I have placed this file on my Desktop: Figure 4: The DL4CV Ubuntu VM.ova file. Once you have downloaded the VirtualMachine.zip file, unarchive it and you’ll find a file named DL4CV Ubuntu VM.ova.
#Pycharm ubuntu download
The file is approximately 4GB so depending on your internet connection this download make take some time to complete. Now that VirtualBox is installed you need to download the pre-configured Ubuntu virtual machine associated with your purchase of Deep Learning for Computer Vision with Python: Figure 3: Downloading the pre-configured Ubuntu deep learning virtual machine.
#Pycharm ubuntu install
To install VirtualBox, first visit the downloads page and then select the appropriate binaries for your operating system: Figure 1: VirtualBox downloads.įrom there install the software on your system following the provided instructions - I’ll be using macOS in this example, but again, these instructions will also work on Linux and Windows as well: Figure 2: Installing VirtualBox on macOS Step #2: Download your deep learning virtual machine The virtual machine that will be imported into VirtualBox is the guest machine. We call the physical hardware VirtualBox is running on your host machine. VirtualBox will run on macOS, Linux, and Windows. The first step is to download VirtualBox, a free open source platform for managing virtual machines.
![pycharm ubuntu pycharm ubuntu](https://linuxize.com/post/how-to-install-pycharm-on-ubuntu-18-04/ubuntu-pycharm-settings_hu082684a3077a84c241c47c2afa6338b4_78331_768x0_resize_q75_lanczos.jpg)
Your deep learning + Python virtual machine
![pycharm ubuntu pycharm ubuntu](https://tecadmin.net/wp-content/uploads/2017/03/pycharm-community-launch.png)
#Pycharm ubuntu how to
![pycharm ubuntu pycharm ubuntu](https://1.bp.blogspot.com/-3a7P6Qdq6Zw/WU0j-fDwWSI/AAAAAAAAIcI/tS6khVrS8HkvoNzEWvLVf036o61-7IbEwCLcBGAs/s1920/pycharm-1.jpg)
Of course, configuring your own deep learning + Python + Linux development environment can be quite the tedious task, especially if you are new to Linux, a beginner at working the command line/terminal, or a novice when compiling and installing packages by hand. When it comes to working with deep learning + Python I highly recommend that you use a Linux environment.ĭeep learning tools can be more easily configured and installed on Linux, allowing you to develop and run neural networks quickly.