# Using MXNet-Neuron Model Serving
In this tutorial, you learn to use a pre-trained MXNet model to perform real-time image classification with Multi Model Server (MMS). MMS is a flexible and easy-to-use tool for serving deep learning models that are trained using any machine learning or deep learning framework. This tutorial includes a compilation step using AWS Neuron and an implementation of MMS using MXNet.
For more information about the Neuron SDK, see the [AWS Neuron SDK documentation](https://awsdocs-neuron.readthedocs-hosted.com/en/latest/neuron-guide/neuron-frameworks/mxnet-neuron/index.html).
**Topics**
+ [Prerequisites](#tutorial-inferentia-mxnet-neuron-serving-prerequisites)
+ [Activate the Conda Environment](#tutorial-inferentia-mxnet-neuron-serving-activate)
+ [Download the Example Code](#tutorial-inferentia-mxnet-neuron-serving-download)
+ [Compile the Model](#tutorial-inferentia-mxnet-neuron-serving-compile)
+ [Run Inference](#tutorial-inferentia-mxnet-neuron-serving-inference)
## Prerequisites
Before using this tutorial, you should have completed the set up steps in [Launching a DLAMI Instance with AWS Neuron](tutorial-inferentia-launching.md). You should also have a familiarity with deep learning and using the DLAMI.
## Activate the Conda Environment
Activate the MXNet-Neuron conda environment by using the following command:
```
source activate aws_neuron_mxnet_p36
```
To exit the current conda environment, run:
```
source deactivate
```
## Download the Example Code
To run this example, download the example code using the following commands:
```
git clone https://github.com/awslabs/multi-model-server
cd multi-model-server/examples/mxnet_vision
```
## Compile the Model
Create a Python script called `multi-model-server-compile.py` with the following content. This script compiles the ResNet50 model to the Inferentia device target.
```
import mxnet as mx
from mxnet.contrib import neuron
import numpy as np
path='http://data.mxnet.io/models/imagenet/'
mx.test_utils.download(path+'resnet/50-layers/resnet-50-0000.params')
mx.test_utils.download(path+'resnet/50-layers/resnet-50-symbol.json')
mx.test_utils.download(path+'synset.txt')
nn_name = "resnet-50"
#Load a model
sym, args, auxs = mx.model.load_checkpoint(nn_name, 0)
#Define compilation parameters# - input shape and dtype
inputs = {'data' : mx.nd.zeros([1,3,224,224], dtype='float32') }
# compile graph to inferentia target
csym, cargs, cauxs = neuron.compile(sym, args, auxs, inputs)
# save compiled model
mx.model.save_checkpoint(nn_name + "_compiled", 0, csym, cargs, cauxs)
```
To compile the model, use the following command:
```
python multi-model-server-compile.py
```
Your output should look like the following:
```
...
[21:18:40] src/nnvm/legacy_json_util.cc:209: Loading symbol saved by previous version v0.8.0. Attempting to upgrade...
[21:18:40] src/nnvm/legacy_json_util.cc:217: Symbol successfully upgraded!
[21:19:00] src/operator/subgraph/build_subgraph.cc:698: start to execute partition graph.
[21:19:00] src/nnvm/legacy_json_util.cc:209: Loading symbol saved by previous version v0.8.0. Attempting to upgrade...
[21:19:00] src/nnvm/legacy_json_util.cc:217: Symbol successfully upgraded!
```
Create a file named `signature.json` with the following content to configure the input name and shape:
```
{
"inputs": [
{
"data_name": "data",
"data_shape": [
1,
3,
224,
224
]
}
]
}
```
Download the `synset.txt` file by using the following command. This file is a list of names for ImageNet prediction classes.
```
curl -O https://s3.amazonaws.com/model-server/model_archive_1.0/examples/squeezenet_v1.1/synset.txt
```
Create a custom service class following the template in the `model_server_template` folder. Copy the template into your current working directory by using the following command:
```
cp -r ../model_service_template/* .
```
Edit the `mxnet_model_service.py` module to replace the `mx.cpu()` context with the `mx.neuron()` context as follows. You also need to comment out the unnecessary data copy for `model_input` because MXNet-Neuron does not support the NDArray and Gluon APIs.
```
...
self.mxnet_ctx = mx.neuron() if gpu_id is None else mx.gpu(gpu_id)
...
#model_input = [item.as_in_context(self.mxnet_ctx) for item in model_input]
```
Package the model with model-archiver using the following commands:
```
cd ~/multi-model-server/examples
model-archiver --force --model-name resnet-50_compiled --model-path mxnet_vision --handler mxnet_vision_service:handle
```
## Run Inference
Start the Multi Model Server and load the model that uses the RESTful API by using the following commands. Ensure that **neuron-rtd** is running with the default settings.
```
cd ~/multi-model-server/
multi-model-server --start --model-store examples > /dev/null # Pipe to log file if you want to keep a log of MMS
curl -v -X POST "http://localhost:8081/models?initial_workers=1&max_workers=4&synchronous=true&url=resnet-50_compiled.mar"
sleep 10 # allow sufficient time to load model
```
Run inference using an example image with the following commands:
```
curl -O https://raw.githubusercontent.com/awslabs/multi-model-server/master/docs/images/kitten_small.jpg
curl -X POST http://127.0.0.1:8080/predictions/resnet-50_compiled -T kitten_small.jpg
```
Your output should look like the following:
```
[
{
"probability": 0.6388034820556641,
"class": "n02123045 tabby, tabby cat"
},
{
"probability": 0.16900072991847992,
"class": "n02123159 tiger cat"
},
{
"probability": 0.12221276015043259,
"class": "n02124075 Egyptian cat"
},
{
"probability": 0.028706775978207588,
"class": "n02127052 lynx, catamount"
},
{
"probability": 0.01915954425930977,
"class": "n02129604 tiger, Panthera tigris"
}
]
```
To cleanup after the test, issue a delete command via the RESTful API and stop the model server using the following commands:
```
curl -X DELETE http://127.0.0.1:8081/models/resnet-50_compiled
multi-model-server --stop
```
You should see the following output:
```
{
"status": "Model \"resnet-50_compiled\" unregistered"
}
Model server stopped.
Found 1 models and 1 NCGs.
Unloading 10001 (MODEL_STATUS_STARTED) :: success
Destroying NCG 1 :: success
```