TensorFlow Benchmarks for Exxact Server Featuring NVIDIA V100S

For this post, we show deep learning benchmarks for TensorFlow on an Exxact TensorEX Server. To conduct these benchmarks this deep learning server was outfitted with 4 NVIDIA V100S GPUs.

We ran the standard “tf_cnn_benchmarks.py” benchmark script from TensorFlow’s github. To compare, tests were run on the following networks: ResNet-50, ResNet-152, Inception V3, Inception V4 and googlenet. In addition we compared the FP16 to FP32 performance, and used batch size of 128 . The same tests were run using 2 and 4 GPU configurations. All benchmarks were done using ‘vanilla’ TensorFlow settings for FP16 and FP32.

NVIDIA V100S Deep Learning Benchmark Snapshot

As we see, running FP16 gives a great boost to performance in the overall images/sec metric. If you’re able to train using FP16 vs FP32, we recommend to do so.

NVIDIA V100S Deep Learning Benchmarks FP16

NVIDIA V100S Deep Learning Benchmarks FP32

System Specifications:

Training Parameters

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