How to Install Ubuntu VM on Mac and Run TensorFlow with GPU Support

Are you looking to install Ubuntu VM on your Mac and run TensorFlow with GPU support? Look no further! In this article, we'll walk you through the steps to get started.

Firstly, ensure that you have a Jupyter notebook installed. You can start the server by following the commands provided. Once done, you'll have access to the GPU-enabled Jupyter notebook on your VM.

Alternatively, you can use the Jupyter Docker image that has TensorFlow installed. This route may work similarly to the one we're about to explore.

From the TensorFlow website, follow the documentation to get started with the latest GPU-Jupyter flavor. There are two official ways to run TensorFlow and Jupyter: one from the TensorFlow website and another from Jupyter.

Next, focus on installing the driver for your Nvidia GPU card. You can do this by following the instructions provided on the official Nvidia website. Make sure you select the platform and card series that matches your actual card.

After installing the driver, you should be able to assign the GPU to your VM using Proxmox GPU passthrough. This is a crucial step that requires careful attention, as misconfiguration can lead to server damage or even data loss.

To get started with Proxmox, follow the official documentation for GPU passthrough. Be mindful of the power cable you purchase for your GPU, as there are different types designed for specific use cases.

Once installed, you'll be able to assign the GPU to your VM and start using TensorFlow for your machine learning projects. In this article, we'll also cover some best practices for managing temperature and fan speed on your server.

In conclusion, installing Ubuntu VM on Mac and running TensorFlow with GPU support requires careful planning and attention to detail. By following these steps, you can get started with your machine learning journey and enjoy the benefits of on-premise computing.

Ubuntu vm on mac