Web app is a great tool for simple demo across any platforms. I find it very useful because I can easily show my trained model's output to anyone like a breeze. The catch, however, would be setting up the server, but once the server is up and running, it cannot be any better to boast in your resume and show off to your boss with your trained model.
Here is a tutorial for setting up a simple neural network inference server using WebDNN library. Although its Github
page and official
documentations are very clear, I still had to spend some time to get it to work. In addition, the official documentation instructs users to compile and install emscripten from source, which will consume quite some time; I will show you how to get around this with install pre-compiled version.
First, one needs to clone WebDNN repository from Github:
$ git clone https://github.com/mil-tokyo/webdnn.git && cd webdnn
Note that WebDNN only supports Python3.6+, so you need to install this unless you already have it on the system. To check the your Python3 version, run
$ python3 --version
Make sure that it is 3.6+. There are plenty of resources that you can search on Google on how to install Python 3.6+. For instance, on Mac OS X, using homebrew is probably the easiest way:
$ brew install python3
Once you have Python 3.6+, it is a good idea to create virtual environment for what you will need to do.
$ virtualenv -p `which python3` python3
Now, activate the environment and install necessary packages
$ source python3/bin/activate
$ pip install tensorflow-gpu keras h5py
The Python environment is now complete. You now need to setup emscripten environment.
$ git clone https://github.com/juj/emsdk.git && cd emsdk
$ ./emsdk install latest
$ ./emsdk activate latest
$ source ./emsdk_env.sh
Now, we need to install eigen library.
$ wget http://bitbucket.org/eigen/eigen/get/3.3.3.tar.bz2
$ tar jxf 3.3.3.tar.bz2
$ export CPLUS_INCLUDE_PATH=$PWD/eigen-eigen-67e894c6cd8f
$ cd ..
Finally, we are ready. Let's first create pre-trained ResNet Keras model that we will use. Run Python and run the following lines
$ python
>>> from keras.applications import resnet50
>>> model = resnet50.ResNet50(include_top=True, weights='imagenet')
>>> model.save("resnet50.h5")
Exit Python and run the following
$ python ./bin/convert_keras.py resnet50.h5 --input_shape '(1,224,224,3)' --out output
After some time, it will generates files in
output directory. To run the server, we first need to modify the
example/resnet/script.js file. We must make sure to point to the correct directory by changing the weight path. For the version I have, I modified line 39 to point to
output directory.
let runner = await WebDNN.load(`/output`, {backendOrder: backend_name});
Finally, we are ready to run the server. To start the server, run the following in
webdnn directory
$ python -m http.server
On your web browser, go to address
localhost:8000/example/resnet/index.html
You should be able to test ResNet50 model!
