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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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The Edge AI Server Revolution (Driven by Arm, Of Course)

The past 2 years have seen rapid growth in experimentation and ultimately deployment and adoption of AI/ML at the Edge. This has been fueled by dramatic increases in on-device AI processing capability, and equally dramatic reduction in size and power requirements of devices. The Nvidia Jetson Nano with its GPU and CUDA cores, Google Coral Dev Board containing a TPU for Tensorflow acceleration, and even Microcontrollers running TinyML have quickly gained widespread adoption among developers. These devices are cheap, accurate, and readily available, allowing developers to deploy AI/ML workloads to places that were not practical just a short time ago.


This allows developers to re-think their applications, and begin to migrate AI workloads out of the datacenter, which was the only place to run their AI/ML tasks previously, potentially saving money or improving performance my moving processing closer to where it is needed. This also allows for net-new capability, adding computer vision, object detection, pose estimation, etc, in places that previously were not possible.

In order to help prepare developers and allow them to experiment and build their skills, miniNodes is making available some Edge AI inspired Arm Servers, starting with the Nvidia Jetson Nano. These nodes are intended to be used by engineers and teams just getting started on their Edge AI journey, who are testing their applications and deep learning algorithms. Another use for an Edge AI Arm Server is for light-duty AI processing, where it doesn’t make financial sense to rent big AI servers from the likes of AWS or Azure, and instead a smaller device will work just fine. Finally, developers and teams that do AI training that is not time-sensitive, or relatively small, can achieve significant savings by using a hosted Jetson Nano for their model training, instead of local GPU’s or AWS resources.

Whether you are just getting starting and beginning to explore Edge AI, or have been following the trend and already have Edge AI projects underway, a miniNodes hosted Jetson Nano is a great way to gain hosted AI processing capability or reduce AWS and Azure cloud AI costs.

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The Future of AI Servers

The Future of AI Servers

Following up on the recent announcement of our new Raspberry Pi 4 AI Servers, it seems that AI servers running on Arm processors are gaining more and more traction in the market due to their natural fit at the IoT and Edge layers of infrastructure.  Let’s take a quick look at some of the unique properties that make AI Servers running on Arm a great strategy for AI/ML and AIoT deployments, to help understand why this is so important for the future.

Power – Many IoT deployments do not have luxuries that “regular” servers enjoy such as reliable power and connectivity, or even ample power for that matter.  While Intel has spent decades making excellent, though power hungry processors, Arm has focused on efficiency and battery life, helping to explain they they dominate the market in tablets and smartphones.  This same efficiency is then leveraged by IoT devices running AI workloads, so Edge devices responsible for computer vision, image classification, object detection, deep learning, or other workloads can operate with a much lower thermal footprint than a comparable x86 device.

Size – Similar to the underlying reasons behind power efficiency, the physical size and dimensions of Arm AI Servers can be made smaller than the majority of x86 designs.  Attaching AI Accelerators such as the Gyrfalcon 2801 or 2803 via USB to boards as small as 2 inches square (such as the NanoPi Neo2) is possible, or the addition of a Google Coral TPU via the mini-PCIe slot on a NanoPi T4 bring an enormous amount of inferencing to AI Servers in tiny form factors. 

Cost – Here again, Arm SoC’s and Single Board Computers typically have a rather large cost advantage versus x86 embedded designs.  

Scalability – This is a critical factor in why Arm will play a massive role in the future of AI Servers, and why miniNodes has begun to offer our Raspberry Pi 4 AI Server.  As mentioned, low power, cheap devices make great endpoints, but, there is also a role for “medium” sized AI servers handling larger workloads, and Arm partners are just now starting to bring these products to market.  An example is the SolidRun Janux AI Server, which also makes use of the same Gyrfalcon AI Accelerators used by our nodes.  So, you can get started training your models, testing out your deployment pipeline, understanding the various AI frameworks and algorithms, and getting comfortable with the tools, and very easily scale up as your needs expand.  Of course, once you reach massive amounts of deep learning and AI/ML processing, enterprise Arm server options exist for that as well.

Flexibility – Taking Scalability one step further, the Arm AI servers also allow for a great amount of flexibility in the specific accelerator hardware (Gyrfalcon, Google Coral, Intel Movidius), the frameworks used (Caffe, PyTorch, TensorFlow, TinyML), and the models (ResNet, MobileNet, ShuffleNet) employed.  

Ubiquity – A final piece of the overall AI Server ecosystem is the ease of access to this type of hardware, and low barriers to entry.  The Raspberry Pi and similar types of boards are distributed globally, and readily available in nearly all markets.

As you can see, our view is that the future of AI servers is based on Arm SoC’s, and now is the time to start exploring what that means for your workload.