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Configure and Manage Your Environment with Anaconda

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.

Overview

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

Installation

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.

Linux Mac Windows
64-bit 64-bit (bash installer) 64-bit (bash installer) 64-bit (exe installer)
32-bit 32-bit (bash installer) 32-bit (exe installer)

Install miniconda on your machine. Detailed instructions:

Setup the carnd-term1 environment.

git clone https://github.com/udacity/CarND-Term1-Starter-Kit.git
cd CarND-Term1-Starter-Kit

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

Create carnd-term1. Running this command will create a new conda environment that is provisioned with all libraries you need to be successful in this program.

conda env create -f environment.yml

Note: Some Mac users have reported issues installing TensorFlow using this method. The cause is unknown but seems to be related to pip. For the time being, we recommend opening environment.yml in a text editor and swapping

    - tensorflow==0.12.1

with

    - https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-0.12.1-py3-none-any.whl

If you have encountered a No module named 'requests' error, try to add in a line under 'pip' line in the environment.yml in a text editor

with

    - requests

Note: In Project Behavioral Cloning, classroom workspaces has Keras==2.2.4 version and in Project Traffic Sign Classifier classroom workspaces has Keras==2.0.9. To install Keras==2.2.4 we would recommend opening environment.yml in a text editor and swapping

    - keras==2.0.9

with

    - keras==2.2.4

or write

    pip install keras==2.2.4

Verify that the carnd-term1 environment was created in your environments:

conda info --envs

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

conda clean -tp

Uninstalling

To uninstall the environment:

conda env remove -n carnd-term1

GPU Installation

Prior to installing tensorflow-gpu for Ubuntu or Windows as part of the Anaconda environment for Nvidia GPUs, install the appropriate versions of CUDA Toolkit and cuDNN, along with the necessary Nvidia drivers. See Ubuntu instructions here and Windows instructions here.

When creating the environment, at the Create step above, change the command to:

conda env create -f environment-gpu.yml

Otherwise, follow the same steps as above.

Using Anaconda

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 carnd-term1 environment:

OS X and Linux

$ source activate carnd-term1

Windows

Depending on shell either:

$ source activate carnd-term1

or

$ activate carnd-term1

That's it. Now all of the carnd-term1 libraries are available to you.

To exit the environment when you have completed your work session, simply close the terminal window.