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From Aaron Markham <aaron.s.mark...@gmail.com>
Subject Re: Some feedback from MXNet Zhihu topic
Date Wed, 19 Sep 2018 21:39:37 GMT
Thanks for this translation and feedback Qing!
I've addressed point 3 of the documentation feedback with this PR:
https://github.com/apache/incubator-mxnet/pull/12604
I'm not sure how to take the first two points without some explicit URLs
and examples, so if anyone has those I'd be happy to take a look if there's
some glitch vs missing or wrong docs.

Also, I would agree that there should be some more simple examples. Often
times the examples are too complicated and unclear about what is important
or not. The audience targeting is for deep learning practitioners, not
"newbies".

And on a related note, I'd really like to pull the Gluon stuff into the API
section. It's confusing as its own navigation item and orphaned
information. It could have a navigation entry at the top of the API list
like "Python: Gluon" or just "Gluon" then list "Python: Module" or just
"Python". Or running this the other way, the Gluon menu could have API and
Tutorials and be more fleshed out, though this is not my preference. Either
way, it needs some attention.

Cheers,
Aaron

On Wed, Sep 19, 2018 at 11:04 AM Qing Lan <lanking520@live.com> wrote:

> Hi all,
>
> There was a trend topic<https://www.zhihu.com/question/293996867> in
> Zhihu (a famous Chinese Stackoverflow+Quora) asking about the status of
> MXNet in 2018 recently. Mu replied the thread and obtained more than 300+
> `like`.
> However there are a few concerns addressed in the comments of this thread,
> I have done some simple translation from Chinese to English:
>
> 1. Documentations! Until now, the online doc still contains:
>                 1. Depreciated but not updated doc
>                 2. Wrong documentation with poor description
>                 3. Document in Alpha stage such as you must install `pip
> –pre` in order to run.
>
> 2. Examples! For Gluon specifically, many examples are still mixing
> Gluon/MXNet apis. The mixure of mx.sym, mx.nd mx.gluon confused the users
> of what is the right one to choose in order to get their model to work. As
> an example, Although Gluon made data encapsulation possible, still there
> are examples using mxn.io.ImageRecordIter with tens of params (feels like
> gluon examples are simply the copy from old Python examples).
>
> 3. Examples again! Comparing to PyTorch, there are a few examples I don't
> like in Gluon:
>                 1. Available to run however the code structure is still
> very complicated. Such as example/image-classification/cifar10.py. It
> seemed like a consecutive code concatenation. In fact, these are just a
> series of layers mixed with model.fit. It makes user very hard to
> modify/extend the model.
>                 2. Only available to run with certain settings. If users
> try to change a little bit in the model, crashes will happen. For example,
> the multi-gpu example in Gluon website, MXNet hide the logic that using
> batch size to change learning rate in a optimizer. A lot of newbies didn't
> know this fact and they would only find that the model stopped converging
> when batch size changed.
>                 3. The worst scenario is the model itself just simply
> didn't work. Maintainers in the MXNet community didn't run the model (even
> no integration test) and merge the code directly. It makes the script not
> able run till somebody raise the issues and fix it.
>
> 4. The Community problem. The core advantage for MXNet is it's scalability
> and efficiency. However, the documentation of some tools are confusing.
> Here are two examples:
>
>                 1. im2rec contains 2 versions, C++ (binary) and python.
> But nobody would thought that the argparse in these tools are different (in
> the meantime, there is no appropriate examples to compare with, users could
> only use them by guessing the usage).
>
>                 2. How to combine MXNet distributed platform with
> supercomputing tool such as Slurm? How do we do profiling and how to debug.
> A couples of companies I knew thought of using MXNet for distributed
> training. Due to lack of examples and poor support from the community, they
> have to change their models into TensorFlow and Horovod.
>
> 5. The heavy code base. Most of the MXNet examples/source
> code/documentation/language binding are in a single repo. A git clone
> operation will cost tens of Mb. The New feature PR would takes longer time
> than expected. The poor reviewing response / rules keeps new contributors
> away from the community. I remember there was a call for
> document-improvement last year. The total timeline cost a user 3 months of
> time to merge into master. It almost equals to a release interval of
> Pytorch.
>
> 6. To Developers. There are very few people in the community discussed the
> improvement we can take to make MXNet more user-friendly. It's been so easy
> to trigger tens of stack issues during coding. Again, is that a requirement
> for MXNet users to be familiar with C++? The connection between Python and
> C lacks a IDE lint (maybe MXNet assume every developers as a VIM master).
> API/underlying implementation chaged frequently. People have to release
> their code with an achieved version of MXNet (such as TuSimple and MSRA).
> Let's take a look at PyTorch, an API used move tensor to device would raise
> a thorough discussion.
>
> There will be more comments translated to English and I will keep this
> thread updated…
> Thanks,
> Qing
>

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