ONNX Runtime is a high-performance inference engine for deploying machine learning models. It supports models in the Open Neural Network Exchange (ONNX) format, which is an open standard for representing machine learning models. ONNX Runtime enables developers to run models across a variety of platforms and devices, optimizing performance and compatibility.
When working with ONNX Runtime, you might encounter the following error message: ONNXRuntimeError: [ONNXRuntimeError] : 8 : INVALID_ARGUMENT : Invalid attribute value. This error typically occurs during model loading or execution, indicating an issue with the model's attributes.
The error code INVALID_ARGUMENT suggests that an attribute within the ONNX model has an invalid value or type. Attributes in ONNX models are used to define various parameters and configurations for operations. If these attributes are not correctly defined, ONNX Runtime will raise this error.
Begin by examining the attributes of the model. You can use tools like ONNX's official Python API to load and inspect the model. Here's a basic example:
import onnx
# Load the model
model = onnx.load('your_model.onnx')
# Inspect the model's nodes
for node in model.graph.node:
print(node.name, node.attribute)
Ensure that each attribute has a valid value and type. Refer to the ONNX Operators documentation to verify the expected types and ranges for attributes.
If you find any discrepancies, modify the attributes accordingly. You can use the ONNX Python API to update the model:
# Example: Updating an attribute
for node in model.graph.node:
if node.name == 'target_node':
for attr in node.attribute:
if attr.name == 'target_attribute':
attr.i = new_value # Set the correct value
# Save the updated model
onnx.save(model, 'updated_model.onnx')
After making changes, test the model again with ONNX Runtime to ensure the error is resolved. You can use the ONNX Runtime documentation for guidance on running models.
By carefully inspecting and correcting the attributes in your ONNX model, you can resolve the INVALID_ARGUMENT error. Ensuring that all attributes are correctly defined will help maintain the integrity and performance of your machine learning models in ONNX Runtime.
(Perfect for DevOps & SREs)
(Perfect for DevOps & SREs)
