"ONNX Runtime GPU: The Missing DLL Mystery" - Solving the ONNX Runtime GPU Error in PyInstaller Executables
The Problem:
You've built a deep learning model using ONNX Runtime GPU, packaged it with PyInstaller for distribution, and then... the dreaded error message strikes: "ONNX Runtime GPU: Failed to find ONNX Runtime library 'onnxruntime_providers_shared.dll'. " This error prevents your application from utilizing the power of the GPU for inference, limiting performance and frustrating your efforts.
The Scenario:
Let's imagine you've spent weeks training a cutting-edge object detection model. You've meticulously crafted your Python code using ONNX Runtime GPU, achieving blazing-fast inference speeds on your local machine. Now, you're excited to share your creation with the world by packaging it into an executable with PyInstaller. But upon running the generated EXE file, the dreaded error appears, leaving you scratching your head.
Original Code (Simplified Example):
import onnxruntime as ort
# ... your model loading and inference code
# Use the GPU provider
sess_options = ort.SessionOptions()
sess_options.graph_optimization_level = ort.GraphOptimizationLevel.ORT_ENABLE_ALL
sess_options.enable_mem_pattern = True
sess_options.enable_cpu_mem_arena = False
sess = ort.InferenceSession("your_model.onnx", sess_options)
# ... your inference code
Analysis and Clarification:
The culprit lies in the PyInstaller packaging process and its handling of dependencies. PyInstaller bundles your Python code and its dependencies into a single executable. However, it doesn't automatically include all required external libraries, especially those associated with specific environments or hardware like the ONNX Runtime GPU provider.
The Solution:
The key to fixing this is to explicitly tell PyInstaller about the onnxruntime_providers_shared.dll and its dependencies. Here's how:
-
Locate the DLL: The
onnxruntime_providers_shared.dllis usually located within the ONNX Runtime installation directory. For example, on Windows, it might be atC:\Program Files\ONNX Runtime\lib. -
Specify Additional Files: Modify your PyInstaller command to include the DLL and its related files:
pyinstaller --hidden-import=onnxruntime --add-data "C:\Program Files\ONNX Runtime\lib;." --add-data "C:\Program Files\ONNX Runtime\lib\cuda;." your_script.py--hidden-import=onnxruntime: Tells PyInstaller to include ONNX Runtime as a hidden import.--add-data "path/to/dll;.": Copies theonnxruntime_providers_shared.dlland any other necessary files into the executable's directory.
-
Ensure CUDA/cuDNN Compatibility: If using CUDA, you need to also include the CUDA and cuDNN libraries. Make sure you've installed the correct versions of CUDA and cuDNN, and specify their paths in the
--add-dataparameter.
Additional Considerations:
- Environment Consistency: It's essential to maintain consistency between your development environment and the target runtime environment. If your model was trained using specific CUDA or cuDNN versions, make sure these are the same on the machine where the executable will be run.
- Package Size: Adding extra files can significantly increase your executable's size. Consider using a more optimized approach like distributing your model separately from the executable or using external dependencies management tools.
- Virtual Environments: Using a virtual environment for your project can help manage dependencies and avoid conflicts, leading to a smoother packaging process.
Conclusion:
By understanding the root cause of the "onnxruntime_providers_shared.dll" error and following these steps, you can successfully package your ONNX Runtime GPU-powered applications with PyInstaller and distribute them without encountering this common issue.
Resources:
- ONNX Runtime Documentation: https://onnxruntime.ai/docs/
- PyInstaller Documentation: https://pyinstaller.readthedocs.io/en/stable/
- CUDA Toolkit: https://developer.nvidia.com/cuda-downloads
- cuDNN: https://developer.nvidia.com/cudnn
