The best part is that Windows allows third-party apps and equipment to interact freely.When it comes to working with deep learning + Python I highly recommend that you use a Linux environment.Difference Between Computer Science and Software Engineering: This tutorial cover definition, Differences, Challenges, Best Practices of Computer Science and Software EngineeringDescription: Anaconda offers its data science and machine learning capabilities via a number of different product editions. Windows: When it comes to the hardware options of the Windows operating system then there is a long line for it. Mac operating system is also not compatible with other devices too which closes many horizons for its usability.
Windows Vs Hine Learning Install VirtualBox ForHow to access the pre-installed deep learning libraries on the virtual machine.Let’s go ahead and get started. How to import the pre-configured Ubuntu virtual machine for deep learning. How to download and install VirtualBox for managing, creating, and importing virtual machines. After you purchase your copy you’ll be able to download the virtual machine and get started with deep learning immediately.In the remainder of this tutorial I’ll show you: The tool enables you to perform data science and machine learning on Linux, Windows, and Mac OS.Deep learning tools can be more easily configured and installed on Linux, allowing you to develop and run neural networks quickly.Amazon AWS-Certified-Machine-Learning-Specialty Value Pack (Frequently Bought Together) Online Test Engine supports Windows / Mac / Android / iOS, etc..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.In order to help you jump start your deep learning + Python education, I have created an Ubuntu virtual machine with all necessary deep learning libraries you need to successful (including Keras, TensorFlow, scikit-learn, scikit-image, OpenCV, and others) pre-configured and pre-installed.This virtual machine is part of all three bundles of my book, Deep Learning for Computer Vision with Python.Step #3: Import the deep learning virtual machine into VirtualBoxGo ahead and open up the VirtualBox manager.From there select File => Import Appliance. I have placed this file on my Desktop: Figure 4: The DL4CV Ubuntu VM.ova file.This is the actual file that you will be importing into the VirtualBox manager. The virtual machine that will be imported into VirtualBox is the guest machine.To install VirtualBox, first visit the downloads page and then select the appropriate binaries for your operating system: Figure 1: VirtualBox downloads.From 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 machineNow 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.The file is approximately 4GB so depending on your internet connection this download make take some time to complete.Once you have downloaded the VirtualMachine.zip file, unarchive it and you’ll find a file named DL4CV Ubuntu VM.ova. Access the Python development environment inside the deep learning virtual machine.The first step is to download VirtualBox, a free open source platform for managing virtual machines.VirtualBox will run on macOS, Linux, and Windows.We call the physical hardware VirtualBox is running on your host machine. Download and import your pre-configured Ubuntu deep learning virtual machine. In the following sections I’ll show you how easy it is to import your Ubuntu deep learning virtual machine.This tutorial is broken down into three parts to make it easy to digest and understand:Copy and paste from the virtual machine to your host (and vice versa) Step #6: (Optional) Install Guest Additions on virtual machineAn optional step you may wish to perform is installing the VirtualBox Guest Additions on your machine.The Guest Additions package allow you to: Step #5: Access the deep learning Python virtual environmentThe next step after logging into the VM is to launch a terminal: Figure 10: Launching a terminal window.From there, execute workon dl4cv to access the Python + deep learning development environment: Figure 11: Accessing the dl4cv deep learning + Python development environment.Notice that my prompt now has the text (dl4cv) preceding it, implying that I am inside the dl4cv Python virtual environment.You can run pip freeze to see all the Python libraries installed.I have included a screenshot below demonstrating how to import Keras, TensorFlow, and OpenCV from a Python shell: Figure 12: Importing Keras, TensorFlow, and OpenCV into our deep learning Python virtual environment. Step #4: Boot the deep learning virtual machineNow that the deep learning virtual machine has been imported we need to boot it.From the VirtualBox manager select the “DL4CV Ubuntu VM” on the left pane of the window and then click “Start”: Figure 8: Booting the pre-configured Ubuntu deep learning virtual machine.Once the virtual machine has booted you can login using the following credentials:Figure 9: Logging into the deep learning virtual machine. Use Sublime Text as a lightweight code editor. Tips for using the deep learning virtual machineWhen using the Ubuntu VirtualBox virtual machine for deep learning I recommend the following: I would recommend forwarding the receipt email to yourself so you can login to your inbox via Firefox and then download the code + datasets.You may also use your favorite SFTP/FTP client to transfer the code from your host machine to the virtual machine.Of course, you can always manually write the code inside Ubuntu virtual machine using the built-in text editor as you follow along with the book. Zip archives from the “Your Purchase” page after buying your copy of Deep Learning for Computer Vision with Python. Executing code from Deep Learning for Computer Vision with Python on your virtual machineThere are multiple methods to access the Deep Learning for Computer Vision with Python source code + datasets from your virtual machine.By far the easiest method is to simply open Firefox and download the. From the VirtualBox menu at the top of your screen. Form there you’ll have access to deep learning/computer vision libraries such as TensorFlow, Keras, OpenCV, scikit-learn, scikit-image, etc. How do I run Python scripts that access deep learning libraries?The Deep Learning for Computer Vision with Python virtual machine uses Python virtual environments to help organize Python modules and keep them separate from the system install of Python.To access the virtual environment simply execute workon dl4cv from the shell. From there, click the “Text Entry” button at the bottom of the panel: Figure 13: Selecting “Text Entry” in the deep learning virtual image.Lastly, click the “+” icon, select your keyboard layout, and click “Add”: Figure 14: Updating the keyboard layout in the Ubuntu virtual machine.You may need to reboot your system for these changes to take effect. If you are using a keyboard in a different language than English please add the keyboard layout for your specific language.To accomplish this, first open the system settings application and select “Keyboard”. The username and password is not working for me.The keyboard layout chosen for the Ubuntu virtual machine is the standard English layout. What is the username and password for the Ubuntu deep learning virtual machine?The username is pyimagesearch and the password is deeplearning. Kontakt player osx 64 bitIf you’re on Windows you might also need to disable Hyper-V mode. What do I do?If you are getting an error message similar to the following: Figure 13: Resolving “VT-x/AMD-V hardware acceleration is not available for your system” errors.Then you likely need to check your BIOS and ensure virtualization is enabled. I am receiving an error message related to “VT-x/AMD-V hardware acceleration is not available for your system”. On your physical computer cannot be accessed by the virtual machine.If you would like to use your GPU for deep learning I would suggest configuring your native development environment. Peripherals such as your GPU, USB ports, etc.
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