This MNIST classification demo consists of two parts: a PaddlePaddle inference server and a Javascript front end. We will start them separately.

We will use Docker to run the demo, if you are not familiar with Docker, please checkout this tutorial.

The inference server can be used to inference any model trained by PaddlePaddle. Please see here for more details.

1. Download the MNIST inference model topylogy and parameters to the current working directory.

   wget https://s3.us-east-2.amazonaws.com/models.paddlepaddle/end-to-end-mnist/inference_topology.pkl
   wget https://s3.us-east-2.amazonaws.com/models.paddlepaddle/end-to-end-mnist/param.tar

2. Run following command to start the inference server:

   docker run --name paddle_serve -v `pwd`:/data -d -p 8000:80 -e WITH_GPU=0 paddlepaddle/book:serve

   The above command will mount the current working directory to the /data directory inside the docker container. The inference server will load the model topology and parameters that we just downloaded from there.

   After you are done with the demo, you can run docker stop paddle_serve to stop this docker container.

1. Run the following command

   docker run -it -p 5000:5000 -e BACKEND_URL=http://localhost:8000/ paddlepaddle/book:mnist

   BACKEND_URL in the above command specifies the inference server endpoint. If you started the inference server on another machine, or want to visit the front end remotely, you may want to change its value.

2. Visit http://localhost:5000 and you will see the PaddlePaddle MNIST demo.

We have already prepared the pre-built docker image paddlepaddle/book:mnist, here is the command if you want to build the docker image again.

docker build -t paddlepaddle/book:mnist .

Thanks to the great project https://github.com/sugyan/tensorflow-mnist . Most of the code in this project comes from there.