Missing Python Development Headers

Missing Python Development Headers | Generated by AI

Home 2025.11

Understanding the Error

This is a compilation error from PyTorch’s Torch Inductor (part of the torch.compile backend), which dynamically generates and compiles optimized C++ code to speed up model execution. The process failed during the C++ compilation step using g++. Let’s break it down:

Key Parts of the Error

Root Cause: fatal error: Python.h: No such file or directory
- The generated C++ file (cxyve26btthvhcuhmj6fjdn6e6op2ypybj7jmfcjx275gxkffhg5.cpp) includes #include <Python.h> on line 94.
- Python.h is a core header from the Python C API, used here because Torch Inductor wraps Python objects (e.g., tensors) in C++ for performance.
- The compiler (g++) is told to look in /usr/include/python3.13 via the -I flag, but the file isn’t there—meaning the Python development headers are missing.
Command Breakdown:
- g++ is compiling a temporary C++ source file into a shared library (.so file) for use in your Python session.
- Flags like -I/usr/include/python3.13 (include paths), -ltorch (link PyTorch libs), and optimizations (-O3, -mavx2) are standard for Torch’s codegen.
- It’s using Python 3.13 (from the include path and your site-packages dir), Torch installed in ~/.local/lib/python3.13/site-packages/torch, and OpenMP for parallelism.
Full Traceback Context:
- torch._inductor.exc.InductorError: Wrapper for inductor-specific failures.
- exc.CppCompileError: Raised when the C++ build fails.
- Suggestions like TORCHDYNAMO_VERBOSE=1 or TORCH_LOGS="+dynamo" are for debugging—enable them to see more about the code generation step.

This typically happens in environments where Python is installed (e.g., via apt or pyenv), but the development headers (which include Python.h) are not. It’s common on Linux distros like Ubuntu/Debian.

Why This Happens

PyTorch’s inductor needs to interface with Python at the C level for things like tensor manipulation.
If you’re running in a container, virtual env, or minimal OS install, dev headers might be skipped to save space.
Python 3.13 is relatively new (released Oct 2024), so ensure your system packages are up-to-date.

How to Fix It

Install Python Dev Headers (most common fix):
- On Ubuntu/Debian:
```
sudo apt update
sudo apt install python3.13-dev
```
  (If Python 3.13 isn’t in repos yet, add a PPA or build from source.)
- On Fedora/RHEL:
```
sudo dnf install python3.13-devel
```
- On macOS (with Homebrew):
```
brew install python@3.13
```
  (This usually includes headers.)
- Verify: After install, check ls /usr/include/python3.13/Python.h exists.
Reinstall/Upgrade PyTorch (if headers are present but mismatched):
```
pip uninstall torch
pip install torch --upgrade
```
Ensure your PyTorch version supports Python 3.13 (nightly builds do; stable might lag).
Environment-Specific Tweaks:
- If using Conda: conda install python=3.13 (includes headers).
- In Docker: Add RUN apt-get install -y python3.13-dev to your Dockerfile.
- Set export CPATH=/usr/include/python3.13:$CPATH if paths are quirky.
Debug Further:
- Run with export TORCHDYNAMO_VERBOSE=1 before your PyTorch code to trace the exact graph being compiled.
- If it persists, the issue might be a Torch bug—check PyTorch GitHub issues for “InductorError Python.h”.

After fixing, retry your torch.compile call. If you’re training/inferring a model, this should resolve without changing your code. If you share more context (e.g., your OS or the code triggering this), I can refine the advice!

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