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MegEngine/imperative/python/megengine
Qsingle b4f9703f48 fix(overflow): fix the overflow of the long_scalars in network_node and module_stats 3 years ago
..
amp refactor(imperative): move python code of elemwise/reduce/conv2d/bn to c++ 3 years ago
autodiff fix(mge/gm): fix missing dtype checking while attach tensors 3 years ago
core perf(imperative): improve matmul/batch_matmul 3 years ago
data docs(compose): update compose API docstring and example 3 years ago
distributed docs(example): replace dist.help testcode with doctest format 3 years ago
dtr refactor(imperative): remove command buffer 3 years ago
experimental refactor(dispatch): switch to new dispatch system 3 years ago
functional perf(imperative): improve matmul/batch_matmul 3 years ago
hub feat(imperative): mge hub pretrained support s3 3 years ago
jit feat(subgraph): subgraph builder supports jit and custom grad 3 years ago
module docs(functional): replace megengine function testcode with doctest format 3 years ago
optimizer perf(dispatch): speed up dispatch system 3 years ago
quantization docs(imperative): fix multiple target warning when cross-reference 3 years ago
random docs(functional): replace elem testcode with doctest format 3 years ago
tools Revert "fix(mge): replace _full_sync by sync" 3 years ago
traced_module perf(mge): move Const into C++ 3 years ago
utils fix(overflow): fix the overflow of the long_scalars in network_node and module_stats 3 years ago
__init__.py feat(mge): add warning message when mismatched cuda sm is detected 3 years ago
_multistream.py perf(mge): improve cross stream memory borrowing 3 years ago
config.py fix(mge): control compute_mode by context 3 years ago
conftest.py ci(pytest): fixture doctest namespace in pytest config 3 years ago
device.py feat(mge): add warning message when mismatched cuda sm is detected 3 years ago
logger.py fix(mge): fix some minor problems 3 years ago
serialization.py fix(imperative): fix the default pickle protocol version of save 3 years ago
tensor.py perf(mge): improve cross stream memory borrowing 3 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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