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README.md

Prerequisites

Ensure you have Docker installed on your machine. And you must also have NVIDIA CUDA and CuDNN installed in your machine.

Then, you must check your machine's CUDA version.

nvcc -V

You must change base image name and pytorch version compatible with your CUDA version.

Building the Docker Image

Clone this repository and navigate into the current directory(nougat/docker). You can build the Docker image by running:

docker build -t <image-name> .

Replace with a name of your choice. This will be used to refer to the image later. Please be patient as this operation can take a while. It needs to pull the CUDA-capable image from NVIDIA’s Docker repository and install several libraries. Image size will be about 17GB.

Running the Docker Container

You can run your Docker container with the following command:

docker run -it -d -p <your-port>:8503 --gpus all <image-name>

Replace with the port number you wish to expose on your host machine to access the nougat API server. This can be any valid port number. Replace with the name you chose earlier during the build step.

Testing the API Server

Once the Docker container is running, you can access the nougat API server. You can easily check connection by running:

curl -X 'GET' \
  'http://127.0.0.1:<your-port>/'

It can be take a while for loading API server, because the server have to download nougat model at startup.

If connection is successful, you can get response looks like this.

{"status-code":200,"data":{}}

Using the API Server

To get a prediction of a PDF file by making a POST request to http://127.0.0.1:<your-port>/predict/. It also accepts parameters start and stop to limit the computation to select page numbers (boundaries are included).

The response is a string with the markdown text of the document.

curl -X 'POST' \
  'http://127.0.0.1:<your-port>/predict/' \
  -H 'accept: application/json' \
  -H 'Content-Type: multipart/form-data' \
  -F 'file=@<PDFFILE.pdf>;type=application/pdf'

To use the limit the conversion to pages 1 to 5, use the start/stop parameters in the request URL: http://127.0.0.1:<your-port>/predict/?start=1&stop=5