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From Sheng Zha <szha....@gmail.com>
Subject Re: [GitHub] szha closed pull request #11154: Revert "[MXNET-503] Website landing page for MMS (#11037)"
Date Mon, 11 Jun 2018 02:42:57 GMT
Thanks, Henri. I was reverting the commit on a PR that another committer
didn't intend to merge but only realized afterwards. Given that it wasn't
convenient for him to revert and the negative effect, I committed the
revert and cc'd the original committer in the PR, both as notification and
as a proof of the claim.

-sz

On Sun, Jun 10, 2018 at 10:21 PM, Hen <bayard@apache.org> wrote:

> 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 <git@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)
> >
> >
> >
> >
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