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The Open Neural Network Exchange (ONNX) [ˈɒnɪks] [2] is an open-source artificial intelligence ecosystem [3] of technology companies and research organizations that establish open standards for representing machine learning algorithms and software tools to promote innovation and collaboration in the AI sector.
XNU ("X is Not Unix") is the computer operating system (OS) kernel developed at Apple Inc. since December 1996 for use in the Mac OS X (now macOS) operating system and released as free and open-source software as part of the Darwin OS, which, in addition to being the basis for macOS, is also the basis for Apple TV Software, iOS, iPadOS, watchOS, visionOS, and tvOS.
OpenVINO IR [5] is the default format used to run inference. It is saved as a set of two files, *.bin and *.xml, containing weights and topology, respectively.It is obtained by converting a model from one of the supported frameworks, using the application's API or a dedicated converter.
It samples software at set time intervals (or driven by hardware performance monitors events) taking snapshots of the stack, showing the functions which require more of the application's resources. Includes tools to analyze the data produced by a sampling run. Since Mac OS X 10.7, it is not on the Apple site any more and was replaced by ...
Mac OS Runtime for Java (MRJ, originally Macintosh Runtime for Java) was Apple's proprietary virtual machine for Java-based applications in the classic Mac OS (i.e. versions prior to Mac OS X). Both a runtime environment and a software development kit (SDK) are available.
Darwin is the core Unix-like operating system of macOS, iOS, watchOS, tvOS, iPadOS, audioOS, visionOS, and bridgeOS.It previously existed as an independent open-source operating system, first released by Apple Inc. in 2000.
He advised developers to separate user interface code from the rest of their application development to ensure "a great UI experience on every platform (Mac, Linux, Android, iOS, Windows and Web)" without being dependent on third party APIs.
The library is designed to reduce computing power and memory use and to train large distributed models with better parallelism on existing computer hardware. [2] [3] DeepSpeed is optimized for low latency, high throughput training.