Split the unattended run into a GPU-free phase and a GPU phase

The target machine's card is busy with someone else's job, so a single
end-to-end script stalls on work that does not actually need a GPU.

Compiling the CUDA extensions needs nvcc, not a device, and downloading 18 GB
of data needs neither. Those are the slow parts (~50 min + ~30 min), so phase A
now runs entirely without the card:

  run_setup.sh   bootstrap, conda, extensions, patches, data      no GPU
  run_train.sh   voxel_max measurement, training, evaluation      GPU

run_setup reports the GPU but never fails on it, and verify_env.py gained
SKIP_CUDA_CHECK so import coverage still runs when no device is visible.
TORCH_CUDA_ARCH_LIST is stated rather than probed, since the card may be
unavailable at build time.

run_train waits for the GPU instead of failing when it is busy: it polls until
enough VRAM frees up (12h default), so it can be queued ahead of time. Past the
deadline it proceeds anyway and lets the measured voxel_max adapt to whatever
is actually free.

keepalive.sh now takes the phase to supervise. Replaces run_all.sh and RUN.md
with SETUP.md and TRAIN.md. Adds selfcheck.sh, which syntax-checks every script
and flags CRLF endings - a shell script with either fails at its first line,
which for an unattended weekend run means losing the weekend.

Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>

scripts/verify_env.py

@@ -62,12 +62,23 @@ def main() -> int:
     if root is None:
         ok = False
 
+    # SKIP_CUDA_CHECK exists for the GPU-free setup phase: the extensions can be
+    # built and imported without a card present, and querying the device would
+    # fail on a machine whose GPU is absent or still occupied. Import coverage
+    # is unaffected -- only the device query is skipped.
+    skip_cuda = bool(os.environ.get("SKIP_CUDA_CHECK"))
+
     try:
         import torch
-        print(f"torch {torch.__version__} | cuda {torch.version.cuda} | "
-              f"available {torch.cuda.is_available()}")
-        if torch.cuda.is_available():
-            print(f"device: {torch.cuda.get_device_name(0)}")
+        line = f"torch {torch.__version__} | cuda {torch.version.cuda}"
+        if skip_cuda:
+            print(line + " | device check skipped (SKIP_CUDA_CHECK)")
+        else:
+            print(line + f" | available {torch.cuda.is_available()}")
+            if torch.cuda.is_available():
+                print(f"device: {torch.cuda.get_device_name(0)}")
+            else:
+                print("device: none visible -- fine for setup, required to train")
     except Exception as e:  # noqa: BLE001
         print(f"torch import failed: {e}")
         return 1