# PaddlePaddle inference and training script
This directory contains configuration and instructions to run the PaddlePaddle + nGraph for a local training and inference.

# How to build PaddlePaddle framework with NGraph engine
In order to build the PaddlePaddle + nGraph engine and run proper script,  follow up a few steps:
1. Install PaddlePaddle project
2. set env exports for nGraph and OpenMP
3. run the inference/training script

Currently supported models:
* ResNet50 (inference and training).

Only support Adam optimizer yet.

Short description of aforementioned steps:

## 1. Install PaddlePaddle
Follow PaddlePaddle [installation instruction](https://github.com/PaddlePaddle/models/tree/develop/fluid/PaddleCV/image_classification#installation) to install PaddlePaddle. If you [build from source](https://github.com/PaddlePaddle/FluidDoc/blob/develop/doc/fluid/beginners_guide/install/compile/compile_Ubuntu_en.md), please use the following cmake arguments and ensure to set `-DWITH_NGRAPH=ON`. 
```
cmake .. -DCMAKE_BUILD_TYPE=Release -DWITH_GPU=OFF -DWITH_MKL=ON -DWITH_MKLDNN=ON  -DWITH_NGRAPH=ON
```
Note: MKLDNN and MKL are required.

## 2. Set env exports for nGraph and OMP
Set the following exports needed for running nGraph:
```
export FLAGS_use_ngraph=true
export OMP_NUM_THREADS=<num_cpu_cores>
```

If multiple threads are used, you may export the following for better performance:
```
export KMP_AFFINITY=granularity=fine,compact,1,0
```

## 3. How the benchmark script might be run.
If everything built successfully, you can run command in ResNet50 nGraph session in script [run.sh](https://github.com/PaddlePaddle/models/blob/develop/fluid/PaddleCV/image_classification/run.sh) to start the benchmark job locally. You will need to uncomment the `#ResNet50 nGraph` part of script.

Above is training job using the nGraph, to run the inference job using the nGraph:

Please download the pre-trained resnet50 model from [supported models](https://github.com/PaddlePaddle/models/tree/72dcc7c1a8d5de9d19fbd65b4143bd0d661eee2c/fluid/PaddleCV/image_classification#supported-models-and-performances) for inference script.