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Re: python3-tensorflow; lintian tag for pretrained neural net

Hi -science and -devel,

this section is for -science

As said by Yunqiang, tensorflow itself is a very complex system and it
is even worse that we have to write our own build system for it.  Our
current build system for Tensorflow 1.X (in experimental) is frozen[1]
and I'm basically not going to update it anymore, because it will soon
be replaced by Yunqiang's when Tensorflow 2.X comes out, which is
expected to happen in spring 2019. At that time the python3 interface
will also be available. Python3 package[2] for TF1.X is hopeless since
I've already given up.

TF2.0 is expected to be significantly different compared to the 1.X
series. So whoever wants to package Tensorflow-related applications,
please make sure your upstream will be happy to support TF2.0 when it's

[1] Yunqiang have more time than me to work on this.
[2] Although the current build system is not far from building a working
    python3 interface package.

this section is for -devel

Apart from that, I'm thinking whether we should add one more
experimental lintian tag

  X: possibly-pretrained-neural-network /usr/share/.../foobar.binaryblob

which reminds package maintainers that the license problem underlying
pretrained networks is still *unresolved* if the training data is not
available under a free software license or any compliant one.

However it is not quite easy to detect suspicious files since there are
way too much means to serialize neural nets and store them in some kind
of format. The only reasonable solution I came up with is[3]

  if is_binary_blob(file) and
     any(x in filename for x in (list_of_popular_pretrained_model_names))

Accordingly, since such a tag is proposed as sort of reminder to package
maintainers, we should also determine what to do if a license-problematic
pretrained net sneaked into the archive by chance.

I raised this topic again because I really see an upstream embed
pretrained net in the source[4].

[3] A similar existing lintian tag could be "...lena..." which
    blacklists some certain hashsums. However hashsum matching will never
    work for neural networks.
[4] https://github.com/pytorch/pytorch/tree/master/caffe2/mobile/contrib/arm-compute/models
    FYI: squeezenet is a typical pretrained net expecially for mobile devices.
    FYI: pytorch is a strong competitor to TF.

On Tue, Dec 11, 2018 at 02:53:47PM +0800, YunQiang Su wrote:
> Andreas Tille <andreas@an3as.eu> 于2018年12月11日周二 下午2:40写道:
> >
> > I intend to package deepbinner[1] which needs python3-tensorflow.
> > Is there any reason you do not provide this Python3 module in
> > tensorflow source package?
> The build system of tensorflow is quite complicated, and we are working on it.
> tensorflow is still in experimental now, we are improving it.

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