chore: add large graph render scripts to scripts/

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

Author: johnmarktaylor91

scripts/CLAUDE.md

@@ -5,6 +5,8 @@
 | File | Purpose |
 |------|---------|
 | `check_flops_coverage.py` | Reports FLOPs module coverage against all decorated torch functions |
+| `run_250k.py` | Render a 250k-node graph with ELK layout |
+| `run_1M.py` | Render a 1M-node graph with ELK (phased: construct → log → render) |
 
 ## check_flops_coverage.py
 

scripts/run_1M.py

@@ -0,0 +1,48 @@
+"""Standalone script: render a 1M-node graph with ELK stress + topo seeding."""
+
+import os
+import sys
+import time
+
+sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..", "tests"))
+
+import torch
+from example_models import RandomGraphModel
+from torchlens import log_forward_pass
+
+outdir = os.path.join("tests", "test_outputs", "visualizations", "large")
+os.makedirs(outdir, exist_ok=True)
+
+# Phase 1: Model construction
+t0 = time.time()
+model = RandomGraphModel(target_nodes=1000000, seed=42)
+x = torch.randn(2, 64)
+t1 = time.time()
+print(f"Phase 1 — Model construction: {t1 - t0:.1f}s", flush=True)
+
+# Phase 2: log_forward_pass (logging + postprocessing)
+ml = log_forward_pass(model, x, layers_to_save=None, detect_loops=False)
+t2 = time.time()
+print(f"Phase 2 — log_forward_pass:    {t2 - t1:.1f}s ({len(ml)} layers)", flush=True)
+
+# Phase 3: Render
+ml.render_graph(
+    vis_opt="unrolled",
+    vis_nesting_depth=1000,
+    vis_outpath=os.path.join(outdir, "elk_1M"),
+    save_only=True,
+    vis_fileformat="svg",
+    vis_node_placement="elk",
+)
+t3 = time.time()
+print(f"Phase 3 — ELK render:          {t3 - t2:.1f}s", flush=True)
+
+total = t3 - t0
+print(f"Total: {total:.1f}s ({total / 60:.1f} min)", flush=True)
+
+svg_path = os.path.join(outdir, "elk_1M.svg")
+if os.path.exists(svg_path):
+    size_mb = os.path.getsize(svg_path) / 1024 / 1024
+    print(f"Output: {svg_path} ({size_mb:.0f} MB)")
+
+ml.cleanup()

scripts/run_250k.py

@@ -0,0 +1,30 @@
+"""Standalone script: render a 250k-node graph with ELK."""
+
+import os
+import sys
+import time
+
+sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..", "tests"))
+
+import torch
+from example_models import RandomGraphModel
+from torchlens import show_model_graph
+
+outdir = os.path.join("tests", "test_outputs", "visualizations", "large")
+os.makedirs(outdir, exist_ok=True)
+
+model = RandomGraphModel(target_nodes=250000, seed=42)
+x = torch.randn(2, 64)
+
+print("Starting 250k ELK render...", flush=True)
+t0 = time.time()
+show_model_graph(
+    model,
+    x,
+    vis_node_placement="elk",
+    vis_fileformat="svg",
+    save_only=True,
+    vis_outpath=os.path.join(outdir, "elk_250k"),
+)
+elapsed = time.time() - t0
+print(f"Done in {elapsed:.1f}s ({elapsed / 60:.1f} min)")