Edit, July 2015:
A mere two weeks after I posted this, pip version 7 was released and changed things again. If you have the wheel library and pip version 7 or above, wheel building and caching is now switched on and run by default, you just need to pip install away and the caching happens in the background.
This can result in mild disbelief when numpy installs near-instantly the second time around, with no configuration! Anyway, I'll leave the rest of this post here for posterity - the note about caching the pip and wheel packages using older pip versions may still be useful to some.
I've previously posted on making pip usable offline by caching package files in a local directory. That approach can probably be considered out-of-date now, though the information is still useful if you're stuck with an old version of pip. I expected to be able to run:
pip install --user --upgrade pip
to get the latest version of pip whenever I was logged in, but this doesn't seem to work, so unless you're going to do a system-wide (sudo) upgrade you still need to upgrade pip for each new virtualenv you create. You can do that offline (if you're prepared) using the following pip commands. First, while you're still online (you only have to run this once, to download the pip and wheel source tarballs):
pip install --download=$HOME/.cache/pip/tarballs pip wheel
Then, when you've just created a new virtualenv (while on or offline):
pip install --no-index --find-links=file://$HOME/.cache/pip/tarballs pip wheel
I recommend saving that second command in a script (or you could even alias it to something).
So, now we've got the latest pip (version 6.1.1 as of writing). This gives you a much improved alternative to caching source-tarballs. Since version 1.6 / 6.0, (see release notes), Pip provides support for the Wheel format, allowing caching of pre-built packages. With pip version 6.1, this seems to be stable and very useful.
The new wheel format not only provides download-caching, but does away with build times, by caching a pre-built binary on command. This is awesome - it makes virtualenvs feel much more lightweight (no waiting five-to-ten minutes to spin one up with numpy, scipy, and astropy), It's also a massive boon if you're running automated install testing with tox, since this may run a fresh install for each test-run. You can find usage details here: https://pip.pypa.io/en/latest/reference/pip_wheel.html but I'll give you the basics below.
First, we set up some environment variables, so that you don't have to supply the wheel-cache directory on the command-line every time (default is current working directory, which is not what you want for sharing e.g. a single numpy build between projects). You can set it via environment variables, e.g. (in your .bashrc):
export WHEELHOUSE="${HOME}/.cache/pip/wheelhouse"
export PIP_FIND_LINKS="file://${WHEELHOUSE}"
export PIP_WHEEL_DIR="${WHEELHOUSE}"
Then (e.g. for a numpy install):
pip install wheel #Only needed if caching new packages pip wheel numpy #First time only, to seed the cache. pip install numpy #Takes less than 2 seconds. Amaze.
And you're away.
Hat-tip to @ionelmc for pulling this information into one easy-to-read post, which I found when infuriated by tox build-times. There's also a nice write-up of the wheel format by Armin Ronacher (of flask fame), but although I came across and skim-read the article some months ago, I but never got around to actually trying it out till now. So it goes.