How to pass list/array of request objects to tensorflow serving in one server call? - tensorflow

Question

After loading the wide and deep model, i was able to make prediction for one request object using the map of features and then serializing it to string for predictions as shown below-
is there a way we can create a batch of requests objects and send them for prediction to tensorflow server?
Code for single prediction looks like this-
for (each feature in feature list) {
Feature feature = null;
feature = Feature.newBuilder().setBytesList(BytesList.newBuilder().addValue(ByteString.copyFromUtf8("dummy string"))).build();
if (feature != null) {
inputFeatureMap.put(name, feature);
}
}
//Converting features(in inputFeatureMap) corresponding to one request into 'Features' Proto object
Features features = Features.newBuilder().putAllFeature(inputFeatureMap).build();
inputStr = Example.newBuilder().setFeatures(features).build().toByteString();
}
TensorProto proto = TensorProto.newBuilder()
.addStringVal(inputStr)
.setTensorShape(TensorShapeProto.newBuilder().addDim(TensorShapeProto.Dim.newBuilder().setSize(1).build()).build())
.setDtype(DataType.DT_STRING)
.build();
PredictRequest req = PredictRequest.newBuilder()
.setModelSpec(ModelSpec.newBuilder()
.setName("your serving model name")
.setSignatureName("serving_default")
.setVersion(Int64Value.newBuilder().setValue(modelVer)))
.putAllInputs(ImmutableMap.of("inputs", proto))
.build();
PredictResponse response = stub.predict(req);
System.out.println(response.getOutputsMap());
Is there a way we can send the list of Features Object for predictions, something similar to this-
List<Features> = {someway to create array/list of inputFeatureMap's which can be converted to serialized string.}

Answer 1

For anyone stumbling here, I found a simple workaround with Example proto to do batch request. I will borrow code from this question and modify it for the batch.
Features features =
Features.newBuilder()
.putFeature("Attribute1", feature("A12"))
.putFeature("Attribute2", feature(12))
.putFeature("Attribute3", feature("A32"))
.putFeature("Attribute4", feature("A40"))
.putFeature("Attribute5", feature(7472))
.putFeature("Attribute6", feature("A65"))
.putFeature("Attribute7", feature("A71"))
.putFeature("Attribute8", feature(1))
.putFeature("Attribute9", feature("A92"))
.putFeature("Attribute10", feature("A101"))
.putFeature("Attribute11", feature(2))
.putFeature("Attribute12", feature("A121"))
.putFeature("Attribute13", feature(24))
.putFeature("Attribute14", feature("A143"))
.putFeature("Attribute15", feature("A151"))
.putFeature("Attribute16", feature(1))
.putFeature("Attribute17", feature("A171"))
.putFeature("Attribute18", feature(1))
.putFeature("Attribute19", feature("A191"))
.putFeature("Attribute20", feature("A201"))
.build();
Example example = Example.newBuilder().setFeatures(features).build();
String pfad = System.getProperty("user.dir") + "\\1511523781";
try (SavedModelBundle model = SavedModelBundle.load(pfad, "serve")) {
Session session = model.session();
final String xName = "input_example_tensor";
final String scoresName = "dnn/head/predictions/probabilities:0";
try (Tensor<String> inputBatch = Tensors.create(new byte[][] {example.toByteArray(), example.toByteArray(), example.toByteArray(), example.toByteArray()});
Tensor<Float> output =
session
.runner()
.feed(xName, inputBatch)
.fetch(scoresName)
.run()
.get(0)
.expect(Float.class)) {
System.out.println(Arrays.deepToString(output.copyTo(new float[4][2])));
}
}
Essentially you can pass each example as an object in byte[4][] and you will receive the result in the same shape float[4][2]