I import tensorflow (version 1.13.1) and need ConfigProto:
import tensorflow as tf
config = tf.ConfigProto(intra_op_parallelism_threads=8,
inter_op_parallelism_threads=8,
allow_soft_placement=True,device_count = {'CPU' : 1, 'GPU' : 1})
I get this error:
AttributeError: module 'tensorflow' has no attribute 'ConfigProto'
How do I resolve this?
6 Answers
ConfigProto disappeared in tf 2.0, so an elegant solution is:
import tensorflow as tf
and then replace:
tf.ConfigProto by tf.compat.v1.ConfigProto
In fact, the compatibility built in 2.0 to get tf 1.XX: tf.compat.v1 is really helpful.
Useful link: Migrate your tensorflow 1. code to tensorflow 2.:
I had similar issues, when upgraded to Python 3.7 & Tensorflow 2.0.0 (from Tensorflow 1.2.0)
This is an easy one and works!
If you don't want to touch your code, just add these 2 lines in the main.py file w/ Tensorflow code:
import tensorflow.compat.v1 as tf
tf.disable_v2_behavior()
And that's it!!
NOW Everything should run seamlessly :)
Just an addition to others looking for an answer for Tensorflow v2
As the others have mentioned, you can use the back-compatability to v1. But Tensorflow v2 does actually come with its own implementation of this. It is just a hidden experimental feature.
This is how to allow the GPU to grow in memory in Tensorflow v2:
# Allow memory growth for the GPU
physical_devices = tf.config.experimental.list_physical_devices('GPU')
tf.config.experimental.set_memory_growth(physical_devices[0], True)
More info found @Tensorflow
If using tensorflow version > 2.0:
config = tf.compat.v1.ConfigProto()
config.gpu_options.allow_growth=True
sess = tf.compat.v1.Session(config=config)
I was with a similar error, but i had tensorflow 1.14, ubuntu 18.04 and GTX 1050ti. So a installed properly conda (lastest version - 5.1) even with this the error persisted, so a upgraded tensorflow/tensorflow-gpu to -version tensorflow==2.0.0-beta0 and worked for me.
Info:
RTX 2080
ubuntu 16.04
cuda 10.0
cuDNN v7.4.1.5
Python V 3.5
pip list:
tensorflow (1.13.1)
tensorflow-gpu (1.13.1)
tf-nightly-gpu (1.14.1.dev20190509)
Code:
import tensorflow as tf
from tensorflow import keras
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
sess = tf.Session(config=config)
output:
Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 7439 MB memory) -> physical GPU (device: 0, name: GeForce RTX 2080, pci bus id: 0000:02:00.0, compute capability: 7.5)
That works for me !
