ONNX Runtime is a high-performance inference engine for machine learning models in the Open Neural Network Exchange (ONNX) format. It is designed to accelerate the deployment of machine learning models across various platforms and devices, providing a flexible and efficient solution for model inference.
While using ONNX Runtime, you might encounter the following error message: ONNXRuntimeError: [ONNXRuntimeError] : 5 : INVALID_PROTOBUF : Protobuf parsing failed. This error indicates that there is an issue with parsing the ONNX model file.
Protocol Buffers (Protobuf) is a method developed by Google for serializing structured data. It is used extensively in ONNX models to define the model structure and parameters.
The error occurs when the ONNX model file is not a valid Protobuf file. This can happen if the model file is corrupted, improperly exported, or not in the correct format.
Ensure that the ONNX model file is not corrupted. You can do this by checking the file size and comparing it with the expected size. If the file is significantly smaller than expected, it might be incomplete.
If the file is corrupted or improperly exported, re-export the model from the original framework (e.g., PyTorch, TensorFlow) using the appropriate export function. For example, in PyTorch, use the torch.onnx.export() function to export the model:
import torch
# Assuming 'model' is your PyTorch model
# and 'dummy_input' is a sample input tensor
torch.onnx.export(model, dummy_input, "model.onnx")
Ensure that the export process completes without errors.
Use the ONNX library to validate the model file. You can use the following command to check the model:
import onnx
# Load the ONNX model
model = onnx.load("model.onnx")
# Check the model
onnx.checker.check_model(model)
If the model is valid, the checker will not raise any errors.
For more information on exporting models to ONNX, refer to the PyTorch ONNX documentation or the TensorFlow SavedModel guide. These resources provide detailed instructions on exporting models in the correct format.
(Perfect for DevOps & SREs)
(Perfect for DevOps & SREs)
