We modified the blog post and acknowledged Zhi's contributions. It reads now: "*Special thanks to the dmlc/nnvm community and Zhi Zhang, whose ONNX code was used as a reference for this implementation."*
Regards, Steffen On Fri, Nov 17, 2017 at 10:36 AM Tianqi Chen <[email protected]> wrote: > 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 <[email protected]> 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 > > > > >
