5/5/2023 0 Comments Pycharm docker machine mac![]() ![]() ![]() But as I said, it is not straightforward. These containers are like lightweight virtual machines, as they use the operating system of the host machine to run, and don’t need another full operating system inside them. This way you don’t have to mess up your host system. The right wayĪs I have learned, there is a solution for this problem: Docker!ĭocker allows you to run your program within a container that has the environment and dependencies you need. ![]() So, I do not recommend installing the dependencies that Ubuntu 18.04 needs to run TensorFlow on your system. It is possible to install the previous version on this system, but doing this is way more complex than you would think and, in my case, after one full day of trying, the configuration that allowed me to use the GPU crashed my system when I restarted the computer. The main problem is that, as of now, TensorFlow needs Nvidia CUDA 9.0 Toolkit to run, but I am using Linux Mint 19 which, being based on Ubuntu 18.04, installs CUDA 9.1. After all that effort, I want to share here my errors and the steps that eventually lead me to success. Eventually, I needed to go through many tutorials and Stack Overflow questions to finally get my model running on GPU. Well… it turns out that running TensorFlow with your local GPU is not really straightforward. So, I turn quickly to try and run my old models on the GPU and see how fast it is. I have acquired a laptop with a Nvidia GPU to help me in my learning of AI and Neural Nets. ![]()
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