Is there anyone who can reproduce the result ?

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Hi ,everyone

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I run the command in Readme.md.
python augment.py --name cifar10 --dataset cifar10 --genotype "Genotype( normal=[[('sep_conv_3x3', 0), ('dil_conv_5x5', 1)], [('skip_connect', 0), ('dil_conv_3x3', 2)], [('sep_conv_3x3', 1), ('skip_connect', 0)], [('sep_conv_3x3', 1), ('skip_connect', 0)]], normal_concat=range(2, 6), reduce=[[('max_pool_3x3', 0), ('max_pool_3x3', 1)], [('max_pool_3x3', 0), ('skip_connect', 2)], [('skip_connect', 3), ('max_pool_3x3', 0)], [('skip_connect', 2), ('max_pool_3x3', 0)]], reduce_concat=range(2, 6))"

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My enviroment is pytorch 1.2 and device is a single RTX2080Ti , but 600 epoch seems will cost at about 20 hours , it's a little longer to me , is there any method that we can accelerate convergence , such as change the SGD optimizer to Adam ?