使用pytorch做分布式訓練時，遇到錯誤：

Error: mkl-service + Intel(R) MKL: MKL_THREADING_LAYER=INTEL is incompatible with libgomp.so.1 library.Try to import numpy first or set the threading layer accordingly. Set MKL_SERVICE_FORCE_INTEL to force it.

解決方案1：在環(huán)境變量增加設置

export MKL_SERVICE_FORCE_INTEL=1

解決方案2：在環(huán)境變量增加設置

export MKL_THREADING_LAYER=GNU

問題分析：

Grepping conda manifests, libgomp is pulled in by?libgcc-ng, which is in turn pulled in by, uh, pretty much everything. So the culprit is more likely to be whoever's setting?MKL_THREADING_LAYER=INTEL. As far as that goes, well, it's weird.

import osdef print_layer(prefix):print(f'{prefix}: {os.environ.get("MKL_THREADING_LAYER")}')if __name__ == '__main__':print_layer('Pre-import')import numpy as npfrom torch import multiprocessing as mpprint_layer('Post-import')mp.set_start_method('spawn')p = mp.Process(target=print_layer, args=('Child',))p.start()p.join()

See, if torch is imported before numpy then the child process here gets a GNU threading layer (even though the parent doesn't have the variable defined).

Pre-import: None Post-import: None Child: GNU

But if the imports are swapped so numpy is imported before torch, the child process gets an INTEL threading layer

Pre-import: None Post-import: None Child: INTEL

So I suspect numpy - or ones of its imports - is messing with the?env?parameter of?Popen, but half an hour's search and I can't figure out how.

Ref:?https://github.com/pytorch/pytorch/issues/37377

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