Cloud-Native technologies offer the flexibility and agility needed for rapid product development and continual product upgrades.

Jetson brings Cloud-Native to the edge and enables technologies like containers and container orchestration which revolutionized cloud applications.

NVIDIA JetPack includes NVIDIA Container Runtime with Docker integration, enabling GPU accelerated containerized applications on Jetson platform. Developers can package their applications for Jetson with all its dependencies into a single container that is guaranteed to work in any deployment environment.

Manage the lifecycle of your containerized application on Jetson platform at scale using container orchestration technologies like Kubernetes.


Several development and deployment containers for Jetson are hosted on NVIDIA NGC:

  • L4T-Base Container: Enables running containerized applications on Jetson by including necessary components from the root file system including CUDA, cuDNN and TensorRT. This container is used as a base image while containerizing applications on Jetson.

  • DeepStream Container: Contains plugins and libraries that are part of DeepStream SDK. Different variants of the DeepStream container like Base, Samples and IoT are available for Jetson.

  • TensorFlow Container: Contains TensorFlow pre-installed in a Python 3.6 environment. Developers can use this to set up a TensorFlow development environment quickly. This container can be used as a base image for containerizing TensorFlow applications.

  • PyTorch Container: Contains PyTorch and TourchVision pre-installed in a Python 3.6 environment. Developers can quickly set up a PyTorch development environment and can use this container as a base image for containerizing PyTorch applications.

  • Machine Learning Container: Contains TensorFlow, PyTorch, JupyterLab, and other popular ML and data science frameworks such as scikit-learn, scipy, and Pandas pre-installed in a Python 3.6 environment.

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