|Number of watchers on Github||1217|
|Number of open issues||76|
|Average time to close an issue||14 days|
|Average time to merge a PR||1 day|
|Open pull requests||4+|
|Closed pull requests||13+|
|Last commit||about 2 years ago|
|Repo Created||about 3 years ago|
|Repo Last Updated||about 1 year ago|
|Organization / Author||yahoo|
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CaffeOnSpark brings deep learning to Hadoop and Spark clusters. By combining salient features from deep learning framework Caffe and big-data frameworks Apache Spark and Apache Hadoop, 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).
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) over distributed datasets.
CaffeOnSpark was developed by Yahoo for large-scale distributed deep learning on our Hadoop clusters 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 (see our blog) over alternative deep learning solutions.
share_in_parallel: falseis required for layer configuration.
CaffeOnSpark supports both Spark 1.x and 2.x. For Spark 2.0, our default settings are:
Please join CaffeOnSpark user group for discussions and questions.
The use and distribution terms for this software are covered by the Apache 2.0 license. See LICENSE file for terms.