Scaling TensorFlow and Caffe to 256 GPUs

IBM Research unveiled a “Distributed Deep Learning” (DDL) library that enables cuDNN-accelerated deep learning frameworks like TensorFlow, Caffe, Torch and Chainer to scale to tens of IBM servers leveraging hundreds of GPUs.

“With the DDL library, it took us just 7 hours to train ImageNet-22K using ResNet-101 on 64 IBM Power Systems servers that have a total of 256 NVIDIA P100 GPU accelerators in them,” mentioned Sumit Gupta, VP, HPC, AI & Machine Learning at IBM Cognitive Systems. “16 days down to 7 hours changes the workflow of data scientists. That’s a 58x speedup!”

According to the researcher’s paper,  the team achieved deep learning records in image recognition accuracy and training times when using the new library and 256 GPUs.

A technical preview of DDL is available in version 4 of IBM’s PowerAI enterprise deep learning software, which makes this cluster scaling feature available to any organization using deep learning for training their AI models.

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One thought on “Scaling TensorFlow and Caffe to 256 GPUs

  1. Yan Bellavance on August 8, 2017 at 9:48 pm said:

    Time To Compute and Flops Per Second can now be swapped like orthogonal axis.
    Take forever to compute a problem
    Use Infinite Computing power to compute any problem instantly

    The power of vertical computing is amazing.