Run resnet50 benchmark on GCE

run_resnet50.sh

gcloud compute ssh $INSTANCE_NAME

# Clone TensorFlow benchmark repository.
git clone https://github.com/tensorflow/benchmarks.git && cd benchmarks
git reset --hard 1e7d788042dfc6d5e5cd87410c57d5eccee5c664
cd scripts/tf_cnn_benchmarks

## Synthetic data test

# 8 GPUs
python tf_cnn_benchmarks.py \
    --batch_size=364 \
    --num_batches=100 \
    --model=resnet50 \
    --optimizer=momentum \
    --variable_update=replicated \
    --all_reduce_spec=nccl \
    --use_fp16=True \
    --nodistortions \
    --gradient_repacking=2 \
    --compute_lr_on_cpu=True \
    --single_l2_loss_op=True \
    --xla_compile=True \
    --num_gpus=8 \
    --loss_type_to_report=base_loss

# 1 GPU
python tf_cnn_benchmarks.py \
    --batch_size=364 \
    --num_batches=100 \
    --model=resnet50 \
    --optimizer=momentum \
    --use_fp16=True \
    --nodistortions \
    --compute_lr_on_cpu=True \
    --single_l2_loss_op=True \
    --xla_compile=True \
    --loss_type_to_report=base_loss

## Real data test
# add --data_dir=/data/imagenet to the 1 or 8 GPU command.