Hi Yuhao, BigDL looks very promising and it's a framework we're considering using. It seems the general approach to high performance DL is via GPUs. Your project mentions performance on a Xeon comparable to that of a GPU, but where does this claim come from? Can you provide benchmarks?
Thanks, Michael > On Feb 27, 2017, at 11:11 PM, Yuhao Yang <hhb...@gmail.com> wrote: > > Welcome to try and contribute to our BigDL: > https://github.com/intel-analytics/BigDL > <https://github.com/intel-analytics/BigDL> > > It's native on Spark and fast by leveraging Intel MKL. > > 2017-02-23 4:51 GMT-08:00 Joeri Hermans <joeri.raymond.e.herm...@cern.ch > <mailto:joeri.raymond.e.herm...@cern.ch>>: > Hi Nikita, > > We are actively working on this: https://github.com/cerndb/dist-keras > <https://github.com/cerndb/dist-keras> This will allow you to run Keras on > Spark (with distributed optimization algorithms) through pyspark. I recommend > you to check the examples > https://github.com/cerndb/dist-keras/tree/master/examples > <https://github.com/cerndb/dist-keras/tree/master/examples>. However, you > need to be aware that distributed optimization is a research topic, and has > several approaches and caveats you need to be aware of. I wrote a blog post > on this if you like to have some additional information on this topic > https://db-blog.web.cern.ch/blog/joeri-hermans/2017-01-distributed-deep-learning-apache-spark-and-keras > > <https://db-blog.web.cern.ch/blog/joeri-hermans/2017-01-distributed-deep-learning-apache-spark-and-keras> > > However, if you don't want to use a distributed optimization algorithm, we > also support a "sequential trainer" which allows you to train a model on > Spark dataframes. > > Kind regards, > > Joeri > ________________________________________. > From: Nick Pentreath [nick.pentre...@gmail.com > <mailto:nick.pentre...@gmail.com>] > Sent: 23 February 2017 13:39 > To: dev@spark.apache.org <mailto:dev@spark.apache.org> > Subject: Re: Implementation of RNN/LSTM in Spark > > The short answer is there is none and highly unlikely to be inside of Spark > MLlib any time in the near future. > > The best bets are to look at other DL libraries - for JVM there is > Deeplearning4J and BigDL (there are others but these seem to be the most > comprehensive I have come across) - that run on Spark. Also there are various > flavours of TensorFlow / Caffe on Spark. And of course the libs such as > Torch, Keras, Tensorflow, MXNet, Caffe etc. Some of them have Java or Scala > APIs and some form of Spark integration out there in the community (in > varying states of development). > > Integrations with Spark are a bit patchy currently but include the "XOnSpark" > flavours mentioned above and TensorFrames (again, there may be others). > > On Thu, 23 Feb 2017 at 14:23 n1kt0 <nikita.balysc...@googlemail.com > <mailto:nikita.balysc...@googlemail.com><mailto:nikita.balysc...@googlemail.com > <mailto:nikita.balysc...@googlemail.com>>> wrote: > Hi, > can anyone tell me what the current status about RNNs in Spark is? > > > > -- > View this message in context: > http://apache-spark-developers-list.1001551.n3.nabble.com/Implementation-of-RNN-LSTM-in-Spark-tp14866p21060.html > > <http://apache-spark-developers-list.1001551.n3.nabble.com/Implementation-of-RNN-LSTM-in-Spark-tp14866p21060.html> > Sent from the Apache Spark Developers List mailing list archive at Nabble.com. > > --------------------------------------------------------------------- > To unsubscribe e-mail: dev-unsubscr...@spark.apache.org > <mailto:dev-unsubscr...@spark.apache.org><mailto:dev-unsubscr...@spark.apache.org > <mailto:dev-unsubscr...@spark.apache.org>> > > > --------------------------------------------------------------------- > To unsubscribe e-mail: dev-unsubscr...@spark.apache.org > <mailto:dev-unsubscr...@spark.apache.org> > >
