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Advisories
A significant pull request was merged in on July 26, 2013 around 9pm (CST) that provides an enhancement to the way the book's Vagrant-based virtual machine is configured. If you were an adopter of the book's virtual machine on or before 26 July 2013, you will probably want to do complete the following steps to get in a good spot for moving forward with the code. If you pulled the code after July 26, this advisory will not affect you.
- Save any work in IPython Notebook that you don't want to lose by copying the ipynb file to another location (or by using
git stash) -
vagrant destroy- Kill the existing virtual machine -
git pull- Update your repository's code -
vagrant plugin install vagrant-berkshelf- Install a required Vagrant plugin -
vagrant up- Re-bootstrap your virtual machine with Chef-based configuration management. The first bootstrap takes ~20 minutes, which is significantly faster than the previous approach.
Update on 29 July: Thanks to David Rush (@DDucks) for pointing out that you should do the vagrant destory before doing the git pull.
Enjoy!
In completing a final review of the manuscript and doing another round of testing on the code, I realized that the time that it takes for Vagrant to perform synchronization of the thousands of files that are unarchived from the Enron corpus can take a very long time -- potentially upwards of 2 hours on some systems. Although preprocessing the original data is a very worthwhile exercise, a 2 hour delay is far from acceptable for users who are taking advantage of the virtual machine to streamline learning.
As a reasonable workaround, I checked in a highly compressed version of the output from Example 6.3 so that readers who are either not interested in the details of preprocessing the original corpus or just don't have the time to wait can proceed throughout the real substance of the chapter without additional delays. The IPython Notebook for Chapter 6 has been updated with notes that clearly explain the steps involved.
Bottom line: You can opt to downloading the original Enron corpus and you won't need to execute Examples 6.2 or 6.3 if you'd like to opt-out of the potentially time consuming preprocessing.
Enjoy!