I would suggest you to install Miniconda if you do not have conda already.
I would suggest you to use conda (Ananconda/Miniconda) to create a separate environment and install tensorflow-gpu, cudnn and cudatoolkit.Miniconda has a much smaller footprint than Anaconda. will work until it reaches this line: model.fit (x=train_images, y=train_labels, epochs=10)Solution. At this point the GPU will show up: print (tf.config.list_physical_devices ('GPU')) Now running the entire notebook, including compiling the training images, preprocessing, etc. The cuda and cudnn dlls have been copied into the bundle folder by pyinstaller. Using tensorflow-gpu it works as script but not with pyinstaller. Using tensorflow it works nicely both as script and with pyinstaller. They start …Trying to distribute a program that relies on tensorflow. See NVidia's support matrix.-jupyter tags include Jupyter and some TensorFlow tutorial notebooks. Sysadmins already installed latest Cuda drivers, but unfortunately that's not enough to use GPUs in Tensorflow, as each version of TF can be very picky when it comes to the particular set of Cuda Toolkit + CuDNN versions.NOTE: GPU versions of TensorFlow 1.13 and above (this includes the latest-tags) require an NVidia driver that supports CUDA 10. I have access to a virtual machine with Ubuntu 20.04 setup and GPUs.
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