Скачать или смотреть Resolving CUDA & TensorRT Issues on Ubuntu 22.04

If you're facing issues with TensorFlow not recognizing TensorRT on Ubuntu 22.04, this guide walks you through resolving `CUDA` and `TensorRT` compatibility problems for your deep learning setup.
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Resolving CUDA & TensorRT Issues on Ubuntu 22.04: A Comprehensive Guide

If you are a developer working with deep learning frameworks like TensorFlow, you may have encountered compatibility issues while setting up your development environment—especially when integrating CUDA and TensorRT on Ubuntu 22.04. A common issue arises where TensorFlow fails to import due to drivers mismatches, resulting in warnings and errors. In this blog, we will explore these common issues and how to resolve them effectively.

Identifying the Problem

When you attempt to use TensorFlow with TensorRT on Ubuntu 22.04, you might see warnings like:

[[See Video to Reveal this Text or Code Snippet]]

or errors such as:

[[See Video to Reveal this Text or Code Snippet]]

These issues often stem from incompatible versions of your driver and CUDA toolkit, which indicates that your system might have remnants of conflicting Nvidia packages.

Explanation of the Error Messages

Driver Mismatch: The error log indicates a mismatch between the CUDA driver's kernel version and the installed output shared object (DSO) version. Here's a representative line from the error message:

[[See Video to Reveal this Text or Code Snippet]]

This means that the configuration is broken due to parts of both drivers being installed.

TF-TRT Warning: This warning indicates that TensorFlow cannot locate TensorRT, which may be a result of the compatibility issues mentioned earlier, as TensorRT relies on CUDA for its operations.

Steps to Resolve the Issue

1. Remove Conflicting Nvidia Packages

To fix the driver mismatch, start by completely removing any Nvidia repository references and the associated packages on your system. Here's a concise step-by-step guide:

Remove Nvidia packages: Use the following command in your terminal to purge Nvidia drivers:

[[See Video to Reveal this Text or Code Snippet]]

Reinstall Ubuntu: While a full reinstallation of Ubuntu may seem extreme, it often ensures a clean environment. If that's too impractical, at least clean all remnants of the Nvidia repository.

2. Install Compatible Drivers and Packages

Once you have cleansed your system of conflicting packages, reinstall the appropriate components:

Install Nvidia 510 Driver: Use the Ubuntu Additional Drivers tool to install the nvidia-510 driver.

Install CUDA Toolkit: Install the nvidia-cuda-toolkit.

Install cuDNN: Also install nvidia-cudnn from the official Ubuntu repositories.

3. Set Up TensorRT Correctly

To ensure that TensorRT works properly alongside TensorFlow, follow these configurations:

Download TensorRT: Obtain the TAR installer (choose the version that corresponds to your CUDA version, e.g., TensorRT 8.6 EA for Linux x86_64 and CUDA 11.0-11.8).

Unpack it: Extract the TAR file to /opt.

[[See Video to Reveal this Text or Code Snippet]]

Update LD_LIBRARY_PATH: Set the library path to include TensorRT:

[[See Video to Reveal this Text or Code Snippet]]

4. Additional Configurations for TensorFlow

Symlink for libdevice: If you encounter issues related to TensorFlow not finding libdevice, set up a symbolic link:

[[See Video to Reveal this Text or Code Snippet]]

5. Testing the Setup

After completing these installations and configurations, try running TensorFlow again with a simple command:

[[See Video to Reveal this Text or Code Snippet]]

If there are no errors, congratulations! You have successfully resolved your CUDA and TensorRT compatibility issues on Ubuntu 22.04.

Conclusion

Setting up a development environment for deep learning can be complex, especially with various dependencies and package conflicts. By methodically addressing the driver mismatches and ensuring all