[MXNET-503] Website landing page for MMS (apache#11037)

* new landing page for MMS

* added links

* more links

* updates from feedback

* added docker hub info

* addressed comments

docs/mms/index.md

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+# 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)