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MegEngine/imperative/python/src
Megvii Engine Team b9e850a479 test(imperative): check env values after each pytest 3 years ago
..
common.cpp fix(subgraph): fix device recognition and scalar propagate 3 years ago
common.h style(all): reformat c++ code 3 years ago
grad.cpp fix(autodiff): fix some bugs in relation to 2nd order grad 3 years ago
grad.h fix(autodiff): fix some bugs in relation to 2nd order grad 3 years ago
grad_override.cpp perf(imperative): improve reduce op performance 3 years ago
graph_rt.cpp feat(mge): support dump with specific format 3 years ago
graph_rt.h style(all): reformat c++ code 3 years ago
helper.cpp feat(mgb/dtype): add dtype qint1 3 years ago
helper.h style(all): reformat c++ code 3 years ago
imperative_rt.cpp style(all): reformat c++ code 3 years ago
imperative_rt.h chore(mge): update copyright years 4 years ago
module.cpp fix(imperative): destruct dnn handles at last 3 years ago
module_trace.h perf(dispatch): improve performance of dispatch system 3 years ago
numpy_dtypes.cpp perf(mge/utils): move convert_input into C++ 3 years ago
numpy_dtypes.h perf(mge/utils): move convert_input into C++ 3 years ago
numpy_dtypes_bfloat16.cpp style(all): reformat c++ code 3 years ago
numpy_dtypes_intbx.cpp fix(imperative): fix error message for tensors with intbx data type 3 years ago
ops.cpp test(imperative): check env values after each pytest 3 years ago
ops.h style(all): reformat c++ code 3 years ago
pyext17.h style(all): reformat c++ code 3 years ago
tensor.cpp fix(imperative): restrict value converts to symbolvar 3 years ago
tensor.h perf(imperative): enable memory forwarding for imperative 3 years ago
tensor_utils.cpp feat(mge/indexing): support newaxis 3 years ago
tensor_utils.h perf(mge/utils): move astensor1d into C++ 3 years ago
transformation.h fix(autodiff): fix some bugs in relation to 2nd order grad 3 years ago
utils.cpp fix(fastrun): fix persistent_cache in redis 3 years ago
utils.h chore(mge): update copyright years 4 years ago
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MegEngine 安装包中集成了使用 GPU 运行代码所需的 CUDA 环境，不用区分 CPU 和 GPU 版。 如果想要运行 GPU 程序，请确保机器本身配有 GPU 硬件设备并安装好驱动。 如果你想体验在云端 GPU 算力平台进行深度学习开发的感觉，欢迎访问 MegStudio 平台

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