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Conda is an open-source, [2] cross-platform, [3] language-agnostic package manager and environment management system. It was originally developed to solve package management challenges faced by Python data scientists , and today is a popular package manager for Python and R .
PyTorch is a machine learning library based on the Torch library, [4] [5] [6] used for applications such as computer vision and natural language processing, [7] originally developed by Meta AI and now part of the Linux Foundation umbrella.
In January 2019, the TensorFlow team released a developer preview of the mobile GPU inference engine with OpenGL ES 3.1 Compute Shaders on Android devices and Metal Compute Shaders on iOS devices. [30] In May 2019, Google announced that their TensorFlow Lite Micro (also known as TensorFlow Lite for Microcontrollers) and ARM's uTensor would be ...
In computing, CUDA is a proprietary [2] parallel computing platform and application programming interface (API) that allows software to use certain types of graphics processing units (GPUs) for accelerated general-purpose processing, an approach called general-purpose computing on GPUs.
Tensor Processing Unit (TPU) is an AI accelerator application-specific integrated circuit (ASIC) developed by Google for neural network machine learning, using Google's own TensorFlow software. [2] Google began using TPUs internally in 2015, and in 2018 made them available for third-party use, both as part of its cloud infrastructure and by ...
The wsl.exe command accesses and manages Linux distributions in WSL via command-line interface (CLI) – for example via Command Prompt or PowerShell. With no arguments it enters the default distribution shell. It can list available distributions, set a default distribution, and uninstall distributions. [30]
As of July 2017, the Graphics Core Next instruction set has seen five iterations. The differences between the first four generations are rather minimal, but the fifth-generation GCN architecture features heavily modified stream processors to improve performance and support the simultaneous processing of two lower-precision numbers in place of a single higher-precision number.
Graphics device drivers are implemented using two components: a UMD (user-mode driver) and a KMD (kernel-mode driver). Starting with Linux kernel 4.2 AMD Catalyst and Mesa will share the same Linux kernel driver: amdgpu. Amdgpu provides interfaces defined by DRM and KMS.