Quick benchmarking resnet50 with MKL and without MKL on TF

quick_tfcnnbenchmarks.sh

#Quick benchmarking resnet50 with MKL and NO MKL on TF

#Following are steps:
# Create MKL virtual envirnoment
# pip install a TF1.8 MKL Wheel
# git clone TF_CNN benchmarks.
# run resnet50 inference benchmark.
#
# Create NO-MKL virtual envirnoment
# pip install a TF1.8 Wheel this will install eigen based TF
# git clone TF_CNN benchmarks.
# run resnet50 inference benchmark.
#

#Install virtualenv
pip install virtualenv

###
# Create a MKL virtual env
###
cd ~
virtualenv --python=/usr/bin/python2.7 tfcnnbench_mkl  
source tfcnnbench_mkl/bin/activate

cd tfcnnbench_mkl/ 
#Install TF 1.8 MKL wheel.
wget https://www.dropbox.com/s/fd5y2681vvlwejl/tensorflow-1.8.0rc1-cp27-cp27mu-linux_x86_64.whl                                                                            
pip install tensorflow-1.8.0rc1-cp27-cp27mu-linux_x86_64.whl   

# Clone benchmark scripts
git clone https://github.com/tensorflow/benchmarks.git   
cd benchmarks/scripts/tf_cnn_benchmarks/  

# Assign num_cores to the number of physical cores on your machine
num_cores=36

# Set environment variables
export KMP_AFFINITY=granularity=fine,verbose,compact,1,0
export KMP_BLOCKTIME=1
export KMP_SETTINGS=1
export OMP_NUM_THREADS=$num_cores
export OMP_PROC_BIND=true

#networks=( alexnet googlenet inception3 resnet50 resnet152 vgg16 )
network=resnet50

# Run Inference benchmark, please note the data format is NCHW is optimized for MKL
python tf_cnn_benchmarks.py  --data_format NCHW --model $network --batch_size 32 --num_batches 30 \
--num_intra_threads $num_cores --num_inter_threads 1 --forward_only=True

deactivate

###
# Create a NO-MKL virtual env
###

cd ~
virtualenv --python=/usr/bin/python2.7 tfcnnbench_no_mkl  
source tfcnnbench_no_mkl/bin/activate

cd tfcnnbench_no_mkl/ 

# Clone benchmark scripts
git clone https://github.com/tensorflow/benchmarks.git   
cd benchmarks/scripts/tf_cnn_benchmarks/  

# Assign num_cores to the number of physical cores on your machine
num_cores=36

# Set environment variables
export KMP_AFFINITY=granularity=fine,verbose,compact,1,0
export KMP_BLOCKTIME=1
export KMP_SETTINGS=1
export OMP_NUM_THREADS=$num_cores
export OMP_PROC_BIND=true

#networks=( alexnet googlenet inception3 resnet50 resnet152 vgg16 )
network=resnet50

# Run Inference benchmark, please note the data format is NHWC
python tf_cnn_benchmarks.py  --data_format NHWC --model $network --batch_size 32 --num_batches 30 \
--num_intra_threads $num_cores --num_inter_threads 1 --forward_only=True

deactivate