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AI Server Workflow, Nvidia Transfer Learning Toolkit Example

Continuing our series on Edge AI Servers and the rapid transition underway as developers migrate their workloads from traditional datacenters / clouds to smaller, distributed workloads running closer to users, let’s investigate a specific Nvidia AI Server workflow where a hosted Jetson Nano or Jetson Xavier NX could make sense.

At GTC in May 2021, Nvidia launched the Transfer Learning Toolkit (TLT) 3.0, which is designed to help users build customized AI models quickly and easily.  The process is rather straightforward:  Instead of creating and training a model from scratch, which is very time consuming, you instead take a pre-trained model such as PeopleNet, FaceDetectIR, ResNet, MobileNet, etc, add in your custom object (image for vision applications, sound for audio or language models), re-train with the added content, and then you can leverage the resulting output model for inferencing on smaller, edge devices.

The Transfer Learning Toolkit is part of the larger TAO (Train, Adapt, Optimize) platform, and is intended to be run on big hardware.  Their requirements state:

  • 32 GB system RAM
  • 32 GB of GPU RAM
  • 8 core CPU
  • 100 GB of SSD space
  • TLT is supported on A100, V100 and RTX 30×0 GPUs. 

However, the end result is a model that can run on a much smaller device like a Jetson Nano, TX2, or Xavier NX.

Looking closer at the TLT Quick Start documentation, installation begins with setting up your workstation, or launching a cloud VM like an AWS EC2 P3 or G4 instance which have Volta or Turing GPUs.  There are a series of prerequisites to install, a TLT container that is downloaded from the Nvidia Container Registry, and once your python environment is setup, you launch a Jupiter notebook that will help guide you through the rest.  

There is also a sample Computer Vision Jupyter notebook that can get you up and running quickly, located here:

Once you have the Transfer Learning Toolkit workflow established, you can begin testing the output and resulting models on Jetson devices.  This is where a hosted Jetson Nano functioning as an AI Server might make sense, as you could simply some automation or CI/CD workflow for training with TLT and testing the results on the Jetson.  Then, if everything passes, results are highly accurate, detection, segmentation, etc are all performing well, then you could deploy to your production devices in the field.

This is of course only one example of the value of hosted AI servers, and we’ll continue looking at more use cases in the near future! 

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