I want to know if CNTK dead? Release notes on GitHub dated 03/31/2019: "Today’s 2.7 release will be the last main release of CNTK." I've spent months developing software using CNTK and now it appears to be a waste of time and money. I've search for an answer on numerous sites and still no answer. stackoverflow is one of the sites recommend by Microsoft.
From KedengMS, one of the maintainers for CNTK. Reposted from github.
Thanks for all the CNTK supporters, and I am privileged to have worked
on it, and learned a lot in the process. You can continue to use CNTK
for training and inference in the way it currently is, as other
Microsoft internal teams that still runs old models even in
BrainScript or NDL. Stopping adding new features does not mean CNTK is
no longer open source, it just means that going forward, there will be
no new GPU support (say, CUDA 11+), and no major new features added.
For different user scenarios, I think you may have different choices:
Deep learning newcomers: IMO CNTK is still a good entry to understand basics of deep learning, if you found CNTK
documents/tutorials/examples useful. Once you learnt the basic, it
won't be too hard to switch between frameworks. However, the DL field
is changing rapidly and CNTK has already lagged behind in a lot of
ways, so if you need more advanced features like dynamic graph,
PyTorch would be a better choice.
Model maintainers: If you already have CNTK models working, and to maintain it just means training with new data, you can continue to use
CNTK the way you currently use it. Actually, teams inside Microsoft
are doing this too. If there are serious bugs preventing productivity,
they still will be fixed. For inference, you can continue to use CNTK
C/C++/Python/C#/Java APIs, or you may export CNTK models in ONNX
format, and use ONNX Runtime or ORT as a slimmer and faster inference
engine. You'll be surprised to find how much faster it is comparing to
CNTK, and how slimmer the setup is (forget about OpenMPI when you just
need inference!). ORT currently provides C/C++/Python/C# interfaces.
Model builders: If you have CNTK model, and want to use features that are not currently supported in CNTK, please consider switch to
other frameworks like TensorFlow/PyTorch/etc. Our team has done lots
of data reader work inside PyTorch to ensure teams in Microsoft can
switch from CNTK to PyTorch. Besides, we are also in the process of
migrating CNTK specific distributed trainer like BMUF to PyTorch.
Hopefully you'll find that useful too when migrating your model.
The good thing about open source is that the community can continue to
fork/evolve if needed, unlike other Microsoft products that only ship
binaries (Win7 I am looking at you).
Related
Is there any equivalent/alternate library to fastai in tensorfow for easier training and debugging deep learning models including analysis on results of trained model in Tensorflow.
Fastai is built on top of pytorch looking for similar one in tensorflow.
The obvious choice would be to use tf.keras.
It is bundled with tensorflow and is becoming its official "high-level" API -- to the point where in TF 2 you would probably need to go out of your way not using it at all.
It is clearly the source of inspiration for fastai to easy the use of pytorch as Keras does for tensorflow, as mentionned by the authors time and again:
Unfortunately, Pytorch was a long way from being a good option for part one of the course, which is designed to be accessible to people with no machine learning background. It did not have anything like the clear simple API of Keras for training models. Every project required dozens of lines of code just to implement the basics of training a neural network. Unlike Keras, where the defaults are thoughtfully chosen to be as useful as possible, Pytorch required everything to be specified in detail. However, we also realised that Keras could be even better. We noticed that we kept on making the same mistakes in Keras, such as failing to shuffle our data when we needed to, or vice versa. Also, many recent best practices were not being incorporated into Keras, particularly in the rapidly developing field of natural language processing. We wondered if we could build something that could be even better than Keras for rapidly training world-class deep learning models.
I am newbie in Tensorflow
Actually, I am testing some example in Tensorflow web-site, and I start to understand some features of the framwork, but what I don't understand is how I can design my architecture, I mean number of layers, type of Layer "conv, pool...", and if it is necessery to do that, because there are many predifined architectures like AmexNet,
Thanks,
I would strongly recommend working through their hands on tutorial, depending on if you have previous ML experience (https://www.tensorflow.org/get_started/mnist/pros) or not (https://www.tensorflow.org/get_started/mnist/beginners). The questions you are asking are answered in there.
The question on using predefined architectures or self defined depends on your use case. If you want to do something easy like classifying if there is only a car in the scene or not a more shallow architecture might work better, because it is faster and a more deep one is overkill. However most architectures are similar to the ones already defined in literature.
Another question that arises naturally, while talking about pre defined architecture is about transfer learning / fine tuning. Often pre defined architectures are already learned on some big dataset (mostly ImageNet) and already perform really well out of the box for many tasks. With little training data it makes a lot of sense to use this. With lots of training data it can hinder your progress though.
I want to study on the research of deep learning, but I don't know which framwork should I choice between TensorFlow and PaddlePaddle. who can make a contrast between the two frameworks? which one is better? especially in the running efficiency of CPU
It really depends what you are shooting for...
If you plan on training, CPU is not going to work well for you. Use colab or kaggle.
Assuming you do get a GPU, it depends if you want to focus on classification or object detection.
If you focus on classification, Keras is probably the easiest to work with or pytorch if you want some advanced stuff and to be able to change things.
If you plan on object detection, things are getting complicated... Inference is reasonably easy but training is complicated. There are actually 4 platforms you should consider:
Tensorflow - powerful but very difficult to work with. If you do not use Keras (and for OD you usually can't), you need to preprocess the dataset into tfrecords and it is a pain. The OD Api has very cryptic messages and it is very sensitive to the combination of tf version and api version. On the other hand, cool models like efficientdet are more or less easy to use.
MMdetection - very powerful framework, has lots of advanced models and once you understand how to work with it, you can easily work with and of the models it supports. Downside is that some models are slow to arrive (efficientdet, for example)
paddlepaddle - if you know Chinese, this should work ok, maybe. The documentation is a bit behind and usually requires lots of improvisation. Basically it is similar to mmdetection just with a few unique models and a few missing models.
detectron2 - I didn't work with this one, but it seems to support only a few models.
You probably need first to define for yourself what do you want to do and then choose.
Good luck!
It is not that trivial. Some models run faster with one kind of framework others with another. Furthermore, it depends on the hardware as well. See this blog. If inference is your only concern, then you can develop your model in any of the popular frameworks like TensorFlow, PyTorch, etc. In the end convert your model to ONNX format and benchmark its performance with DNN-Bench to choose the best inference engine for your application.
I heard Google has over 100 engineers working on Tensorflow. So, what does Google gain from distributing this expensive engine for free? Will Google later charge corporate Tensorflow users? ?
What is google’s plan for Tensorflow? will it be free forever? like a deep learning version of python?
Some people tell me it's to reduce new employee training time. If so, why did Baidu develop it's own deep learning library when that means they should spend extra time training it's employees who are used to using Tensorflow, how to use Baidu's deep learning library
Well if everyone is using tensorflow there are more contributions made to it by the public. That helps google for free.
Even if it doesn't become a product that they sell they can still make money from it.
The thing is they might be able to sell services around it like lessons. They can also sell hardware specifically optimized for tensorflow etc.
Almost every company had its own machine learning framework. The reason they open sourced them was because each of theirs were going to fall much behind if it wasn't open source. A prime example is how Torch was doing so great up until tensorflow became open source as well. (With google's backing and because it's in python instead of lua are a few reasons tf became more popular).
Are there instructions or some documentation somewhere or could somebody describe how to deploy the models available as "Parsey's Cousins" (see https://github.com/tensorflow/models/blob/master/syntaxnet/universal.md) with SyntaxNet under Tensorflow Serving? Even deploying just Parsey is a rather complex undertaking that is not really documented anywhere, but how to do this for the additional 40 languages?
This pull request partially addresses your request, but it still has some issues: https://github.com/tensorflow/models/pull/250.
We do have some tentative plans to provide easier integration between SyntaxNet and Tensorflow Serving, but no precise timeline.
Just for the benefit of anyone else who finds this question, after some digging around on GitHub, one can find the following issue started by Johann Petrak:
https://github.com/dsindex/syntaxnet/issues/7
a model from parsey's cousin is not able to export by that patch due to version mismatch
So whilst some people have been able to modify syntaxnet so that it works with Tensorflow Serving, this seems to be at the cost of using a version which is not compatible with Parsey's Cousins.
Currently the only way to get Tensorflow Serving working with languages other than English is to use something like dsindex's code and train your own models.