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Linux, macOS, Windows: Python: Python: Only on Linux No Yes No Yes Yes Keras: François Chollet 2015 MIT license: Yes Linux, macOS, Windows: Python: Python, R: Only if using Theano as backend Can use Theano, Tensorflow or PlaidML as backends Yes No Yes Yes [20] Yes Yes No [21] Yes [22] Yes MATLAB + Deep Learning Toolbox (formally Neural Network ...
macOS i386, x86-64 and AArch64, iOS, Linux, FreeBSD, NetBSD, Windows: No ? Apache License v2.0 with LLVM Exceptions 16.0.5, 2 June 2023 mdb: 1999 Solaris standard debugger (adb) successor Any compiled to machine code: Solaris: Yes (Memory debugger) ? CDDL: 2016 TotalView: 1998 Source code and memory serial and parallel debugger C++, C, CUDA ...
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]
CUDA 9.0–9.2 comes with these other components: CUTLASS 1.0 – custom linear algebra algorithms, NVIDIA Video Decoder was deprecated in CUDA 9.2; it is now available in NVIDIA Video Codec SDK; CUDA 10 comes with these other components: nvJPEG – Hybrid (CPU and GPU) JPEG processing; CUDA 11.0–11.8 comes with these other components: [19 ...
C, Java, C#, Fortran, Python 1970 many components Not free Proprietary: General purpose numerical analysis library. Math.NET Numerics: C. Rüegg, M. Cuda, et al. C#, F#, C, PowerShell 2009 4.7.0, November 2018 Free MIT/X11: General purpose numerical analysis and statistics library for the .NET framework and Mono, with optional support for ...
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Free/open source - BSD version is part of 4.2BSD and GNU version is part of GNU Binutils (by GNU Project) HWPMC: FreeBSD 6.0+ System-level and process-level counting and sampling hardware performance monitoring framework supporting multiple architectures. BSD Instana: Linux, Windows, iOS, Android, Azure, AWS, AIX, Solaris, HP/UX, zOS, zLinux
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 inference performance across major cloud platforms.