7 ms·

BigDL: Distributed Deep Learning on Apache Spark

112 points by ubolonton_ 10y ago

rustyconover 10y ago
It does not appear that this uses a GPU at all. Which is okay of course but may not win any speed contests.
- wcrichton 10y ago
  Intel is pushing their own chips for deep learning (i.e. Xeon and Xeon Phi). They claim that using MKL on these chips gets comparable performance to using Caffe/cuDNN on a high-end GPU.
  - Eridrus 10y ago
    Phi may yet win out, I saw a talk from an NLP researcher about Seq2Seq models and someone asked him what his wish for hardware was and it was for GPU cores to not be as stuck in lock step with each other control-wise. Not sure if he knew about the Phi's though.
- sandGorgon 10y ago
  gpu acceleration in spark in generally production ready - https://databricks.com/blog/2016/10/27/gpu-acceleration-in-databricks.html https://databricks.com/blog/2016/10/27/gpu-acceleration-in-d... in fact looks like you can use tensorflow models in spark with GPU - https://databricks.com/blog/2016/12/21/deep-learning-on-databricks.html https://databricks.com/blog/2016/12/21/deep-learning-on-data...
  - agibsonccc 10y ago
    Tensorframes despite the marketing is already defunct. It hasn't seen a commit since august. https://github.com/databricks/tensorframes https://github.com/databricks/tensorframes Spark is just a data access layer here. It's not even remotely gpu friendly. Most people also still relies on mesos or yarn for running distributed. The library you're using matters alot. Mesos just added gpu support: http://mesos.apache.org/documentation/latest/gpu-support/ http://mesos.apache.org/documentation/latest/gpu-support/ Yarn can sorta support it with node labeling for job completion but it's still kinda hacky. The real work in this space (without the marketing) is done by IBM: http://www.slideshare.net/ishizaki/exploiting-gpus-in-spark http://www.slideshare.net/ishizaki/exploiting-gpus-in-spark When spark can (without "production ready" buzzwords) run gpus like this out of the box then we're talking. For now spark needs a companion library to work with gpus though.
zero-x 10y ago
Hmm, seems interesting but wondering how it compares to H2O's Sparkling Water. Been using that for clients and i love it.
- deleted 10y ago
  [deleted]
- happynewyear 10y ago
  We don't seem to hear much about 0xdata on hn.
sandGorgon 10y ago
Isn't spark more versatile than tensorflow at this point? It does graph processing and deep learning. Plus it's built for distributed processing. Pyspark makes it easy to use.
- Eridrus 10y ago
  Spark doesn't do GPU acceleration, which is super important if you don't have a lot of spare CPU capacity. The one time I tried to train a DL model on CPU it was 48x slower, and with communication overhead it would have taken more than 48 cores to match the single GPH. And given you want to do hyperparameter search on top of that, those CPU cores start adding up.
  - sandGorgon 10y ago
    that doesnt seem right. Databricks seems to have this in production. https://databricks.com/blog/2016/10/27/gpu-acceleration-in-databricks.html https://databricks.com/blog/2016/10/27/gpu-acceleration-in-d... https://databricks.com/blog/2016/12/21/deep-learning-on-databricks.html https://databricks.com/blog/2016/12/21/deep-learning-on-data... I havent used this feature - but are you sure ?
    - nl 10y ago
      This seems exactly right. The Databricks GPU stuff isn't generally available and is built on TF anyway.
      - sandGorgon 10y ago
        hmm.. atleast databricks claims that the GPU clusters are beta, but generally available https://docs.databricks.com/user-guide/clusters/gpu.html https://docs.databricks.com/user-guide/clusters/gpu.html https://databricks.com/blog/2016/12/21/deep-learning-on-databricks.html https://databricks.com/blog/2016/12/21/deep-learning-on-data... In addition, IBM has multiple projects around GPU aware spark - https://github.com/IBMSparkGPU https://github.com/IBMSparkGPU http://www.spark.tc/gpu-acceleration-on-apache-spark-2/ http://www.spark.tc/gpu-acceleration-on-apache-spark-2/
mmrezaie 10y ago
Some projects like SparkNet or DeepLearning4j+spark or even Sparkling Water are kinda doing the same thing. So, how this is compared to them?
- blueyes 10y ago
  This is a classic vanity deep-learning framework that Intel built due to NIH syndrome. It's like DSSTNE. Doomed to be abandoned. I can't a worse way to position a deep learning library than to say: this only works on CPUs. When you look at Intel's track record with software, especially their Trusted Analytics Platform this year, BigDL's prospects are poor. I'm just waiting for IBM to copy this move and come out with yet another deep learning lib: YADLL.
vonnik 10y ago
Deeplearning4j does this already. It has a huge community, a Scala API and does model import from Keras. It's important to note that Spark is not an efficient computation later -- it's best if used for fast ETL. If you get that wrong, your going to be training slow. https://deeplearning4j.org https://deeplearning4j.org https://github.com/deeplearning4j/ScalNet https://github.com/deeplearning4j/ScalNet https://deeplearning4j.org/model-import-keras https://deeplearning4j.org/model-import-keras https://gitter.im/deeplearning4j/deeplearning4j https://gitter.im/deeplearning4j/deeplearning4j
- stevehiehn 10y ago
  I started experimenting with DL4J about a month ago. The getting started example apps are actually pretty good. You can pretty much clone the repo and run them.
- agibsonccc 10y ago
  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...
ris 10y ago
A rather transparent attempt to pull people away from GPUs and instead use massive farms of (intel powered) machines.

112 points by ubolonton_ 10y ago

rustyconover 10y ago

It does not appear that this uses a GPU at all. Which is okay of course but may not win any speed contests.

wcrichton 10y ago
Intel is pushing their own chips for deep learning (i.e. Xeon and Xeon Phi). They claim that using MKL on these chips gets comparable performance to using Caffe/cuDNN on a high-end GPU.
- Eridrus 10y ago
  Phi may yet win out, I saw a talk from an NLP researcher about Seq2Seq models and someone asked him what his wish for hardware was and it was for GPU cores to not be as stuck in lock step with each other control-wise. Not sure if he knew about the Phi's though.
sandGorgon 10y ago
gpu acceleration in spark in generally production ready - https://databricks.com/blog/2016/10/27/gpu-acceleration-in-databricks.html https://databricks.com/blog/2016/10/27/gpu-acceleration-in-d... in fact looks like you can use tensorflow models in spark with GPU - https://databricks.com/blog/2016/12/21/deep-learning-on-databricks.html https://databricks.com/blog/2016/12/21/deep-learning-on-data...
- agibsonccc 10y ago
  Tensorframes despite the marketing is already defunct. It hasn't seen a commit since august. https://github.com/databricks/tensorframes https://github.com/databricks/tensorframes Spark is just a data access layer here. It's not even remotely gpu friendly. Most people also still relies on mesos or yarn for running distributed. The library you're using matters alot. Mesos just added gpu support: http://mesos.apache.org/documentation/latest/gpu-support/ http://mesos.apache.org/documentation/latest/gpu-support/ Yarn can sorta support it with node labeling for job completion but it's still kinda hacky. The real work in this space (without the marketing) is done by IBM: http://www.slideshare.net/ishizaki/exploiting-gpus-in-spark http://www.slideshare.net/ishizaki/exploiting-gpus-in-spark When spark can (without "production ready" buzzwords) run gpus like this out of the box then we're talking. For now spark needs a companion library to work with gpus though.

zero-x 10y ago

Hmm, seems interesting but wondering how it compares to H2O's Sparkling Water. Been using that for clients and i love it.

deleted 10y ago
[deleted]
happynewyear 10y ago
We don't seem to hear much about 0xdata on hn.

sandGorgon 10y ago

Isn't spark more versatile than tensorflow at this point? It does graph processing and deep learning. Plus it's built for distributed processing. Pyspark makes it easy to use.

Eridrus 10y ago
Spark doesn't do GPU acceleration, which is super important if you don't have a lot of spare CPU capacity. The one time I tried to train a DL model on CPU it was 48x slower, and with communication overhead it would have taken more than 48 cores to match the single GPH. And given you want to do hyperparameter search on top of that, those CPU cores start adding up.
- sandGorgon 10y ago
  that doesnt seem right. Databricks seems to have this in production. https://databricks.com/blog/2016/10/27/gpu-acceleration-in-databricks.html https://databricks.com/blog/2016/10/27/gpu-acceleration-in-d... https://databricks.com/blog/2016/12/21/deep-learning-on-databricks.html https://databricks.com/blog/2016/12/21/deep-learning-on-data... I havent used this feature - but are you sure ?
  - nl 10y ago
    This seems exactly right. The Databricks GPU stuff isn't generally available and is built on TF anyway.
    - sandGorgon 10y ago
      hmm.. atleast databricks claims that the GPU clusters are beta, but generally available https://docs.databricks.com/user-guide/clusters/gpu.html https://docs.databricks.com/user-guide/clusters/gpu.html https://databricks.com/blog/2016/12/21/deep-learning-on-databricks.html https://databricks.com/blog/2016/12/21/deep-learning-on-data... In addition, IBM has multiple projects around GPU aware spark - https://github.com/IBMSparkGPU https://github.com/IBMSparkGPU http://www.spark.tc/gpu-acceleration-on-apache-spark-2/ http://www.spark.tc/gpu-acceleration-on-apache-spark-2/

mmrezaie 10y ago

Some projects like SparkNet or DeepLearning4j+spark or even Sparkling Water are kinda doing the same thing. So, how this is compared to them?

blueyes 10y ago
This is a classic vanity deep-learning framework that Intel built due to NIH syndrome. It's like DSSTNE. Doomed to be abandoned. I can't a worse way to position a deep learning library than to say: this only works on CPUs. When you look at Intel's track record with software, especially their Trusted Analytics Platform this year, BigDL's prospects are poor. I'm just waiting for IBM to copy this move and come out with yet another deep learning lib: YADLL.

vonnik 10y ago

Deeplearning4j does this already. It has a huge community, a Scala API and does model import from Keras. It's important to note that Spark is not an efficient computation later -- it's best if used for fast ETL. If you get that wrong, your going to be training slow. https://deeplearning4j.org https://deeplearning4j.org https://github.com/deeplearning4j/ScalNet https://github.com/deeplearning4j/ScalNet https://deeplearning4j.org/model-import-keras https://deeplearning4j.org/model-import-keras https://gitter.im/deeplearning4j/deeplearning4j https://gitter.im/deeplearning4j/deeplearning4j

stevehiehn 10y ago
I started experimenting with DL4J about a month ago. The getting started example apps are actually pretty good. You can pretty much clone the repo and run them.
agibsonccc 10y ago
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...

ris 10y ago

A rather transparent attempt to pull people away from GPUs and instead use massive farms of (intel powered) machines.