AWS sagemaker abalone example pipeline endpoint json rejected

Question

We've just created a train/build/deploy template in AWS SageMaker which provides a deployment of an Abalone model.  We're trying to test it via the Test Inference endpoint, but the JSON there is rejected with the following message:

> Error invoking endpoint: Received client error (415) from model with message "application/json is not an accepted ContentType: csv, libsvm, parquet, recordio-protobuf, text/csv, text/libsvm, text/x-libsvm, application/x-parquet, application/x-recordio-protobuf.". See https://eu-west-1.console.aws.amazon.com/cloudwatch/home?region=eu-west-1#logEventViewer:group=/aws/sagemaker/Endpoints/USEngProbOfConversion-staging in account 607522716587 for more information.

However the Test Inference endpoint only allows us to hit the endpoint with JSON - what can we do?  Here's a screenshot (if this dropbox embed works):

![](https://www.dropbox.com/s/x79l8xxqklgbxx4/Screenshot%202022-06-30%20at%2017.48.35.png?raw=1)

but that's not working so here's the request dump:

```
{
  "body": {
    "s-x": "M",
    "length": 3,
    "diameter": 5,
    "height": 7,
    "whole_weight": 45,
    "shucked_weight": 34,
    "viscera_weight": 23,
    "shell_weight": 76
  },
  "contentType": "application/json",
  "endpointName": "USEngProbOfConversion-staging",
  "customURL": "",
  "customHeaders": [
    {
      "Key": "sm_endpoint_name",
      "Value": "USEngProbOfConversion-staging"
    },
    {
      "Key": "",
      "Value": ""
    }
  ]
}
```

Answer

So as you saw already, the SageMaker Studio "Test inference" UI currently only supports JSON format... But this is a constraint of the UI, not your endpoint.

If you want to test your deployed endpoint with **non-JSON data**, you can do this from code (e.g. from a notebook):
- Using the `sagemaker` Python SDK, create a [Predictor](https://sagemaker.readthedocs.io/en/stable/api/inference/predictors.html) specifying your endpoint name and the relevant de/serializers (from `sagemaker.(de)serializers` - for example [sagemaker.serializers.CSVSerializer](https://sagemaker.readthedocs.io/en/stable/api/inference/serializers.html#sagemaker.serializers.CSVSerializer)). Then call `predictor.predict(data)`.
- Using a `boto3.`[client("sagemaker-runtime")](https://boto3.amazonaws.com/v1/documentation/api/latest/reference/services/sagemaker-runtime.html), serialize your data to required format yourself (e.g. `"M,3,5,7,45,34,23,76"`) and then call `invoke_endpoint()`.

This would be necessary if you're using a pre-built algorithm that doesn't support JSON as a request/response format. Since you're using the Abalone pipeline example, I guess it's likely you're using [XGBoost as a pre-built SageMaker algorithm](https://docs.aws.amazon.com/sagemaker/latest/dg/xgboost.html#InputOutput-XGBoost)?

**Alternatively**, if you're building a custom algorithm with your own training script **OR** would be interested in [using XGBoost as a script-mode framework](https://docs.aws.amazon.com/sagemaker/latest/dg/xgboost.html#xgboost-modes) - more information [in the SageMaker SDK doc](https://sagemaker.readthedocs.io/en/stable/frameworks/xgboost/using_xgboost.html)), you may like to extend your algorithm to accept `application/json` requests and return JSON responses.

The process for this will vary a little by framework, but should be documented [here for XGBoost](https://sagemaker.readthedocs.io/en/stable/frameworks/xgboost/using_xgboost.html#sagemaker-xgboost-model-server). Essentially, you'll want to provide a script file e.g. `inference.py` which defines special functions `input_fn()` and `output_fn()`. You can provide implementations of these functions that accept `application/json` content types and de/serialize appropriately.

That way you could make your deployed endpoint support JSON format and therefore be able to use the test UI in SageMaker Studio.

AWS sagemaker abalone example pipeline endpoint json rejected

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