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The Kubernetes API can be extended using Custom Resources, which represent objects that are not part of the standard Kubernetes installation. These custom resources are declared using Custom Resource Definitions (CRDs), which is a kind of resource that can be dynamically registered and unregistered without shutting down or restarting a cluster ...
Python is a widely used general-purpose, high-level, interpreted, programming language. [16] Python supports multiple programming paradigms, including object-oriented, imperative, functional and procedural paradigms. It features a dynamic type system, automatic memory management, a standard library, and strict use of whitespace. [17]
ProGet currently supports a growing list of package managers, including NuGet, Chocolatey, Bower, npm, Maven, PowerShell, RubyGems, Helm for Kubernetes, Debian, Python, and Visual Studio Extensions (.vsix). ProGet also supports Docker containers, Jenkins build artifacts (through a plugin) and vulnerability scanning.
FastAPI is a high-performance web framework for building HTTP-based service APIs in Python 3.8+. [3] It uses Pydantic and type hints to validate , serialize and deserialize data. FastAPI also automatically generates OpenAPI documentation for APIs built with it. [ 4 ]
Dask is an open-source Python library for parallel computing.Dask [1] scales Python code from multi-core local machines to large distributed clusters in the cloud. Dask provides a familiar user interface by mirroring the APIs of other libraries in the PyData ecosystem including: Pandas, scikit-learn and NumPy.
YAML (/ ˈ j æ m əl /, rhymes with camel [4]) was first proposed by Clark Evans in 2001, [15] who designed it together with Ingy döt Net [16] and Oren Ben-Kiki. [16]Originally YAML was said to mean Yet Another Markup Language, [17] because it was released in an era that saw a proliferation of markup languages for presentation and connectivity (HTML, XML, SGML, etc.).
Checkmk is a software system developed in Python and C++ for IT Infrastructure monitoring. It is used for the monitoring of servers, applications, networks, cloud infrastructures (public, private, hybrid), containers, storage, databases and environment sensors.
Data and model versioning is the base layer [21] of DVC for large files, datasets, and machine learning models. It allows the use of a standard Git workflow, but without the need to store those files in the repository. Large files, directories and ML models are replaced with small metafiles, which in turn point to