CaffeOnSpark: Deep Learning On Spark Cluster - Databricks

CaffeOnSpark: Deep Learning On Spark Cluster

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Deep learning is a critical capability for gaining intelligence from datasets. Many existing frameworks require a separated cluster for deep learning, and multiple programs have to be created for a typical machine learning pipeline. The separated clusters require large datasets to be transferred between clusters, and introduce unwanted system complexity and latency for end-to-end learning.

Yahoo introduced CaffeOnSpark to alleviate those pain points and bring deep learning onto Hadoop and Spark clusters. By combining salient features from deep learning framework Caffe and big-data framework Apache Spark, CaffeOnSpark enables distributed deep learning on a cluster of GPU and CPU servers. The framework is complementary to non-deep learning libraries MLlib and Spark SQL, and its data-frame style API provides Spark applications with an easy mechanism to invoke deep learning over distributed datasets. Its server-to-server direct communication (Ethernet or InfiniBand) achieves faster learning and eliminates scalability bottleneck. 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. Our early benchmark indicates 18x speedup for deep networks. CaffeOnSpark has been in use by Yahoo for image search, content classification and several other use cases. Recently, we have released CaffeOnSpark at under Apache 2.0 License.

In this talk, we will provide a technical overview of CaffeOnSpark, its API and deployment on a private cloud or public cloud (AWS EC2). We will share our experience on applying CaffeOnSpark to various use cases, and discuss potential areas of community collaboration.

Learn more:

  • Deep Learning on Databricks
  • A Vision for Making Deep Learning Simple
  • Caffe
  • About Andy Feng

    Andy Feng is a VP Architect at Nvidia, building solutions to empower advanced AI research and applications in variety compute environments. Previously, he was a VP Architect at Yahoo, leading the architecture and design of big data and machine learning initiatives.

    About Jun Shi

    Jun Shi is a Principal Engineer at Yahoo who specializes in machine learning platforms and large-scale machine learning algorithms. Prior to Yahoo, he was designing wireless communication chips at Broadcom, Qualcomm and Intel.