I have watched the issue for around two days. Here are my two cents.
First of all, there is no legal constraint to enforce you do anything, but
as you said(which I fully agree on), we need to assume others have best
intentions and give goodwill
- It is great to reuse code, that is what open-source is about
- It is un-arguably true that Zhi created and maintained most of part of
the original code. While there are minor contributions from other
contributors. I think Zhi should be personally acknowledged at least(he
deserve more than that).
- As an analogy, you are not the only one creating the onnx-mxnet
repo, but never the less you are listed as the author, instead of simply
saying that comes from AWS
- I would recommend you start with the files of nnvm as your first commit,
then apply changes to it.
- This will take around 5 min or so, copy the file from nnvm, commit,
override with your new file, commit
- It makes it clear what changes are being done
- It makes your life easier to adopt new patches when there is a
bugfix in nnvm or vice versa
- Please maintain it, instead of leaving the job to the community. As with
every great prize comes with great responsibility, it is great that you
push out the repo and takes the credit for doing it. The deep learning
serializable IR land is still unstable and there demand the efforts to put
in to maintain the code to keep up with the breaking changes and add
coverage.
Congrats on the release
Tianqi
On Thu, Nov 16, 2017 at 2:04 PM, Lupesko, Hagay <lupe...@gmail.com> wrote:
> Hey folks,
>
>
>
> Today AWS announced contributing ONNX-MXNet, an open source Python package
> that imports ONNX models into MXNet. @roshrini and I (@lupesko) have worked
> on the code, which is now publicly available [1], and published a blog post
> demonstrating usage of the package [2]. Special thanks to dmlc/nnvm team,
> whose ONNX code was used as a reference for this implementation.
>
>
>
> What is ONNX?
>
> ONNX is an open source format to encode deep learning models. ONNX defines
> a format to store neural network's computational graph, as well as a
> storage format for operators used within a neural network graph. For more
> details, check out onnx.ai [3].
>
>
>
> Why I think ONNX is important for MXNet?
>
> ONNX is an emerging standard, that holds a lot of potential for Deep
> Learning practitioners. With ONNX, people can create and train a network
> with framework A, and deploy it for inference with framework B. The blog
> post we published demonstrates using a Super Res model trained with
> PyTorch, and importing it into MXNet Symbolic API for inference. I strongly
> believe that adopting ONNX early on adds value for deep learning
> practitioners, and thus supporting it adds value for MXNet as well.
>
>
>
> As for next steps, I was thinking that porting the functionality and code
> into MXNet is the logical next step.
>
> Would love to get the community's feedback and contributions!
>
>
>
> [1] https://github.com/onnx/onnx-mxnet
>
> [2] https://aws.amazon.com/blogs/ai/announcing-onnx-support-
> for-apache-mxnet/
>
> [3] https://onnx.ai
>
>
