![]() They want you to install TensorFlow using native pip through Python 3.5.x. If you would have read through the entire document, it would have stated that the Anaconda installation is community supported, not officially supported. That is why you better search for an Anaconda guide, for example. Therefore it is recommended to keep to a full service approach which Anaconda offers, where all dependencies are kept right, even if you enter conda install -all. If all of the dependencies are so important and so easily wrong when updated separately, like you could do with pip, any install that you do by yourself using pip might crash your sensitive tensorflow install. For tensorflow, you have to install version CUDA Toolkit 10.1 although 11.0 is already available, so that your whole card must run on a lower version than possible only to support Tensorflow - even if some games would like to have version 11.0. It is still the approach to understand the recommended different envs). Pytorch uses a cuda that is installed inside Pytorch. Therefore you should consider a separate environment for both Tensorflow and Pytorch, since any update of the conda cudatoolkit to version 11.0 could harm the dependency condition of Pytorch (Though this is not completely right. And tensorflow needs tensorflow-gpu to reach the standalone cuda install. ![]() We see that at the moment, Pytorch supports version 10.2, Tensorflow supports 10.1, and it is not just the version that differs: mind that "CUDA Toolkit" (standalone) and cudatoolkit (conda binary install) are different! One is a a standalone / executable install, the other is a binary install. I am not a professional, I have little knowledge of the seemingly chaotic world of different install methods. Libraries, just create a different environment and play around without If we want to use a different Python version or package This can save time and energyĪnaconda can be used across different platforms, Windows, macOS, and Has a lot of advantages, such as independently installing/updating py2_olvįor a Python developer or a data science researcher, using Anaconda On your follow-up, If you have multiple environments, you can switch between them on Pycharm by changing the interpreter. One of the nice things with environments is you can have one with Python=2 (latest python 2), one with Python=3, another with Python=2.7 etc conda create -name py Python=2) you probably only have root. You can see all your environments with: conda info -envs But unless you create some environment (using e.g. If you see no prompt, it is the default, root environment. You should be able to access both (it is also possible just installing keras would install tensorflow, if its a dependancy) Install tensorflow and keras on the same one and only root environment you have. You run all these on command prompt.īottom line is, unless you have multiple environments (I highly recommend it so you can try different things) I cannot see you using activate. ![]() 'Activate' changes from one environment to the other, so unless you have multiple environments you shouldn't need it. I assume compiling tensorflow might not be completely trivial. Conda stuff is kind of pre-compiled to work with your machine/anaconda environment, while pip stuff is usually compiled on the spot. Using conda or pip installs stuff at your current installation. ![]() You can create as many environments as you want, think of each as a separate installation of python. Then you have a default environment, called root. I assume you installed python using anaconda. ![]()
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