Source: thenewstack.io
Tutorial: Deploying TensorFlow Models at the Edge with NVIDIA Jetson Nano aCategory: Kubernetes, Ubuntu, Docker, shell, artificial-intelligence
In this tutorial, we will explore the idea of running TensorFlow models as microservices at the edge. For the completeness of the tutorial, we will run a single node K3s on Jetson Nano.
Check the version of Docker runtime with the below command: Since Docker supports custom runtimes, we can use the standard Docker CLI with --runtime nvidia switch to use NVIDIA’s container runtime.
For the AI workloads running in K3s, we need access to the GPU which is available only through the nvidia-docker runtime.
We will also add a couple of other switches which makes it easy to use the kubectl CLI with K3s.
Check the version of Docker runtime with the below command: Since Docker supports custom runtimes, we can use the standard Docker CLI with --runtime nvidia switch to use NVIDIA’s container runtime.
For the AI workloads running in K3s, we need access to the GPU which is available only through the nvidia-docker runtime.
We will also add a couple of other switches which makes it easy to use the kubectl CLI with K3s.
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