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=2g 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 2g. This sets the shared memory storage to 2GB. Since NCCL will
create shared memory segments, this argument is essential for the JupyterNotebook to work with NCCL.
(See also NVIDIA/nccl#290).
The folder structure of the docker image will be
/workspace/
├── gluonnlp
├── horovod
├── mxnet
├── notebooks
├── data
If you have a multi-GPU instance, e.g., g4dn.12xlarge, p2.8xlarge, p3.8xlarge, you can try to run the following command to verify the installation of horovod + MXNet
docker run --gpus all --rm -it --shm-size=4g gluonai/gluon-nlp:gpu-latest \
horovodrun -np 2 python3 -m pytest /workspace/horovod/horovod/test/test_mxnet.py
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 .