Python3.5执行model.fit()报错instruction not allowed求助

Hey there, let’s work through this instruction not allowed crash you’re hitting when running model.fit() in Python 3.5 on your Debian 9 Ubuntu server. Since you mentioned this worked fine before and suspect file permissions might be the culprit, here are some actionable steps to diagnose and fix the issue:

• Double-check file and directory permissions
  Let’s start with the permission angle you suspect. The Python process needs read access to your training data and model files, plus write access if you’re saving checkpoints or logs during training.

  • Use ls -l /path/to/your/data and ls -l /path/to/your/model to inspect ownership and permissions for all relevant files/directories.
  • If the files are owned by a different user (like root), adjust ownership with chown -R your_username:your_group /path/to/your/files (swap in your actual user/group details).
  • Set appropriate permissions: use chmod -R 644 /path/to/files for read-only access, or chmod -R 664 /path/to/files if you need read/write. Avoid overly permissive settings like 777 unless absolutely necessary.
  • Also, make sure your script isn’t trying to write to restricted system directories (like /usr or /root). If it is, either run the script with sudo (use caution!) or move your output targets to a user-owned directory (e.g., ~/training-results).

• Check CPU instruction set compatibility
  The instruction not allowed error often ties to CPU architecture mismatches—especially if you’re using optimized ML libraries (like TensorFlow or PyTorch) that were compiled for a different CPU type. Even if it worked before, a recent update could have broken this:

  • Run cat /proc/cpuinfo to see your CPU’s supported instruction sets (look for flags like avx, avx2, sse4).
  • Verify your ML framework version matches your CPU’s capabilities. If you installed a pre-built wheel that uses advanced instructions your CPU doesn’t support, try switching to a more compatible build. For example, install the CPU-only variant of TensorFlow/PyTorch, which is compiled for broader compatibility.
  • If you built the library from source previously, double-check that the compile flags match your CPU’s supported instructions. A recent recompile with incorrect flags could be causing this crash.

• Review recent environment changes
  Since the code worked before, something must have shifted. Let’s look for recent tweaks:

  • Did you update Python, your ML framework, or any dependencies lately? Try rolling back to a known-good version with pip install package-name==specific-version (e.g., pip install tensorflow==1.15 if that’s what you used before).
  • Check for system updates: look at /var/log/apt/history.log to see if kernel, glibc, or other core packages were updated recently. Temporarily reverting critical updates can help pinpoint if that’s the cause.
  • Check resource limits with ulimit -a—if new restrictions on memory, file handles, or CPU usage were added, they could be causing the process to terminate unexpectedly.

• Debug with system tracing tools
  Get more granular details about where the crash happens:

  • Run your script with strace to track system calls: strace -f python3 your-script.py. Look for the last system call before the crash—it might highlight a permission issue or an instruction-related failure.
  • Use gdb to get a backtrace: start with gdb python3, then run run your-script.py. When the process crashes, type bt to see exactly where in the code (or underlying library) the error occurs.

• Test with a minimal reproducible example
  Create a tiny script that runs model.fit() with a small dummy dataset (no real data needed). If this still crashes, the issue is likely with your environment; if it works, the problem is tied to your specific data or model setup. This helps narrow down whether it’s truly a permission issue or something else.

Quick note: Start with the permission and CPU compatibility checks first—these are the most common triggers for this specific error in ML training workflows.

内容的提问来源于stack exchange，提问作者Jérémie