This is a placeholder for providing help and tips on common issues encountered in the book. Right now, this page is intentionally left blank as no known issues have been reported.
Please use this discussion page if you have any issues with file downloads.
If you are viewing the notebook code in JupyterLab rather than VSCode, note that JupyterLab (in its default setting) has had scrolling bugs in recent versions. My recommendation is to go to Settings -> Settings Editor and change the "Windowing mode" to "none" (as illustrated below), which seems to address the issue.
If you are a Linux user and see an InductorError: CppCompileError: C++ compile error when executing torch.compile containing the following lines:
Python.h: No such file or directory
81 | #include <Python.h>
| ^~~~~~~~~~
compilation terminated.it indicates they your Python runtime may be lacking some C++ header files required for compiling the model for CPU usage.
You could for example check if the file exists: ls -l /usr/include/python3.12/Python.h.
If it doesn't exist, you could then try to install a different Python runtime via
sudo apt-get install -y python3.12-dev build-essentialOr you could disable the C++ requirements in PyTorch before calling torch.compile:
import torch
import torch._inductor.config as inductor_config
inductor_config.cpp_wrapper = False
compiled_model = torch.compile(model)Also see #192 for more context.
In train_rlvr_grpo (Chapter 6), a Ctrl+C triggers the KeyboardInterrupt handler to save a -interrupt checkpoint. If you press Ctrl+C a second time before the save completes, it can interrupt torch.save mid-write and leave a truncated .pth file. Wait for the -interrupt checkpoint message before exiting.
Corrupted model checkpoints usually raise load errors or fail during evaluation; another telltale sign is that they are much smaller than the expected ~1.5 GB.
For other issues, please feel free to open a new GitHub Issue.