On 08/01/2018 15:48, Jakob Schiøtz wrote:
Hi Kenneth,
I have now tested your TensorFlow 1.4.0 eb on our machines with a real-world
script. It works, but it runs three times slower than with the prebuild
TensorFlow 1.2.1 :-(
The prebuild version complains that it was build without AVX2 etc, so I do not
really understand why it is so much slower to use the version compiled from
source - assuming of course that there is not a factor three performance loss
between 1.2.1 and 1.4.0; which seems unlikely.
Wow, that must be wrong somehow...
Is this on the GPU systems?
You're not comparing a GPU-enabled TF 1.2 with a CPU-only TF 1.4 built
with EB, are you?
If you are, then a only factor 3 slower using only CPU is actually quite
impressive vs GPU-enabled build. ;-)
How are you benchmarking this exactly?
When I was trying with the script from
https://github.com/tensorflow/benchmarks/tree/master/scripts/tf_cnn_benchmarks,
I saw 7x better performance when building TF 1.4.0 from source on Intel
Haswell (no GPU) compared to a conda install (which is basically the
same as using the binary wheel).
On a GPU system (NVIDIA K40) with the TF 1.4.0 binary wheel I saw
another 8x performance increase over the EB-installed-from-source
CPU-only TF 1.4.0 installation.
Here's the command I was running (don't forget the change --device when
running on a GPU system):
python tf_cnn_benchmarks.py --device cpu --batch_size=32
--model=resnet50 --variable_update=parameter_server --data_format NHWC
regards,
Kenneth
Best regards
Jakob
On 5 Jan 2018, at 13:57, Kenneth Hoste <kenneth.ho...@ugent.be> wrote:
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/
--
Jakob Schiøtz, professor, Ph.D.
Department of Physics
Technical University of Denmark
DK-2800 Kongens Lyngby, Denmark
http://www.fysik.dtu.dk/~schiotz/
