TensorFrames: Deep Learning with TensorFlow on Apache Spark (Tim Hunter)
Since the creation of Apache Spark, I/O throughput has increased at a faster pace than processing speed. In a lot of big data applications, the bottleneck is increasingly the CPU. With the release of Apache Spark 2.0 and Project Tungsten, Spark runs a number of control operations close to the metal. At the same time, there has been a surge of interest in using GPUs (the Graphics Processing Units o
GitHub - databricks/tensorframes: [DEPRECATED] Tensorflow wrapper for DataFrames on Apache Spark
Note: TensorFrames is deprecated. You can use pandas UDF instead. Experimental TensorFlow binding for Scala and Apache Spark. TensorFrames (TensorFlow on Spark DataFrames) lets you manipulate Apache Spark's DataFrames with TensorFlow programs. This package is experimental and is provided as a technical preview only. While the interfaces are all implemented and working, there are still some areas o
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[1511.06051] SparkNet: Training Deep Networks in Spark
Training deep networks is a time-consuming process, with networks for object recognition often requiring multiple days to train. For this reason, leveraging the resources of a cluster to speed up training is an important area of work. However, widely-popular batch-processing computational frameworks like MapReduce and Spark were not designed to support the asynchronous and communication-intensive
Large Scale Distributed Deep Learning on Hadoop... | Hadoop at Yahoo
By Cyprien Noel, Jun Shi and Andy Feng (@afeng76), Yahoo Big ML Team Introduction In the last 10 years, Yahoo has progressively invested in building and scaling Apache Hadoop clusters with a current footprint of more than 40,000 servers and 600 petabytes of storage spread across 19 clusters. As discussed at the 2015 Hadoop Summit, we have developed scalable machine learning algorithms on these clu
SparkNet: Training Deep Networks on Spark
Training deep networks is a time-consuming process, with networks for object recognition often requiring multiple days to train. For this reason, leveraging the resources of a cluster to speed up training is an important area of work. However, widely-popular batch-processing computational frameworks like MapReduce and Spark were not designed to support the asynchronous and communication-intensive
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GPU Acceleration in Databricks
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GitHub - amplab/SparkNet: Distributed Neural Networks for Spark
Real-time Recommendations using Spark Comcast Labs
