These instructions and environment setups were taken from a public repository for a Udacity course on self-driving cars. Check out the full repo here. If you want tensorflow and keras and some additional libraries relating to computer vision, then clone that repository and follow the steps in their readme. Bonus: their setup comes with a Docker container too. But this repo contains most libraries needed for computer vision and image processing, including Jupyter notebook.

Per the Anaconda docs:

Conda is an open source package management system and environment management system for installing multiple versions of software packages and their dependencies and switching easily between them. It works on Linux, OS X and Windows, and was created for Python programs but can package and distribute any software.

Using Anaconda consists of the following:

1. Install miniconda on your computer
2. Create a new conda environment using this project
3. Each time you wish to work, activate your conda environment

Download the latest version of miniconda that matches your system.

NOTE: There have been reports of issues creating an environment using miniconda v4.3.13. If it gives you issues try versions 4.3.11 or 4.2.12 from here.

Install miniconda on your machine. Detailed instructions:

• Linux: http://conda.pydata.org/docs/install/quick.html#linux-miniconda-install
• Mac: http://conda.pydata.org/docs/install/quick.html#os-x-miniconda-install
• Windows: http://conda.pydata.org/docs/install/quick.html#windows-miniconda-install

Setup your cvpy environment.

git clone https://github.com/alkasm/cvpy
cd cvpy

If you are on Windows, rename
meta_windows_patch.yml to
meta.yml

Create cvpy. Running this command will create a new conda environment that is provisioned with all libraries you need.

conda env create -f environment.yml

Verify that the cvpy environment was created in your environments:

conda info --envs

Cleanup downloaded libraries (remove tarballs, zip files, etc):

conda clean -tp

To uninstall the environment:

conda env remove -n cvpy

Now that you have created an environment, in order to use it, you will need to activate the environment. This must be done each time you begin a new working session i.e. open a new terminal window.

Activate the cvpy environment:

$ source activate cvpy

Depending on shell either:

$ source activate cvpy

or

$ activate cvpy

You're done!