OCaml ONNX runtime powered by onnxruntime.
This is an early prototype of bindings to the onnxruntime C library. Only a small subset of the functions are available though it should be enough to run basic models.
This has been tested with release 1.10.0 of onnxruntime. To try it out, download the onnxruntime library, uncompress it in the onnxruntime directory and run the following.
LIBONNXRUNTIME=onnxruntime dune runtest
Exporting a PyTorch model:
import torch
import torch.nn as nn
import torch.nn.init as init
class AddOne(nn.Module):
def __init__(self):
super(AddOne, self).__init__()
def forward(self, x):
return x + 1
torch_model = AddOne()
x = torch.randn(1, requires_grad=True)
torch_out = torch_model(x)
torch.onnx.export(torch_model, # model being run
x, # model input (or a tuple for multiple inputs)
"add_one.onnx", # where to save the model (can be a file or file-like object)
export_params=True, # store the trained parameter weights inside the model file
opset_version=10, # the ONNX version to export the model to
do_constant_folding=True, # whether to execute constant folding for optimization
input_names = ['input'], # the model's input names
output_names = ['output'], # the model's output names
)