Gluon is an attempt by Microsoft and Amazon to regain some influence in AI tools. Keras looked like it was going to become the standard high-level API, but now Theano is dead, and CNTK and MxNet are controlled by Google's rivals, and they're ganging up against Google's tools. Francois Chollet is committed to keeping Keras neutral, but he's still a Google engineer, and that probably makes Microsoft and Amazon nervous. This is the equivalent of Microsoft creating C# in response to Java. The company that controls the API, has enormous influence on the ecosystem built atop the API, just like Google has had with Android, or MSFT with Windows. MSFT and AMZN are carving out their own user base, or trying to, at the price of fragmenting the Python community.
Gluon – Deep Learning API from AWS and Microsoft
61–70 of 85 posts
Re: Gluon – Deep Learning API from AWS and Microsoft
#62Earlier quoted context omitted.
As a long-time Java developer, Python was a beauty. It brings back the joy of programming and makes data manipulation a breeze. C# is better than Java, but it's still not as elegant/simple/clean as Python for data science.
Python is only fun for tiny projects. Once you reach 120k LOC in a project, refactoring in Python is an insanity even with PyCharm, and debugging becomes impossible, too. Have you tried Kotlin?
Python in data/ml rarely goes into that scale. It is used for Training. Several thousands line per project at most, it is tractable and I don't think machine learning models really can be refactored or debugged like a web application.
Re: Gluon – Deep Learning API from AWS and Microsoft
#63Earlier quoted context omitted.
Data Engineer here... How do you get to 120K LOC without splitting up your infrastructure? If anything, it is poor design on your part. Python is beautiful. I've used it at 3 different companies now, 2 of which i encouraged them to try it out and they have nothing but love for it.
Even if you split your infrastructure, have you ever tried refactoring larger projects, with many contributors, while ensuring API contracts are kept? Without a strict and static type system it becomes quite problematic to ensure new code keeps the API contract, unless you have unit codes for every possible value. A good type system accelerates your coding speed, compared to writing equivalent unit tests, and it impr…
> A good type system accelerates your coding speed, compared to writing equivalent unit tests, and it improves your quality, compared to no testing.
Coding speed is least of my concern for a ML project, to be honest. And unit tests aren't useful either, since ML by large is not deterministic. A lot u said is true for web application, but didn't really apply for a ML project
Re: Gluon – Deep Learning API from AWS and Microsoft
#64Earlier quoted context omitted.
Mostly because Python is a great "glue" language. It isn't performant enough to implement the actual low-level computation, but is better at running other applications, getting data from them, feeding them into other applications (a.k.a pipelines).
Sure but if I have invested a lot in MS technologies I am hesitant to learn and implement things in a completely new language if I can find something that is more fitting to the stack I already use.
Python is easy to pick up (coming from someone who loves the MS stack). Don't let it being a new language deter you.
Re: Gluon – Deep Learning API from AWS and Microsoft
#65Earlier quoted context omitted.
“accelerates coding speed” this is debatable
Well, the comparison was to writing unit tests that provide the same safety as an equivalent type system. And compared to that, the type system is certainly faster.
Numpy enforces type consistency within arrays. Type errors are still possible but generally rarer and are noticed sooner than base Python.
Re: Gluon – Deep Learning API from AWS and Microsoft
#66Earlier quoted context omitted.
https://jeffknupp.com/blog/2017/09/15/python-is-the-fastest-... > Python's Buffer Protocol: The #1 Reason Python Is The Fastest Growing Programming Language Today > The buffer protocol was (and still is) an extremely low-level API for direct manipulation of memory buffers by other libraries. These are buffers created and used by the interpreter to store certain types of data (initially, primarily "array-like" structu…
That doesn’t sound like a very satisfying reason to me. Isn’t a ByteArrayBuffer in Java pretty much the same (its underlying implementation is a char[] which can be used directly from C)? Is there perhaps another factor, such as an existing ecosystem or that it’s widely used in the academic field?
>Is there perhaps another factor, such as an existing ecosystem or that it’s widely used in the academic field?
Yes, it's widely used in science in general. Don't underestimate the learning curves of other languages when your audience is scientists and mathematicians. Python is incredibly easy to use, even when using numpy and other scientific tools.
Re: Gluon – Deep Learning API from AWS and Microsoft
#67Gluon is an attempt by Microsoft and Amazon to regain some influence in AI tools. Keras looked like it was going to become the standard high-level API, but now Theano is dead, and CNTK and MxNet are controlled by Google's rivals, and they're ganging up against Google's tools. Francois Chollet is committed to keeping Keras neutral, but he's still a Google engineer, and that probably makes Microsoft and Amazon nervous.…
Unlike Keras and Tensorflow, Gluon is Define-by-run Deeplearning framework like Pytorch, Chainer.
Network definition/debugging/flexibility are really better with dynamic network (define-by-run).
That's why Facebook seem to use Pytorch for research and caffe2 for deployment. Gluon/Mxnet can do both define-by-run with Gluon API and "standard" define-and-run with it's Module API.
Re: Gluon – Deep Learning API from AWS and Microsoft
#68Gluon is an attempt by Microsoft and Amazon to regain some influence in AI tools. Keras looked like it was going to become the standard high-level API, but now Theano is dead, and CNTK and MxNet are controlled by Google's rivals, and they're ganging up against Google's tools. Francois Chollet is committed to keeping Keras neutral, but he's still a Google engineer, and that probably makes Microsoft and Amazon nervous.…
I think you had a justifiable devil's advocate until your claim about "Fragmenting"
What exactly is so bad about competition?
Re: Gluon – Deep Learning API from AWS and Microsoft
#69Gluon is an attempt by Microsoft and Amazon to regain some influence in AI tools. Keras looked like it was going to become the standard high-level API, but now Theano is dead, and CNTK and MxNet are controlled by Google's rivals, and they're ganging up against Google's tools. Francois Chollet is committed to keeping Keras neutral, but he's still a Google engineer, and that probably makes Microsoft and Amazon nervous.…
I think you had a justifiable devil's advocate until your claim about "Fragmenting" What exactly is so bad about competition?
What is so bad about standardization?
Re: Gluon – Deep Learning API from AWS and Microsoft
#70This is interesting, but because of the growth of the number of ML frameworks and languages, when new ones pop up it would be great for them to release methods to transfer existing models to their language. I would love some extra compatibility with AWS for deploying deep learning models in prod but since I already have existing models running in production, it's a hard sale for me to re-train and re-implement from s…
Recently there is more and more initiative to have standard format in deeplearning environnement.
dlpack for tensor format (https://github.com/dmlc/dlpack)
onnx for saved NN (https://github.com/onnx/onnx)
and tvm for execution (http://tvmlang.org/2017/10/06/nnvm-compiler-announcement.htm...)