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Preparing the PC for TensorflowSetting up a virtual Python environment | ||||||||
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TensorFlow relies on the cuda toolkit and on the NVidia display driver. Even after installing the newest version of cuda, TensorFlow did not see the GPU. An intensive search with Google finally showed that (at the moment of writing this page) TensorFlow version 2 only works with cuda 11.8 and cuDNN 8.6. | ||||||||
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< < | https://medium.com/@juancrrn/installing-cuda-and-cudnn-in-ubuntu-20-04-for-deep-learning-dad8841714d6 describes how to install cuda-11.8 and cudnn-8.6 and how to test the installation. | |||||||
> > | https://medium.com/@juancrrn/installing-cuda-and-cudnn-in-ubuntu-20-04-for-deep-learning-dad8841714d6 and https://gist.github.com/MihailCosmin/affa6b1b71b43787e9228c25fe15aeba decribe how to install cuda-11.8 and cudnn-8.6 and how to test the installation. | |||||||
The script testGPU.py below shows the number of GPUs that TensorFlow has fouind:
#!/home/uli/.virtualenvs/AI/bin/python import tensorflow as tf print("Num GPUs Available: ", len(tf.config.list_physical_devices('GPU'))) |
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< < | TensorFlow relies on the cuda toolkit and on the NVidia display driver. I had difficulties to install these with the .deb files but succeeded to do so using the .run file. | |||||||
> > | When looking at the Software & Updates program we see that the nvidia-driver-535 is used to control the NVidia GeForce GTX 950 module. Make sure that the nouveau driver is disabled! | |||||||
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< < | To run the installer script I had to boot into single user mode, avoiding to start the desktop system. This can be done typing "e" at the boot screen and adding "single" to the boot command. | |||||||
> > | TensorFlow relies on the cuda toolkit and on the NVidia display driver. Even after installing the newest version of cuda, TensorFlow did not see the GPU. An intensive search with Google finally showed that (at the moment of writing this page) TensorFlow version 2 only works with cuda 11.8 and cuDNN 8.6. | |||||||
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< < | In the "Additional Drivers" tab of the Software & Updates program you see that we are using the manually installed NVidia driver: | |||||||
> > | https://medium.com/@juancrrn/installing-cuda-and-cudnn-in-ubuntu-20-04-for-deep-learning-dad8841714d6 describes how to install cuda-11.8 and cudnn-8.6 and how to test the installation. | |||||||
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> > | The script testGPU.py below shows the number of GPUs that TensorFlow has fouind:
#!/home/uli/.virtualenvs/AI/bin/python import tensorflow as tf print("Num GPUs Available: ", len(tf.config.list_physical_devices('GPU'))) | |||||||
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< < | When trying a very simple TensorFlow program:
#!/usr/bin/python3 import tensorflow as tf import numpy as np from tensorflow import keras model = tf.keras.Sequential([keras.layers.Dense(units=1, input_shape=[1])]) | |||||||
> > | After installation of cuda-11.8 and cuDNN-8.6 this is what I get, when I run the script: | |||||||
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< < | I get: | |||||||
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> > | To get rid of these warnings I first had to install tensorrt from the NVidia WEB site. To irradicate the NUMA mode warning I followed: https://askubuntu.com/questions/1379119/how-to-set-the-numa-node-for-an-nvidia-gpu-persistently When running the testGPU program now, things seem to be ok and I should be able to use my NVidia GPU. | |||||||
-- Uli Raich - 2022-01-31
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< < | When trying a very simple TensorFlow program: | |||||||
> > | When trying a very simple TensorFlow program: | |||||||
#!/usr/bin/python3 import tensorflow as tf import numpy as np from tensorflow import keras model = tf.keras.Sequential([keras.layers.Dense(units=1, input_shape=[1])]) I get: |
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Preparing the PC for TensorflowSetting up a virtual Python environmentThe procedure is described in https://www.freecodecamp.org/news/virtualenv-with-virtualenvwrapper-on-ubuntu-18-04. Once his is done you can create a new virtual environment with: mkvirtualenv name_of_env (e.g. mkvirtualenv AI) switch to a virtual environment with the "workon" command: e.g. workon AISetting up the GPUWhen trying my first simple TensorFlow program I saw this warning: My PC, being a "gaming machine", uses an nVIDIA GeForce GTX 950M graphics card whose GPU (Graphics Processing Unit) can be used by TensorFlow to speed up number crunching. TensorFlow relies on the cuda toolkit and on the NVidia display driver. I had difficulties to install these with the .deb files but succeeded to do so using the .run file. To run the installer script I had to boot into single user mode, avoiding to start the desktop system. This can be done typing "e" at the boot screen and adding "single" to the boot command. In the "Additional Drivers" tab of the Software & Updates program you see that we are using the manually installed NVidia driver: When trying a very simple TensorFlow program: #!/usr/bin/python3import tensorflow as tf import numpy as np from tensorflow import keras model = tf.keras.Sequential([keras.layers.Dense(units=1, input_shape=[1])]) I get: -- Uli Raich - 2022-01-31 Comments
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