Updating readme

examples/onnxruntime/resnet50/README.md

@@ -9,25 +9,52 @@ This example demonstrates how to perform an MIGraphX Python API inference throug
 - [**Running this Example**](#Running-this-Example)
 
 ## Basic Setup
-Before running inference we must first install MIGraphX via the Docker method as it also downloads onnxruntime into the dockerfile created.
+Before running inference we must first install MIGraphX via the Docker method as it also downloads onnxruntime into the dockerfile created. 
 
-## Running this Example
+Starting from project root:
+```
+$ cd AMDMIGraphX
+$ docker build -t migraphx .
+$ docker --device='/dev/kfd' --device='/dev/dri' <your docker settings and mounts> --group-add video -it migraphx
+```
+
+The dockerfile will install the latest supported version of ROCm with all the depandacnies needed for MIGraphX
 
-This directory contains everything needed to perform an inference once MIGraphX has been installed in the docker contaienr
+Once the docker file has been installed and you're inside the folder run
 
 ```
-$ mkdir build
-$ cd build
-$ CXX=/opt/rocm/llvm/bin/clang++ cmake ..
-$ make -j$(nproc) package
-$ dpkg -i *.deb
+$ rbuild develop -d deps -B build
+$ cd build 
+$ make -j$(nproc) package && dpkg -i *.deb
 ```
-Once you've build and installed MIGraphX as a deb package go to the examples folder, run the pre-req script to build and install
-onnxruntime and then install the approrpaite version of pytorch
 
+to verify migraphx has been installed correclty in the docker run dpkg --list or dpkg -l
+
+```
+$ dpkg -l | grep migraphx 
+$ ii  migraphx                      2.7.0                             amd64        AMD's graph optimizer
+$ ii  migraphx-dev                  2.7.0                             amd64        AMD's graph optimizer
 ```
+
+## Running this Example
+
+This directory contains everything needed to perform an inference once MIGraphX has been installed in the docker container
+Once you've build and installed MIGraphX as a deb package, go to the examples folder, run the pre-req script to build and install
+onnxruntime and then install the approrpaite version of pytorch from project root.
+
+```
+$ 
 $ cd examples/onnxruntime/resnet50
 $ ./prereq_steps.sh
 $ pip list | grep onnxruntime
 $ python resnet50.py
 ```
+
+example output:
+
+African grey 0.5883207
+kite 0.06284781
+goldfinch 0.01847724
+macaw 0.014789124
+fire screen 0.013297303
+resnet50, time = 88.89 ms