import tensorflow.compat.v1 as tf config = tf.ConfigProto() config.gpu_options.allow_growth = True with tf.Session(config=config) as sess: # do some Tensorflow operations here
import tensorflow.compat.v1 as tf config = tf.ConfigProto() config.inter_op_parallelism_threads = 8 config.intra_op_parallelism_threads = 8 with tf.Session(config=config) as sess: # do some Tensorflow operations here
import tensorflow.compat.v1 as tf config = tf.ConfigProto() config.log_device_placement = True config.save_summary_steps = 100 config.save_checkpoints_steps = 500 with tf.Session(config=config) as sess: # do some Tensorflow operations hereIn this example, we create a `ConfigProto` object and set the `log_device_placement`, `save_summary_steps`, and `save_checkpoints_steps` properties to enable checkpointing of our Tensorflow models. This will save the model every 500 training steps and log the device placement for each operation. Overall, `tensorflow.compat.v1.ConfigProto` is a very useful class for fine-tuning the behavior of Tensorflow in Python code. It provides a lot of flexibility and configurability, and is an essential part of working with Tensorflow models in Python.