Installing TensorFlow on Pop!_OS using Tensorman

Pop!_OS allows easy installation and management of Tensorflow using 'tensorman'.

Installing TensorFlow on Pop!_OS using Tensorman

First, make sure you have all the updates installed:

sudo apt update
sudo apt full-upgrade

Then, install the tensorman package:

sudo apt install tensorman

 In order to get Nvidia CUDA support, install the nvidia docker package:

sudo apt install nvidia-docker2

How to Install TensorFlow 2.0 on Anaconda (with CUDA support)

TensorFlow 2.0 has been released for a few months now. This latest version comes with many new features and improvements, such as eager execution, multi-GPU support, tighter Keras integration, and new deployment options such as TensorFlow Serving.

So, it's time we all switched to TensorFlow 2.0.

The Anaconda-native TensorFlow 2.0 packages are now available in the main conda repository. So, let's see how we can install TensorFlow 2.0 on Anaconda Python. This method will work on both Windows and Linux. And, if you have a CUDA capable NVIDIA GPU, you can enable GPU support as well.

Step 1.Creating a New Conda Environment

We'll start by creating a new conda environment. (I'll name it 'tensorflow2'. You can choose another name if you like):

conda create --name tensorflow2 python=3.7 anaconda

Create a new conda environment

Installing OpenCV from source on Anaconda Python on Ubuntu 16.10

I recently switched to Linux for my Machine Learning experiments, and I did a post on How to install Keras and Anaconda Python on Ubuntu 16.10.

Now, I wanted to install OpenCV on Ubuntu also. Since OpenCV does not have a pre-built package for Linux, it meant I had to compile OpenCV from source.

OpenCV 3.1 running on Lubuntu 16.10

Adrian of PyImageSearch has recently done a post about how to compile OpenCV on Ubuntu 16.04 using virtualenv. I followed his steps as a base, but had to make numerous adjustments to some of the packages which gets installed (e.g. libpng-dev, libhdf5-serial-dev) and the build commands due to the changes from Ubuntu 16.04 to 16.10, and because I'm using Anaconda environments rather than virtualenv.

I'll be installing OpenCV 3.1, and will be using the Lubuntu 16.10 virtual machine which I used in my earlier post. But the steps and commands will be exactly the same for any flavor of Ubuntu 16.10.

First, as a habit, get and install the latest updates for Ubuntu,

 sudo apt-get update   
 sudo apt-get upgrade   

Then (if you have not done already) install the necessary build tools,

 sudo apt-get install build-essential cmake git unzip pkg-config  

Then, we install the following packages which allows OpenCV to interact with various image and video formats,

 sudo apt-get install libjpeg8-dev libtiff5-dev libjasper-dev libpng-dev  
 sudo apt-get install libavcodec-dev libavformat-dev libswscale-dev libv4l-dev  
 sudo apt-get install libxvidcore-dev libx264-dev  

Note: on Ubuntu 16.04, the package name for libpng was libpng12-dev. But on 16.10, it should be libpng-dev.

Setting up Keras and Anaconda Python on Ubuntu 16.10

I’ve been using Anaconda Python for most of my Machine Learning experiments, mainly because of the flexibility it gives with the isolated Python environments. I recently did a post on how to install Keras on Anaconda on Windows.

I’m planning to switch to Linux for few of my experiments, so I decided to try out setting up Anaconda Python and Keras from scratch on Ubuntu. I’ll be using the latest Ubuntu 16.10 (Yakkety Yak) 64-Bit for this.

Note: The screenshots I captured are from a virtual machine with Lubuntu 16.10 (the LXDE flavor of Ubuntu). But the steps and commands are exactly the same for the standard Ubuntu desktop as well.

First and foremost, get and install the latest updates in Ubuntu, (Reboot the machine if necessary after updating.)

 sudo apt-get update  
 sudo apt-get upgrade  

Then, we’ll install the following necessary packages,

 sudo apt-get install build-essential cmake git unzip pkg-config  
 sudo apt-get install libopenblas-dev liblapack-dev  

Now, on to installing Anaconda. Head over to the Anaconda Python Downloads page, and get the Linux installer for Anaconda. We’ll be getting the Python 3.5 64-Bit package.

Download the Anaconda Python 3.5 64-Bit package for Linux

This will download a file named Anaconda3-4.2.0-Linux-x86_64.sh (the version numbers might be different based on the latest version available at the time of the download).

Switching between TensorFlow and Theano on Keras

Keras speeds up the task of building Neural Networks by providing high-level simplified functions to create and manipulate neural models. It itself does not provide the lower level neural and deep learning functions, but it’s rather meant to be run on an engine – which Keras refers to as a “backend” - which would provide such low-level functions.

Currently, Keras supports two such backends – TensorFlow and Theano.

The current version of Keras (v1.1.0 at the time of this writing) uses TensorFlow by default.

Most models written on top of Keras can be switched to a different backend without changes – at least it’s what’s said in the documentation. I’m yet to test this.

Which backend Kesas will use is defined in the Keras config file, which is located in the .keras directory in your home directory:
e.g.: on linux it would be ~/.keras/keras.json and on windows you can get to it on %USERPROFILE%\.keras\keras.json

For the default of using the TensorFlow backend, use the following config,

 {  
   "image_dim_ordering": "tf",  
   "epsilon": 1e-07,  
   "floatx": "float32",  
   "backend": "tensorflow"  
 }  

Notice the "backend" is set to "tensorflow" and "image_dim_ordering" is set to "tf".

To use the Theano backend, use the following,

 {  
   "image_dim_ordering": "th",   
   "epsilon": 1e-07,   
   "floatx": "float32",   
   "backend": "theano"  
 }  

Apart from the obvious "backend": "theano", note that "image_dim_ordering" is set to "th".

See my new post to see what the image_dim_ordering parameter in Keras does, and why is it important to set it properly.

Update: If you use Jupyter notebooks, and need to switch between TensorFlow and Theano backends quite often, fellow blogger desertnaut has a solution to dynamically switch the backend. Check out his solution at: Dynamically switch Keras backend in Jupyter notebooks

Related posts:
What is the image_dim_ordering parameter in Keras, and why is it important

Related links:
https://keras.io/backend/

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