Nuclio

Note: Port 1024-65535 has been automatically mapped. Nuclio is a high-performance "serverless" framework that focuses on data, I/O and compute-intensive workloads. It is deeply integrated with popular data science tools such as Jupyter and Kubeflow, supports multiple data and streaming sources, and supports execution on CPU and GPU. The Nuclio project began in 2017 and continues to grow rapidly, with many start-ups and large companies already using Nuclio in production. Nuclio can be run as a stand-alone Docker container or deployed on an existing Kubernetes cluster. You can view deployment instructions in the Nuclio documentation. You can also use Nuclio through fully managed application services (in the cloud or on-premises) in the Iguazio Data Science Platform. If you want to create and manage Nuclio functions through code, such as from the Jupyter Notebook, you can check out the Nuclio Jupyter project, which provides a Python package and SDK for creating and deploying Nuclio functions from the Jupyter Notebook. Nuclio is also an important part of the new open source MLRun library (for data science automation and tracking) and the open source Kubeflow Pipelines framework (for building and deploying portable, extensible ML workflows). Nuclio is very fast: a single function instance can process hundreds of thousands of HTTP requests or data records per second, 10 to 100 times faster than some other frameworks. Nuclio also has high security: it integrates with Kaniko, allowing Docker images to be built in a way that is safe at runtime and usable for production. For further support and questions, you can join Nuclio's Slack workspace.