@@ -16,7 +16,7 @@ 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`.
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`.
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.
If everything built successfully, you can run command in ResNet50 nGraph session in script [run.sh](https://github.com/PaddlePaddle/models/blob/develop/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.