XXX Failure Of TensorRT X.Y When Running XXX On GPU XXX

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TensorRT X.Y Failure: XXX on GPU XXX

Description

When attempting to run a model on a GPU using TensorRT X.Y, I encountered a failure that resulted in the following error log:

Error: [TRT] failed to create engine

This error occurred when I tried to run the model on a specific GPU, and I was unable to identify the root cause of the issue.

Environment

TensorRT Version: X.Y NVIDIA GPU: GeForce RTX 3080 NVIDIA Driver Version: 470.57.02 CUDA Version: 11.4 CUDNN Version: 8.1 Operating System: Ubuntu 20.04 Python Version: 3.9.7 Tensorflow Version: 2.4.1 PyTorch Version: 1.9.0 Baremetal or Container: Baremetal

Relevant Files

Steps To Reproduce

To reproduce this issue, follow these steps:

  1. Install TensorRT X.Y using the following command:
sudo apt-get install nvidia-tensorrt
  1. Clone the model repository using the following command:
git clone https://github.com/username/model.git
  1. Navigate to the model directory and run the following command to build the model:
python build_model.py
  1. Run the following command to run the model on the GPU:
python run_model.py
  1. Observe the error log and note the following error message:
Error: [TRT] failed to create engine

Commands or Scripts

  • build_model.py:
import tensorrt as trt
import numpy as np

# Define the model architecture
model = trt.Model()
model.add(trt.layers.Conv2D(32, (3, 3), activation='relu'))
model.add(trt.layers.MaxPool2D((2, 2)))
model.add(trt.layers.Flatten())
model.add(trt.layers.Dense(128, activation='relu'))
model.add(trt.layers.Dense(10, activation='softmax'))

# Serialize the model
model.serialize('model.trt')
  • run_model.py:
import tensorrt as trt
import numpy as np

# Load the serialized model
model = trt.Model('model.trt')

# Create an engine
engine = trt.create_engine(model)

# Run the engine
output = engine.run(np.random.rand(1, 3, 224, 224))

# Print the output
print(output)

Have you tried the latest release?

Yes, I have tried the latest release of TensorRT, but the issue persists.

Can this model run on other frameworks?

Yes, the model can run on other frameworks such as TensorFlow and PyTorch. However, the issue only occurs when running the model on the GPU using TensorRT.

Additional Information

  • GPU Usage: The GPU usage is 100% when running the model on the GPU.
  • Memory Usage: The memory usage is 80% when running the model on the GPU.
  • Error Log: The error log is as:
Error: [TRT] failed to create engine

This error log indicates that the engine creation failed, but it does not provide any additional information about the root cause of the issue.

Possible Solutions

  1. Update NVIDIA Driver: Update the NVIDIA driver to the latest version.
  2. Update CUDA Version: Update the CUDA version to the latest version.
  3. Update CUDNN Version: Update the CUDNN version to the latest version.
  4. Check GPU Compatibility: Check the GPU compatibility with the model.
  5. Check Model Architecture: Check the model architecture for any issues.

Conclusion

The TensorRT X.Y failure on GPU XXX is a complex issue that requires a thorough investigation. The error log indicates that the engine creation failed, but it does not provide any additional information about the root cause of the issue. Possible solutions include updating the NVIDIA driver, CUDA version, and CUDNN version, checking the GPU compatibility, and checking the model architecture for any issues.
TensorRT X.Y Failure: XXX on GPU XXX - Q&A

Q: What is the root cause of the TensorRT X.Y failure on GPU XXX?

A: The root cause of the TensorRT X.Y failure on GPU XXX is not immediately clear from the error log. However, it is possible that the issue is related to the GPU compatibility, CUDA version, or CUDNN version.

Q: How can I troubleshoot the issue?

A: To troubleshoot the issue, you can try the following steps:

  1. Update NVIDIA Driver: Update the NVIDIA driver to the latest version.
  2. Update CUDA Version: Update the CUDA version to the latest version.
  3. Update CUDNN Version: Update the CUDNN version to the latest version.
  4. Check GPU Compatibility: Check the GPU compatibility with the model.
  5. Check Model Architecture: Check the model architecture for any issues.

Q: Can I run the model on other frameworks?

A: Yes, the model can run on other frameworks such as TensorFlow and PyTorch. However, the issue only occurs when running the model on the GPU using TensorRT.

Q: What is the error log indicating?

A: The error log is indicating that the engine creation failed. However, it does not provide any additional information about the root cause of the issue.

Q: How can I resolve the issue?

A: To resolve the issue, you can try the following steps:

  1. Update NVIDIA Driver: Update the NVIDIA driver to the latest version.
  2. Update CUDA Version: Update the CUDA version to the latest version.
  3. Update CUDNN Version: Update the CUDNN version to the latest version.
  4. Check GPU Compatibility: Check the GPU compatibility with the model.
  5. Check Model Architecture: Check the model architecture for any issues.

Q: Can I get more information about the error log?

A: Yes, you can get more information about the error log by checking the TensorRT logs. You can do this by setting the TRT_LOG_LEVEL environment variable to DEBUG and running the model again.

Q: How can I set the TRT_LOG_LEVEL environment variable?

A: To set the TRT_LOG_LEVEL environment variable, you can use the following command:

export TRT_LOG_LEVEL=DEBUG

Then, run the model again using the following command:

python run_model.py

This will generate a more detailed log that can help you identify the root cause of the issue.

Q: What are the possible solutions to the issue?

A: The possible solutions to the issue include:

  1. Update NVIDIA Driver: Update the NVIDIA driver to the latest version.
  2. Update CUDA Version: Update the CUDA version to the latest version.
  3. Update CUDNN Version: Update the CUDNN version to the latest version.
  4. Check GPU Compatibility: Check the GPU compatibility with the model.
  5. Check Model Architecture: Check the model architecture for any issues.

Q: How can I check the GPU compatibility?

A: To check the GPU compatibility, you can use the following command:

nvidia-smi

This will display information about the GPU, including its model, memory, and driver version.

Q: How can I check the model architecture?

A: To check the model architecture, you can use the following command:

python model.py

This will display the model architecture, including the layers and their connections.

Q: What are the benefits of using TensorRT?

A: The benefits of using TensorRT include:

  1. Improved Performance: TensorRT can improve the performance of deep learning models by up to 10x.
  2. Reduced Memory Usage: TensorRT can reduce the memory usage of deep learning models by up to 50%.
  3. Improved Accuracy: TensorRT can improve the accuracy of deep learning models by up to 5%.

Q: What are the limitations of using TensorRT?

A: The limitations of using TensorRT include:

  1. Complexity: TensorRT can be complex to use, especially for beginners.
  2. Limited Support: TensorRT may not support all deep learning frameworks or models.
  3. Limited Documentation: TensorRT may not have extensive documentation or tutorials.

Q: How can I get started with TensorRT?

A: To get started with TensorRT, you can follow these steps:

  1. Install TensorRT: Install TensorRT using the official installation instructions.
  2. Read the Documentation: Read the TensorRT documentation to learn about its features and usage.
  3. Try the Examples: Try the TensorRT examples to get a feel for how it works.
  4. Experiment with Different Models: Experiment with different models and configurations to see how TensorRT can improve their performance.