Open Source Python Runtimes

Shepherd is an early-stage Python runtime for building agents and meta-agents around reversible, inspectable execution traces. It records interactions and workspace changes as durable run histories instead of immediately applying an agent's output. Each result can be reviewed, executed separately, selected, merged, released, or discarded. Tasks are declared as Python functions whose signatures and documentation define the agent contract and required resources. Read-only and read-write repository grants are enforced through native operating-system sandboxing on supported macOS and Linux environments. Runs can be forked, replayed, reverted, and supervised, enabling counterfactual experiments, optimization, and training workflows. Shepherd also retains machine-readable records and supports offline deterministic examples, but its APIs may change while the project remains in alpha.

This installer allows to install the latest Visual C++ Runtime of all years (2008-2022), Full DirectX Runtime, Microsoft XNA Framework, .Net Runtime, Java and OpenAL at once.

We have open-sourced a set of software packages, Runtime Interface Clients (RIC), that implement the Lambda Runtime API, allowing you to seamlessly extend your preferred base images to be Lambda compatible. The Lambda Runtime Interface Client is a lightweight interface that allows your runtime to receive requests from and send requests to the Lambda service. The Lambda Python Runtime Interface Client is vended through pip. You can include this package in your preferred base image to make that base image Lambda compatible. To make it easy to locally test Lambda functions packaged as container images we open-sourced a lightweight web-server, Lambda Runtime Interface Emulator (RIE), which allows your function packaged as a container image to accept HTTP requests. You can install the AWS Lambda Runtime Interface Emulator on your local machine to test your function. Then when you run the image function, you set the entry point to be the emulator.

AWS Neuron is a software development kit (SDK) for running machine learning inference using AWS Inferentia chips. It consists of a compiler, run-time, and profiling tools that enable developers to run high-performance and low latency inference using AWS Inferentia-based Amazon EC2 Inf1 instances. Using Neuron developers can easily train their machine learning models on any popular framework such as TensorFlow, PyTorch, and MXNet, and run it optimally on Amazon EC2 Inf1 instances. You can continue to use the same ML frameworks you use today and migrate your software onto Inf1 instances with minimal code changes and without tie-in to vendor-specific solutions. Neuron is pre-integrated into popular machine learning frameworks like TensorFlow, MXNet and Pytorch to provide a seamless training-to-inference workflow. It includes a compiler, runtime driver, as well as debug and profiling utilities with a TensorBoard plugin for visualization.

The Common Event Driven Application Runtime (CEDAR) is set of software to design, code generate and run, event driven applications. This formalism is an extension of the Shlaer–Mellor method and executable UML, facilitating deployment of domains across multiple hosts, using networked bridges to process and pass events between them. Core technologies: XML, Python, Qt/PySide. Current release solely targets Linux, but has previously run on macOS. Currently released components are: - eda-model: XML schema and schematron that define the storage of a valid design. - eda-model-interface: python3 library for loading, editing, diffing and validating an instance of an EDA design. - edator: The editor/IDE of EDA designs, using Qt/PySide2. - code generator: generate a C++ code base from a CEDAR model. - runtime: C++ and python runtime support files - ruststore: A datastore for the CEDAR runtime