Tensorflow 2.1+ What’s going on? With the new Tensorflow 2.1 release, the default tensorflow pip package contains both CPU and GPU versions of TF. In previous TF versions, not finding the CUDA libraries would emit an error and raise an exception, while now the library dynamically searches for the correct CUDA version and, if it … Read more
UPDATE 8/1730/2020: TF 2.3 has finally done it: all cases run as fast, or notably faster, than any previous version. Further, my previous update was unfair to TF; my GPU was to blame, has been overheating lately. If you see a rising stem plot of iteration times, it’s a reliable symptom. Lastly, see a dev’s … Read more
According to TF 1:1 Symbols Map, in TF 2.0 you should use tf.compat.v1.Session() instead of tf.Session() https://docs.google.com/spreadsheets/d/1FLFJLzg7WNP6JHODX5q8BDgptKafq_slHpnHVbJIteQ/edit#gid=0 To get TF 1.x like behaviour in TF 2.0 one can run import tensorflow.compat.v1 as tf tf.disable_v2_behavior() but then one cannot benefit of many improvements made in TF 2.0. For more details please refer to the migration guide … Read more
You can set the fraction of GPU memory to be allocated when you construct a tf.Session by passing a tf.GPUOptions as part of the optional config argument: # Assume that you have 12GB of GPU memory and want to allocate ~4GB: gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.333) sess = tf.Session(config=tf.ConfigProto(gpu_options=gpu_options)) The per_process_gpu_memory_fraction acts as a hard upper bound … Read more