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Helmut Hoffer von Ankershoffen experimenting with arm64 based NVIDIA Jetson (Nano and AGX Xavier) edge devices running Kubernetes (K8s) for machine learning (ML) including Jupyter Notebooks, TensorFlow Training and TensorFlow Serving using CUDA for smart IoT.
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9
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enh(tensorflow-serving/webservice): provide meta information such as tf model and JETSON_MODEL as well as timestamp and duration; provide make target to access public endpoint on polarize.ai
98b4745View on GitHubrefactor(workflow): automatically repack CUDA ml libraries as part of provisioning; automatically create k8s join token as part of provisioning; enh(images): provide more metadata; doc: more hints
2de8877View on GitHubenh: images published on Docker Hub are now tagged with release versions
9575c6dView on GitHubdoc: enhance; feat: make publish-all; enh(custom-builder): allow passing any options from skaffold; enh(tensorflow-serving-base/xavier): build with compute capability for TX2; fix(tensorflow-serving-base/nano): set correct compute capability
2aced5fView on GitHubfeat: TensorFlow Serving base image; feat: Webservice as facade for TensorFlow Serving; test: container structure tests
fd7124fView on GitHubfeat: automatically extract deb packages from host, ml-base image; refactor: cleanup; other: start with TensorFlow Serving
c41a923View on GitHub