ONNX Runtime

Direct ONNX model inference via ONNX Runtime 1.10 — no conversion to SameDiff required

ONNX Runtime

The nd4j-onnxruntime module provides direct ONNX model inference via ONNX Runtime (ORT) 1.10. Unlike SameDiff ONNX import, there is no conversion of the model into a SameDiff graph. The ONNX model runs natively inside the ORT C++ runtime, which is accessed from Java via JavaCPP bindings.

This is the fastest path for ONNX inference when you do not need to inspect or modify the graph.

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When to Use ONNX Runtime vs SameDiff Import

Requirement	Use ORT	Use SameDiff Import
Pure inference, minimum latency	Yes	No
Inspect or modify the graph in Java	No	Yes
Further training in Java	No	Yes
Op coverage: maximum ONNX compatibility	Yes (ORT is the reference)	Partial
Custom Java op integration	No	Yes
GPU execution	Yes (CUDA EP)	Via ND4J CUDA backend

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Maven Dependency

<dependency>
    <groupId>org.eclipse.deeplearning4j</groupId>
    <artifactId>nd4j-onnxruntime</artifactId>
    <version>${dl4j.version}</version>
</dependency>

The module bundles native ORT binaries for Linux x86_64, Windows x86_64, and macOS x86_64. ARM platforms require building from source.

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Basic Usage

import org.nd4j.onnxruntime.runner.OnnxRuntimeRunner;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.factory.Nd4j;

import java.util.LinkedHashMap;
import java.util.Map;

// Load the ONNX model
OnnxRuntimeRunner runner = OnnxRuntimeRunner.builder()
        .modelUri("path/to/model.onnx")
        .build();

// Prepare input data
INDArray input = Nd4j.rand(1, 3, 224, 224);  // NCHW image

// Build input map — keys must match ONNX model input names
Map<String, INDArray> inputs = new LinkedHashMap<>();
inputs.put("input", input);

// Run inference
Map<String, INDArray> outputs = runner.exec(inputs);

// Retrieve output
INDArray predictions = outputs.get("output");
System.out.println("Output shape: " + java.util.Arrays.toString(predictions.shape()));

// Close the runner when done
runner.close();

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Finding Input and Output Names

Use Python to inspect the ONNX model for input and output tensor names before running in Java:

import onnx

model = onnx.load("model.onnx")
print("Inputs:")
for inp in model.graph.input:
    print(f"  {inp.name}")

print("Outputs:")
for out in model.graph.output:
    print(f"  {out.name}")

Alternatively, use ONNX Runtime Python API:

import onnxruntime as ort

sess = ort.InferenceSession("model.onnx")
for inp in sess.get_inputs():
    print(f"Input: {inp.name}, shape: {inp.shape}, dtype: {inp.type}")
for out in sess.get_outputs():
    print(f"Output: {out.name}, shape: {out.shape}")

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OnnxRuntimeRunner Builder Options

OnnxRuntimeRunner runner = OnnxRuntimeRunner.builder()
        .modelUri("path/to/model.onnx")     // path to .onnx file (required)
        .build();

The runner manages the ORT session lifecycle. Close it when done to release native resources:

try (OnnxRuntimeRunner runner = OnnxRuntimeRunner.builder()
        .modelUri("model.onnx")
        .build()) {

    Map<String, INDArray> outputs = runner.exec(inputs);
    // use outputs...
}

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Batched Inference

ONNX Runtime supports batched inference when the model has a dynamic batch dimension:

# Python export with dynamic batch
torch.onnx.export(
    model, dummy, "model.onnx",
    dynamic_axes={"input": {0: "batch_size"}, "output": {0: "batch_size"}}
)

// Java: batch of 8
INDArray batchInput = Nd4j.rand(8, 3, 224, 224);
inputs.put("input", batchInput);
Map<String, INDArray> outputs = runner.exec(inputs);
// outputs["output"] shape: [8, num_classes]

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Data Types

ONNX Runtime accepts various tensor data types. ND4J INDArray types are mapped:

ND4J DataType	ONNX Tensor Type
FLOAT	FLOAT (float32)
DOUBLE	DOUBLE (float64)
INT32	INT32
INT64	INT64
BOOL	BOOL
FLOAT16	FLOAT16

Ensure your INDArray has the correct data type before passing to the runner. Cast if necessary:

INDArray floatInput = Nd4j.rand(1, 3, 224, 224).castTo(org.nd4j.linalg.api.buffer.DataType.FLOAT);

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Complete Example: Image Classification

import org.nd4j.onnxruntime.runner.OnnxRuntimeRunner;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.factory.Nd4j;

import java.util.LinkedHashMap;
import java.util.Map;

public class OrtInferenceExample {

    public static void main(String[] args) throws Exception {
        String modelPath = "resnet50.onnx";

        try (OnnxRuntimeRunner runner = OnnxRuntimeRunner.builder()
                .modelUri(modelPath)
                .build()) {

            // Simulate a preprocessed image [batch=1, channels=3, H=224, W=224]
            INDArray image = Nd4j.rand(new int[]{1, 3, 224, 224})
                    .castTo(org.nd4j.linalg.api.buffer.DataType.FLOAT);

            Map<String, INDArray> inputs = new LinkedHashMap<>();
            inputs.put("input", image);

            long start = System.currentTimeMillis();
            Map<String, INDArray> outputs = runner.exec(inputs);
            long elapsed = System.currentTimeMillis() - start;

            INDArray logits = outputs.get("output");
            int topClass = logits.argMax(1).getInt(0);

            System.out.printf("Top class: %d  (inference time: %d ms)%n", topClass, elapsed);
        }
    }
}

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Troubleshooting

Native library not found: verify that the nd4j-onnxruntime artifact was downloaded correctly and that the platform classifier matches your OS/architecture. On Linux, ensure libonnxruntime.so is accessible.

Wrong input name: the input name passed to the runner must exactly match the ONNX model's input tensor name. Use the Python inspection snippet to verify.

Data type mismatch: ORT is strict about tensor data types. If the model expects FLOAT and you provide DOUBLE, an error is thrown. Cast the array to the expected type before inference.

Session already closed: OnnxRuntimeRunner is AutoCloseable. After calling close() or exiting a try-with-resources block, the session is released and cannot be reused.