On 04/01/2018 16:37, Jakob Schiøtz wrote:
Dear Kenneth, Pablo and Maxime,
Thanks for your feedback. Yes, I will try to see if I can build from source,
but I will focus on the foss toolchain since we use that one for our Python
here (we do not have the Intel MPI license, and the iomkl toolchain could not
built Python last time I tried).
I assume the reason for building from source is to ensure consistent library
versions etc. If that proves very difficult, could we perhaps in the interim
have builds (with a -bin suffix?) using the prebuilt wheels?
The main reason for building from source is performance and
compatibility with the OS.
The binary wheels that are available for TensorFlow are not compatible
with older OS versions like CentOS 6, as I experienced first-hand when
trying to get it to work on an older (GPU) system.
Since the compilation from source with CUDA support didn't work yet, I
had to resort to injecting a newer glibc version in the 'python' binary,
which was not fun (well...).
For CPU-only installations, you really have no other option than
building from source, since the binary wheels were not built with AVX2
instructions for example, which leads to large performance losses (some
quick benchmarking showed a 7x increase in performance for TF 1.4 built
with foss/2017b over using the binary wheel).
For GPU installations, a similar concern arises, although it may be less
severe there, depending on what CUDA compute capabilities the binary
wheels were built with (I only tested the wheels on old systems with
NVIDIA K20x/K40 GPUs, so there I doubt you'll get much performance
increase when building from source).
If it turns out to be too difficult or time-consuming to get the build
from source with CUDA support to work, then we can of course progress
with sticking to the binary wheel releases for now, I'm not going to
oppose that.
regards,
Kenneth
Best regards
Jakob
On 4 Jan 2018, at 15:29, Kenneth Hoste <kenneth.ho...@ugent.be> wrote:
Dear Jakob,
On 04/01/2018 10:23, Jakob Schiøtz wrote:
Hi,
I made a TensorFlow easyconfig a while ago depending on Python with the foss
toolchain; and including a variant with GPU support (PR 4904). The latter has
not yet been merged, probably because it is annoying to have something that can
only build on a machine with a GPU (it fails the sanity check otherwise, as
TensorFlow with GPU support cannot load on a machine without it).
Not being able to test this on a non-GPU system is a bit unfortunate, but
that's not a reason that it hasn't been merged yet, that's mostly due to a lack
of time from my side to get back to it...
Since I made that PR, two newer releases of TensorFlow have appeared (1.3 and
1.4). There are easyconfigs for 1.3 with the Intel tool chain. I am
considering making easyconfigs for TensorFlow 1.4 with Python-3.6.3-foss-2017b
(both with and without GPU support), but first I would like to know if anybody
else is doing this - it is my impression that somebody who actually know what
they are doing may be working on TensorFlow. :-)
I have spent quite a bit of time puzzling together an easyblock that supports
building TensorFlow from source, see [1].
It already works for non-GPU installations (see [2] for example), but it's not
entirely finished yet because:
* building from source with CUDA support does not work yet, the build fails
with strange Bazel errors...
* there are some issues when the TensorFlow easyblock is used together with
--use-ccache and the Intel compilers;
because two compiler wrappers are used, they end up calling each other resulting in a
"fork bomb" style situation...
I would really like to get it finished and have easyconfigs available for
TensorFlow 1.4 and newer where we properly build TensorFlow from source rather
than using the binary wheels...
Are you up for giving it a try, and maybe helping out with the problems
mentioned above?
regards,
Kenneth
[1] https://github.com/easybuilders/easybuild-easyblocks/pull/1287
[2] https://github.com/easybuilders/easybuild-easyconfigs/pull/5499
Best regards
Jakob
--
Jakob Schiøtz, professor, Ph.D.
Department of Physics
Technical University of Denmark
DK-2800 Kongens Lyngby, Denmark
http://www.fysik.dtu.dk/~schiotz/
--
Jakob Schiøtz, professor, Ph.D.
Department of Physics
Technical University of Denmark
DK-2800 Kongens Lyngby, Denmark
http://www.fysik.dtu.dk/~schiotz/
