I was not even thinking we needed to modify the tool shed to implement
this. I was hoping (?) you could just modify:

lib/galaxy/tools/deps/__init__.py

to implement this. If some tool contains the tag

  <requirement type="package" version="1.7.1">numpy</requirement>

then if there is a manually installed tool_dependency in
`tool_dependency_dir/numpy/1.7.1/env.sh` that would take precedence
over the tool shed installed version (would that be something like
`tool_dependency_dir/numpy/1.7.1/owner/name/changeset/env.sh`)? Let me
know if this is way off base.

There is a lot you could do to make this more complicated of course -
an interface for mapping exact tool shed dependencies to manually
installed ones, the ability to auto-compile tool shed dependencies
against manually installed libraries, etc..., but I am not sure those
complexities are buying you anything really.

Thoughts?

-John



On Thu, Sep 26, 2013 at 5:47 PM, Greg Von Kuster <g...@bx.psu.edu> wrote:
> Hi John,
>
> On Sep 26, 2013, at 5:27 PM, John Chilton <chil...@msi.umn.edu> wrote:
>
>> My recommendation would be make the tool dependency install work on as
>> many platforms as you can and not try to optimize in such a way that
>> it is not going to work - i.e. favor reproduciblity over performance.
>> If a system administrator or institution want to sacrifice
>> reproduciblity and optimize specific packages they should be able to
>> do so manually. Its not just Atlas and CPU throttling right? Its
>> vendor versions of MPI, GPGPU variants of code, variants of OpenMP,
>> etc....  Even if the tool shed provided some mechanism for determining
>> if some particular package optimization is going to work, perhaps its
>> better to just not enable it by default because frequently these cause
>> slightly different results than the unoptimized version.
>>
>> The problem with this recommendation that is Galaxy currently provides
>> no mechanism for doing so. Luckily this is easy to solve and the
>> solution solves other problems. If the tool dependency resolution code
>> would grab the manually configured dependency instead of the tool shed
>> variant when available, instead of favoring the opposite, then it
>> would be really easy to add in an optimized version of numpy or an MPI
>> version of software X.
>
> How would you like this to happen?  Would it work to provide an admin the 
> ability to create a ToolDependency object and point it to a "manually 
> configured dependency" in whatever location on disk the admin chooses via a 
> new UI feature?  Or do you have a different idea?
>
> Thanks,
>
> Greg Von Kuster
>
>
>>
>> Whats great is this solves other problems as well. For instance, our
>> genomics Galaxy web server runs Debian but the worker nodes run
>> CentOS. This means many tool shed installed dependencies do not work.
>> JJ being the patient guy he is goes in and manually updates the tool
>> shed installed env.sh files to load modules. Even if you think not
>> running the same version of the OS on your server and worker nodes is
>> a bit crazy, there is the much more reasonable (common) case of just
>> wanting to submit to multiple different clusters. When I was talking
>> with the guys at NCGAS they were unsure how to do this, this one
>> change would make that a lot more tenable.
>>
>> -John
>>
>> On Thu, Sep 26, 2013 at 1:29 PM, Björn Grüning
>> <bjoern.gruen...@pharmazie.uni-freiburg.de> wrote:
>>> Hi,
>>>
>>>> Hi Bjoern,
>>>>
>>>> Is there anything else we (the Galaxy community) can do to help
>>>> sort out the ATLAS installation problems?
>>>
>>> Thanks for asking. I have indeed a few things I would like some
>>> comments.
>>>
>>>> Another choice might be to use OpenBLAS instead of ATLAS, e.g.
>>>> http://stackoverflow.com/questions/11443302/compiling-numpy-with-openblas-integration
>>>
>>> I have no experience with it. Does it also need to turn off CPU
>>> throttling? I would assume so, or how is it optimizing itself?
>>>
>>>> However, I think we build NumPy without using ATLAS or any
>>>> BLAS library. That seems like the most pragmatic solution
>>>> in the short term - which I think is what Dan tried here:
>>>> http://testtoolshed.g2.bx.psu.edu/view/blankenberg/package_numpy_1_7
>>>
>>> I can remove them if that is the consensus.
>>>
>>> A few points:
>>> - fixing the atlas issue can speed up numpy, scipy, R considerably (by
>>> 400% in some cases)
>>> - as far as I understand that performance gain is due to optimizing
>>> itself on specific hardware, for atlas there is no way around to disable
>>> CPU throttling (How about OpenBlas?)
>>> - it seems to be complicated to deactivate CPU throttling on OS-X
>>> - binary installation does not make sense in that case, because ATLAS is
>>> self optimizing
>>> - Distribution shipped ATLAS packages are not really faster
>>>
>>> Current state:
>>> - Atlas tries two different commands to deactivate CPU throttling. Afaik
>>> that only works on some Ubuntu versions, where no root privileges are
>>> necessary.
>>> - If atlas fails for some reason, numpy/R/scipy installation should not
>>> be affected (that's was at least the aim)
>>>
>>> Questions:
>>> - Is it worth the hassle for some speed improvements? pip install numpy,
>>> would be so easy?
>>>
>>> - If we want to support ATLAS, any better idea to how to implement it?
>>> Any Tool Shed feature that can help? -> interactive installation?
>>>       - can we flag a tool dependency as optional? So it can fail?
>>>
>>> - Can anyone help with testing and fixing it?
>>>
>>>
>>> Any opinions/comments?
>>> Bjoern
>>>
>>>> Thanks,
>>>>
>>>> Peter
>>>
>>>
>>>
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