bharatk-parallel 547a4fbfe8 Fixed Singualrity related problems 3 tahun lalu
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English 93182c4a52 Added Rapids and Deepstream labs 3 tahun lalu
Dockerfile 93182c4a52 Added Rapids and Deepstream labs 3 tahun lalu
README.md 547a4fbfe8 Fixed Singualrity related problems 3 tahun lalu
Singularity 93182c4a52 Added Rapids and Deepstream labs 3 tahun lalu

README.md

openacc-training-materials

Training materials provided by OpenACC.org. The objective of this lab is to give an introduction to using Nvidia DeepStream Framework in a Intelligent Video Analytics Domain.

Prerequisites

To run this tutorial you will need a machine with NVIDIA GPU.

Creating containers

To start with, you will have to build a Docker or Singularity container.

Docker Container

To build a docker container, run: sudo docker build --network=host -t <imagename>:<tagnumber> .

For instance: sudo docker build -t myimage:1.0 .

and to run the container, run: sudo docker run --rm -it --gpus=all --network=host -p 8888:8888 myimage:1.0

Once inside the container launch the jupyter notebook by typing the following command jupyter notebook --no-browser --allow-root --ip=0.0.0.0 --port=8888 --NotebookApp.token="" --notebook-dir=/opt/nvidia/deepstream/deepstream-5.0/python

Then, open the jupyter notebook in browser: http://localhost:8888 Start working on the lab by clicking on the Start_Here.ipynb notebook.

Singularity Container

To build the singularity container, run: sudo singularity build --sandbox <image_name>.simg Singularity

and copy the files to your local machine to make sure changes are stored locally: singularity run --writable <image_name>.simg cp -rT /opt/nvidia/deepstream/deepstream-5.0/ ~/workspace

Then, run the container: singularity run --nv --writable <image_name>.simg jupyter notebook --no-browser --allow-root --ip=0.0.0.0 --port=8888 --NotebookApp.token="" --notebook-dir=~/workspace/python

Then, open the jupyter notebook in browser: http://localhost:8888 Start working on the lab by clicking on the Start_Here.ipynb notebook.

Troubleshooting

Q. "ResourceExhaustedError" error is observed while running the labs A. Currently the batch size and network model is set to consume 16GB GPU memory. In order to use the labs without any modifications it is recommended to have GPU with minimum 16GB GPU memory. Else the users can play with batch size to reduce the memory footprint