cntk - 微软放弃CNTK了吗？

Question

我想知道CNTK死了吗？ GitHub 上发布日期为 2019 年 3 月 31 日的发行说明:“今天的 2.7 版本将是 CNTK 的最后一个主要版本。”我花了几个月的时间使用 CNTK 开发软件，现在看来这是浪费时间和金钱。我在许多网站上搜索了答案，但仍然没有答案。 stackoverflow 是 Microsoft 推荐的网站之一。

Answer 1

来自 KedengMS，CNTK 的维护者之一。转自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).