Update README.md to use more standard markup for command line instructions

README.md

@@ -13,46 +13,64 @@ cd horovod-environment
 
 # request gpu resources (one way of doing it), this needs to be done everytime
 
-`sinteractive --ntasks=8 --time=08:00:00 --exclusive --partition=pascalnodes -N2 --gres=gpu:4`
+```
+sinteractive --ntasks=8 --time=08:00:00 --exclusive --partition=pascalnodes -N2 --gres=gpu:4
+```
 
 # load modules, this needs to be done everytime
-`module load Anaconda3/5.2.0`
+```
+module load Anaconda3/5.2.0
 
-`module load cuda91`
+module load cuda91
 
-`module load OpenMPI/3.1.2-gcccuda-2018b`
+module load OpenMPI/3.1.2-gcccuda-2018b
+```
 
 # create anaconda environment
 Download distribLearn2.yml from this repo
 
-`conda env create -f distribLearn2.yml --name distributedLearning`
+```
+conda env create -f distribLearn2.yml --name distributedLearning
+```
 
 ## source activate env needs to be done everytime
-`source activate distributedLearning`
+```
+source activate distributedLearning
+```
 
 These next 3 bits only need to be done to setup the env
 
-`conda update automat`
+```
+conda update automat
 
-`pip uninstall horovod`
+pip uninstall horovod
 
-`pip install --no-cache-dir horovod`
+pip install --no-cache-dir horovod
+```
 
 # navigate to an example
-`This can be downloaded from https://github.com/uber/horovod`
+This can be downloaded from https://github.com/uber/horovod
 
-`cd /data/user/blazerid/horovod-master/examples/`
+```
+cd /data/user/blazerid/horovod-master/examples/
 
-`mpirun -np 8 -bind-to none -map-by slot -mca pml ob1 -mca btl_tcp_if_include ib0 python keras_mnist.py`
+mpirun -np 8 -bind-to none -map-by slot -mca pml ob1 -mca btl_tcp_if_include ib0 python keras_mnist.py
+```
 
 # or run benchmarks
 
-`git clone -b cnn_tf_v1.10_compatible https://github.com/tensorflow/benchmarks`
+```
+git clone -b cnn_tf_v1.10_compatible https://github.com/tensorflow/benchmarks
+
+cd benchmarks/
 
-`cd benchmarks/`
+mpirun -np 8 -bind-to none -map-by slot -mca pml ob1 -mca btl_tcp_if_include ib0 python scripts/tf_cnn_benchmarks/tf_cnn_benchmarks.py --model resnet101 --batch_size 64 --variable_update horovod
+```
 
-`mpirun -np 8 -bind-to none -map-by slot -mca pml ob1 -mca btl_tcp_if_include ib0 python scripts/tf_cnn_benchmarks/tf_cnn_benchmarks.py --model resnet101 --batch_size 64 --variable_update horovod`
+For the resnet101 benchmark test, 
 
 running using 4 GPUs across 1 nodes gives: total images/sec: 491.34
+
 running using 8 GPUs across 2 nodes gives: total images/sec: 915.31
+
 running using 12 GPUs across 3 nodes gives: total images/sec: 1450.00