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TLT Face Mask Detection - NGC Collections Demo

Introduction

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!

If you missed the session and live Q&A - don't worry! You can watch it all on demand here and you can get in touch with the presenters here.

Step-By-Step Guide

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:

Step 1

Fetch the TLT container from NGC:

docker pull nvcr.io/nvidia/tlt-streamanalytics:v2.0_py3\n

Step 2

docker run --gpus all -it -v \"/path/to/dir/on/host\":\"/path/to/dir/in/docker\" \\\n-p 80:8888 nvcr.io/nvidia/tlt-streamanalytics:v2.0_py3 /bin/bash\n

Step 3

Fetch the Jupyter Notebook tutorial:

ngc registry resource download-version \"nvidia/gtcfallngcdemo:1.0.0\"\n

Step 4

Start the notebook:

jupyter notebook --ip 0.0.0.0 --port 8888 --allow-root\n

Step 5

Follow the steps in the notebook to replicate the demo (or tweak to be even more awesome)..

Step 6

Show off your work by tweeting @nvidiaai with the hashtag #NGC

Viewing the Jupyter Notebook in NGC

To take a look at this, or any other, notebook; follow these steps:

Navigate to the File Browser tab of the asset in NGC
Select the version you'd like to see
Under the actions menu (three dots) for the .ipynb file select \"View Jupyter\"
There you have it! You can read a notebook for documentation and copy code samples without ever leaving NGC.

System Requirements

Getting Support

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:

Hints and Tips - Ask questions about this demo in our general forum..

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Extra points are available for anyone who follows these instructions but modifies it for their own use case. \n\nYou can let us know what you build by tweeting @nvidiaai and showing off your work! \n\nIf you missed the session and live Q&A - don't worry! You can watch it all on demand [here](https://nvidia.com/gtc) and you can get in touch with the presenters [here](https://twitter.com/intent/tweet?text=.+%40chrisparsonsdev+%40adelhallak+%23NGC).\n\n## Step-By-Step Guide\n\nOk - 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](https://developer.nvidia.com/blog/securing-and-accelerating-end-to-end-ai-workflows-with-the-ngc-private-registry/) are geared towards simplifying the workflow. Follow these steps to get the notebook running: \n\n*Step 1*\n\nFetch the TLT container from NGC: \n\n```\ndocker pull nvcr.io/nvidia/tlt-streamanalytics:v2.0_py3\n```\n\n*Step 2*\n\nLog in to the TLT container: \n\n```\ndocker run --gpus all -it -v \"/path/to/dir/on/host\":\"/path/to/dir/in/docker\" \\\n-p 80:8888 nvcr.io/nvidia/tlt-streamanalytics:v2.0_py3 /bin/bash\n```\n\n*Step 3*\n\nFetch the Jupyter Notebook tutorial: \n\n```\nngc registry resource download-version \"nvidia/gtcfallngcdemo:1.0.0\"\n```\n\n*Step 4* \n\nStart the notebook:\n\n```\njupyter notebook --ip 0.0.0.0 --port 8888 --allow-root\n```\n\n*Step 5*\n\nFollow the steps in the notebook to replicate the demo (or tweak to be even more awesome)..\n\n*Step 6* \n\nShow off your work by [tweeting @nvidiaai](https://twitter.com/intent/tweet?text=.+%40nvidiaai+look+at+the+awesome+demo+I+created+with+%23NGC) with the hashtag #NGC\n\n\n## Viewing the Jupyter Notebook in NGC\n\nTo take a look at this, or any other, notebook; follow these steps:\n\n1. Navigate to the File Browser tab of the asset in NGC\n2. Select the version you'd like to see\n3. Under the actions menu (three dots) for the .ipynb file select \"View Jupyter\"\n4. There you have it! You can read a notebook for documentation and copy code samples without ever leaving NGC.\n\n## System Requirements\n\n1. [Nvidia Driver](https://github.com/NVIDIA/nvidia-docker/wiki/Frequently-Asked-Questions#how-do-i-install-the-nvidia-driver)\n2. [Docker CE](https://docs.docker.com/v17.12/install/)\n3. [Nvidia-Docker](https://github.com/NVIDIA/nvidia-docker#quickstart)\n\n## Getting Support\n\nNeed help with anything on NGC including this notebook? Head over to our online community on the [developer forums](https://forums.developer.nvidia.com/c/accelerated-computing/nvidia-gpu-cloud-ngc-users/25) 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: \n\n* [Hints and Tips](https://forums.developer.nvidia.com/c/accelerated-computing/nvidia-gpu-cloud-ngc-users/general/35) - Ask questions about this demo in our general forum..","displayName","TLT Face Mask Demo",["D",1709059000048],"publisher","NVIDIA","latestVersion","versions","collectionCounts","imageUrl","/api/image/nvidia/-/resources/tltfacemaskdemo","rawImageUrl","https://assets.nvidiagrid.net/ngc/logos/Tutorials.png","canGuestDownload","latestVersionSizeInBytes","licenseTerms","isMultinodeEnabled","isMultiArchitecture","bias","explainability","privacy","safetyAndSecurity","featureLabels",[],"displayLabels",[237,238,239,240,241,242],"policyLabels",[],"systemLabels",[],"products",[],"hasNimMetadata",{"_205":258,"_244":258,"_245":259,"_247":248},{"_205":256,"_244":256,"_245":257,"_247":248},{"_205":254,"_244":254,"_245":255,"_247":248},{"_205":252,"_244":252,"_245":253,"_247":248},{"_205":250,"_244":250,"_245":251,"_247":248},{"_205":243,"_244":243,"_245":246,"_247":248},"Tutorial","value","filter",{"_249":73,"_244":243},"category","label","field","TLT",{"_249":248,"_244":250},"notebook",{"_249":248,"_244":252},"Example",{"_249":248,"_244":254},"COVID19",{"_249":248,"_244":256},"Collections",{"_249":248,"_244":258},{"_107":1238,"_109":1239},"ngcApps",[1192],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1173":1174,"_1175":1176,"_1177":1178,"_1179":1180,"_1181":271,"_138":295,"_1182":1183,"_1184":55,"_1185":86,"_1186":86,"_1187":1188,"_1189":86,"_1190":1191},"amplitude",{"_1170":1171,"_807":-7,"_1172":30},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmVuYWJsZUNzYVdpZGdldCUyMiUzQWZhbHNlJTJDJTIyZW5hYmxlUHJvZHVjdEdyb3VwaW5nQnlTdWJzY3JpcHRpb24lMjIlM0FmYWxzZSUyQyUyMmNvbnRhaW5lclNjYW5uaW5nUmVkZXNpZ25NMSUyMiUzQXRydWUlN0Q=","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1164":1165,"_1166":1167,"_1168":1169},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"locale","en","ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"1.10.0","defaultSearchSuggestions",{"_1047":180,"_1048":1049,"_1050":1051,"_1052":1053},"announcements",[],"consentState",{"_334":1037,"_1038":30,"_1039":30,"_1040":86,"_1041":1042},"consentConfig",{"_316":317,"_318":319,"_320":321,"_322":323,"_324":325,"_326":327,"_328":329,"_330":331,"_332":333,"_334":335},"showContactAdmin",1785135249163,[],[],[309,310,311,312,313,314],{"_44":59,"_46":70,"_48":102,"_50":-5,"_52":-5,"_54":55},{"_44":59,"_46":68,"_48":102,"_50":-5,"_52":-5,"_54":55},{"_44":59,"_46":66,"_48":102,"_50":-5,"_52":-5,"_54":55},{"_44":59,"_46":60,"_48":102,"_50":-5,"_52":-5,"_54":55},{"_44":56,"_46":57,"_48":49,"_50":51,"_52":315,"_54":55},{"_44":45,"_46":47,"_48":49,"_50":51,"_52":53,"_54":55},4,"mainInfoText","We and our third-party partners (including social media, advertising, and analytics partners) use cookies and other tracking technologies to collect, store, monitor, and process certain information about you when you visit our website. The information collected might relate to you, your preferences, or your device. We use that information to make the site work, analyze performance and traffic on our website, provide a more personalized web experience, and assist in our marketing efforts.

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