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From Mu Li <limu...@gmail.com>
Subject Re: [GitHub] szha closed pull request #11154: Revert "[MXNET-503] Website landing page for MMS (#11037)"
Date Mon, 11 Jun 2018 02:56:23 GMT
Hi Sheng,

I suggest to put down a reason for such actions later. It may confuse other contributors,
e.g., Steffen raised his concern in a private thread.

Best
Mu

> On Jun 10, 2018, at 7:42 PM, Sheng Zha <szha.pvg@gmail.com> wrote:
> 
> 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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