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AMDgpu is an open source device driver for the Linux operating system developed by AMD to support its Radeon lineup of graphics cards (GPUs). It was announced in 2014 as the successor to the previous radeon device driver as part of AMD's new "unified" driver strategy, [3] and was released on April 20, 2015.
The main AMD GPU software stacks are fully supported on Linux: GPUOpen for graphics, and ROCm for compute. GPUOpen is most often merely a supplement, for software utilities, to the free Mesa software stack that is widely distributed and available by default on most Linux distributions .
Installation instructions are provided for Linux and Windows in the official AMD ROCm documentation. ROCm software is currently spread across several public GitHub repositories. Within the main public meta-repository , there is an XML manifest for each official release: using git-repo , a version control tool built on top of Git , is the ...
In September 2022, Meta announced that PyTorch would be governed by the independent PyTorch Foundation, a newly created subsidiary of the Linux Foundation. [23] PyTorch 2.0 was released on 15 March 2023, introducing TorchDynamo, a Python-level compiler that makes code run up to 2x faster, along with significant improvements in training and ...
Linux device drivers for AMD hardware in August 2016 Screenshot of glxinfo showing OpenGL information with glxgears running on a Linux system with AMDGPU kernel module. AMD's proprietary driver, AMD Catalyst for their Radeon, is available for Microsoft Windows and Linux (formerly fglrx). A current version can be downloaded from AMD's site, and ...
The torch package also simplifies object-oriented programming and serialization by providing various convenience functions which are used throughout its packages. The torch.class(classname, parentclass) function can be used to create object factories ().
TensorFlow is Google Brain's second-generation system. Version 1.0.0 was released on February 11, 2017. [17] While the reference implementation runs on single devices, TensorFlow can run on multiple CPUs and GPUs (with optional CUDA and SYCL extensions for general-purpose computing on graphics processing units). [18]
Mesa implements a translation layer between a graphics API such as OpenGL and the graphics hardware drivers in the operating system kernel. The supported version of the different graphic APIs depends on the driver, because each hardware driver has its own implementation (and therefore status).