Common data formats for inference - Amazon SageMaker AI
Convert data for inference request serialization Convert data for inference response deserializationCommon request formats for all algorithmsUse batch transform with built-in algorithms

Common data formats for inference

Amazon SageMaker AI algorithms accept and produce several different MIME types for the HTTP payloads used in retrieving online and mini-batch predictions. You can use multiple AWS services to transform or preprocess records before running inference. At a minimum, you need to convert the data for the following:

  • Inference request serialization (handled by you)

  • Inference request deserialization (handled by the algorithm)

  • Inference response serialization (handled by the algorithm)

  • Inference response deserialization (handled by you)

Convert data for inference request serialization

Content type options for Amazon SageMaker AI algorithm inference requests include: text/csv, application/json, and application/x-recordio-protobuf. Algorithms that don't support all of these types can support other types. XGBoost, for example, only supports text/csv from this list, but also supports text/libsvm.

For text/csv, the value for the Body argument to invoke_endpoint should be a string with commas separating the values for each feature. For example, a record for a model with four features might look like 1.5,16.0,14,23.0. Any transformations performed on the training data should also be performed on the data before obtaining inference. The order of the features matters and must remain unchanged.

application/json is more flexible and provides multiple possible formats for developers to use in their applications. At a high level, in JavaScript, the payload might look like the following: 

let request = {
  // Instances might contain multiple rows that predictions are sought for.
  "instances": [
    {
      // Request and algorithm specific inference parameters.
      "configuration": {},
      // Data in the specific format required by the algorithm.
      "data": {
         "<field name>": dataElement
       }
    }
  ]
}

You have the following options for specifying the dataElement:

Protocol buffers equivalent

// Has the same format as the protocol buffers implementation described for training.
let dataElement = {
  "keys": [],
  "values": [],
  "shape": []
}

Simple numeric vector 

// An array containing numeric values is treated as an instance containing a
// single dense vector.
let dataElement = [1.5, 16.0, 14.0, 23.0]

// It will be converted to the following representation by the SDK.
let converted = {
  "features": {
    "values": dataElement
  }
}

For multiple records

let request = {
  "instances": [
    // First instance.
    {
      "features": [ 1.5, 16.0, 14.0, 23.0 ]
    },
    // Second instance.
    {
      "features": [ -2.0, 100.2, 15.2, 9.2 ]
    }
  ]
}

Convert data for inference response deserialization

Amazon SageMaker AI algorithms return JSON in several layouts. At a high level, the structure is:

let response = {
  "predictions": [{
    // Fields in the response object are defined on a per algorithm-basis.
  }]
}

The fields that are included in predictions differ across algorithms. The following are examples of output for the k-means algorithm.

Single-record inference 

let response = {
  "predictions": [{
    "closest_cluster": 5,
    "distance_to_cluster": 36.5
  }]
}

Multi-record inference

let response = {
  "predictions": [
    // First instance prediction.
    {
      "closest_cluster": 5,
      "distance_to_cluster": 36.5
    },
    // Second instance prediction.
    {
      "closest_cluster": 2,
      "distance_to_cluster": 90.3
    }
  ]
}

Multi-record inference with protobuf input 

{
  "features": [],
  "label": {
    "closest_cluster": {
      "values": [ 5.0 ] // e.g. the closest centroid/cluster was 1.0
    },
    "distance_to_cluster": {
      "values": [ 36.5 ]
    }
  },
  "uid": "abc123",
  "metadata": "{ "created_at": '2017-06-03' }"
}

SageMaker AI algorithms also support the JSONLINES format, where the per-record response content is same as that in JSON format. The multi-record structure is a collection of per-record response objects separated by newline characters. The response content for the built-in KMeans algorithm for 2 input data points is:

{"distance_to_cluster": 23.40593910217285, "closest_cluster": 0.0}
{"distance_to_cluster": 27.250282287597656, "closest_cluster": 0.0}

While running batch transform, we recommended using the jsonlines response type by setting the Accept field in the CreateTransformJobRequest to application/jsonlines.

Common request formats for all algorithms

Most algorithms use many of the following inference request formats.

JSON request format

Content type: application/JSON

Dense format

let request =   {
    "instances":    [
        {
            "features": [1.5, 16.0, 14.0, 23.0]
        }
    ]
}


let request =   {
    "instances":    [
        {
            "data": {
                "features": {
                    "values": [ 1.5, 16.0, 14.0, 23.0]
                }
            }
        }
    ]
}

Sparse format

{
	"instances": [
		{"data": {"features": {
					"keys": [26, 182, 232, 243, 431],
					"shape": [2000],
					"values": [1, 1, 1, 4, 1]
				}
			}
		},
		{"data": {"features": {
					"keys": [0, 182, 232, 243, 431],
					"shape": [2000],
					"values": [13, 1, 1, 4, 1]
				}
			}
		},
	]
}

JSONLINES request format

Content type: application/JSONLINES

Dense format

A single record in dense format can be represented as either:

{ "features": [1.5, 16.0, 14.0, 23.0] }

or:

{ "data": { "features": { "values": [ 1.5, 16.0, 14.0, 23.0] } }

Sparse Format

A single record in sparse format is represented as:

{"data": {"features": { "keys": [26, 182, 232, 243, 431], "shape": [2000], "values": [1, 1, 1, 4, 1] } } }

Multiple records are represented as a collection of single-record representations, separated by newline characters:

{"data": {"features": { "keys": [0, 1, 3], "shape": [4], "values": [1, 4, 1] } } }
{ "data": { "features": { "values": [ 1.5, 16.0, 14.0, 23.0] } }
{ "features": [1.5, 16.0, 14.0, 23.0] }

CSV request format

Content type: text/CSV; label_size=0

Note

CSV support is not available for factorization machines.

RECORDIO request format

Content type: application/x-recordio-protobuf

Use batch transform with built-in algorithms

While running batch transform, we recommended using the JSONLINES response type instead of JSON, if supported by the algorithm. To do this, set the Accept field in the CreateTransformJobRequest to application/jsonlines.

When you create a transform job, the SplitType must be set based on the ContentType of the input data. Similarly, depending on the Accept field in the CreateTransformJobRequest, AssembleWith must be set accordingly. Use the following table to set these fields:

ContentType Recommended SplitType
application/x-recordio-protobuf RecordIO
text/csv Line
application/jsonlines Line
application/json None
application/x-image None
image/* None
Accept Recommended AssembleWith
application/x-recordio-protobuf None
application/json None
application/jsonlines Line

For more information on response formats for specific algorithms, see the following: