ONNX Installation Fails On S390x: A Troubleshooting Guide

Oct 21, 2025 by ADMIN 58 views

Hey guys! Ever run into the frustrating issue of pip install onnx failing on your s390x architecture? You're definitely not alone! This guide breaks down a common problem encountered when trying to install ONNX (Open Neural Network Exchange) versions 1.17, 1.19.0, and 1.19.1 on s390x systems. We'll dive deep into the reasons behind these failures and provide you with clear, step-by-step instructions to troubleshoot and hopefully get ONNX up and running. So, let's jump right in and get those models working!

Understanding the ONNX Installation Issues on s390x

So, what's the deal with these ONNX installation failures? The issue primarily arises on s390x architecture due to a few key factors. Let's break them down:

Version 1.17.0 and Segmentation Faults: When attempting to install ONNX version 1.17.0 on s390x, a segmentation fault is a common error. This nasty error indicates a memory access violation, meaning the program is trying to access a memory location it shouldn't. In this case, it happens during the build process, specifically when compiling the protocol buffer definitions. This is a low-level error that often points to incompatibilities or bugs in the code related to the specific architecture.

Digging Deeper into Segmentation Faults: Segmentation faults are notorious for being tricky to debug. They often arise from memory corruption, pointer errors, or other low-level issues. When you encounter this during the ONNX installation, it's a strong sign that there's a problem with the compiled code or its interaction with the system's memory management. The error message you see, such as the one involving protoc-gen-mypy.sh and a core dump, gives us clues about where the failure occurs during the build process, specifically within the protocol buffer generation.

Why Does This Happen on s390x? The s390x architecture has specific hardware and software characteristics that can expose bugs or incompatibilities not seen on other platforms like x86. This could be related to how memory is managed, how instructions are executed, or even how the compiler optimizes code for this particular architecture. The fact that this error consistently occurs on s390x for version 1.17.0 suggests a deeper issue with the code's compatibility with this platform.

The Importance of Protocol Buffers: Protocol Buffers (protobuf) are a crucial part of ONNX. They are a language-neutral, platform-neutral, extensible mechanism for serializing structured data. ONNX uses protobuf to define the structure of its models and data. When the protoc-gen-mypy.sh script fails, it indicates that the code responsible for generating Python code from the protobuf definitions is crashing. This is a critical step in the installation process, as it makes the ONNX model definitions accessible from Python.

What You Can Do: When facing a segmentation fault, there are several troubleshooting steps you can take. First, ensure that your system meets the minimum requirements for ONNX. Second, try different versions of Python and ONNX to see if the issue is specific to certain versions. Third, check for any known issues or bug reports related to ONNX and s390x. Finally, you may need to dig into the build logs and error messages to identify the exact point of failure and potentially look for workarounds or patches. Remember, dealing with segmentation faults often requires patience and a methodical approach.
Versions 1.19.0 and 1.19.1 and Dependency Conflicts: ONNX versions 1.19.0 and 1.19.1 introduce a different set of challenges. The requirements.txt file for these versions pulls in the ml_dtypes package, which in turn, has a strong dependency on the latest version of NumPy (specifically, NumPy 2.0 or later). This can lead to installation failures due to conflicts with existing NumPy installations or incompatibilities with other libraries in your environment. This is especially problematic in Python 3.10.10.

The Role of ml_dtypes: The ml_dtypes package is designed to provide a consistent set of data types across different machine learning frameworks. While this is a noble goal, its tight coupling with NumPy 2.0 can create headaches. NumPy is the fundamental package for numerical computation in Python, and many other libraries depend on it. Forcing an upgrade to NumPy 2.0 can break existing projects or lead to unexpected behavior if other dependencies are not compatible. This is a classic example of a dependency conflict, where different libraries require conflicting versions of the same dependency.

Why NumPy 2.0 Matters: NumPy 2.0 introduces significant changes and improvements, but these come at the cost of potential backward compatibility issues. Libraries that were written for older versions of NumPy may not work correctly with the new version. The error message you see, related to hwy/ops/ppc_vsx-inl.h and __builtin_s390_vfll, suggests that the NumPy 2.0 version is using new or modified code paths that are incompatible with the s390x architecture. This is a common scenario when dealing with low-level numerical libraries, as they often have architecture-specific optimizations and code.

The Impact on Your Projects: If your project relies on specific versions of NumPy or other numerical libraries, a forced upgrade to NumPy 2.0 can be a major roadblock. It can require significant code changes, testing, and debugging to ensure that everything works correctly. This is why managing dependencies carefully is crucial in software development. Tools like virtual environments and dependency management systems like pipenv or conda can help you isolate your project's dependencies and avoid conflicts.

What You Can Do: If you encounter this issue, there are several ways to tackle it. First, you can try installing ONNX in a virtual environment to isolate its dependencies from your system-wide Python installation. Second, you can try downgrading NumPy to a compatible version before installing ONNX. Third, you can explore alternative ways to install ONNX, such as using a pre-built binary or building it from source with specific configurations. The key is to identify the root cause of the conflict and find a solution that doesn't break your existing projects.
Missing Git and Abseil Issues (Python 3.12 & 3.13): For Python 3.12 and 3.13, the installation process might fail due to the inability to find Git, which is needed to clone the Abseil library. Abseil is a collection of C++ library code designed to augment the C++ standard library, and it's a dependency for ONNX. If Git isn't available on your system or properly configured, the build process will be interrupted.

Abseil's Role in ONNX: Abseil is a critical component of ONNX, providing a range of utility functions and data structures that ONNX relies on. These include things like string manipulation, data containers, and concurrency primitives. By using Abseil, ONNX can leverage well-tested and optimized code, rather than reinventing the wheel. However, this also means that Abseil becomes a dependency, and if it's not available, ONNX won't build.

Why Git Matters: Git is a distributed version control system, and it's the backbone of many open-source projects. When a project depends on other libraries, it often uses Git to download and manage those dependencies. In this case, ONNX uses Git to download the Abseil library from its source repository. If Git isn't installed or configured correctly, the download will fail, and the build process will grind to a halt. This is why it's essential to have Git installed and accessible in your system's PATH.

The Importance of Build Tools: Building software from source often involves a complex chain of tools and processes. These tools include compilers, linkers, build systems, and dependency managers. CMake, which is mentioned in the error message, is a cross-platform build system that automates the process of generating build files for different environments. When these tools encounter problems, such as a missing dependency or a configuration error, the build process can fail in unpredictable ways. This is why it's crucial to have a clear understanding of the build process and the tools involved.

What You Can Do: The solution to this issue is relatively straightforward: make sure Git is installed and accessible on your system. You can usually install Git using your system's package manager (e.g., apt-get install git on Debian/Ubuntu, yum install git on CentOS/RHEL). Once Git is installed, the ONNX installation process should be able to proceed without issues. If you're still encountering problems, double-check that Git is in your system's PATH and that you have the necessary permissions to access it.

Step-by-Step Reproduction Instructions and Solutions

Alright, let's get practical. Here are the reproduction steps for each failure scenario, along with the solutions to get you back on track.

Reproduction and Solution for ONNX 1.17.0 (Segmentation Fault)

Reproduction:

Using Docker (This is the easiest way to reproduce the environment):

docker run -it --entrypoint bash icr.io/ibmz/python:3.10.10-bullseye
pip install numpy==2.0.1
pip install protobuf==6.32.0
pip install onnx==1.17.0

This sequence consistently leads to a segmentation fault during the build process.

Solution:

Unfortunately, there isn't a simple workaround for this issue with ONNX 1.17.0. The segmentation fault indicates a deeper incompatibility. Here's what you can try:

Try a different ONNX version: The most straightforward solution is to try installing a different version of ONNX. Consider using a more recent version (like 1.19.0 or later) or an older, more stable release. Newer versions often have bug fixes and improvements that might address the segmentation fault.
Check for known issues: Search the ONNX GitHub repository for existing issues related to segmentation faults on s390x. There might be ongoing discussions or solutions provided by the ONNX community.
Build from source (advanced): If you're comfortable with building software from source, you can try cloning the ONNX repository and building it yourself. This gives you more control over the build process and allows you to apply patches or make modifications if necessary. However, this is an advanced approach that requires familiarity with CMake and C++.

Reproduction and Solution for ONNX 1.19.0 & 1.19.1 (Dependency Conflicts)

Reproduction:

Using Docker:

docker run -it --entrypoint bash icr.io/ibmz/python:3.10.10-bullseye
pip install numpy==2.0.1
pip install protobuf==6.32.0
pip install onnx==1.19.0

This will likely fail due to the ml_dtypes dependency pulling in NumPy 2.0, causing incompatibility issues.

Solutions:

Use a virtual environment: The best practice is to isolate your project's dependencies using a virtual environment. This prevents conflicts with system-wide packages. You can create a virtual environment using venv:
```
python3 -m venv .venv
source .venv/bin/activate
pip install numpy==2.0.1
pip install protobuf==6.32.0
pip install onnx==1.19.0
```
Downgrade NumPy (if necessary): If you need to use a specific version of NumPy for other parts of your project, you might need to downgrade NumPy before installing ONNX:

pip install "numpy<2.0" pip install onnx==1.19.0 ```

However, be aware that this might introduce other compatibility issues.

Check ONNX compatibility: Always refer to the ONNX documentation to see the recommended versions of NumPy and other dependencies. Using compatible versions will minimize the risk of conflicts.

Reproduction and Solution for Missing Git (Python 3.12 & 3.13)

Reproduction:

Using Docker:

docker run -it --entrypoint bash icr.io/ibmz/python:3.12-bookworm
pip install numpy==2.0.1
pip install protobuf==6.32.0
pip install onnx==1.19.0

This will fail with an error message indicating that Git could not be found.

Solution:

Install Git: The most straightforward solution is to install Git on your system. The exact command will vary depending on your operating system.
- On Debian/Ubuntu:
```
apt-get update
apt-get install git
```
- On CentOS/RHEL:
```
yum install git
```
Verify Git installation: After installing Git, verify that it's accessible by running git --version in your terminal. If Git is installed correctly, this command will display the Git version.
Retry ONNX installation: Once Git is installed, retry the pip install onnx command. The installation should proceed without the Git-related error.

Additional Tips and Troubleshooting

Here are some extra tips to help you navigate ONNX installation issues on s390x:

Check your environment: Make sure you have the necessary build tools installed, such as CMake, a C++ compiler (like GCC or Clang), and Python development headers. These tools are often required for building Python packages with native extensions.
Use verbose mode: When installing ONNX with pip, use the -v or -vvv flags to get more detailed output. This can help you pinpoint the exact stage where the installation is failing and identify the root cause of the problem.
Consult the ONNX documentation: The official ONNX documentation is a valuable resource for troubleshooting installation issues. It often includes information about known problems and workarounds.
Search online forums and communities: If you're stuck, try searching online forums, such as Stack Overflow, or ONNX-specific communities. Other users might have encountered similar issues and found solutions.

Conclusion

Installing ONNX on s390x can sometimes feel like navigating a maze, but hopefully, this guide has shed some light on the common pitfalls and provided you with the tools to overcome them. Whether it's dealing with segmentation faults, dependency conflicts, or missing build tools, understanding the underlying issues is key to finding the right solution. So, go forth, install ONNX, and get those models running smoothly! Remember, we're all in this together, and the ONNX community is a great resource if you need further assistance. Happy modeling, guys!