名称 最后提交 最后更新
..
mkldnn [MKL-DNN] Tensor modifications revert (#16462)
CMakeLists.txt Refine operator cmake (#14413)
elementwise_add_op.cc add elementwise_add_grad_grad op (#17366)
elementwise_add_op.cu add elementwise_add_grad_grad op (#17366)
elementwise_add_op.h add elementwise_add_grad_grad op (#17366)
elementwise_div_op.cc Double backward elementwise div (#17416)
elementwise_div_op.cu Double backward elementwise div (#17416)
elementwise_div_op.h Double backward elementwise div (#17416)
elementwise_floordiv_op.cc add floordiv and mod op; test=develop
elementwise_floordiv_op.cu fix time; test=develop
elementwise_floordiv_op.h fix time; test=develop
elementwise_max_op.cc Fix some grad op desc makers (#16633)
elementwise_max_op.cu Fix Eigen macro when using GPU
elementwise_max_op.h Fix some grad op desc makers (#16633)
elementwise_min_op.cc Fix some grad op desc makers (#16633)
elementwise_min_op.cu Fix Eigen macro when using GPU
elementwise_min_op.h Fix some grad op desc makers (#16633)
elementwise_mod_op.cc Mod floordiv (#17251)
elementwise_mod_op.cu fix time; test=develop
elementwise_mod_op.h fix time; test=develop
elementwise_mul_op.cc add double grad for elementwise_mul op (#17255)
elementwise_mul_op.cu Optimize the elementwise op using eigen (#15494)
elementwise_mul_op.h Optimize the elementwise op using eigen (#15494)
elementwise_op.h add elementwise_add_grad_grad op (#17366)
elementwise_op_function.h Double backward elementwise div (#17416)
elementwise_pow_op.cc
elementwise_pow_op.cu
elementwise_pow_op.h
elementwise_sub_op.cc
elementwise_sub_op.cu
elementwise_sub_op.h

项目简介

PArallel Distributed Deep LEarning: Machine Learning Framework from Industrial Practice (『飞桨』核心框架,深度学习&机器学习高性能单机、分布式训练和跨平台部署)

:rocket: Github 镜像仓库 :rocket:

源项目地址 :arrow_down: :arrow_down: :arrow_down:

https://github.com/paddlepaddle/paddle

deep-learningdistributed-trainingefficiencymachine-learningneural-networkpaddlepaddlepythonscalability

发行版本 60

PaddlePaddle 2.5.0 Release Note

全部发行版

贡献者 246

全部贡献者

开发语言

  • C++ 49.8 %
  • Python 41.0 %
  • Cuda 7.0 %
  • CMake 1.1 %
  • Shell 0.6 %
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