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Benchmarks

128-GPU Benchmark

The above benchmark was done on 32 servers with 4 Pascal GPUs each connected by RoCE-capable 25 Gbit/s network. Horovod achieves 90% scaling efficiency for both Inception V3 and ResNet-101, and 79% scaling efficiency for VGG-16.

To reproduce the benchmarks:

  1. Install Horovod using the instructions provided on the Horovod on GPU page.

  2. Clone https://github.com/tensorflow/benchmarks:

$ git clone https://github.com/tensorflow/benchmarks
$ cd benchmarks

  1. Run the benchmark. Examples below are for Open MPI.

    1. If you have a network with RoCE / InfiniBand (recommended):
    $ mpirun -np 16 \
    -H server1:4,server2:4,server3:4,server4:4 \
    -bind-to none -map-by slot \
    -x NCCL_DEBUG=INFO -x LD_LIBRARY_PATH \
    -mca pml ob1 -mca btl_openib_receive_queues P,128,32:P,2048,32:P,12288,32:P,65536,32 \
    \
    python scripts/tf_cnn_benchmarks/tf_cnn_benchmarks.py \
        --model resnet101 \
        --batch_size 64 \
        --variable_update horovod
    1. If you have a plain TCP network:
    $ mpirun -np 16 \
    -H server1:4,server2:4,server3:4,server4:4 \
    -bind-to none -map-by slot \
    -x NCCL_DEBUG=INFO -x LD_LIBRARY_PATH \
    \
    python scripts/tf_cnn_benchmarks/tf_cnn_benchmarks.py \
        --model resnet101 \
        --batch_size 64 \
        --variable_update horovod

  2. At the end of the run, you will see the number of images processed per second:

total images/sec: 1656.82

Real data benchmarks

The benchmark instructions above are for the synthetic data benchmark.

To run the benchmark on a real data, you need to download the ImageNet dataset and convert it using the TFRecord preprocessing script.

Now, simply add --data_dir /path/to/imagenet/tfrecords --data_name imagenet --num_batches=2000 to your training command:

  1. If you have a network with RoCE / InfiniBand (recommended):
$ mpirun -np 16 \
    -H server1:4,server2:4,server3:4,server4:4 \
    -bind-to none -map-by slot \
    -x NCCL_DEBUG=INFO -x LD_LIBRARY_PATH \
    -mca pml ob1 -mca btl_openib_receive_queues P,128,32:P,2048,32:P,12288,32:P,65536,32 \
    \
    python scripts/tf_cnn_benchmarks/tf_cnn_benchmarks.py \
        --model resnet101 \
        --batch_size 64 \
        --variable_update horovod \
        --data_dir /path/to/imagenet/tfrecords \
        --data_name imagenet \
        --num_batches=2000
  1. If you have a plain TCP network:
$ mpirun -np 16 \
    -H server1:4,server2:4,server3:4,server4:4 \
    -bind-to none -map-by slot \
    -x NCCL_DEBUG=INFO -x LD_LIBRARY_PATH \
    \
    python scripts/tf_cnn_benchmarks/tf_cnn_benchmarks.py \
        --model resnet101 \
        --batch_size 64 \
        --variable_update horovod \
        --data_dir /path/to/imagenet/tfrecords \
        --data_name imagenet \
        --num_batches=2000