Use online predictions when you are making requests in response to application input or in situations that require timely inference (real-time responses).
This page shows you how to format online prediction requests using the Online Prediction API for your custom-trained models and provides examples of requests and responses. After formatting your request, you can get an online prediction.
Before you begin
Before formatting a request to make online predictions, perform the following steps:
- Export your model artifact for prediction.
Deploy the model resource to an endpoint.
This action associates compute resources with the model so that it can serve online predictions with low latency.
Check the status of the
DeployedModelcustom resource of your model and ensure it is ready to accept prediction requests:kubectl --kubeconfig PREDICTION_CLUSTER_KUBECONFIG get -f DEPLOYED_MODEL_NAME.yaml -o jsonpath='{.status.primaryCondition}'Replace the following:
PREDICTION_CLUSTER_KUBECONFIG: the path to the kubeconfig file in the prediction cluster.DEPLOYED_MODEL_NAME: the name of theDeployedModeldefinition file.
The primary condition must show that the
DeployedModelis ready.The following output shows a sample response:
{"firstObservedTime":"2024-01-19T01:18:30Z","lastUpdateTime":"2024-01-19T01:35:47Z","message":"DeployedModel is ready", "observedGeneration":1, "reason":"Ready", "resourceName":"my-tf-model","type":"DeployedModel"}Check the status of the
Endpointcustom resource and ensure it is ready to accept prediction requests:kubectl --kubeconfig PREDICTION_CLUSTER_KUBECONFIG get -f ENDPOINT_NAME.yaml -o jsonpath='{$.status.conditions[?(@.type == "EndpointReady")]}'Replace the following:
PREDICTION_CLUSTER_KUBECONFIG: the path to the kubeconfig file in the prediction cluster.ENDPOINT_NAME: the name of theEndpointdefinition file.
The
statusfield of theEndpointReadycondition must show aTruevalue.The following output shows a sample response:
{"lastTransitionTime":"2024-01-19T05:12:26Z","message":"Endpoint Ready", "observedGeneration":1,"reason":"ResourceReady","status":"True","type":"EndpointReady"}%
Format your input for online predictions
Online Prediction has the following two methods to send requests:
- Prediction request: send a request to the
predictmethod to get an online prediction. - Raw prediction request: send a request to the
rawPredictmethod, which lets you use an arbitrary HTTP payload rather than following a JSON format.
If you require low latency, get raw predictions because rawPredict skips the
serialization steps and directly forwards the request to the prediction
container.
This section shows how to format and encode your prediction input instances
using JSON, which is required if you are using the predict method. This
information is not required if you are using the rawPredict method.
If you're using the Vertex AI SDK for Python to send prediction requests, specify
the list of instances without the instances field. For example, specify [
["the","quick","brown"], ... ] instead of { "instances": [
["the","quick","brown"], ... ] }.
Format instances as JSON strings
The basic format for Online Prediction is a list of data instances. These can be either plain lists of values or members of a JSON object, depending on how you configured your inputs in your training application. TensorFlow models can accept more complex inputs.
The following example shows an input tensor and an instance key to a TensorFlow model:
{"values": [1, 2, 3, 4], "key": 1}
The makeup of the JSON string can be complex as long as it follows these rules:
The top level of instance data must be a JSON object, which is a dictionary of key-value pairs.
Individual values in an instance object can be strings, numbers, or lists. You can't embed JSON objects.
Lists must contain only items of the same type (including other lists). Don't mix strings and numerical values.
You pass input instances for Online Prediction as the message body for
the predict call. Learn more about the
request body's formatting requirements.
Make each instance an item in a JSON array, and provide the array as the
instances field of a JSON object like in the following example:
{"instances": [
{"values": [1, 2, 3, 4], "key": 1},
{"values": [5, 6, 7, 8], "key": 2}
]}
Encode binary data for prediction input
You can't format binary data as the UTF-8 encoded strings that JSON supports. If you have binary data in your inputs, use base64 encoding to represent it. You require the following special formatting:
Format your encoded string as a JSON object with a single key named
b64. In Python 3, base64 encoding outputs a byte sequence. Convert this sequence to a string to make it JSON-serializable:{'image_bytes': {'b64': base64.b64encode(jpeg_data).decode()}}In your TensorFlow model code, name the aliases for your binary input and output tensors so that they end with
_bytes.
Request and response examples
This section describes the format of the Online Prediction request and response bodies with examples for TensorFlow and PyTorch.
Request body details
TensorFlow
The request body contains data with the following structure (JSON representation):
{
"instances": [
<value>|<simple/nested list>|<object>,
...
]
}
The instances[] object is required and it must contain the list of
instances to get predictions for.
The structure of each element of the instances list is determined by your model's input definition. Instances can include named inputs (as objects) or can contain only unlabeled values.
Not all data includes named inputs. Some instances are JSON values (boolean, number, or string). However, instances are often lists of values or complex nested lists.
The following are some examples of request bodies:
- CSV data with each row encoded as a string value:
{"instances": ["1.0,true,\\"x\\"", "-2.0,false,\\"y\\""]}
- Plain text:
{"instances": ["the quick brown fox", "the lazy dog"]}
- Sentences encoded as lists of words (vectors of strings):
{
"instances": [
["the","quick","brown"],
["the","lazy","dog"],
...
]
}
- Floating point scalar values:
{"instances": [0.0, 1.1, 2.2]}
- Vectors of integers:
{
"instances": [
[0, 1, 2],
[3, 4, 5],
...
]
}
- Tensors (in this case, two-dimensional tensors):
{
"instances": [
[
[0, 1, 2],
[3, 4, 5]
],
...
]
}
- Images, which can be represented different ways:
In this encoding scheme the first two dimensions represent the rows and columns of the image, and the third dimension contains lists (vectors) of the R, G, and B values for each pixel:
{
"instances": [
[
[
[138, 30, 66],
[130, 20, 56],
...
],
[
[126, 38, 61],
[122, 24, 57],
...
],
...
],
...
]
}
Data encoding
JSON strings must be encoded as UTF-8. To send binary data, you must
base64-encode the data and mark it as binary. To mark a JSON string as
binary, replace it with a JSON object with a single attribute named
b64:
{"b64": "..."}
The following example shows two serialized tf.Examples
instances, requiring base64 encoding (fake data, for illustrative purposes
only):
{"instances": [{"b64": "X5ad6u"}, {"b64": "IA9j4nx"}]}
The following example shows two JPEG image byte strings, requiring base64 encoding (fake data, for illustrative purposes only):
{"instances": [{"b64": "ASa8asdf"}, {"b64": "JLK7ljk3"}]}
Multiple input tensors
Some models have an underlying TensorFlow graph that accepts multiple input tensors. In this case, use the names of JSON key-value pairs to identify the input tensors.
For a graph with input tensor aliases tag (string) and image
(base64-encoded string):
{
"instances": [
{
"tag": "beach",
"image": {"b64": "ASa8asdf"}
},
{
"tag": "car",
"image": {"b64": "JLK7ljk3"}
}
]
}
For a graph with input tensor aliases tag (string) and image
(3-dimensional array of 8-bit ints):
{
"instances": [
{
"tag": "beach",
"image": [
[
[138, 30, 66],
[130, 20, 56],
...
],
[
[126, 38, 61],
[122, 24, 57],
...
],
...
]
},
{
"tag": "car",
"image": [
[
[255, 0, 102],
[255, 0, 97],
...
],
[
[254, 1, 101],
[254, 2, 93],
...
],
...
]
},
...
]
}
PyTorch
If your model uses a PyTorch prebuilt container,
the default handlers of TorchServe expect each instance to be wrapped in a
data field. For example:
{
"instances": [
{ "data": , <value> },
{ "data": , <value> }
]
}
Response body details
If the call is successful, the response body contains one prediction entry per instance in the request body, given in the same order:
{
"predictions": [
{
object
}
],
"deployedModelId": string
}
If prediction fails for any instance, the response body contains no predictions. Instead, it contains a single error entry:
{
"error": string
}
The predictions[] object contains the list of predictions, one for each
instance in the request.
On error, the error string contains a message describing the problem. The
error is returned instead of a prediction list if an error occurred while
processing any instance.
Even though there is one prediction per instance, the format of a prediction is not directly related to the format of an instance. Predictions take whatever format is specified in the outputs collection defined in the model. The collection of predictions is returned in a JSON list. Each member of the list can be a value, a list, or a JSON object of any complexity. If your model has more than one output tensor, each prediction is a JSON object containing a key-value pair for each output. The keys identify the output aliases in the graph.
Response body examples
The following examples show some possible responses for TensorFlow:
A set of predictions for three input instances, where each prediction is an integer value:
{"predictions": [5, 4, 3], "deployedModelId": 123456789012345678 }A more complex set of predictions, each containing two named values that correspond to output tensors, named
labelandscores, respectively. The value oflabelis the predicted category (car or beach) andscorescontains a list of probabilities for that instance across the possible categories:{ "predictions": [ { "label": "beach", "scores": [0.1, 0.9] }, { "label": "car", "scores": [0.75, 0.25] } ], "deployedModelId": 123456789012345678 }A response when there is an error processing an input instance:
{"error": "Divide by zero"}