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An output of pip install virtualenv. Pip's command-line interface allows the install of Python software packages by issuing a command: pip install some-package-name. Users can also remove the package by issuing a command: pip uninstall some-package-name
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.
[50] [51] The company's co-founder and CEO laid out the Tegra processor roadmap with Ubuntu Unity at the 2013 GPU Technology Conference. [52] Nvidia's Unified Memory driver (nvidia-uvm.ko), which implements memory management for Pascal and Volta GPUs on Linux, is MIT licensed.
CUDA code runs on both the central processing unit (CPU) and graphics processing unit (GPU). NVCC separates these two parts and sends host code (the part of code which will be run on the CPU) to a C compiler like GNU Compiler Collection (GCC) or Intel C++ Compiler (ICC) or Microsoft Visual C++ Compiler, and sends the device code (the part which will run on the GPU) to the GPU.
In June 2020, a benchmark with 173 tests on WSL 2 (20H2) with an AMD Ryzen Threadripper 3970X showed an average of 87% of the performance of native Ubuntu 20.04 LTS. In contrast, WSL 1 had only 70% of the performance of native Ubuntu. WSL 2 improves I/O performance, providing a near-native level. [49]
Konsole, KDE's terminal application, and Dolphin, KDE's file manager, two of KDE's core applications. The KDE Gear is a set of applications and supporting libraries that are developed by the KDE community, [4] primarily used on Linux-based operating systems but mostly multiplatform, and released on a common release schedule.
It is also used for Downstream development for enhancing OpenHarmony base in global and western markets for compatibility and interoperability with connected IoT systems as well as custom third-party support on-device AI features on custom frameworks such as Tensorflow, CUDA and others, alongside native Huawei MindSpore solutions across the ...
VDPAU was originally designed by Nvidia for their PureVideo SIP block present on their GeForce 8 series and later GPUs. [8]On March 9, 2015, Nvidia released VDPAU version 1.0 which supports High Efficiency Video Coding (HEVC) decoding for the Main, Main 4:4:4, Main Still Picture, Main 10, and Main 12 profiles.