Simplify Deep Learning Library usage on Linux and Windows!

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No, I'm not dead ;) I've been very busy with my Ph.D (and playing Path of Exile, let's be honest...) and haven't had time to write something here in a long time.

Until now, there was too way to use my Deep Learning Library (DLL) project:

  1. Write a C++ program that uses the library

  2. Install DLL and write a configuration file to define your network and the problem to solve

The first version gives you all the features of the tool and allows you to build exactly what you need. The second version is a bit more limited, but does not require any C++ knowledge. However, it still does require a recent C++ compiler and build system.

Due to the high C++ requirements that are not met by Visual Studio and the fact that I don't work on Windows, this platform is not supported by the tool. Until now!

I've added a third option to use DLL in the form of a Docker image to make the second option even easier and allow the use of DLL on Windows. All you need is Docker, which is available on Linux, Mac and Windows. This is still limited to the second option in that you need to write a configuration describing the network, but you need to build DLL and don't need to install all its dependencies.

Usage

To install the image, you can simply use docker pull:

docker pull wichtounet/docker-dll

Then, to run it, you have to create a folder containing a dll.conf file and mount in the container at /dll/data/. There are some examples in the image repository. For instance, on Linux from the cloned repository:

docker run -v ${pwd}/rbm_mnist/:/dll/data/ wichtounet/docker-dll

or on Windows:

docker run -v /c/Users/Baptiste/rbm_mnist/:/dll/data wichtounet/docker-dll

This will automatically run the actions specified in the configuration file and train your network.

Conclusion

I would really have thought this would be harder, but it turned out that Docker is a very good solution to deploy multiplatform demo tools :)

As of now, there is only support for mnist data format in the tool in this form, but I plan to add basic CSV support as well in the near future.

I hope that this will help people willing to try the library with a simpler usage.

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