## Development and deployment of a CNN component using EMADL2CPP
# Development and deployment of a CNN component using EMADL2CPP
## Prerequisites
* Linux. Ubuntu Linux 16.04 and 18.04 were used during testing.
* Deep learning backend:
* MXNet
* training - generated is Python code. Required is Python 2.7 or higher, Python packages `h5py`, `mxnet` (for training on CPU) or e.g. `mxnet-cu75` for CUDA 7.5 (for training on GPU with CUDA, concrete package should be selected according to CUDA version). Follow [official instructions on MXNet site](https://mxnet.incubator.apache.org/install/index.html?platform=Linux&language=Python&processor=CPU)
* prediction - generated code is C++. Install MXNet using [official instructions on MXNet site](https://mxnet.incubator.apache.org/ for CPP package).
* prediction - generated code is C++. Install MXNet using [official instructions on MXNet site](https://mxnet.incubator.apache.org) for C++.
### HowTo
1. Define a EMADL component containing architecture of a neural network and save it in a `.emadl` file. For more information on architecture language please refer to [CNNArchLang project](https://git.rwth-aachen.de/monticore/EmbeddedMontiArc/languages/CNNArchLang). An example of NN architecture:
...
...
@@ -60,45 +60,49 @@ configuration VGG16{
}
```
3. Generate GPL code for the specified deep learning backend using the jar package of a EMADL2CPP generator. The generator receives the following command line parameters:
*-m path to directory with EMADL models
*-r name of the root model
*-o output path
*-b backend
*`-m` path to directory with EMADL models
*`-r` name of the root model
*`-o` output path
*`-b` backend
Assume both the architecture definition `VGG16.emadl`and the corresponding training configuration `VGG16.cnnt` are located in a folder `models` and the target code should be generated into `target` folder using `MXNet` backend. An example of a command is then:
