This notebook, in conjunction with the TLT Collection was used as part of the NGC GTC Fall 2020 content.
Feel free to follow the steps in this overivew and the accompanying notebook, to replicate our demo for yourself. Extra points are available for anyone who follows these instructions but modifies it for their own use case.
You can let us know what you build by tweeting @nvidiaai and showing off your work!
Ok - so how do you get this example up and running? Well, thanks to the power of NGC, deploying this demo couldn't be easier. We've taken care of most of the heavy lifting and the NGC Catalog and Private Registry are geared towards simplifying the workflow. Follow these steps to get the notebook running:
Fetch the TLT container from NGC:
docker pull nvcr.io/nvidia/tlt-streamanalytics:v2.0_py3
Log in to the TLT container:
docker run --gpus all -it -v "/path/to/dir/on/host":"/path/to/dir/in/docker" \ -p 80:8888 nvcr.io/nvidia/tlt-streamanalytics:v2.0_py3 /bin/bash
Fetch the Jupyter Notebook tutorial:
ngc registry resource download-version "nvidia/gtcfallngcdemo:1.0.0"
Start the notebook:
jupyter notebook --ip 0.0.0.0 --port 8888 --allow-root
Follow the steps in the notebook to replicate the demo (or tweak to be even more awesome)..
Show off your work by tweeting @nvidiaai with the hashtag #NGC
To take a look at this, or any other, notebook; follow these steps:
Need help with anything on NGC including this notebook? Head over to our online community on the developer forums and ask away. You can get help with everything from our CSP integrations through to submitting feature requests. Take a look at some of our favourite forums below: