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chore: rerun tutorial notebooks
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‎docs/tutorials/notebooks/advanced_training.ipynb‎

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},
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"cell_type": "code",
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"execution_count": null,
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"id": "ca8e3e4d",
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"# These tutorials use small synthetic datasets and short training runs, which\n",
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"# surfaces a few non-actionable framework messages. Quieten them so the output\n",
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"# stays focused on the tutorial; none of them affect correctness.\n",
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"warnings.filterwarnings(\"ignore\", message=\".*n_quantiles.*\")\n",
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"warnings.filterwarnings(\"ignore\", message=\".*does not have many workers.*\")\n",
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"warnings.filterwarnings(\"ignore\", message=\".*have no logger configured.*\")\n",
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"warnings.filterwarnings(\"ignore\", message=\".*lr_patience.*\")\n",
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"warnings.filterwarnings(\"ignore\", message=\".*Checkpoint directory.*\")\n",
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"warnings.filterwarnings(\"ignore\")\n",
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"logging.getLogger(\"lightning.pytorch\").setLevel(logging.ERROR)"
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],
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"metadata": {
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"kernelspec": {
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"display_name": ".venv",
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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‎docs/tutorials/notebooks/experimental.ipynb‎

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"cell_type": "code",
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"# These tutorials use small synthetic datasets and short training runs, which\n",
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"# surfaces a few non-actionable framework messages. Quieten them so the output\n",
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"# stays focused on the tutorial; none of them affect correctness.\n",
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"warnings.filterwarnings(\"ignore\", message=\".*n_quantiles.*\")\n",
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"warnings.filterwarnings(\"ignore\", message=\".*does not have many workers.*\")\n",
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"warnings.filterwarnings(\"ignore\", message=\".*have no logger configured.*\")\n",
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"warnings.filterwarnings(\"ignore\", message=\".*lr_patience.*\")\n",
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"warnings.filterwarnings(\"ignore\", message=\".*Checkpoint directory.*\")\n",
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"warnings.filterwarnings(\"ignore\")\n",
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"logging.getLogger(\"lightning.pytorch\").setLevel(logging.ERROR)"
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],
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"metadata": {
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"display_name": "Python 3",
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‎docs/tutorials/notebooks/hpo.ipynb‎

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"cell_type": "code",
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"execution_count": null,
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"# These tutorials use small synthetic datasets and short training runs, which\n",
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"# surfaces a few non-actionable framework messages. Quieten them so the output\n",
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"# stays focused on the tutorial; none of them affect correctness.\n",
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"warnings.filterwarnings(\"ignore\", message=\".*n_quantiles.*\")\n",
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"warnings.filterwarnings(\"ignore\", message=\".*does not have many workers.*\")\n",
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"warnings.filterwarnings(\"ignore\", message=\".*have no logger configured.*\")\n",
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"warnings.filterwarnings(\"ignore\", message=\".*lr_patience.*\")\n",
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"warnings.filterwarnings(\"ignore\", message=\".*Checkpoint directory.*\")\n",
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"warnings.filterwarnings(\"ignore\")\n",
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"logging.getLogger(\"lightning.pytorch\").setLevel(logging.ERROR)"
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]
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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‎docs/tutorials/notebooks/imbalance_classification.ipynb‎

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"# These tutorials use small synthetic datasets and short training runs, which\n",
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"# surfaces a few non-actionable framework messages. Quieten them so the output\n",
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"# stays focused on the tutorial; none of them affect correctness.\n",
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"warnings.filterwarnings(\"ignore\", message=\".*n_quantiles.*\")\n",
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"warnings.filterwarnings(\"ignore\", message=\".*does not have many workers.*\")\n",
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"warnings.filterwarnings(\"ignore\", message=\".*have no logger configured.*\")\n",
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"warnings.filterwarnings(\"ignore\", message=\".*lr_patience.*\")\n",
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"warnings.filterwarnings(\"ignore\", message=\".*Checkpoint directory.*\")\n",
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"warnings.filterwarnings(\"ignore\")\n",
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"logging.getLogger(\"lightning.pytorch\").setLevel(logging.ERROR)"
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"metadata": {
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"display_name": "Python 3",
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‎docs/tutorials/notebooks/observability.ipynb‎

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"# These tutorials use small synthetic datasets and short training runs, which\n",
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"# surfaces a few non-actionable framework messages. Quieten them so the output\n",
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"# stays focused on the tutorial; none of them affect correctness.\n",
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"warnings.filterwarnings(\"ignore\", message=\".*n_quantiles.*\")\n",
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"warnings.filterwarnings(\"ignore\", message=\".*does not have many workers.*\")\n",
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"warnings.filterwarnings(\"ignore\", message=\".*have no logger configured.*\")\n",
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"warnings.filterwarnings(\"ignore\", message=\".*lr_patience.*\")\n",
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"warnings.filterwarnings(\"ignore\", message=\".*Checkpoint directory.*\")\n",
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"warnings.filterwarnings(\"ignore\", message=\".*logging interval.*\")\n",
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"warnings.filterwarnings(\"ignore\", message=\".*IProgress not found.*\")\n",
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"\n",
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"# Lightning prints a device banner and a parameter-count table on every fit.\n",
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"# They are useful in isolation but drown out the observability messages this\n",
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"# tutorial is about, so raise these loggers to ERROR. DeepTab's own events are\n",
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"# emitted separately and are unaffected.\n",
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"for _name in (\n",
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" \"lightning\",\n",
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" \"lightning.pytorch\",\n",
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" \"lightning.pytorch.callbacks.model_summary\",\n",
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" \"lightning.pytorch.utilities.rank_zero\",\n",
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" \"lightning.pytorch.accelerators\",\n",
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" \"pytorch_lightning\",\n",
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"):\n",
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" logging.getLogger(_name).setLevel(logging.ERROR)"
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"warnings.filterwarnings(\"ignore\")\n",
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"logging.getLogger(\"lightning.pytorch\").setLevel(logging.ERROR)\n"
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"Your sink received the full event stream with its structured payloads, ready to forward to whatever backend you use. Because no `ObservabilityConfig` was attached, DeepTab created no run directory of its own: your code is in full control."
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"cell_type": "markdown",
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"source": [
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"## 8. Side-by-side: what each configuration leaves on disk\n",
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"\n",
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"The trees below are the canonical shapes you can expect. Timestamps and ids vary per run; the structure does not.\n",
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"\n",
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"**No observability** — only the best-weights checkpoint:\n",
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"\n",
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"```text\n",
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"01_no_observability/\n",
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" checkpoints/\n",
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" best_model.ckpt\n",
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"```\n",
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"\n",
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"**Minimal `ObservabilityConfig`** — self-describing run directory:\n",
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"\n",
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"```text\n",
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"02_minimal/\n",
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" runs/demo/<timestamp>_<run_id>/\n",
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" config.yaml # full estimator configuration\n",
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" summary.json # final metrics\n",
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" artifacts/ # reserved for run artifacts\n",
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" checkpoints/\n",
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" best_model.ckpt\n",
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"```\n",
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"\n",
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"**`structured_logging=True, log_to_file=True`** — adds the event log:\n",
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"\n",
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"```text\n",
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"04_with_file/\n",
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" runs/demo/<timestamp>_<run_id>/\n",
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" config.yaml\n",
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" lifecycle.jsonl # one JSON event per line\n",
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" summary.json\n",
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" artifacts/\n",
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" checkpoints/\n",
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" best_model.ckpt\n",
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"```\n",
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"\n",
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"**`experiment_trackers=[\"tensorboard\"]`** — adds a TensorBoard tree:\n",
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"\n",
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"```text\n",
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"06_tensorboard/\n",
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" runs/demo/<timestamp>_<run_id>/\n",
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" config.yaml\n",
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" summary.json\n",
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" artifacts/\n",
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" checkpoints/best_model.ckpt\n",
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" tensorboard/demo/<timestamp>_<run_id>/\n",
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" events.out.tfevents...\n",
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" hparams.yaml\n",
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"```\n",
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"\n",
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"**`experiment_trackers=[\"mlflow\"]`** — adds a local MLflow store:\n",
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"\n",
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"```text\n",
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"07_mlflow/\n",
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" runs/demo/<timestamp>_<run_id>/\n",
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" config.yaml\n",
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" summary.json\n",
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" artifacts/\n",
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" checkpoints/best_model.ckpt\n",
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" mlflow/\n",
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" backend/mlflow.db # run metadata (SQLite)\n",
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" artifacts/<mlflow_run_id>/artifacts/\n",
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" config.yaml\n",
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" summary.json\n",
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" best_model/... # logged model checkpoint\n",
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" checkpoints/best_model.ckpt\n",
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"```\n",
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"\n",
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"**`logger=...` + a tracker** — your Lightning logger sits beside DeepTab's trees:\n",
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"\n",
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"```text\n",
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"08_byo_logger/\n",
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" csv/mlp/version_0/\n",
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" hparams.yaml\n",
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" metrics.csv\n",
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" runs/demo/<timestamp>_<run_id>/...\n",
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" tensorboard/demo/<timestamp>_<run_id>/...\n",
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"```"
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]
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‎docs/tutorials/notebooks/skewed_regression.ipynb‎

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"# These tutorials use small synthetic datasets and short training runs, which\n",
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"# surfaces a few non-actionable framework messages. Quieten them so the output\n",
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"# stays focused on the tutorial; none of them affect correctness.\n",
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"warnings.filterwarnings(\"ignore\", message=\".*n_quantiles.*\")\n",
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"warnings.filterwarnings(\"ignore\", message=\".*does not have many workers.*\")\n",
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"warnings.filterwarnings(\"ignore\", message=\".*have no logger configured.*\")\n",
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"warnings.filterwarnings(\"ignore\", message=\".*lr_patience.*\")\n",
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"warnings.filterwarnings(\"ignore\", message=\".*Checkpoint directory.*\")\n",
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‎docs/tutorials/notebooks/uncertainty_quantification.ipynb‎

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"# These tutorials use small synthetic datasets and short training runs, which\n",
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"# surfaces a few non-actionable framework messages. Quieten them so the output\n",
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"# stays focused on the tutorial; none of them affect correctness.\n",
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"warnings.filterwarnings(\"ignore\", message=\".*n_quantiles.*\")\n",
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"warnings.filterwarnings(\"ignore\", message=\".*does not have many workers.*\")\n",
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"warnings.filterwarnings(\"ignore\", message=\".*have no logger configured.*\")\n",
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"warnings.filterwarnings(\"ignore\", message=\".*lr_patience.*\")\n",
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"warnings.filterwarnings(\"ignore\", message=\".*Checkpoint directory.*\")\n",
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