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TensorFlow is available on 64-bit Linux, macOS, Windows, and mobile computing platforms including Android and iOS. [ citation needed ] Its flexible architecture allows for easy deployment of computation across a variety of platforms (CPUs, GPUs, TPUs ), and from desktops to clusters of servers to mobile and edge devices .
Unlike OpenCL, CUDA-enabled GPUs are only available from Nvidia as it is proprietary. [28] [2] Attempts to implement CUDA on other GPUs include: Project Coriander: Converts CUDA C++11 source to OpenCL 1.2 C. A fork of CUDA-on-CL intended to run TensorFlow. [29] [30] [31] CU2CL: Convert CUDA 3.2 C++ to OpenCL C. [32]
C++, Wolfram Language, CUDA: Wolfram Language: Yes No Yes No Yes Yes [75] Yes Yes Yes Yes [76] Yes Software Creator Initial release Software license [a] Open source Platform Written in Interface OpenMP support OpenCL support CUDA support ROCm support [77] Automatic differentiation [2] Has pretrained models Recurrent nets Convolutional nets RBM/DBNs
The latest releases also have packages that allow for easy setup for TensorFlow and CUDA. [5] [6] Pop!_OS is maintained primarily by System76, with the release version source code hosted in a GitHub repository. Unlike many other Linux distributions, it is not community-driven, although outside programmers can contribute, view and modify the ...
Tensor Processing Unit (TPU) is an AI accelerator application-specific integrated circuit (ASIC) developed by Google for neural network machine learning, using Google's own TensorFlow software. [2]
Altimesh Hybridizer created by Altimesh compiles Common Intermediate Language to CUDA binaries. [15] [16] It supports generics and virtual functions. [17] Debugging and profiling is integrated with Visual Studio and Nsight. [18] It is available as a Visual Studio extension on Visual Studio Marketplace.
CuPy is an open source library for GPU-accelerated computing with Python programming language, providing support for multi-dimensional arrays, sparse matrices, and a variety of numerical algorithms implemented on top of them. [3]
Keras was first independent software, then integrated into the TensorFlow library, and later supporting more. "Keras 3 is a full rewrite of Keras [and can be used] as a low-level cross-framework language to develop custom components such as layers, models, or metrics that can be used in native workflows in JAX, TensorFlow, or PyTorch — with ...