限制 Tensorflow CPU 和内存使用

Question

I've seen several questions about GPU Memory with Tensorflow but I've installed it on a Pine64 with no GPU support.

That means I'm running it with very limited resources (CPU and RAM only) and Tensorflow seems to want it all, completely freezing my machine.

Is there a way to limit the amount of processing power and memory allocated to Tensorflow? Something similar to bazel's own --local_resources flag?

Answer 1

This will create a session that runs one op at a time, and only one thread per op

sess = tf.Session(config=
    tf.ConfigProto(inter_op_parallelism_threads=1,
                   intra_op_parallelism_threads=1))

Not sure about limiting memory, it seems to be allocated on demand, I've had TensorFlow freeze my machine when my network wanted 100GB of RAM, so my solution was to make networks that need less RAM

Answer 2

For TensorFlow 2.x this has been answered in the following thread :

In Tensorflow 2.x, there is no session anymore. Directly use the config API to set the parallelism at the start of the program.

import tensorflow as tf

tf.config.threading.set_intra_op_parallelism_threads(2)
tf.config.threading.set_inter_op_parallelism_threads(2)
with tf.device('/CPU:0'):
    model = tf.keras.models.Sequential([...

https://www.tensorflow.org/api_docs/python/tf/config/threading