Add an end-to-end runnable graphnet demo on the bundled Prometheus data

README.md

@@ -40,7 +40,7 @@ src/spine/
   train.py    reader-agnostic fit() assembly
 configs/      Hydra groups: backbone/ task/ optimizer/ scheduler/ callbacks/ trainer/ data/
 integrations/spine_graphnet/  graphnet integration: DeepIce backbone + reader adapter
-examples/     Hydra launcher (train_curtain.py)
+examples/     Hydra launcher (train_curtain.py) + runnable graphnet demo (graphnet_demo.py)
 tests/        core-independence gate (spine imports no graphnet)
 ```
 
@@ -65,6 +65,21 @@ python examples/train_curtain.py \
     data.train_selection=train.parquet data.val_selection=val.parquet \
     callbacks=curtain_auc task/objectives=v2 trainer.devices=4
 ```
+
+An end-to-end demo of the graphnet frame needs no data of your own: it runs on
+the Prometheus example file bundled with a graphnet checkout, pretrains a tiny
+DeepIce on CPU in about a minute, and loads the exported encoder back into a
+stock graphnet DeepIce:
+```
+python examples/graphnet_demo.py --out curtain_demo_out
+```
+The same run is also available through the Hydra path: stage the demo inputs
+once, then launch from the experiment config:
+```
+python examples/graphnet_demo.py --prepare-only
+python examples/train_curtain.py +experiment=prometheus_demo
+```
+
 ## 📥 Reading data
 SPINE mandates **no reader**. Provide any PyTorch `Dataset` satisfying the
 contract stated canonically in `spine/data/datamodule.py`:

configs/data/prometheus_demo.yaml

@@ -0,0 +1,14 @@
+# graphnet's bundled Prometheus demo file, staged locally by
+# `python examples/graphnet_demo.py --prepare-only` (copies the db and writes
+# geometry + selections into curtain_demo_out/). Paths are relative to the
+# invocation cwd (the launcher keeps cwd via hydra.job.chdir=false).
+db: curtain_demo_out/prometheus-events.db
+train_selection: curtain_demo_out/train_selection.parquet
+val_selection: curtain_demo_out/val_selection.parquet
+n_train: 0
+reader:
+  # the demo module next to the launcher: `python examples/train_curtain.py`
+  # puts examples/ on sys.path, so the demo's reader resolves without living
+  # in an installed package
+  _target_: graphnet_demo.demo_reader
+  db: ${data.db}

configs/experiment/prometheus_demo.yaml

@@ -0,0 +1,37 @@
+# @package _global_
+# The bundled-Prometheus demo through the Hydra path -- same tiny run as
+# examples/graphnet_demo.py. Stage its inputs once, then launch:
+#   python examples/graphnet_demo.py --prepare-only
+#   python examples/train_curtain.py +experiment=prometheus_demo
+defaults:
+  - override /data: prometheus_demo
+  - override /callbacks: curtain_auc
+
+geo: curtain_demo_out/prometheus_demo_geometry.npz
+geo_sensor_key: sensor_id
+out: curtain_demo_out/curtain_prometheus_demo.pth
+
+# graphnet's own detector for this file supplies the standardization, so the
+# encoder pretrained here matches a downstream graphnet fine-tune's inputs
+scaler:
+  _target_: spine_graphnet.scaling.DetectorScaler
+  detector:
+    _target_: graphnet.models.detector.prometheus.Prometheus
+  feature_names: [sensor_pos_x, sensor_pos_y, sensor_pos_z, t, charge]
+
+task:
+  dt_scale: 100.0
+
+backbone:
+  d_model: 32
+  depth: 2
+  head_size: 8
+  depth_rel: 1
+  n_rel: 1
+  seq_length: 32
+
+trainer:
+  batch: 8
+  num_workers: 0
+  max_epochs: 15
+  patience: 10

configs/task/curtain.yaml

@@ -1,6 +1,6 @@
 # The CURTAIN pretext task -- a self-contained plugin config that pulls in its
 # own objectives. The launcher injects the runtime geo + scaler and
-# recursive-instantiates the rest. Sampler knobs (holdout_mode, q_lo/q_hi,
+# recursive-instantiates the rest. Sampler knobs (q_lo/q_hi,
 # pos_k, neg_anchor, rand_neg_frac, min_visible, min_future, resample_tries)
 # are task arguments with their defaults in CurtainTask -- override like
 # task.q_lo=0.5.