Skip to content

Troubleshooting

By following these troubleshooting steps, you can effectively address and resolve common issues encountered while using the Final State Transformer toolkit.

Common Issues and Solutions

Installation Problems

  • Unable to install dependencies: Ensure you have the correct version of Python and pip installed. Run python --version and pip --version to verify. If the versions are incorrect, download and install the appropriate versions from the official Python website.

  • Errors during pip install -r requirements.txt: Check the error message for missing packages or compatibility issues. Ensure your system has the necessary compilers and libraries.

  • Problems with virtual environment activation.: Ensure the virtual environment is correctly set up. On Unix-based systems, use source venv/bin/activate. If activation fails, check the path and permissions.

Data Loading Errors

  • Incorrect data format: Ensure your data is in the expected format, such as HDF5. Refer to the data preparation section of the documentation for specific formatting requirements. Verify the integrity of the data files and ensure there are no missing or corrupted entries.

  • FileNotFoundError or path issues: Double-check the paths specified in your configuration files. Ensure the paths are absolute or correctly relative to the working directory. Confirm that the files exist at the specified locations.

Training Failures

  • Model training crashes or does not start: Review the configuration files for any incorrect or missing parameters. Ensure that the dataset paths, model parameters, and training hyperparameters are correctly specified. Check the logs for specific error messages and troubleshoot accordingly.

  • CUDA-related errors (if using GPU): Verify that CUDA is properly installed and that your GPU drivers are up to date. Use nvidia-smi to check GPU status. Ensure that the TensorFlow or PyTorch version you are using is compatible with your CUDA version.

  • Memory errors or out-of-memory issues: Reduce batch size or use a smaller model architecture to fit within your system's memory limits. If using a GPU, ensure that other processes are not occupying significant GPU memory.

Evaluation Discrepancies

  • Unexpectedly low performance metrics: Verify that the model checkpoint being loaded for evaluation is the correct one. Ensure that the evaluation dataset is correctly formatted and preprocessed. Cross-check the evaluation script parameters and configuration.

  • Inconsistent results between training and evaluation: Check for data leakage, where information from the training set inadvertently ends up in the evaluation set. Ensure that data augmentation and preprocessing steps are applied consistently during training and evaluation.

Debugging Tips

  • Log Files: Thoroughly review log files generated during training and evaluation for detailed error messages and warnings. Logs can provide insights into the stages where issues occur and help identify the root cause.

  • Verbose Mode: Run scripts in verbose mode to get more detailed output. This can be done by adding verbosity flags (e.g., --verbose or -v) to your command, which can help in pinpointing where the process is failing.

  • Dependency Checks: Regularly check for updates to dependencies and ensure compatibility. Using a package manager like pip-tools can help manage and update dependencies systematically.

  • Sanity Checks: Perform sanity checks on your data and model by running a few quick epochs with a small subset of the data. This can help identify obvious issues before committing to longer training sessions.

Specific Error Messages and Their Resolutions

  • ModuleNotFoundError: Ensure that all necessary packages are installed. If you are working in a virtual environment, ensure it is activated. You may also need to update your PYTHONPATH to include the directory where the module resides.

  • ValueError: Shapes (X, Y) and (A, B) are incompatible: This error typically indicates a mismatch in expected input shapes. Verify the dimensions of your input data and ensure they match the model's expected input. Adjust preprocessing steps if necessary.

  • TypeError: object of type 'NoneType' has no len(): This error suggests that a variable expected to be a list or similar iterable is None. Trace the variable through your code to ensure it is being properly initialized and populated.