TVM Key features and brief samples.
Nd4j allows execution of models via javacpp's tvm bindings using nd4j's INDArray as a data structure. Leveraging the nd4j-tvm interop is fairly simple.
A TVM model can be loaded and executed as follows:
This outputs a result with a map of output names to the ndarray result from tvm.
In maven, add the following dependency:
Note, this depends on javacpp's tvm bindings. This means tvm's native binaries are managed by javacpp. Javacpp will bundle all native binaries for all platforms by default unless you specify a platform. You can do this by specifying a -Dplatform=your-platform-of-choice. You may find more here in the javacpp docs.
Onnx interop Key features and brief samples.
Nd4j allows execution of models via onnx runtime using nd4j's INDArray as a data structure. Leveraging the nd4j-onnxruntime interop is fairly simple. An onnx model can be loaded and executed as follows:
This outputs a result with a map of output names to the ndarray result from onnx.
In maven, add the following dependency:
Tensorflow interop Key features and brief samples.
Nd4j allows execution of models via javacpp's tensorflow bindings using nd4j's INDArray as a data structure. Leveraging the nd4j-tensorflow interop is fairly simple.
Note, this is based on tensorflow 1.x. TF java 2 will be coming at a later time.
A tensorflow model can be loaded and executed as follows:
This outputs a result with a map of output names to the ndarray result from tensorflow.
In maven, add the following dependency:
Note, this depends on javacpp's onnxruntime bindings. This means onnx runtime's native binaries are managed by javacpp. Javacpp will bundle all native binaries for all platforms by default unless you specify a platform. You can do this by specifying a -Dplatform=your-platform-of-choice. You may find more in the javacpp docs.
Note, this depends on javacpp's tensorflow bindings. This means tensorflow's native binaries are managed by javacpp. Javacpp will bundle all native binaries for all platforms by default unless you specify a platform. You can do this by specifying a -Dplatform=your-platform-of-choice. You may find more in the javacpp docs.