Fix GLIBCXX_3.4.29 Not Found Error with ONNX Runtime & Datasets

Zartom
Aug 26
8 min read

Encountering an ImportError: /lib/x86_64-linux-gnu/libstdc++.so.6: version GLIBCXX_3.4.29' not found when working with Python, particularly with libraries like ONNX Runtime and Hugging Face Datasets, points to a common yet frustrating dependency conflict. This specific error arises because a package you're trying to import was compiled using a newer version of the C++ standard library (libstdc++) than what your operating system currently provides. It’s akin to trying to run a modern application on an older operating system that lacks essential system components. The problem often surfaces when importing multiple libraries that have different C++ runtime requirements, leading to a clash during the dynamic linking process.

This guide addresses a common Python environment issue where importing libraries like onnxruntime or datasets fails due to a missing symbol version in the system’s C++ standard library. We will explain the root cause and provide practical solutions to resolve this ImportError.

Understanding the GLIBCXX Version Error

The error message ImportError: /lib/x86_64-linux-gnu/libstdc++.so.6: version GLIBCXX_3.4.29' not found indicates that a Python package (in this case, pyarrow, a dependency of datasets) was compiled against a newer version of the GNU Standard C++ Library (libstdc++) than what is available on your Ubuntu system. This often happens when a package is built with a newer compiler toolchain, and its dynamic linking requirements exceed the capabilities of the host system’s shared libraries.

Specifically, the pyarrow library, essential for the datasets library to function, requires GLIBCXX_3.4.29. Your Ubuntu 20.04 LTS system, by default, might only provide an older version, such as GLIBCXX_3.4.26. This mismatch prevents the dynamic linker from resolving the necessary symbols, leading to the import failure when both libraries are loaded.

Why it Happens with Specific Imports

The peculiar behavior where importing onnxruntime or datasets individually works, but importing both (or even just datasets after onnxruntime) fails, suggests an interaction during the dynamic loading process. When onnxruntime is loaded, it might establish certain environment conditions or load its own compatible libraries. Subsequently, when datasets (via pyarrow) attempts to load, it might clash with or require a different, newer version of libstdc++ that was not loaded or made available by the earlier import.

This dependency conflict can be subtle. Sometimes, the order of import matters because different libraries might have slightly different assumptions about the available C++ runtime environment. The presence of onnxruntime might indirectly trigger a path or load a library that causes pyarrow’s specific linkage to fail, even if onnxruntime itself doesn’t directly use the missing symbol version.

Environment Details

You are using Python 3.12.9 on Ubuntu 20.04.5 LTS, with datasets==3.5.0 and onnxruntime==1.21.0. These versions are relatively recent. Ubuntu 20.04 LTS, being an older LTS release, typically ships with an older GCC version and thus an older libstdc++.so.6. Newer Python packages, especially those with C++ extensions like pyarrow, are often compiled with more recent toolchains that utilize newer C++ standard library features.

The discrepancy between the compilation environment of your Python packages and the system’s available shared libraries is the fundamental cause. Resolving this requires either updating the system libraries or ensuring your Python environment uses compatible versions.

Resolving the GLIBCXX Version Mismatch

The most direct way to fix this ImportError is to ensure that your Python environment can access a compatible version of the C++ standard library. This typically involves either updating your system’s libraries or, more practically for isolated environments, recompiling or reinstalling the problematic packages (like pyarrow) within an environment that has access to a newer libstdc++.

Method 1: Update System Libraries (Use with Caution)

The ideal, but often disruptive, solution is to update your Ubuntu system to a newer release or manually update the C++ standard library. However, modifying system libraries on an LTS release like Ubuntu 20.04 can lead to system instability if not done carefully. A safer approach within a controlled environment is to use newer build tools.

You can try installing a newer version of GCC and its associated libraries, and then ensure your Python packages are installed or reinstalled in a way that utilizes these newer tools. This is complex and generally not recommended unless you are very comfortable with system administration. The preferred method is usually to manage dependencies within your Python environment.

Method 2: Reinstall PyArrow in a Compatible Environment

A more robust solution is to recreate your Python environment using a base image or system that has a newer libstdc++, or to explicitly rebuild pyarrow against a newer compiler available within your current environment. If you are using Conda, you might try creating a new environment with a newer Python version or a different distribution base that includes updated libraries.

For instance, you could try installing pyarrow from a conda channel known to provide recent builds, or if building from source, ensure you use a recent GCC. However, the simplest approach is often to ensure your base Python installation or Conda environment is up-to-date.

Method 3: Using a Newer Conda Environment

Given you are using a Conda environment (env-312), a common fix is to create a new environment with a Python version that is more likely to have compatible pre-compiled packages available, or to explicitly install pyarrow and its dependencies from channels that are known to be up-to-date. Sometimes, simply updating Conda itself can help.

Try creating a new environment: conda create -n new_env python=3.12 pyarrow datasets onnxruntime. This command attempts to resolve all dependencies from the default channels, which might include correctly compiled versions of pyarrow. If this fails, consider explicitly specifying channels known for recent builds, like conda-forge.

Demonstrating a Fix with Conda

Let’s illustrate how to create a new Conda environment and install the necessary packages. This approach isolates the dependencies and often pulls in correctly compiled binaries.

Step 1: Create a New Conda Environment

Open your terminal and run the following command. This creates a new environment named onnx_test_env using Python 3.12 and attempts to install the required libraries. Conda’s package manager is adept at resolving complex dependency chains.

The command conda create -n onnx_test_env python=3.12 pyarrow datasets onnxruntime -c conda-forge is a good starting point, explicitly using the conda-forge channel, which is often a source for more up-to-date packages.

Step 2: Activate and Test the New Environment

After the environment is created, activate it using conda activate onnx_test_env. Once activated, you can run your Python script or try importing the libraries directly within the Python interpreter to see if the ImportError is resolved.

If the issue persists, it might indicate that even conda-forge doesn’t have a perfectly compatible build for your specific Ubuntu setup, or that another system library is interfering. In such rare cases, consulting specific package documentation or forums for workarounds might be necessary.

Step 3: Verifying the Solution

After activating the new environment, start a Python interpreter (type python) and execute the imports: import onnxruntime as ort followed by from datasets import load_dataset. If no error occurs, your problem is solved. The key is that the new environment likely installed a version of pyarrow compiled with a compiler that matches your system’s libstdc++ capabilities.

The fix relies on Conda’s ability to find or build packages that are compatible with the target environment, effectively bypassing the system’s potentially outdated libraries by providing a self-contained, compatible set of dependencies.

Key Takeaways for Fixing GLIBCXX Errors

The GLIBCXX_3.4.29' not found error stems from a mismatch between the C++ standard library version used to compile Python packages (like pyarrow) and the version available on your operating system. The most reliable fix, especially when using Conda, is to create a new, clean environment with the desired Python version and explicitly install the necessary packages, often leveraging channels like conda-forge for up-to-date builds.

Always ensure your environment management tools (like Conda or venv) are up-to-date, and consider that newer OS versions or specialized container images (like Docker) might offer a more straightforward path to resolving such low-level dependency issues for cutting-edge libraries.

Similar Python Dependency Issues

Here are a few related problems and their typical solutions:

ImportError: libcudart.so.11.0: cannot open shared object file

This usually means CUDA toolkit libraries are not found or not installed correctly in your PATH. Ensure your NVIDIA drivers and CUDA toolkit are properly installed and configured.

ModuleNotFoundError: No module named 'some_package'

The simplest fix is to install the missing package using pip or conda: pip install some_package or conda install some_package.

DLL load failed: The specified module could not be found

Common on Windows, this indicates missing DLLs required by a Python package. Often resolved by reinstalling the package, ensuring build tools are present, or installing Visual C++ Redistributable packages.

Segmentation Fault when Importing Library

This is a more severe error, often indicating a deeper incompatibility or bug, possibly related to memory management or C++ runtime issues, similar to the GLIBCXX problem.

Version Conflicts between Libraries

When Package A requires Package C v1.0 and Package B requires Package C v2.0, you might encounter errors. Using virtual environments and careful dependency management is key.

Code Illustrations for Environment Management

These examples demonstrate common practices for managing Python environments to avoid dependency conflicts.

Creating a Conda Environment with Specific Packages

# Create a new conda environment named 'ml_env' with Python 3.10
# and install common ML libraries.
conda create -n ml_env python=3.10 numpy pandas scikit-learn

This command sets up an isolated environment with specified Python version and libraries, helping to prevent version conflicts.

Exporting and Importing Conda Environment Configurations

# Export the current environment to a file named environment.yml
conda env export > environment.yml

# Create a new environment from the exported file
conda env create -f environment.yml

Sharing environment configurations via environment.yml ensures reproducibility and simplifies setting up identical environments on different machines.

Using pip with a requirements.txt File

# Create a requirements.txt file listing all dependencies
# Example content:
# numpy==1.23.0
# pandas==1.5.0
# scikit-learn==1.2.0

# Install packages from the requirements file
pip install -r requirements.txt

Managing dependencies with requirements.txt is a standard practice for Python projects, facilitating easy setup and deployment.

Checking Installed Package Versions

# List all installed packages and their versions in the current environment
conda list
# Or for pip environments:
pip freeze

Regularly checking installed package versions helps in identifying potential conflicts or outdated dependencies before they cause issues.

TroubleshootingGLIBCXXErrors withldd

# Check which libstdc++.so.6 is being used by a Python executable
ldd $(which python)
# Check which libstdc++.so.6 is being used by pyarrow
# (This is harder to do directly without running pyarrow)

The ldd command can help identify which shared libraries your executables and libraries are linked against, aiding in diagnosing GLIBCXX or other linking errors.

Symptom	Cause	Solution
ImportError: GLIBCXX_3.4.29 not found	Package compiled with newer GCC/libstdc++ than system provides.	Create a new Conda environment with up-to-date packages (e.g., from conda-forge) or recompile packages.
DLL load failed (Windows)	Missing required DLLs for a package.	Reinstall package, ensure build tools are present, or install Visual C++ Redistributable.
ModuleNotFoundError	Package not installed in the current environment.	Install the package using pip or conda (e.g., pip install package_name).
Segmentation Fault	Deep incompatibility, memory error, or C++ runtime issue.	Often requires environment cleanup, package reinstallation, or checking system compatibility.
Version Conflicts	Two or more packages require different, incompatible versions of a shared dependency.	Use virtual environments (venv, Conda) and careful dependency management (e.g., requirements.txt, environment.yml).