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MegEngine/imperative/python/megengine
Megvii Engine Team 30ef28b0fa build(imperative): generate stub files for core ops 3 years ago
..
amp fix(imperative/amp): add is_scalar check in elemwise and concat 3 years ago
autodiff revert: chore(mge/misc): api converage 3 years ago
core build(imperative): generate stub files for core ops 3 years ago
data Merge pull request #492 from tpoisonooo:fix-imagenet-untar 3 years ago
distributed revert: chore(mge/misc): api converage 3 years ago
dtr chore(license): remove all license in file header 3 years ago
experimental chore(license): remove all license in file header 3 years ago
functional build(imperative): generate stub files for core ops 3 years ago
hub revert: chore(mge/misc): api converage 3 years ago
jit revert: chore(mge/misc): api converage 3 years ago
module docs(imperative): adjust bn doc 3 years ago
optimizer fix(imperative): fix inplace operation of optim 3 years ago
quantization revert: chore(mge/misc): api converage 3 years ago
random chore(license): remove all license in file header 3 years ago
tools feat(imperative): load_nerwork_and_run enable weight preprocess 3 years ago
traced_module fix(mge/traced_module): fix visualization bug by adding Node to non-const leaf 3 years ago
utils fix(mge/utils): fix using wrong function in register_hook_module 3 years ago
__init__.py feat(whl): check broken env before import 2/2 3 years ago
_env_initlization.py feat(whl): check broken env before import 2/2 3 years ago
_multistream.py perf(mge): improve cross stream memory borrowing 3 years ago
config.py fix(mge): control compute_mode by context 4 years ago
conftest.py ci(pytest): fixture doctest namespace in pytest config 4 years ago
device.py fix(mge/device): add missed API to __all__ scope 3 years ago
logger.py revert: chore(mge/misc): api converage 3 years ago
serialization.py revert: chore(mge/misc): api converage 3 years ago
tensor.py docs(mge/tensor): add dtype and device doc ref for typehint result 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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