How Spark Illuminates Deep Learning

Data scientists everywhere are delving more deeply into deep learning (DL). 

Developers of convolutional, recurrent, and other DL models use Spark in their projects for the following reasons:

  • Available platforms, libraries, and tools: Spark lets DL developers quickly train and deploy multi-layered neural nets using libraries and compute clusters that are already at their disposal.
  • Familiar computing model and development framework: Spark allows DL developers to get up to speed on distributed architectures without having to master an unfamiliar DL-specific computing model such as NVIDIA’s CUDA.
  • Flexible execution and deployment options: Spark facilitates developer experimentation with DL architectures that incorporate model training on Spark clusters (horizontally scalable, CPU-based, multi-node, in-memory) alongside DL-optimized accelerators that do fast matrix manipulations with DL-optimized algorithm libraries on DL-optimized, GPU-based single-node co-processers.

Read the source article at Datanami


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