Use Vagrant to automatically configure a Ubuntu VM from scratch and bootstrap it with SaltStack to install the SciPy stack with a mix of Ubuntu packages and virtualenvs.

To get started install:

• Virtualbox (Virtual Machine runner)
• Vagrant (Command-line tool to control VMs)

It can be helpful to tell vagrant to automatically install the virtualbox guest additions for the matching version to solve shared folder issues:

[host]$ vagrant plugin install vagrant-vbguest

Then you can start the VM with:

[host]$ vagrant up

The first start takes a couple of minutes to download the VM image, boot it, and install most of the Scipy stack packages with Salt. Subsequent calls to vagrant up will be much faster.

Once started you can ssh into the VM with:

[host]$ vagrant ssh

SSH agent forwarding is enabled by default to be able to use git from inside the VM if needed.

Once there you can activate one of the virtualenvs, for instance for the Python 3.4 virtualenv:

[guest]$ workon 3.4
(3.4)[guest]$ ipython

You can suspend / resume the VM with to temporarily free the memory on the host with vagrant suspend / vagrant resume. Or shutfown and destroy the VM completely to also free the disk with:

[host]$ vagrant destroy

Edit the Vagrantfile to customize:

• VM image (Ubuntu 13.04 64 bits by default)
• VM RAM size
• shared folders
• port forwarding

The configuration is a bunch of text files in:

salt/roots/salt/

In particular the Ubuntu packages and virtualenv configuration is in:

salt/roots/salt/python-venvs.sls

Read the SaltStack documentation to get started with custom state files.

If you edit the salt configuration you can ask salt to quickly take all your changes into accout from inside the VM:

[guest]$ sudo salt-call state.highstate

Alternatively you can reboot the whole VM with from outside of the VM:

[host]$ vagrant reload

You can even shutdown and delete the VM to restart the provisioning from scratch with:

[host]$ vagrant destroy
[host]$ vagrant up

To speedup the dev setup, this repo comes with a wheelhouse folder with pre-built numpy and scipy whl packages.

To build wheel packages for newer versions of numpy and scipy you can use (for instance with Python 3.4):

[guest]$ workon 3.4
(3.4)[guest]$ pip wheel --download-cache=~/pip-cache \
                        --wheel-dir=/vagrant/wheelhouse numpy
(3.4)[guest]$ pip install /vagrant/wheelhouse/numpy-*-cp34-cp34m-linux_x86_64.whl

(3.4)[guest]$ pip wheel --download-cache=~/pip-cache \
                        --wheel-dir=/vagrant/wheelhouse scipy
(3.4)[guest]$ pip install /vagrant/wheelhouse/scipy-*-cp34-cp34m-linux_x86_64.whl