It wasn't clear why this was commit was reverted. Things that stood out as odd:
* I didn't see an email to dev@ on the topic of a revert. * Rather than reverting, if there is a minor item requiring a fix it could simply be fixed; if a major item then it should be raised on dev@. * I didn't see a reason to revert in the revert PR (11154). * The original PR has github:szha asking for github:piiswrong to review with no context; I'm concerned that it was implied that the commit could not go in without this review. * I don't see anything in the original PR to earn a revert. At best 'github:john-andrilla' being asked if "a flexible, scalable, multi-framework serving solution" was okay. * I find it odd that github:lupesko is a reviewer. Hen On Tue, Jun 5, 2018 at 5:08 PM, GitBox <g...@apache.org> wrote: > szha closed pull request #11154: Revert "[MXNET-503] Website landing page > for MMS (#11037)" > URL: https://github.com/apache/incubator-mxnet/pull/11154 > > > > > This is a PR merged from a forked repository. > As GitHub hides the original diff on merge, it is displayed below for > the sake of provenance: > > As this is a foreign pull request (from a fork), the diff is supplied > below (as it won't show otherwise due to GitHub magic): > > diff --git a/docs/mms/index.md b/docs/mms/index.md > deleted file mode 100644 > index ff6edae414b..00000000000 > --- a/docs/mms/index.md > +++ /dev/null > @@ -1,114 +0,0 @@ > -# Model Server for Apache MXNet (incubating) > - > -[Model Server for Apache MXNet (incubating)](https://github. > com/awslabs/mxnet-model-server), otherwise known as MXNet Model Server > (MMS), is an open source project aimed at providing a simple yet scalable > solution for model inference. It is a set of command line tools for > packaging model archives and serving them. The tools are written in Python, > and have been extended to support containers for easy deployment and > scaling. MMS also supports basic logging and advanced metrics with Amazon > CloudWatch integration. > - > - > -## Multi-Framework Model Support with ONNX > - > -MMS supports both *symbolic* MXNet and *imperative* Gluon models. While > the name implies that MMS is just for MXNet, it is in fact much more > flexible, as it can support models in the [ONNX](https://onnx.ai) format. > This means that models created and trained in PyTorch, Caffe2, or other > ONNX-supporting frameworks can be served with MMS. > - > -To find out more about MXNet's support for ONNX models and using ONNX > with MMS, refer to the following resources: > - > -* [MXNet-ONNX Docs](../api/python/contrib/onnx.md) > -* [Export an ONNX Model to Serve with MMS](https://github.com/ > awslabs/mxnet-model-server/docs/export_from_onnx.md) > - > -## Getting Started > - > -To install MMS with ONNX support, make sure you have Python installed, > then for Ubuntu run: > - > -```bash > -sudo apt-get install protobuf-compiler libprotoc-dev > -pip install mxnet-model-server > -``` > - > -Or for Mac run: > - > -```bash > -conda install -c conda-forge protobuf > -pip install mxnet-model-server > -``` > - > - > -## Serving a Model > - > -To serve a model you must first create or download a model archive. Visit > the [model zoo](https://github.com/awslabs/mxnet-model-server/ > docs/model_zoo.md) to browse the models. MMS options can be explored as > follows: > - > -```bash > -mxnet-model-server --help > -``` > - > -Here is an easy example for serving an object classification model. You > can use any URI and the model will be downloaded first, then served from > that location: > - > -```bash > -mxnet-model-server \ > - --models squeezenet=https://s3.amazonaws.com/model-server/ > models/squeezenet_v1.1/squeezenet_v1.1.model > -``` > - > - > -### Test Inference on a Model > - > -Assuming you have run the previous `mxnet-model-server` command to start > serving the object classification model, you can now upload an image to its > `predict` REST API endpoint. The following will download a picture of a > kitten, then upload it to the prediction endpoint. > - > -```bash > -curl -O https://s3.amazonaws.com/model-server/inputs/kitten.jpg > -curl -X POST http://127.0.0.1:8080/squeezenet/predict -F > "data=@kitten.jpg" > -``` > - > -The predict endpoint will return a prediction response in JSON. It will > look something like the following result: > - > -``` > -{ > - "prediction": [ > - [ > - { > - "class": "n02124075 Egyptian cat", > - "probability": 0.9408261179924011 > - }, > -... > -``` > - > -For more examples of serving models visit the following resources: > - > -* [Quickstart: Model Serving](https://github.com/ > awslabs/mxnet-model-server/README.md#serve-a-model) > -* [Running the Model Server](https://github.com/ > awslabs/mxnet-model-server/docs/server.md) > - > - > -## Create a Model Archive > - > -Creating a model archive involves rounding up the required model > artifacts, then using the `mxnet-model-export` command line interface. The > process for creating archives is likely to evolve. As the project adds > features, we recommend that you review the following resources to get the > latest instructions: > - > -* [Quickstart: Export a Model](https://github.com/ > awslabs/mxnet-model-server/README.md#export-a-model) > -* [Model Artifacts](https://github.com/awslabs/mxnet-model-server/ > docs/export_model_file_tour.md) > -* [Loading and Serving Gluon Models](https://github.com/ > awslabs/mxnet-model-server/tree/master/examples/gluon_alexnet) > -* [Creating a MMS Model Archive from an ONNX Model](https://github.com/ > awslabs/mxnet-model-server/docs/export_from_onnx.md) > -* [Create an ONNX model (that will run with MMS) from PyTorch]( > https://github.com/onnx/onnx-mxnet/blob/master/README.md#quick-start) > - > - > -## Using Containers > - > -Using Docker or other container services with MMS is a great way to scale > your inference applications. You can use Docker to pull the latest version: > - > -``` > -docker pull awsdeeplearningteam/mms_gpu > -``` > - > -It is recommended that you review the following resources for more > information: > - > -* [MMS Docker Hub](https://hub.docker.com/u/awsdeeplearningteam/) > -* [Using MMS with Docker Quickstart](https://github. > com/awslabs/mxnet-model-server/docker/README.md) > -* [MMS on Fargate](https://github.com/awslabs/mxnet-model-server/ > docs/mms_on_fargate.md) > -* [Optimized Container Configurations for MMS](https://github.com/ > awslabs/mxnet-model-server/docs/optimized_config.md) > -* [Orchestrating, monitoring, and scaling with MMS, Amazon Elastic > Container Service, AWS Fargate, and Amazon CloudWatch)](https://aws. > amazon.com/blogs/machine-learning/apache-mxnet-model- > server-adds-optimized-container-images-for-model-serving-at-scale/) > - > - > -## Community & Contributions > - > -The MMS project is open to contributions from the community. If you like > the idea of a flexible, scalable, multi-framework serving solution for your > models and would like to provide feedback, suggest features, or even jump > in and contribute code or examples, please visit the [project page on > GitHub](https://github.com/awslabs/mxnet-model-server). You can create an > issue there, or join the discussion on the forum. > - > -* [MXNet Forum - MMS Discussions](https://discuss. > mxnet.io/c/mxnet-model-server) > - > - > -## Further Reading > - > -* [GitHub](https://github.com/awslabs/mxnet-model-server) > -* [MMS Docs](https://github.com/awslabs/mxnet-model-server/docs) > > > > > ---------------------------------------------------------------- > This is an automated message from the Apache Git Service. > To respond to the message, please log on GitHub and use the > URL above to go to the specific comment. > > For queries about this service, please contact Infrastructure at: > us...@infra.apache.org > > > With regards, > Apache Git Services >
