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MegEngine/imperative/python/src
Megvii Engine Team a405461c01 fix(mge/elemwise): fix problem that elemwise.mode is not comparable with string mode 4 years ago
..
common.cpp feat(distributed): auto detect device and backend when init group 4 years ago
common.h chore(mge): update copyright years 4 years ago
grad.cpp chore(mge): update copyright years 4 years ago
grad.h chore(mge): update copyright years 4 years ago
grad_info.h chore(mge): update copyright years 4 years ago
grad_override.cpp chore(mge): update copyright years 4 years ago
graph_rt.cpp refactor(imperative): refactor enum param type caster 4 years ago
graph_rt.h chore(mge): update copyright years 4 years ago
helper.cpp chore(mge): update copyright years 4 years ago
helper.h chore(mge): update copyright years 4 years ago
imperative_rt.cpp chore(mge): update copyright years 4 years ago
imperative_rt.h chore(mge): update copyright years 4 years ago
intrusive_list.h chore(mge): update copyright years 4 years ago
module.cpp chore(mge): update copyright years 4 years ago
numpy_dtypes.cpp fix(imperative/dtype): fix is_dtype_equal 4 years ago
numpy_dtypes.h chore(mge): update copyright years 4 years ago
numpy_dtypes_bfloat16.cpp chore(mge): update copyright years 4 years ago
numpy_dtypes_intbx.cpp chore(mge): update copyright years 4 years ago
ops.cpp fix(mge/elemwise): fix problem that elemwise.mode is not comparable with string mode 4 years ago
ops.h refactor(imperative): refactor enum param type caster 4 years ago
pyext17.h chore(mge): update copyright years 4 years ago
tensor.cpp fix(mge/core): support non-Tensor args for `core.tensor.utils.get_device` 4 years ago
tensor.h feat(mge/utils): add array method for varnode 4 years ago
trace.cpp fix(imperative): catch python exception in c++ 4 years ago
trace.h chore(mge): update copyright years 4 years ago
trace_info.h chore(mge): update copyright years 4 years ago
utils.cpp perf(fastrun): cache persistent cache 4 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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