they will guide you. We've done that recently for our lapack integration with cpu and gpu as well as various neural net implementations. No offense but it kills me to see comments like this. I see tons of people complaining about features yet doing nothing to add them let alone engaging open source authors. It's kinda funny - every time someone has actually did that I've hired them. The developers that actually take action when engaging open source are amazing people.I have a feeling it's because they take the time to learn and get their feet wet even if it's initimidating. Other neat community initiatives include flink: https://issues.apache.org/jira/browse/FLINK-5782 https://issues.apache.org/jira/browse/FLINK-5782 Nasa (Apache Tika): https://github.com/apache/tika/pull/165 https://github.com/apache/tika/pull/165 A language for our ETL library DataVec (supports binary vectorization AND sql like transformations!): https://github.com/deeplearning4j/DataVec/issues/224 https://github.com/deeplearning4j/DataVec/issues/224 A scala lib like tensorflow built on top of nd4j: https://github.com/ThoughtWorksInc/DeepLearning.scala https://github.com/ThoughtWorksInc/DeepLearning.scala Our spark ml integration: https://github.com/deeplearning4j/deeplearning4j/tree/master/deeplearning4j-scaleout/spark/dl4j-spark-ml https://github.com/deeplearning4j/deeplearning4j/tree/master... The community is very active. We have 4200 people in a gitter room alone: http://gitter.im/deeplearning4j/deeplearning4j http://gitter.im/deeplearning4j/deeplearning4j

mks40 9y ago
I have contributed to dl4j though ;) I did not mean not criticise dl4j at all, I was simply pointing out an example of a feature I know I was missing at a point, I think we are actually agreeing. It does not always make sense to start something from scratch even though it's fun and a great learning experience. The ramp-up to something really useful in deep learning is simply very high. Further, few people can be an expert on the whole stack and I have no problem admitting to myself that even if I spent 2 years writing something from scratch, many parts would simply not be as good as something I could copy from an existing open source library. That's why contributing to open source also makes more sense to me - you get to work on a part that you can be good at. Also should point out that when I was having problems with custom loss functions a year ago you guys were extremely helpful on Gitter in discussing issues.
- agibsonccc 9y ago
  Hard to tell from an HN user name :D. That's great to hear! I get how hard it can be - what you get out of it is learning though. We have some seriously cool examples that are just weekend projects for folks right now with javafx for example: https://github.com/deeplearning4j/dl4j-examples/pull/421 https://github.com/deeplearning4j/dl4j-examples/pull/421 A lot of community contributions are in the examples now, if you haven't used dl4j in a while maybe take a look.
RandyRanderson 9y ago
You need to have some demos that one can download and get to work easily. Before you say "we do - look at this link..." see sentence 1.
- agibsonccc 9y ago
  https://deeplearning4j.org/quickstart https://deeplearning4j.org/quickstart I agree with you if my aim were to mainly promote new users here - I was more targeting someone who knew what dl4j was already and had maybe used it. I usually don't comment unless someone mentions the library by name. 99.99999% of the people who comment on here are going to likely be more interested in python in which I usually point them at keras. Thanks for the feedback though! I'm not sure what to do beyond "git clone and import into intellij". If you'd like feel free to file an issue on I'm guessing? the nd4j repo you were looking at? We always take feedback seriously if people take 5 seconds to post problems they've found. My head of training does our docs and videos and updates the site when he can. Here are some of our youtube videos: https://www.youtube.com/channel/UCa-HKBJwkfzs4AgZtdUuBXQ https://www.youtube.com/channel/UCa-HKBJwkfzs4AgZtdUuBXQ