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
