Search Results for "distribution"

...At present, MNN has been integrated in more than 20 apps of Alibaba Inc, such as Taobao, Tmall, Youku, Dingtalk, Xianyu and etc., covering more than 70 usage scenarios such as live broadcast, short video capture, search recommendation, product searching by image, interactive marketing, equity distribution, security risk control. In addition, MNN is also used on embedded devices, such as IoT. MNN Workbench could be downloaded from MNN's homepage, which provides pretrained models, visualized training tools, and one-click deployment of models to devices. Android platform, core so size is about 400KB, OpenCL so is about 400KB, Vulkan so is about 400KB. ...

...The approach keeps application logic on the BEAM—supervised, fault-tolerant, and hot-reload-friendly—while rendering an HTML/CSS/JS interface inside the system’s embedded browser engine. It offers conveniences for packaging and distribution on Windows, macOS, and Linux, including app metadata, icons, and startup integration. The library exposes desktop-specific affordances such as system tray menus, window management, and notifications, so applications feel native rather than like generic web wrappers. Because LiveView drives the UI, state lives on the server process, enabling real-time updates without heavy client frameworks. ...

...Swift Package Manager, CocoaPods, and Carthage integrations are the preferred and recommended approaches. The Swift Package Manager is a tool for automating the distribution of Swift code and is integrated into Xcode and the Swift compiler. This is the recommended installation method. Updates to SwiftyStoreKit will always be available immediately to projects with SPM. SPM is also integrated directly with Xcode.

...As TensorFlow added a Metal PluggableDevice path, the project directed users toward using Apple’s tensorflow-metal to get GPU acceleration on Mac directly through Metal. The README and releases emphasized this was a macOS-optimized distribution, not an upstream fork of TensorFlow, and it cataloged common installation notes and issues encountered by early Apple Silicon users. In practice, the repo served as a bridge during the transition to native Mac acceleration, making modern TensorFlow viable on Apple hardware while upstream support matured.