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Otherwise you need to write a simple script and run them. If you create a folder in your home you will be able to use the commands from the official documentation: source ~/tensorflow/bin/activate
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Sudo apt-get install build-essential libssl-dev libffi-dev python-devĬreate a folder for your evnironments. Prior creating the environment you need to install several libraries: sudo apt-get install -y python3-pip For example one requires numpy 2.0 while other project requires different one. you can have less problems related to module and required libraries between different projects.if something goes wrong you can easily fix or build new environment.you can have several different versions of tensorflow.I prefer to create virtual environment for tensorflow because:
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To uninstall all graphic drivers related to nvidia do: sudo apt-get remove -purge nvidia* If this is not the case you can reinstall the video card driver. | Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. | GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. In this case you need to check if the GPU drivers are properly installed and working by: +-+ ImportError: libcublas.so.9.0: cannot open shared object file: No such file or directory You can use some of the following commands: lspci | grep -i nvidiaĪnother error which could raised after fresh test of tensorflow with GPU support is: One solution is to set your GPU as CUDA visible device by: CUDA_VISIBLE_DEVICES=0 InvalidArgumentError (see above for traceback): Cannot assign a device to node 'MatMul_1': Could not satisfy explicit device specification '/device:GPU:0' because no devices matching that specification are registered in this process available devices: /job:localhost/replica:0/task:0/cpu:0 You may need to check your GPU information in order to avoid error:
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You may need to install Java: sudo apt-get update Sudo apt-key add /var/cuda-repo-9-1-local/7fa2af80.pubĪdd this to your path by adding line export PATH=/usr/local/cuda-9.0/bin$/usr/local/cuda/extras/CUPTI/lib64 Sudo apt-key add /var/cuda-repo-9-0-local/7fa2af80.pubįor 9.1 sudo dpkg -i cuda-repo-ubuntu-local_9.1.85-1_b Sudo dpkg -i cuda-repo-ubuntu-local_9.0.176-1_b
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Older version of CUDA (like 7.0 and 8.0) can be found here:ĬUDA Toolkit Archive Install Cuda Toolkit Update: you can install Cuda also by: sudo apt install cuda-9-0
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knowledge how to use Linux terminal commands.In order to follow this article you need: But from practical point of view - one and the same NN with the same training set takes 48 hours on 24 CPUs and 4 on a single 1080(used in dual mode - display and compute). Of course this measurement is pretty lame and doesn't take into account many factors. My tests are showing that a single NVidia 1080 is 10 times faster that 24 CPUs used from Google cloud platform. Installing tensorflow on Ubuntu google cloud platform
For pip install of Tensorflow for CPU you can check here: The installation of tensorflow is by Virtualenv. GPU in the example is GTX 1080 and Ubuntu 16(updated for Linux MInt 19). How to install Tensorflow with NVIDIA GPU - using the GPU for computing and display.