benchmarks.md

Benchmarks

The above benchmark was done on 128 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 68% 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

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

   $ 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 -x PATH \
       -mca pml ob1 -mca btl ^openib \
       \
       python scripts/tf_cnn_benchmarks/tf_cnn_benchmarks.py \
           --model resnet101 \
           --batch_size 64 \
           --variable_update horovod

4. 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:

$ 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 -x PATH \
    -mca pml ob1 -mca btl ^openib \
    \
    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