Ml Testing Accelerators
Testing framework for Deep Learning models (Tensorflow and PyTorch) on Google Cloud hardware accelerators (TPU and GPU)
| Project Name | Stars | Downloads | Repos Using This | Packages Using This | Most Recent Commit | Total Releases | Latest Release | Open Issues | License | Language |
|---|---|---|---|---|---|---|---|---|---|---|
| Onnxruntime | 11,668 |  | 8 | 88 | 3 months ago | 39 | November 20, 2023 | 2,222 | mit | C++ |
| ONNX Runtime: cross-platform, high performance ML inferencing and training accelerator | ||||||||||
| Arrayfire | 4,318 | 3 months ago | 325 | bsd-3-clause | C++ | |||||
| ArrayFire: a general purpose GPU library. | ||||||||||
| Spark Rapids | 619 | 1 | 3 months ago | 19 | October 24, 2023 | 1,271 | apache-2.0 | Scala | ||
| Spark RAPIDS plugin - accelerate Apache Spark with GPUs | ||||||||||
| Aparapi | 370 | 17 | 9 | 3 years ago | 19 | July 12, 2021 | 47 | apache-2.0 | Java | |
| The New Official Aparapi: a framework for executing native Java and Scala code on the GPU. | ||||||||||
| Autodock Gpu | 317 | 3 months ago | 58 | gpl-2.0 | C++ | |||||
| AutoDock for GPUs and other accelerators | ||||||||||
| Oneapi.jl | 163 | 4 months ago | 26 | other | Julia | |||||
| Julia support for the oneAPI programming toolkit. | ||||||||||
| Dcompute | 116 | 2 years ago | 2 | October 15, 2017 | 18 | bsl-1.0 | D | |||
| DCompute: Native execution of D on GPUs and other Accelerators | ||||||||||
| Ml Testing Accelerators | 58 | 3 months ago | 35 | apache-2.0 | Jsonnet | |||||
| Testing framework for Deep Learning models (Tensorflow and PyTorch) on Google Cloud hardware accelerators (TPU and GPU) | ||||||||||
| Rayaccel | 50 | 7 years ago | 1 | mit | C | |||||
| Hybrid CPU and GPU ray traced rendering using shared memory. | ||||||||||
| Sneakysnake | 44 | a year ago | 1 | gpl-3.0 | VHDL | |||||
| SneakySnake:snake: is the first and the only pre-alignment filtering algorithm that works efficiently and fast on modern CPU, FPGA, and GPU architectures. It greatly (by more than two orders of magnitude) expedites sequence alignment calculation for both short and long reads. Described in the Bioinformatics (2020) by Alser et al. https://arxiv.org/abs/1910.09020. | ||||||||||
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