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Re: TensorFlow, PyTorch, and manylinux1


I'm reposting my original reply below the current reply (below a dotted
line). It was filtered out because I wasn't subscribed to the relevant
mailing lists.

 tl;dr: manylinux2010 looks pretty promising, because CUDA supports CentOS6
(for now).

In the meanwhile, I dug into what pyarrow does, and it looks like it links
with `static-libstdc++` along with a linker version script [1].

PyTorch did exactly that until Jan this year [2], except that our linker
version script didn't cover the subtleties of statically linking stdc++ as
well as Arrow did. Because we weren't covering all of the stdc++ static
linking subtleties, we were facing huge issues that amplified wheel
incompatibility (import X; import torch crashing under various X). Hence,
we moved since then to linking with system-shipped libstdc++, doing no
static stdc++ linking.

I'll revisit this in light of manylinux2010, and go down the path of static
linkage of stdc++ again, though I'm wary of the subtleties around handling
of weak symbols, std::string destruction across library boundaries [3] and
std::string's ABI incompatibility issues.

I've opened a tracking issue here:
https://github.com/pytorch/pytorch/issues/15294

I'm looking forward to hearing from the TensorFlow devs if manylinux2010 is
sufficient for them, or what additional constraints they have.

As a personal thought, I find multiple libraries in the same process
statically linking to stdc++ gross, but without a package manager like
Anaconda that actually is willing to deal with the C++-side dependencies,
there aren't many options on the table.

References:

[1] https://github.com/apache/arrow/blob/master/cpp/src/arrow/symbols.map
[2] https://github.com/pytorch/pytorch/blob/v0.3.1/tools/pytorch.version
[3] https://github.com/pytorch/pytorch/issues/5400#issuecomment-369428125
............................................................................................................................................................
Hi Philipp,

Thanks a lot for getting a discussion started. I've sunk ~100+ hours over
the last 2 years making PyTorch wheels play well with OpenCV, TensorFlow
and other wheels, that I'm glad to see this discussion started.


On the PyTorch wheels, we have been shipping with the minimum glibc and
libstdc++ versions we can possibly work with, while keeping two hard
constraints:

1. CUDA support
2. C++11 support


1. CUDA support

manylinux1 is not an option, considering CUDA doesn't work out of CentOS5.
I explored this option [1] to no success.

manylinux2010 is an option at the moment wrt CUDA, but it's unclear when
NVIDIA will lift support for CentOS6 under us.
Additionally, CuDNN 7.0 (if I remember) was compiled against Ubuntu 12.04
(meaning the glibc version is newer than CentOS6), and binaries linked
against CuDNN refused to run on CentOS6. I requested that this constraint
be lifted, and the next dot release fixed it.

The reason PyTorch binaries are not manylinux2010 compatible at the moment
is because of the next constraint: C++11.

2. C++11

We picked C++11 as the minimum supported dialect for PyTorch, primarily to
serve the default compilers of older machines, i.e. Ubuntu 14.04 and
CentOS7. The newer options were C++14 / C++17, but we decided to polyfill
what we needed to support older distros better.

A fully fleshed out C++11 implementation landed in gcc in various stages,
with gradual ABI changes [2]. Unfortunately, the libstdc++ that ships with
centos6 (and hence manylinx2010) isn't sufficient to cover all of C++11.
For example, the binaries we built with devtoolset3 (gcc 4.9.2) on CentOS6
didn't run with the default libstdc++ on CentOS6 either due to ABI changes
or minimum GLIBCXX version for some of the symbols being unavailable.

We tried our best to support our binaries running on CentOS6 and above with
various ranges of static linking hacks until 0.3.1 (January 2018), but at
some point hacks over hacks was only getting more fragile. Hence we moved
to a CentOS7-based image in April 2018 [3], and relied only on dynamic
linking to the system-shipped libstdc++.

As Wes mentions [4], an option is to host a modern C++ standard library via
PyPI would put manylinux2010 on the table. There are however subtle
consequences with this -- if this package gets installed into a conda
environment, it'll clobber anaconda-shipped libstdc++, possibly corrupting
environments for thousands of anaconda users (this is actually similar to
the issues with `mkl` shipped via PyPI and Conda clobbering each other).


References:

[1] https://github.com/NVIDIA/nvidia-docker/issues/348
[2] https://gcc.gnu.org/wiki/Cxx11AbiCompatibility
[3]
https://github.com/pytorch/builder/commit/44d9bfa607a7616c66fe6492fadd8f05f3578b93
[4] https://github.com/apache/arrow/pull/3177#issuecomment-447515982
..............................................................................................................................................................................................

On Sun, Dec 16, 2018 at 2:57 PM Wes McKinney <wesmckinn@xxxxxxxxx> wrote:

> Reposting since I wasn't subscribed to developers@xxxxxxxxxxxxxx. I
> also didn't see Soumith's response since it didn't come through to
> dev@xxxxxxxxxxxxxxxx
>
> In response to the non-conforming ABI in the TF and PyTorch wheels, we
> have attempted to hack around the issue with some elaborate
> workarounds [1] [2] that have ultimately proved to not work
> universally. The bottom line is that this is burdening other projects
> in the Python ecosystem and causing confusing application crashes.
>
> First, to state what should hopefully obvious to many of you, Python
> wheels are not a robust way to deploy complex C++ projects, even
> setting aside the compiler toolchain issue. If a project has
> non-trivial third party dependencies, you either have to statically
> link them or bundle shared libraries with the wheel (we do a bit of
> both in Apache Arrow). Neither solution is foolproof in all cases.
> There are other downsides to wheels when it comes to numerical
> computing -- it is difficult to utilize things like the Intel MKL
> which may be used by multiple projects. If two projects have the same
> third party C++ dependency (e.g. let's use gRPC or libprotobuf as a
> straw man example), it's hard to guarantee that versions or ABI will
> not conflict with each other.
>
> In packaging with conda, we pin all dependencies when building
> projects that depend on them, then package and deploy the dependencies
> as separate shared libraries instead of bundling. To resolve the need
> for newer compilers or newer C++ standard library, libstdc++.so and
> other system shared libraries are packaged and installed as
> dependencies. In manylinux1, the RedHat devtoolset compiler toolchain
> is used as it performs selective static linking of symbols to enable
> C++11 libraries to be deployed on older Linuxes like RHEL5/6. A conda
> environment functions as sort of portable miniature Linux
> distribution.
>
> Given the current state of things, as using the TensorFlow and PyTorch
> wheels in the same process as other conforming manylinux1 wheels is
> unsafe, it's hard to see how one can continue to recommend pip as a
> preferred installation path until the ABI problems are resolved. For
> example, "pip" is what is recommended for installing TensorFlow on
> Linux [3]. It's unclear that non-compliant wheels should be allowed in
> the package manager at all (I'm aware that this was deemed to not be
> the responsibility of PyPI to verify policy compliance [4]).
>
> A couple possible paths forward (there may be others):
>
> * Collaborate with the Python packaging authority to evolve the
> manylinux ABI to be able to produce compliant wheels that support the
> build and deployment requirements of these projects
> * Create a new ABI tag for CUDA/C++11-enabled Python wheels so that
> projects can ship packages that can be guaranteed to work properly
> with TF/PyTorch. This might require vendoring libstdc++ in some kind
> of "toolchain" wheel that projects using this new ABI can depend on
>
> Note that these toolchain and deployment issues are absent when
> building and deploying with conda packages, since build- and run-time
> dependencies can be pinned and shared across all the projects that
> depend on them, ensuring ABI cross-compatibility. It's great to have
> the convenience of "pip install $PROJECT", but I believe that these
> projects have outgrown the intended use for pip and wheel
> distributions.
>
> Until the ABI incompatibilities are resolved, I would encourage more
> prominent user documentation about the non-portability and potential
> for crashes with these Linux wheels.
>
> Thanks,
> Wes
>
> [1]:
> https://github.com/apache/arrow/commit/537e7f7fd503dd920c0b9f0cef8a2de86bc69e3b
> [2]:
> https://github.com/apache/arrow/commit/e7aaf7bf3d3e326b5fe58d20f8fc45b5cec01cac
> [3]: https://www.tensorflow.org/install/
> [4]: https://www.python.org/dev/peps/pep-0513/#id50
> On Sat, Dec 15, 2018 at 11:25 PM Robert Nishihara
> <robertnishihara@xxxxxxxxx> wrote:
> >
> > On Sat, Dec 15, 2018 at 8:43 PM Philipp Moritz <pcmoritz@xxxxxxxxx>
> wrote:
> >
> > > Dear all,
> > >
> > > As some of you know, there is a standard in Python called manylinux (
> > > https://www.python.org/dev/peps/pep-0513/) to package binary
> executables
> > > and libraries into a “wheel” in a way that allows the code to be run
> on a
> > > wide variety of Linux distributions. This is very convenient for Python
> > > users, since such libraries can be easily installed via pip.
> > >
> > > This standard is also important for a second reason: If many different
> > > wheels are used together in a single Python process, adhering to
> manylinux
> > > ensures that these libraries work together well and don’t trip on each
> > > other’s toes (this could easily happen if different versions of
> libstdc++
> > > are used for example). Therefore *even if support for only a single
> > > distribution like Ubuntu is desired*, it is important to be manylinux
> > > compatible to make sure everybody’s wheels work together well.
> > >
> > > TensorFlow and PyTorch unfortunately don’t produce manylinux compatible
> > > wheels. The challenge is due, at least in part, to the need to use
> > > nvidia-docker to build GPU binaries [10]. This causes various levels of
> > > pain for the rest of the Python community, see for example [1] [2] [3]
> [4]
> > > [5] [6] [7] [8].
> > >
> > > The purpose of the e-mail is to get a discussion started on how we can
> > > make TensorFlow and PyTorch manylinux compliant. There is a new
> standard in
> > > the works [9] so hopefully we can discuss what would be necessary to
> make
> > > sure TensorFlow and PyTorch can adhere to this standard in the future.
> > >
> > > It would make everybody’s lives just a little bit better! Any ideas are
> > > appreciated.
> > >
> > > @soumith: Could you cc the relevant list? I couldn't find a pytorch dev
> > > mailing list.
> > >
> > > Best,
> > > Philipp.
> > >
> > > [1] https://github.com/tensorflow/tensorflow/issues/5033
> > > [2] https://github.com/tensorflow/tensorflow/issues/8802
> > > [3] https://github.com/primitiv/primitiv-python/issues/28
> > > [4] https://github.com/zarr-developers/numcodecs/issues/70
> > > [5] https://github.com/apache/arrow/pull/3177
> > > [6] https://github.com/tensorflow/tensorflow/issues/13615
> > > [7] https://github.com/pytorch/pytorch/issues/8358
> > > [8] https://github.com/ray-project/ray/issues/2159
> > > [9] https://www.python.org/dev/peps/pep-0571/
> > > [10]
> > >
> https://github.com/tensorflow/tensorflow/issues/8802#issuecomment-291935940
> > >
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