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

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Jun 5, 2018
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[MXNET-503] Website landing page for MMS #11037

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a957754
new landing page for MMS
aaronmarkham May 23, 2018
28bb3f6
added links
aaronmarkham May 23, 2018
dd6aa28
more links
aaronmarkham May 23, 2018
b41c363
updates from feedback
aaronmarkham May 24, 2018
018bf1e
added docker hub info
aaronmarkham Jun 1, 2018
9b08f38
addressed comments
aaronmarkham Jun 1, 2018
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added docker hub info
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@aaronmarkham
aaronmarkham committed Jun 1, 2018
commit 018bf1eb8949f3ebbd27d50be184261571cdc021
13 changes: 10 additions & 3 deletions docs/mms/index.md
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Expand Up @@ -86,11 +86,18 @@ Creating a model archive involves rounding up the required model artifacts, then

## Using Containers
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Should we point users to the Docker Hub builds?
https://hub.docker.com/r/awsdeeplearningteam/mms_cpu and https://hub.docker.com/r/awsdeeplearningteam/mms_gpu

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I added a docker pull command and a link to the parent on docker hub. I think it would be nicer to have an actual organization page and have the repos listed there. One stop shopping, but this should be fine for now!

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👍🏼


Using Docker or other container services with MMS is a great way to scale your inference applications. It is recommended that you review the following resources for more information:
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 Quickstart](https://github.com/awslabs/mxnet-model-server/docker/README.md)
```
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 Configurations](https://github.com/awslabs/mxnet-model-server/docs/optimized_config.md)
* [Optimized Container Configurations for MMS](https://github.com/awslabs/mxnet-model-server/docs/optimized_config.md)
* [Orchestrating, monitoring, and scaling with MMS, AWS Elastic Container Service, AWS Fargate, and AWS CloudWatch)](https://aws.amazon.com/blogs/machine-learning/apache-mxnet-model-server-adds-optimized-container-images-for-model-serving-at-scale/)


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