# Troubleshooting ## The default command did not train anything That is intentional: `python run.py` uses the `plan` action. Use `python run.py run --profile smoke` for a small end-to-end run. ## "Analysis is not feasible for these configurations" A model's estimated analysis exceeds `max_model_evaluations`, `max_saved_bytes` or `max_candidate_points` in `settings_analysis.py`. Nothing was computed. The message names each configuration and the settings to reduce: typically the truths (`truth_design`, `sobol_truths`, `truth_points_per_axis`), `repeats`, `observations` or `candidate_count`. Raise a limit only if the hardware can afford it. Nothing is reduced automatically. Trained models are unaffected: fix the analysis settings and run `analyze`. ## Analysis says there are no trained models `analyze` never trains. It analyzes the models of the latest successful `train` or `run` in the profile's output folder; train first. Changing `settings_training.py` afterwards does not change which models are analyzed; run `train` again to train the new settings. For loading existing weights on a different device, use `restore`. ## A Choice in the analysis settings is rejected Sweeps are for training settings. An analysis is one setting: analyze, change the setting, and analyze again. Compare the saved metrics in between. ## A list did not produce several configurations Literal lists are one setting value. Use `Choice([baseline, alternative, ...])` for alternatives. `Choice([[64, 64], [128, 128]])` is a network-width comparison; `[64, 64]` by itself is just one two-layer architecture. ## A selected parameter does not exist Parameter indices start at zero. `std:1` needs at least two observation dimensions. OFAT alternatives are checked against the baseline, not against a helpful combination of other alternatives. Use a compatible baseline or one atomic dictionary choice for coupled settings. ## An extra metric unexpectedly rebuilt a dataset Check its product settings selector, dependency keys, source dependencies, explicit version, output root, and recorded package versions. Cache keys are conservative. Products whose builders read new settings must declare them; removing dependencies just to force cache reuse would undermine correctness. ## Results of earlier settings are gone That is intended: in an output folder the latest settings win, and the cache keeps only what the last three distinct training settings used. Training starts each run's analysis over; run `analyze` again. The latest settings files are saved as `outputs//settings_training.py` and `settings_analysis.py`. To keep several experiments side by side, give each its own profile. See [Change settings and run again](quickstart.md#change-settings-and-run-again). ## A result was moved and no longer loads Move the whole output root, including `cache`, `source`, and `runs`, rather than one individual run directory. Relative references require their corresponding artifact store. `verify` checks cache integrity but does not recreate missing artifacts. ## CUDA or distributed execution fails immediately Confirm a CUDA-enabled PyTorch installation, visible devices, and worker count. Use `device="cuda"` under `torchrun`; indexed `cuda:N` settings are for a single process. The [CUDA guide](cuda.md) separates independent jobs from collective DDP. Physical GPU availability is not checked merely by generating a plan. ## Coverage or reweighting looks poor Do not infer scientific calibration from passing software tests. Inspect candidate resolution, attained HLD masses, truth repeats, training replicas, reference likelihood results, and overlap/importance-weight ESS. Read the [scientific notes](../SCIENTIFIC_NOTES.md) for the implemented definitions and finite-candidate limitations. ## The documentation has not appeared on GitHub The repository must contain `.github/workflows/docs.yml` at its root. Enable **Settings → Pages → Build and deployment → Source: GitHub Actions**. Publishing is manual: pushes only build-check the documentation, so run the documentation workflow on the desired branch with `deploy` ticked. Inspect the build/deploy jobs in the Actions tab. Detailed steps and permission caveats are in [Publishing the website](../publishing.md).