Difference between revisions of "ML computers tensorflow"
Jump to navigation
Jump to search
| Line 8: | Line 8: | ||
from tensorflow.python.client import device_lib | from tensorflow.python.client import device_lib | ||
print(device_lib.list_local_devices()) | print(device_lib.list_local_devices()) | ||
| − | + | ||
*The GPU should appear as a device. | *The GPU should appear as a device. | ||
Revision as of 22:37, 27 November 2019
General info
- Install the package tensorflow-gpu for GPU acceleration.
- For tensorflow-gpu <= 1.12 you need CUDA9.0 TF1.13,CUDA10.0)
- For tensorflow-gpu >= 1.13 you need CUDA10.0
- By default tensorflow installs the cpu version. For being sure the GPU is being used, run these lines with python3:
from tensorflow.python.client import device_lib print(device_lib.list_local_devices())
- The GPU should appear as a device.
Setting up tensorflow-gpu on Ubuntu 16.04 for python3
- First we install pip3:
Link to wiki python
- Then we do:
pip3 install tensorflow-gpu==1.2.0
- If conflict with setuptools version
- solution:
pip3 install --upgrade setuptools==41.0.0 sudo pip3 install --upgrade tensorflow-gpu==1.2.0
- How to check the version installed:
pip3 show tensorflow-gpu
- Problem: After installing tensorflow jupyter notebook was not working.
- Solution:
pip3 uninstall pyzmq pip3 install pyzmq
- Problem: Error -> ImportError: No module named model_utils
- Solution:
pip3 list
- and installing the same version of tensorflow-estimator than of tensorflow-gpu:
pip3 uninstall tensorflow-estimator pip3 install tensorflow-estimator==1.14.0