Linux
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AMD iGPUs Use 128GB Memory on Linux via GTT
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AMD's integrated GPUs (iGPUs) on Linux can leverage up to 128 GB of system memory as VRAM through a feature called Graphics Translation Table (GTT). This dynamic allocation allows developers to utilize iGPUs for tasks like kernel optimization without impacting the CPU's memory pool until needed. While iGPUs are slower for inference tasks, they offer a cost-effective solution for development and profiling, especially when used alongside a main GPU. This capability is particularly beneficial for those working on hybrid CPU/GPU architectures, enabling efficient memory management and development of large memory AMD GPU kernels. This matters because it opens up new possibilities for affordable and efficient computational development on standard hardware.
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NVIDIA Drops Pascal Support, Impacting Arch Linux
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NVIDIA's decision to drop support for Pascal GPUs on Linux has caused disruptions, particularly for Arch Linux users who rely on these older graphics cards. This change has led to compatibility issues and forced users to seek alternative solutions or upgrade their hardware to maintain system stability and performance. The move highlights the challenges of maintaining support for older technology in rapidly evolving software ecosystems. Understanding these shifts is crucial for users and developers to adapt and ensure seamless operation of their systems.
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TensorFlow 2.15 Hot-Fix for Linux Installation
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A hot-fix has been released for TensorFlow 2.15 to address an installation issue on Linux platforms. The problem arose due to the TensorFlow 2.15.0 Python package requesting unavailable tensorrt-related packages unless pre-installed or additional flags were provided, causing installation errors or downgrades to TensorFlow 2.14. The fix, TensorFlow 2.15.0.post1, removes these dependencies from the tensorflow[and-cuda] installation method, restoring the intended functionality while maintaining support for TensorRT if it is already installed. Users should specify version 2.15.0.post1 or use a fuzzy version specification to ensure they receive the correct version, as the standard version specification will not install the fixed release. This matters because it ensures seamless installation and functionality of TensorFlow 2.15 alongside NVIDIA CUDA, crucial for developers relying on these tools for machine learning projects.