Docker

From Kubernetes Dreams to Docker Reality: Building an ML Inference Cluster on Jetson Nano I set out with a clear goal: stand up a four-node Jetson Nano cluster running k3s, connect it to Splunk’s Deep Learning Toolkit (DLTK), and use the Nano GPUs to serve ML model inference for security analytics use cases — specifically DNS tunneling detection and lateral movement identification from host-based firewall data. The plan was reasonable on paper. Kubernetes gives you scheduling, self-healing, and a clean abstraction over bare hardware. Jetson Nanos are purpose-built for edge AI workloads. Splunk DLTK provides a framework for connecting ML models to security event streams. What could go wrong? ...

Introduction In Part 1, you learned why an edge AI inference pipeline is a compelling approach for security operations, and you understood the key constraints imposed by the Jetson Nano 4GB hardware and DSDL 5.2.3’s actual behavior. In this post, you will build the inference container – every file, every command, and every decision explained. The build covers four areas: the Dockerfile and its critical dependencies, the DSDL-native Flask application that implements the correct wire protocol, TLS certificate generation using a lab CA, and the distribution workflow for deploying the finished image to all four nodes. ...