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 hardware. By providing a consistent API, ONNX Runtime allows developers to run models efficiently on different devices, from cloud servers to edge devices.
When using ONNX Runtime, you might encounter the following error message:
ONNXRuntimeError: [ONNXRuntimeError] : 19 : FAIL : Invalid model path
This error indicates that the model path specified in your code is either incorrect or inaccessible, preventing ONNX Runtime from loading the model for inference.
The error code 19 : FAIL : Invalid model path suggests that ONNX Runtime is unable to locate or access the model file at the given path. This can happen due to several reasons, such as:
Some common causes for this issue include:
To resolve the "Invalid model path" error, follow these steps:
Ensure that the path specified in your code is correct. Double-check for any typographical errors and confirm that the file exists at the specified location. For example:
model_path = "/path/to/your/model.onnx"
Use absolute paths instead of relative paths to avoid confusion.
Ensure that the file has the necessary read permissions. On Unix-like systems, you can use the ls -l command to check permissions:
ls -l /path/to/your/model.onnx
If necessary, adjust the permissions using chmod:
chmod +r /path/to/your/model.onnx
Ensure that the path is correctly formatted for your operating system. For Windows, use double backslashes \\ or raw strings r"path\to\model.onnx" to avoid escape character issues.
Test the accessibility of the path by attempting to open the file in a Python script:
try:
with open("/path/to/your/model.onnx", "rb") as f:
print("File is accessible")
except IOError:
print("File is not accessible")
For more information on ONNX Runtime and troubleshooting, consider visiting the following resources:
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