Using CPU vs GPU Version of TensorFlow

Head to the official TensorFlow installation instructions, and follow the Anaconda Installation instructions. The main difference between this, and what we did in Lesson 1, is that you need the GPU enabled version of TensorFlow for your system. However, before you install TensorFlow into this environment, you need to setup your computer to be GPU enabled with CUDA and CuDNN. The official TensorFlow documentation outline this step by step, but I recommended this tutorial if you are trying to setup a recent Ubuntu install. The main reason is that, at the time of writing (July 2016), CUDA has not yet been built for the most recent Ubuntu version, which means the process is a lot more manual.



Install TensorFlow on Windows

With the release of TensorFlow r0.12, we now provide a native TensorFlow package for Windows 7, 10, and Server 2016. This release enables you to speed up your TensorFlow training with any GPU that runs CUDA 8.

We have published the latest release as a pip package in PyPI, so now you can install TensorFlow with a single command:

     C:\> pip install tensorflow

And for GPU support:

     C:\> pip install tensorflow-gpu

More details about Windows support and all of the other new features in r0.12 are included in the release notes.


Pip installation on Windows

TensorFlow supports only 64-bit Python 3.5 on Windows. We have tested the pip packages with the following distributions of Python:

Both distributions include pip. To install the CPU-only version of TensorFlow, enter the following command at a command prompt:

C:\> pip install --upgrade

To install the GPU version of TensorFlow, enter the following command at a command prompt:

C:\> pip install --upgrade

You can now test your installation.

You can also use Virtualenv or Anaconda environments to manage your installation of TensorFlow on Windows.




Ionic vs Nativescript


  • Native platform layouts
  • JavaScript access to native components and APIs
  • Has excellent performance
  • It is newer and may have less examples

Ionic Framework

  • AngularJS is nice to work with
  • Apache Cordova is polished
  • Has good performance
  • Angular 1.x to Angular 2.x has a huge learning curve
  • Apache Cordova plugins require native knowledge
  • Layouts do not use native components