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 _______________________________________________ Numpy-discussion mailing list Numpy-discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
