Commit 918715c

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HOME ?=/home/${USER}

CAFFE_ON_SPARK ?=$(shell pwd)

LD_LIBRARY_PATH ?=/home/y/lib64:/home/y/lib64/mkl/intel64

LD_LIBRARY_PATH2=${LD_LIBRARY_PATH}:${CAFFE_ON_SPARK}/caffe-public/distribute/lib:${CAFFE_ON_SPARK}/caffe-distri/distribute/lib:/usr/lib64:/lib64

DYLD_LIBRARY_PATH ?=/home/y/lib64:/home/y/lib64/mkl/intel64

DYLD_LIBRARY_PATH2=${DYLD_LIBRARY_PATH}:${CAFFE_ON_SPARK}/caffe-public/distribute/lib:${CAFFE_ON_SPARK}/caffe-distri/distribute/lib:/usr/lib64:/lib64

build:

cd caffe-public; make proto; make -j4 -e distribute; cd ..

export LD_LIBRARY_PATH="${LD_LIBRARY_PATH2}"; mvn package

jar -xvf caffe-grid/target/caffe-grid-0.1-SNAPSHOT-jar-with-dependencies.jar META-INF/native/linux64/liblmdbjni.so

mv META-INF/native/linux64/liblmdbjni.so ${CAFFE_ON_SPARK}/caffe-distri/distribute/lib

buildosx:

cd caffe-public; make proto; make -j4 -e distribute; cd ..

export DYLD_LIBRARY_PATH="${DYLD_LIBRARY_PATH2}"; mvn package

jar -xvf caffe-grid/target/caffe-grid-0.1-SNAPSHOT-jar-with-dependencies.jar META-INF/native/osx64/liblmdbjni.jnilib

mv META-INF/native/osx64/liblmdbjni.jnilib ${CAFFE_ON_SPARK}/caffe-distri/distribute/lib

update:

git submodule init

git submodule update --force

git submodule foreach --recursive git clean -dfx

doc:

rm -rf scala_doc

scaladoc -cp caffe-grid/target/caffe-grid-0.1-SNAPSHOT-jar-with-dependencies.jar:${HOME}/.m2/repository/org/apache/spark/spark-core_2.10/1.6.0/spark-core_2.10-1.6.0.jar:${HOME}/.m2/repository/org/apache/spark/spark-sql_2.10/1.6.0/spark-sql_2.10-1.6.0.jar:${HOME}/.m2/repository/org/apache/spark/spark-mllib_2.10/1.6.0/spark-mllib_2.10-1.6.0.jar:${HOME}/.m2/repository/org/apache/spark/spark-catalyst_2.10/1.6.0/spark-catalyst_2.10-1.6.0.jar:$(shell hadoop classpath) -d scala_doc caffe-grid/src/main/scala/com/yahoo/ml/caffe/*.scala

gh-pages: doc

git checkout gh-pages

git add -r scala_doc

git commit -m "scala_doc update"

git push origin gh-pages

clean:

pushd caffe-public; make clean; popd

pushd caffe-distri; make clean; popd

mvn clean

ALL: build

CaffeOnSpark brings deep learning to Hadoop and Spark clusters. By

combining salient features from deep learning framework

[Caffe](https://github.com/BVLC/caffe) and big-data frameworks [Apache

Spark](http://spark.apache.org/) and [Apache Hadoop](http://hadoop.apache.org/), CaffeOnSpark enables distributed

deep learning on a cluster of GPU and CPU servers.

As a distributed extension of Caffe, CaffeOnSpark supports neural

network model training, testing, and feature extraction. Caffe users

can now perform distributed learning using their existing LMDB data

files and minorly adjusted network configuration (as

[illustrated](../master/data/lenet_memory_train_test.prototxt#L10-L12)).

CaffeOnSpark is a Spark package for deep learning. It is complementary

to non-deep learning libraries MLlib and Spark SQL.

CaffeOnSpark's Scala API provides Spark applications with an easy

mechanism to invoke deep learning (see

[sample](../master/caffe-grid/src/main/scala/com/yahoo/ml/caffe/examples/MyMLPipeline.scala))

over distributed datasets.

CaffeOnSpark was developed by Yahoo for [large-scale distributed deep

learning on our Hadoop

clusters](http://yahoohadoop.tumblr.com/post/129872361846/large-scale-distributed-deep-learning-on-hadoop)

in Yahoo's private cloud. It's been in use by Yahoo for image search,

content classification and several other use cases.

CaffeOnSpark provides some important benefits over alternative deep learning solutions.

* It enables model training, test and feature extraction directly on Hadoop datasets stored in HDFS on Hadoop clusters.

* It turns your Hadoop or Spark cluster(s) into a powerful platform for deep learning, without the need to set up a new dedicated cluster for deep learning separately.

* Server-to-server direct communication (Ethernet or InfiniBand) achieves faster learning and eliminates scalability bottleneck.

* Caffe users' existing datasets (e.g. LMDB) and configurations could be applied for distributed learning without any conversion needed.

* High-level API empowers Spark applications to easily conduct deep learning.

* Incremental learning is supported to leverage previously trained models or snapshots.

* Additional data formats and network interfaces could be easily added.

* It's can be easily deployed on public cloud (ex. AWS EC2) or a private cloud.

Please check CaffeOnSpark [wiki site](../../wiki) for detailed

documentations including [buiding instruction](../../wiki/build), and

getting started guides for [standalone cluster](../../wiki/GetStarted_local)

and [AWS EC2 cluster](../../wiki/GetStarted_EC2).

Please note:

* Batch sizes specified in prototxt files are per device.

* Memory layers should not be shared among GPUs, and thus "share_in_parallel: false" is required for layer configuration.

Please join [CaffeOnSpark user

group](https://groups.google.com/forum/#!forum/caffeonspark-users) for

discussions and questions.

The use and distribution terms for this software are covered by the

Apache 2.0 license. See [LICENSE](LICENSE.txt) file for terms.

/caffe.iml

src/main/java/caffe/Caffe.java

include/jni/com_yahoo_ml_jcaffe_*.h