Search Results for "double-entry"

Train machine learning models within a Docker container using Amazon SageMaker. Amazon SageMaker is a fully managed service for data science and machine learning (ML) workflows. You can use Amazon SageMaker to simplify the process of building, training, and deploying ML models. To train a model, you can include your training script and dependencies in a Docker container that runs your training code. A container provides an effectively isolated environment, ensuring a consistent runtime and...

The ‘setupdocx‘ provides a control layer for continuous documentation by the simplified creation, packaging, and installation of documentation. The provided commands are distributed as entry points and optional base classes for further customization into 'setup.py' - setuptools / distutils. Manages arbitrary document templates for the supported builder, supports multiple builds with arbitrary document layouts, designs, and patched contents. The current release supports the following commands: - build_docx - Enhanced documentation...

A framework to simply understandable control flows in applications. It is easy to manage flow by XML and annotations. There are many simple ways to catch actions such as transition, step entry or leaving, including control the decision similar to 'if' or 'switch' statements.

instantFSM is a header-only C++ state machine library. Its goal is to make state machine declaration and use easy enough that it can be integrated into your projects painlessly. Have a look atp the wiki where you will find a tutorial, samples, and a cheatsheet to get accustomed with instantFSM. The only instantFSM header file is downloadable from the files section or you can check it out from the git repository.

JUMLStateMachine is a Java library implementing a state machine by the UML standard. The purpose for the library is to be easily usable for small state machines, yet extendable and scalable for larger and more complex ones.