Windows wheels, built, but should we deploy?
Hi, Summary: I propose that we upload Windows wheels to pypi. The wheels are likely to be stable and relatively easy to maintain, but will have slower performance than other versions of numpy linked against faster BLAS / LAPACK libraries. Background: There's a long discussion going on at issue github #5479 [1], where the old problem of Windows wheels for numpy came up. For those of you not following this issue, the current situation for community-built numpy Windows binaries is dire: * We have not so far provided windows wheels on pypi, so `pip install numpy` on Windows will bring you a world of pain; * Until recently we did provide .exe "superpack" installers on sourceforge, but these became increasingly difficult to build and we gave up building them as of the latest (1.10.4) release. Despite this, popularity of Windows wheels on pypi is high. A few weeks ago, Donald Stufft ran a query for the binary wheels most often downloaded from pypi, for any platform [2] . The top five most downloaded were (n_downloads, name): 6646, numpy-1.10.4-cp27-none-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl 5445, cryptography-1.2.1-cp27-none-win_amd64.whl 5243, matplotlib-1.4.0-cp34-none-win32.whl 5241, scikit_learn-0.15.1-cp34-none-win32.whl 4573, pandas-0.17.1-cp27-none-win_amd64.whl So a) the OSX numpy wheel is very popular and b) despite the fact that we don't provide a numpy wheel for Windows, matplotlib, sckit_learn and pandas, that depend on numpy, are the 3rd, 4th and 5th most downloaded wheels as of a few weeks ago. So, there seems to be a large appetite for numpy wheels. Current proposal: I have now built numpy wheels, using the ATLAS blas / lapack library - the build is automatic and reproducible [3]. I chose ATLAS to build against, rather than, say OpenBLAS, because we've had some significant worries in the past about the reliability of OpenBLAS, and I thought it better to err on the side of correctness. However, these builds are relatively slow for matrix multiply and other linear algebra routines compared numpy built against OpenBLAS or MKL (which we cannot use because of its license) [4]. In my very crude array test of a dot product and matrix inversion, the ATLAS wheels were 2-3 times slower than MKL. Other benchmarks on Julia found about the same result for ATLAS vs OpenBLAS on 32-bit bit, but a much bigger difference on 64-bit (for an earlier version of ATLAS than we are currently using) [5]. So, our numpy wheels likely to be stable and give correct results, but will be somewhat slow for linear algebra. I propose that we upload these ATLAS wheels to pypi. The upside is that this gives our Windows users a much better experience with pip, and allows other developers to build Windows wheels that depend on numpy. The downside is that these will not be optimized for performance on modern processors. In order to signal that, I propose adding the following text to the numpy pypi front page: ``` All numpy wheels distributed from pypi are BSD licensed. Windows wheels are linked against the ATLAS BLAS / LAPACK library, restricted to SSE2 instructions, so may not give optimal linear algebra performance for your machine. See http://docs.scipy.org/doc/numpy/user/install.html for alternatives. ``` In a way this is very similar to our previous situation, in that the superpack installers also used ATLAS - in fact an older version of ATLAS. Once we are up and running with numpy wheels, we can consider whether we should switch to other BLAS libraries, such as OpenBLAS or BLIS (see [6]). I'm posting here hoping for your feedback... Cheers, Matthew [1] https://github.com/numpy/numpy/issues/5479 [2] https://gist.github.com/dstufft/1dda9a9f87ee7121e0ee [3] https://ci.appveyor.com/project/matthew-brett/np-wheel-builder [4] http://mingwpy.github.io/blas_lapack.html#intel-math-kernel-library [5] https://github.com/numpy/numpy/issues/5479#issuecomment-185033668 [6] https://github.com/numpy/numpy/issues/7372
On Fri, Mar 4, 2016 at 4:42 AM, Matthew Brett <matthew.brett@gmail.com> wrote:
Hi,
Summary:
I propose that we upload Windows wheels to pypi. The wheels are likely to be stable and relatively easy to maintain, but will have slower performance than other versions of numpy linked against faster BLAS / LAPACK libraries.
Background:
There's a long discussion going on at issue github #5479 [1], where the old problem of Windows wheels for numpy came up.
For those of you not following this issue, the current situation for community-built numpy Windows binaries is dire:
* We have not so far provided windows wheels on pypi, so `pip install numpy` on Windows will bring you a world of pain; * Until recently we did provide .exe "superpack" installers on sourceforge, but these became increasingly difficult to build and we gave up building them as of the latest (1.10.4) release.
Despite this, popularity of Windows wheels on pypi is high. A few weeks ago, Donald Stufft ran a query for the binary wheels most often downloaded from pypi, for any platform [2] . The top five most downloaded were (n_downloads, name):
6646, numpy-1.10.4-cp27-none-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl 5445, cryptography-1.2.1-cp27-none-win_amd64.whl 5243, matplotlib-1.4.0-cp34-none-win32.whl 5241, scikit_learn-0.15.1-cp34-none-win32.whl 4573, pandas-0.17.1-cp27-none-win_amd64.whl
So a) the OSX numpy wheel is very popular and b) despite the fact that we don't provide a numpy wheel for Windows, matplotlib, sckit_learn and pandas, that depend on numpy, are the 3rd, 4th and 5th most downloaded wheels as of a few weeks ago.
So, there seems to be a large appetite for numpy wheels.
Current proposal:
I have now built numpy wheels, using the ATLAS blas / lapack library - the build is automatic and reproducible [3].
I chose ATLAS to build against, rather than, say OpenBLAS, because we've had some significant worries in the past about the reliability of OpenBLAS, and I thought it better to err on the side of correctness.
However, these builds are relatively slow for matrix multiply and other linear algebra routines compared numpy built against OpenBLAS or MKL (which we cannot use because of its license) [4]. In my very crude array test of a dot product and matrix inversion, the ATLAS wheels were 2-3 times slower than MKL. Other benchmarks on Julia found about the same result for ATLAS vs OpenBLAS on 32-bit bit, but a much bigger difference on 64-bit (for an earlier version of ATLAS than we are currently using) [5].
So, our numpy wheels likely to be stable and give correct results, but will be somewhat slow for linear algebra.
I would not worry too much about this: at worst, this gives us back the situation where we were w/ so-called superpack, which have been successful in the past to spread numpy use on windows. My main worry is whether this locks us into ATLAS for a long time because of package depending on numpy blas/lapack (scipy, scikit learn). I am not sure how much this is the case. David
I propose that we upload these ATLAS wheels to pypi. The upside is that this gives our Windows users a much better experience with pip, and allows other developers to build Windows wheels that depend on numpy. The downside is that these will not be optimized for performance on modern processors. In order to signal that, I propose adding the following text to the numpy pypi front page:
``` All numpy wheels distributed from pypi are BSD licensed.
Windows wheels are linked against the ATLAS BLAS / LAPACK library, restricted to SSE2 instructions, so may not give optimal linear algebra performance for your machine. See http://docs.scipy.org/doc/numpy/user/install.html for alternatives. ```
In a way this is very similar to our previous situation, in that the superpack installers also used ATLAS - in fact an older version of ATLAS.
Once we are up and running with numpy wheels, we can consider whether we should switch to other BLAS libraries, such as OpenBLAS or BLIS (see [6]).
I'm posting here hoping for your feedback...
Cheers,
Matthew
[1] https://github.com/numpy/numpy/issues/5479 [2] https://gist.github.com/dstufft/1dda9a9f87ee7121e0ee [3] https://ci.appveyor.com/project/matthew-brett/np-wheel-builder [4] http://mingwpy.github.io/blas_lapack.html#intel-math-kernel-library [5] https://github.com/numpy/numpy/issues/5479#issuecomment-185033668 [6] https://github.com/numpy/numpy/issues/7372 _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org https://mail.scipy.org/mailman/listinfo/numpy-discussion
On Fri, Mar 4, 2016 at 12:29 AM, David Cournapeau <cournape@gmail.com> wrote:
On Fri, Mar 4, 2016 at 4:42 AM, Matthew Brett <matthew.brett@gmail.com> wrote:
Hi,
Summary:
I propose that we upload Windows wheels to pypi. The wheels are likely to be stable and relatively easy to maintain, but will have slower performance than other versions of numpy linked against faster BLAS / LAPACK libraries.
Background:
There's a long discussion going on at issue github #5479 [1], where the old problem of Windows wheels for numpy came up.
For those of you not following this issue, the current situation for community-built numpy Windows binaries is dire:
* We have not so far provided windows wheels on pypi, so `pip install numpy` on Windows will bring you a world of pain; * Until recently we did provide .exe "superpack" installers on sourceforge, but these became increasingly difficult to build and we gave up building them as of the latest (1.10.4) release.
Despite this, popularity of Windows wheels on pypi is high. A few weeks ago, Donald Stufft ran a query for the binary wheels most often downloaded from pypi, for any platform [2] . The top five most downloaded were (n_downloads, name):
6646, numpy-1.10.4-cp27-none-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl 5445, cryptography-1.2.1-cp27-none-win_amd64.whl 5243, matplotlib-1.4.0-cp34-none-win32.whl 5241, scikit_learn-0.15.1-cp34-none-win32.whl 4573, pandas-0.17.1-cp27-none-win_amd64.whl
So a) the OSX numpy wheel is very popular and b) despite the fact that we don't provide a numpy wheel for Windows, matplotlib, sckit_learn and pandas, that depend on numpy, are the 3rd, 4th and 5th most downloaded wheels as of a few weeks ago.
So, there seems to be a large appetite for numpy wheels.
Current proposal:
I have now built numpy wheels, using the ATLAS blas / lapack library - the build is automatic and reproducible [3].
I chose ATLAS to build against, rather than, say OpenBLAS, because we've had some significant worries in the past about the reliability of OpenBLAS, and I thought it better to err on the side of correctness.
However, these builds are relatively slow for matrix multiply and other linear algebra routines compared numpy built against OpenBLAS or MKL (which we cannot use because of its license) [4]. In my very crude array test of a dot product and matrix inversion, the ATLAS wheels were 2-3 times slower than MKL. Other benchmarks on Julia found about the same result for ATLAS vs OpenBLAS on 32-bit bit, but a much bigger difference on 64-bit (for an earlier version of ATLAS than we are currently using) [5].
So, our numpy wheels likely to be stable and give correct results, but will be somewhat slow for linear algebra.
I would not worry too much about this: at worst, this gives us back the situation where we were w/ so-called superpack, which have been successful in the past to spread numpy use on windows.
My main worry is whether this locks us into ATLAS for a long time because of package depending on numpy blas/lapack (scipy, scikit learn). I am not sure how much this is the case.
You mean the situation where other packages try to find the BLAS / LAPACK library and link against that? My impression was that neither scipy or scikit-learn do that at the moment, but I'm happy to be corrected. You'd know better than me about this, but my understanding is that BLAS / LAPACK has a standard interface that should allow code to run the same way, regardless of which BLAS / LAPACK library it is linking to. So, even if another package is trying to link against the numpy BLAS, swapping the numpy BLAS library shouldn't cause a problem (unless the package is trying to link to ATLAS-specific stuff, which seems a bit unlikely). Is that right? Cheers, Matthew
On Fri, Mar 4, 2016 at 1:38 PM, Matthew Brett <matthew.brett@gmail.com> wrote:
On Fri, Mar 4, 2016 at 12:29 AM, David Cournapeau <cournape@gmail.com> wrote:
On Fri, Mar 4, 2016 at 4:42 AM, Matthew Brett <matthew.brett@gmail.com> wrote:
Hi,
Summary:
I propose that we upload Windows wheels to pypi. The wheels are likely to be stable and relatively easy to maintain, but will have slower performance than other versions of numpy linked against faster BLAS / LAPACK libraries.
Background:
There's a long discussion going on at issue github #5479 [1], where the old problem of Windows wheels for numpy came up.
For those of you not following this issue, the current situation for community-built numpy Windows binaries is dire:
* We have not so far provided windows wheels on pypi, so `pip install numpy` on Windows will bring you a world of pain; * Until recently we did provide .exe "superpack" installers on sourceforge, but these became increasingly difficult to build and we gave up building them as of the latest (1.10.4) release.
Despite this, popularity of Windows wheels on pypi is high. A few weeks ago, Donald Stufft ran a query for the binary wheels most often downloaded from pypi, for any platform [2] . The top five most downloaded were (n_downloads, name):
6646,
numpy-1.10.4-cp27-none-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl
5445, cryptography-1.2.1-cp27-none-win_amd64.whl 5243, matplotlib-1.4.0-cp34-none-win32.whl 5241, scikit_learn-0.15.1-cp34-none-win32.whl 4573, pandas-0.17.1-cp27-none-win_amd64.whl
So a) the OSX numpy wheel is very popular and b) despite the fact that we don't provide a numpy wheel for Windows, matplotlib, sckit_learn and pandas, that depend on numpy, are the 3rd, 4th and 5th most downloaded wheels as of a few weeks ago.
So, there seems to be a large appetite for numpy wheels.
Current proposal:
I have now built numpy wheels, using the ATLAS blas / lapack library - the build is automatic and reproducible [3].
I chose ATLAS to build against, rather than, say OpenBLAS, because we've had some significant worries in the past about the reliability of OpenBLAS, and I thought it better to err on the side of correctness.
However, these builds are relatively slow for matrix multiply and other linear algebra routines compared numpy built against OpenBLAS or MKL (which we cannot use because of its license) [4]. In my very crude array test of a dot product and matrix inversion, the ATLAS wheels were 2-3 times slower than MKL. Other benchmarks on Julia found about the same result for ATLAS vs OpenBLAS on 32-bit bit, but a much bigger difference on 64-bit (for an earlier version of ATLAS than we are currently using) [5].
So, our numpy wheels likely to be stable and give correct results, but will be somewhat slow for linear algebra.
I would not worry too much about this: at worst, this gives us back the situation where we were w/ so-called superpack, which have been successful in the past to spread numpy use on windows.
My main worry is whether this locks us into ATLAS for a long time because of package depending on numpy blas/lapack (scipy, scikit learn). I am not sure how much this is the case.
You mean the situation where other packages try to find the BLAS / LAPACK library and link against that? My impression was that neither scipy or scikit-learn do that at the moment, but I'm happy to be corrected.
You'd know better than me about this, but my understanding is that BLAS / LAPACK has a standard interface that should allow code to run the same way, regardless of which BLAS / LAPACK library it is linking to. So, even if another package is trying to link against the numpy BLAS, swapping the numpy BLAS library shouldn't cause a problem (unless the package is trying to link to ATLAS-specific stuff, which seems a bit unlikely).
Is that right?
AFAIK, numpy doesn't provide access to BLAS/LAPACK. scipy does. statsmodels is linking to the installed BLAS/LAPACK in cython code through scipy. So far we haven't seen problems with different versions. I think scipy development works very well to isolate linalg library version specific parts from the user interface. AFAIU, The main problem will be linking to inconsistent Fortran libraries in downstream packages that use Fortran. Eg. AFAIU it won't work to pip install a ATLAS based numpy and then install a MKL based scipy from Gohlke. I don't know if there is a useful error message, or if this just results in puzzled users. Josef
Cheers,
Matthew _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org https://mail.scipy.org/mailman/listinfo/numpy-discussion
On Fri, Mar 4, 2016 at 7:30 PM, <josef.pktd@gmail.com> wrote: [...]
AFAIK, numpy doesn't provide access to BLAS/LAPACK. scipy does. statsmodels is linking to the installed BLAS/LAPACK in cython code through scipy. So far we haven't seen problems with different versions. I think scipy development works very well to isolate linalg library version specific parts from the user interface.
Yeah, it should be invisible to users of both numpy and scipy which BLAS/LAPACK is in use under the hood.
AFAIU, The main problem will be linking to inconsistent Fortran libraries in downstream packages that use Fortran. Eg. AFAIU it won't work to pip install a ATLAS based numpy and then install a MKL based scipy from Gohlke.
The specific scenario you describe will be a problem, but not for the reason you state -- the problem is that (IIUC) the Gohlke scipy build has some specific hacks where it "knows" that it can find a copy of MKL buried at a particular location inside the numpy package (and the Gohlke numpy build has a specific hack to put a copy of MKL there). So the Gohlke scipy requires the Gohlke numpy, but this is due to patches that Christoph applies to his builds. AFAIK, outside of downstream packages that poke around the inside of numpy like this, there should be no way for downstream packages to know or care which BLAS/LAPACK implementation numpy is using (except for speed, bugs, etc.). -n -- Nathaniel J. Smith -- https://vorpus.org
Hi, On Fri, Mar 4, 2016 at 8:40 PM, Nathaniel Smith <njs@pobox.com> wrote:
On Fri, Mar 4, 2016 at 7:30 PM, <josef.pktd@gmail.com> wrote: [...]
AFAIK, numpy doesn't provide access to BLAS/LAPACK. scipy does. statsmodels is linking to the installed BLAS/LAPACK in cython code through scipy. So far we haven't seen problems with different versions. I think scipy development works very well to isolate linalg library version specific parts from the user interface.
Yeah, it should be invisible to users of both numpy and scipy which BLAS/LAPACK is in use under the hood.
My impression is that the general mood here is positive, so I plan to deploy these wheels to pypi on Monday, with the change to the pypi text. Please do let me know if there are any strong objections. Cheers, Matthew
+1 from me. I could prepare scipy builds based on these numpy builds. Carl 2016-03-05 19:40 GMT+01:00 Matthew Brett <matthew.brett@gmail.com>:
Hi,
On Fri, Mar 4, 2016 at 7:30 PM, <josef.pktd@gmail.com> wrote: [...]
AFAIK, numpy doesn't provide access to BLAS/LAPACK. scipy does. statsmodels is linking to the installed BLAS/LAPACK in cython code through scipy. So far we haven't seen problems with different versions. I think scipy development works very well to isolate linalg library version specific parts from
On Fri, Mar 4, 2016 at 8:40 PM, Nathaniel Smith <njs@pobox.com> wrote: the
user interface.
Yeah, it should be invisible to users of both numpy and scipy which BLAS/LAPACK is in use under the hood.
My impression is that the general mood here is positive, so I plan to deploy these wheels to pypi on Monday, with the change to the pypi text. Please do let me know if there are any strong objections.
Cheers,
Matthew _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org https://mail.scipy.org/mailman/listinfo/numpy-discussion
On Sat, Mar 5, 2016 at 10:40 AM, Matthew Brett <matthew.brett@gmail.com> wrote:
Hi,
On Fri, Mar 4, 2016 at 8:40 PM, Nathaniel Smith <njs@pobox.com> wrote:
On Fri, Mar 4, 2016 at 7:30 PM, <josef.pktd@gmail.com> wrote: [...]
AFAIK, numpy doesn't provide access to BLAS/LAPACK. scipy does. statsmodels is linking to the installed BLAS/LAPACK in cython code through scipy. So far we haven't seen problems with different versions. I think scipy development works very well to isolate linalg library version specific parts from the user interface.
Yeah, it should be invisible to users of both numpy and scipy which BLAS/LAPACK is in use under the hood.
My impression is that the general mood here is positive, so I plan to deploy these wheels to pypi on Monday, with the change to the pypi text. Please do let me know if there are any strong objections.
Done: (py35) PS C:\tmp> pip install numpy Collecting numpy Downloading numpy-1.10.4-cp35-none-win32.whl (6.6MB) 100% |################################| 6.6MB 34kB/s Installing collected packages: numpy Cheers, Matthew
Thanks Matthew! I just installed it and ran the tests and it all works (except for test_system_info.py that fails because I am missing a vcvarsall.bat on that system but this is expected). -- Olivier
+1 -- thanks for doing all this work. There is a HUGE amount you can do with numpy that doesn't give a whit about how fast .dot() et all are. If you really do need that to be fast as possible, you can pug in a faster build later. This is great. Just as one example -- I teach a general python class every year --I do only one session on numpy/scipy. If I can expect my students to be able to simply pip install the core scipy stack, this will be SO much easier. -CHB On Thu, Mar 3, 2016 at 8:42 PM, Matthew Brett <matthew.brett@gmail.com> wrote:
Hi,
Summary:
I propose that we upload Windows wheels to pypi. The wheels are likely to be stable and relatively easy to maintain, but will have slower performance than other versions of numpy linked against faster BLAS / LAPACK libraries.
Background:
There's a long discussion going on at issue github #5479 [1], where the old problem of Windows wheels for numpy came up.
For those of you not following this issue, the current situation for community-built numpy Windows binaries is dire:
* We have not so far provided windows wheels on pypi, so `pip install numpy` on Windows will bring you a world of pain; * Until recently we did provide .exe "superpack" installers on sourceforge, but these became increasingly difficult to build and we gave up building them as of the latest (1.10.4) release.
Despite this, popularity of Windows wheels on pypi is high. A few weeks ago, Donald Stufft ran a query for the binary wheels most often downloaded from pypi, for any platform [2] . The top five most downloaded were (n_downloads, name):
6646, numpy-1.10.4-cp27-none-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl 5445, cryptography-1.2.1-cp27-none-win_amd64.whl 5243, matplotlib-1.4.0-cp34-none-win32.whl 5241, scikit_learn-0.15.1-cp34-none-win32.whl 4573, pandas-0.17.1-cp27-none-win_amd64.whl
So a) the OSX numpy wheel is very popular and b) despite the fact that we don't provide a numpy wheel for Windows, matplotlib, sckit_learn and pandas, that depend on numpy, are the 3rd, 4th and 5th most downloaded wheels as of a few weeks ago.
So, there seems to be a large appetite for numpy wheels.
Current proposal:
I have now built numpy wheels, using the ATLAS blas / lapack library - the build is automatic and reproducible [3].
I chose ATLAS to build against, rather than, say OpenBLAS, because we've had some significant worries in the past about the reliability of OpenBLAS, and I thought it better to err on the side of correctness.
However, these builds are relatively slow for matrix multiply and other linear algebra routines compared numpy built against OpenBLAS or MKL (which we cannot use because of its license) [4]. In my very crude array test of a dot product and matrix inversion, the ATLAS wheels were 2-3 times slower than MKL. Other benchmarks on Julia found about the same result for ATLAS vs OpenBLAS on 32-bit bit, but a much bigger difference on 64-bit (for an earlier version of ATLAS than we are currently using) [5].
So, our numpy wheels likely to be stable and give correct results, but will be somewhat slow for linear algebra.
I propose that we upload these ATLAS wheels to pypi. The upside is that this gives our Windows users a much better experience with pip, and allows other developers to build Windows wheels that depend on numpy. The downside is that these will not be optimized for performance on modern processors. In order to signal that, I propose adding the following text to the numpy pypi front page:
``` All numpy wheels distributed from pypi are BSD licensed.
Windows wheels are linked against the ATLAS BLAS / LAPACK library, restricted to SSE2 instructions, so may not give optimal linear algebra performance for your machine. See http://docs.scipy.org/doc/numpy/user/install.html for alternatives. ```
In a way this is very similar to our previous situation, in that the superpack installers also used ATLAS - in fact an older version of ATLAS.
Once we are up and running with numpy wheels, we can consider whether we should switch to other BLAS libraries, such as OpenBLAS or BLIS (see [6]).
I'm posting here hoping for your feedback...
Cheers,
Matthew
[1] https://github.com/numpy/numpy/issues/5479 [2] https://gist.github.com/dstufft/1dda9a9f87ee7121e0ee [3] https://ci.appveyor.com/project/matthew-brett/np-wheel-builder [4] http://mingwpy.github.io/blas_lapack.html#intel-math-kernel-library [5] https://github.com/numpy/numpy/issues/5479#issuecomment-185033668 [6] https://github.com/numpy/numpy/issues/7372 _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org https://mail.scipy.org/mailman/listinfo/numpy-discussion
-- Christopher Barker, Ph.D. Oceanographer Emergency Response Division NOAA/NOS/OR&R (206) 526-6959 voice 7600 Sand Point Way NE (206) 526-6329 fax Seattle, WA 98115 (206) 526-6317 main reception Chris.Barker@noaa.gov
participants (7)
-
Carl Kleffner -
Chris Barker -
David Cournapeau -
josef.pktd＠gmail.com -
Matthew Brett -
Nathaniel Smith -
Olivier Grisel