We provide the Docker container with everything set up to run GluonNLP. With the prebuilt docker image, there is no need to worry about the operating systems or system dependencies. You can launch a JupyterLab development environment and try out to use GluonNLP to solve your problem.
You can run the docker with the following command.
docker pull gluonai/gluon-nlp:gpu-latest
docker run --gpus all --rm -it -p 8888:8888 -p 8787:8787 -p 8786:8786 --shm-size=4g gluonai/gluon-nlp:gpu-latest
Here, we open the ports 8888, 8787, 8786, which are used for connecting to JupyterLab.
Also, we set --shm-size to 4g. This sets the shared memory storage to 4GB. Since NCCL will
create shared memory segments, this argument is essential for the JupyterNotebook to work with NCCL.
(See also NVIDIA/nccl#290).
To build a docker image fom the dockerfile, you may use the following command:
docker build -f ubuntu18.04-devel-gpu.Dockerfile -t gluonai/gluon-nlp:gpu-latest .