structure - easy to achieve the result without any frills.  It supports multiple platforms and backends.  It is user friendly framework which runs on both CPU and GPU.  Highly scalability of computation operations. By default, keras runs on top of TensorFlow backend. If you want, you can switch to other backends like Theano or CNTK. Defualt backend configuration is defined inside your root directory under

is using close to all available device memory due to an unexpected memory thrashing when `torch.backends.cudnn.benchmark = True` is used. The performance can be restored by disabling `cudnn.benchmark` autotuning could cause a long startup time or a hang. In these cases, disbale autotuning using `torch.backends.cudnn.benchmark = False`. ‣ GNMTv2 inference performance regression of up to 50% due to an MKL cases this might be manifested as NaNs in the output and we recommend to disable cuDNN via torch.backends.cudnn.enabled = False. ‣ Channels-last memory format is experimental in the 20.07 container. Potential

autopep8 -i --select 例如：autopep8 -i --select E128 tests/keras/backend/test_backends.py 8. 提交时，请使用适当的描述性提交消息。 9. 更新文档。如果引入新功能，请确保包含演示新功能用法的代码片段。 10. 提交你的 PR。如果你的更改已在之前的讨论中获得批准，并且你有完整（并通过）的单元