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The GPU version of tensorflow encountered blas GEMM launch failed error during model training, or keras encountered the same error (keras generally takes tensorflow as the backend, if the GPU version of tensorflow is installed, GPU will be preferred when using keras)

InternalError (see above for traceback): Blas GEMM launch failed : a.shape=(300, 1), b.shape=(1, 10), m=300, n=10, k=1

This is when calling GPU, the video memory allocation encountered a problem. The way to compare security is to allocate memory space for tensorflow or keras before model training. Tensorflow creates a session with the following statement

gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.333)  
sess = tf.Session(config=tf.ConfigProto(gpu_options=gpu_options))

The parameters of keras are set when keras is introduced

import tensorflow as tf
from keras.backend.tensorflow_backend import set_session
config = tf.ConfigProto()
config.gpu_options.allocator_type = 'BFC' #A "Best-fit with coalescing" algorithm, simplified from a version of dlmalloc.
config.gpu_options.per_process_gpu_memory_fraction = 0.3
config.gpu_options.allow_growth = True
set_session(tf.Session(config=config))

If you use IPython notebook, GPU sync failed may appear after completing the above settings, and it should be OK to restart it

From: https://blog.csdn.net/Leo_ Xu06/article/details/82023330

I tried the useful of tensorflow, record it