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As AMDgpu is part of the monolithic Linux kernel, it is shipped by most Linux distributions directly. The package suite / install script amdgpu-pro, distributed by AMD directly from AMD Radeon Software , ships an AMDgpu kernel module somewhat reliably more up-to-date compared to that of kernels shipped in regular operating system 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 ...
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 .
In September 2022, Meta announced that PyTorch would be governed by the independent PyTorch Foundation, a newly created subsidiary of the Linux Foundation. [24] 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 ...
A current version can be downloaded from AMD's site, and some Linux distributions contain it in their repositories. It is in the process of being replaced with an AMDGPU-PRO hybrid driver combining the open-source kernel, X and Mesa multimedia drivers with closed-source OpenGL, OpenCL and Vulkan drivers derived from Catalyst.
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.
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Torch is used by the Facebook AI Research Group, [8] IBM, [9] Yandex [10] and the Idiap Research Institute. [11] Torch has been extended for use on Android [12] [better source needed] and iOS. [13] [better source needed] It has been used to build hardware implementations for data flows like those found in neural networks. [14]