[Numpy-discussion] Managing Python with NumPy and many external libraries on multiple Windows machines

David Cournapeau david@ar.media.kyoto-u.ac...
Mon Apr 27 22:20:06 CDT 2009


Wes McKinney wrote:
> Hello,
>
> I am wondering if anyone can offer some suggestions on this problem.
> Over the last year or so I have been building a number of libraries on
> top of NumPy + SciPy + matplotlib and other libraries which are being
> used for investigative research for my company's problem domain in
> place of, say, Matlab and R (which are more "ready out of the box"
> systems). I have approximately 20 users, all of whom are running
> Windows on a very Microsoft-centric network with databases, etc. Has
> anyone had any luck managing a standardized Python environment on lots
> of Windows machines with a large number of 3rd-party Python libraries?
> Upgrading packages right now involves getting 20 people to click
> through an .exe installer, which is hardly a good solution.

There is no good solution that I know of for python for this problem.
Upgrades on windows and mac os x are usually handled on a
per-application basis, but that requires that you control everything.
Existing solutions (e.g. based on eggs) will require you to do something
in any case.

> For example, I was recently forced to upgrade everyone's NumPy to 1.3
> after I discovered that a DLL I had built against 1.3 was incompatible
> with 1.2.x.

The solution is to build against numpy 1.2.x. You can't expect to link
against a library v2 and runs the application with v1 is loaded when v1
< v2. Very few, if any library handle forward compatibility.

cheers,

David


More information about the Numpy-discussion mailing list