AMD has recently released AMD ROCm 5.7 for the AMD Radeon RX 7900 XTX and Radeon PRO W7900 GPUs, which are specifically designed for Machine Learning (ML) development workflows using PyTorch. This platform has now been extended to include the Radeon RX 7900 XT GPU, which provides more options for AI developers and researchers.
The Radeon RX 7900 XT GPU comes with the same RDNA 3 GPU architecture as the previously mentioned GPUs, with 20GB of fast on-board memory, and 168 AI accelerators. This makes it an excellent solution for accelerating workflows for ML training and inference on a local desktop.
By leveraging the parallel computing power of desktop GPUs, PyTorch users now have more options for a local client solution, which reduces their reliance on cloud-based solutions.
AMD ROCm 5.7 is an open software platform built for flexibility and performance. The HPC and AI communities can gain access to open compute languages, compilers, libraries, and tools designed to accelerate code development and solve the toughest challenges in the world today. The latest version of the AMD ROCm platform adds new functionality while building on the best features from ROCm 5.7 and other previous releases.
AMD’s HIPTensor is a C++ library for accelerating tensor primitives, which generates contraction plans and supports AMD Instinct GPUs.
MIGraphX optimises machine learning inference on AMD hardware via graph-level transformations. It parses ONNX or TensorFlow models into an internal graph representation (IGR), applies optimizations such as operator fusion and constant propagation, generates code for AMD GPU, and provides C++/Python APIs for model management.
In ROCm 5.7, discrete distributions have been optimised that works with recent HIP compiler optimizations, resulting in improved rocRAND RNG performance.
“We are excited about this latest addition to our portfolio. In combination with ROCm, these high-end GPUs make AI more accessible both from a software and hardware perspective, so developers can choose the solution that best fits their needs”, says Erik Hultgren, Software Product Manager at AMD.
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AMD has recently released AMD ROCm 5.7 for the AMD Radeon RX 7900 XTX and Radeon PRO W7900 GPUs, which are specifically designed for Machine Learning (ML) development workflows using PyTorch. This platform has now been extended to include the Radeon RX 7900 XT GPU, which provides more options for AI developers and researchers.
The Radeon RX 7900 XT GPU comes with the same RDNA 3 GPU architecture as the previously mentioned GPUs, with 20GB of fast on-board memory, and 168 AI accelerators. This makes it an excellent solution for accelerating workflows for ML training and inference on a local desktop.
By leveraging the parallel computing power of desktop GPUs, PyTorch users now have more options for a local client solution, which reduces their reliance on cloud-based solutions.
AMD ROCm 5.7 is an open software platform built for flexibility and performance. The HPC and AI communities can gain access to open compute languages, compilers, libraries, and tools designed to accelerate code development and solve the toughest challenges in the world today. The latest version of the AMD ROCm platform adds new functionality while building on the best features from ROCm 5.7 and other previous releases.
AMD’s HIPTensor is a C++ library for accelerating tensor primitives, which generates contraction plans and supports AMD Instinct GPUs.
MIGraphX optimises machine learning inference on AMD hardware via graph-level transformations. It parses ONNX or TensorFlow models into an internal graph representation (IGR), applies optimizations such as operator fusion and constant propagation, generates code for AMD GPU, and provides C++/Python APIs for model management.
In ROCm 5.7, discrete distributions have been optimised that works with recent HIP compiler optimizations, resulting in improved rocRAND RNG performance.
“We are excited about this latest addition to our portfolio. In combination with ROCm, these high-end GPUs make AI more accessible both from a software and hardware perspective, so developers can choose the solution that best fits their needs”, says Erik Hultgren, Software Product Manager at AMD.
,
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end of article