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* prediction - generated code is C++.
## Installation
The two bash scripts found under ... should build and install all prerequisits for all backends as of 26.09.2019.
Note that the installation might take some time (hours) and you will need some disk space (> 60GB) for all backends. Also enough RAM or a big
The two bash scripts found under https://git.rwth-aachen.de/monticore/EmbeddedMontiArc/generators/EMADL2CPP/tree/tensorflow_group/src/main/resources/installation_scripts
should build and install all prerequisits for all backends as of 26.09.2019.
Note that the installation may take some time (hours) and you will need some disk space (> 60GB) for all backends. Also enough RAM or a big
enough swapspace is advisable (>10GB) for the installation of the cpp part of tensorflow. This scripts were tested with a completly clean Ubuntu 16.04,
without system updates installed. Using another Ubuntu version or installing other stuff, system updates included might/ have caused problems.
If you want to install the backends with CUDA GPU support(only MXNet/Gluon and Tensorflow, the used caffe2 version does not work with GPU support anymore),
you have to install CUDA 10.0(!!), CUDNN and NCCL (Obtainable from the nvidai webpage. You can follow their instructions.) inbetween the two scripts.
Furthermore you will have to change the pip commands for mxnet and tensorflow to the respective commented out parts.
Also docker images for the cpu version of each backend are provided at ..., though some of these might be outdated.
Also docker images for the cpu version of each backend are provided at https://git.rwth-aachen.de/monticore/EmbeddedMontiArc/generators/EMADL2CPP/tree/tensorflow_group/src/test/resources/docker,
though some of these might be outdated.
### 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:
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