training on gpu about main_loop_tf HOT 5 CLOSED

I got the memory issue OOM when allocating tensor with shape[1,60,60,1024] [[Node: gpu0_train/my_model/resnet_v2_101/block3/unit_8/bottleneck_ v2/preact/moments/SquaredDifference = SquaredDifference[T=DT_FLOAT, _device="/job:localhost/replica:0/task:0/cpu:0"](gpu0_ train/my_model/resnet_v2_101/block3/unit_7/bottleneck_v2/add, gpu0_train/my_model/resnet_v2_101/block3/unit_8/bottleneck_ v2/preact/moments/StopGradient)]] Which is caused by training on cpu. I changed the devices to gpu but it is still on cpu. Also I changed the with tf.device('/cpu:0') line to with tf.device('/gpu:0'). But nothing changed. How can I make training on one gpu device. Thanks — You are receiving this because you are subscribed to this thread. Reply to this email directly, view it on GitHub <#18>, or mute the thread <https://github.com/notifications/unsubscribe-auth/AEbKHRWQtrsSlgp0rqvX2Aenehs2wexAks5sUDflgaJpZM4OqyJL> .

Thanks for your reply. My GPU is TITAN X with 12 GB free memory. So what does this line says _device="/job:localhost/replica:0/task:0/*cpu*:0" — You are receiving this because you commented. Reply to this email directly, view it on GitHub <#18 (comment)>, or mute the thread <https://github.com/notifications/unsubscribe-auth/AEbKHUngJ7hclGGLgioAB8a1EVGgiFm0ks5sUGALgaJpZM4OqyJL> .