---
title: "SageMaker Image Classification inference"
date: 2020-02-07T00:15:15-05:00
draft: false
algo: [sagescene]
---

Make sure you have trained your model as outlined [here](../../training/sagescenes)

### Using the Console

> On the training job page, click create model. Give the model a name, leave all other options as default and create model
![Create model](/images/sagescene-console-createmodel.png)

> On the next page, click create endpoint, add options as needed on the next page.
![Create endpoint](/images/sagescene-console-createendpoint.png)


### Using the boto3 python SDK

#### Create Model
```python
import boto3
from time import gmtime, strftime

sage = boto3.Session().client(service_name='sagemaker')

model_name="DEMO--classification-model" + time.strftime('-%Y-%m-%d-%H-%M-%S', time.gmtime())
print(model_name)
info = sage.describe_training_job(TrainingJobName=job_name)
model_data = info['ModelArtifacts']['S3ModelArtifacts']
print(model_data)

hosting_image = get_image_uri(boto3.Session().region_name, 'image-classification')

primary_container = {
    'Image': hosting_image,
    'ModelDataUrl': model_data,
}

create_model_response = sage.create_model(
    ModelName = model_name,
    ExecutionRoleArn = role,
    PrimaryContainer = primary_container)

print(create_model_response['ModelArn'])
```

#### Create Endpoint configuration
```python
from time import gmtime, strftime

timestamp = time.strftime('-%Y-%m-%d-%H-%M-%S', time.gmtime())
endpoint_config_name = 'DEMO-imageclassification' + '-epc-' + timestamp
endpoint_config_response = sage.create_endpoint_config(
    EndpointConfigName = endpoint_config_name,
    ProductionVariants=[{
        'InstanceType':'ml.m4.xlarge',
        'InitialInstanceCount':1,
        'ModelName':model_name,
        'VariantName':'AllTraffic'}])

print('Endpoint configuration name: {}'.format(endpoint_config_name))
print('Endpoint configuration arn:  {}'.format(endpoint_config_response['EndpointConfigArn']))
```

### Create Endpoint
```python
# get the status of the endpoint
response = sagemaker.describe_endpoint(EndpointName=endpoint_name)
status = response['EndpointStatus']
print('EndpointStatus = {}'.format(status))


# wait until the status has changed
sagemaker.get_waiter('endpoint_in_service').wait(EndpointName=endpoint_name)


# print the status of the endpoint
endpoint_response = sagemaker.describe_endpoint(EndpointName=endpoint_name)
status = endpoint_response['EndpointStatus']
print('Endpoint creation ended with EndpointStatus = {}'.format(status))

if status != 'InService':
    raise Exception('Endpoint creation failed.')
```


### Perform inference
```python
import boto3
import json
import numpy as np

runtime = boto3.Session().client(service_name='runtime.sagemaker')

with open(file_name, 'rb') as f:
    payload = f.read()
    payload = bytearray(payload)
response = runtime.invoke_endpoint(EndpointName=endpoint_name,
                                   ContentType='application/x-image',
                                   Body=payload)
result = response['Body'].read()
# result will be in json format and convert it to ndarray
result = json.loads(result)

```