nightly benchmark run configureation

engine/benchmark.py

@@ -7,7 +7,7 @@ import sys
 import argparse
 import json
 import logging
-from datetime import datetime, UTC
+from datetime import datetime, timezone
 from pathlib import Path
 
 # clear stale TPU locks on startup
@@ -449,7 +449,7 @@ def _run_with_args(args, compare_robust_override: bool | None = None):
 
     out_dir = Path(args.output_dir)
     out_dir.mkdir(parents=True, exist_ok=True)
-    stamp = datetime.now(UTC).strftime("%Y%m%d_%H%M%S")
+    stamp = datetime.now(timezone.utc).strftime("%Y%m%d_%H%M%S")
     csv_path = out_dir / f"benchmark_{stamp}.csv"
     trace_path = out_dir / f"benchmark_traces_{stamp}.json"
     df.to_csv(csv_path, index=False)
@@ -580,7 +580,7 @@ def run_cli(raw_args: list[str] | None = None):
 
     tiers = _parse_list(args.tiers)
     alpha_values = _parse_float_list(args.alpha_values)
-    run_stamp = datetime.now(UTC).strftime("%m%d-%H%M%S")
+    run_stamp = datetime.now(timezone.utc).strftime("%m%d-%H%M%S")
     compare_enabled = _truthy(os.environ.get("PHANTOM_BENCHMARK_COMPARE_ROBUST"))
     compare_tag = "robust-compare" if compare_enabled else "single-mode"
     modes = (

scripts/ray_distributed_train.py

@@ -15,6 +15,15 @@ def _has_flag(tokens: list[str], name: str) -> bool:
     return any(tok == name or tok.startswith(f"{name}=") for tok in tokens)
 
 
+def _entry_tokens(run_kind: str, entry_args: str) -> list[str]:
+    tokens = shlex.split(entry_args)
+    if run_kind == "benchmark" and not (
+        _has_flag(tokens, "--run-kind") or _has_flag(tokens, "--run-mode")
+    ):
+        return ["--run-kind", "benchmark", *tokens]
+    return tokens
+
+
 def _alive_node_ips() -> list[str]:
     seen: set[str] = set()
     ips: list[str] = []
@@ -33,13 +42,18 @@ def _alive_node_ips() -> list[str]:
 def _train_on_node(
     *,
     root: str,
-    train_args: str,
+    run_kind: str,
+    entry_args: str,
     rank: int,
     world_size: int,
     coordinator_ip: str,
     coordinator_port: int,
     base_seed: int,
     run_group: str,
+    compare_robust: bool,
+    output_root: str,
+    wandb_entity: str,
+    wandb_project: str,
     sync_jax: bool,
 ) -> int:
     env = dict(os.environ)
@@ -53,6 +67,12 @@ def _train_on_node(
     env["JAX_PLATFORMS"] = requested_platform
     # Keep each train process in single-host mode to avoid accidental global stalls.
     env["CLOUD_TPU_TASK_ID"] = "0"
+    if run_kind == "benchmark":
+        env["PHANTOM_BENCHMARK_COMPARE_ROBUST"] = "1" if compare_robust else "0"
+    if wandb_entity:
+        env["WANDB_ENTITY"] = wandb_entity
+    if wandb_project:
+        env["WANDB_PROJECT"] = wandb_project
 
     cwd = str(Path(root))
 
@@ -75,30 +95,62 @@ def _train_on_node(
             [sys.executable, "-c", probe], cwd=cwd, env=env_probe, check=True
         )
 
-    tokens = shlex.split(train_args)
+    tokens = _entry_tokens(run_kind, entry_args)
+    seed = int(base_seed + rank)
     if not _has_flag(tokens, "--seed"):
-        tokens.extend(["--seed", str(base_seed + rank)])
-    if not _has_flag(tokens, "--group"):
+        tokens.extend(["--seed", str(seed)])
+
+    if run_kind == "train" and not _has_flag(tokens, "--group"):
         tokens.extend(["--group", run_group])
 
+    if (
+        run_kind == "benchmark"
+        and output_root
+        and not _has_flag(tokens, "--output-dir")
+    ):
+        out_dir = Path(output_root) / f"rank_{rank}" / f"seed_{seed}"
+        out_dir.parent.mkdir(parents=True, exist_ok=True)
+        tokens.extend(["--output-dir", str(out_dir)])
+
     cmd = [sys.executable, "-m", "engine.train", *tokens]
+    print(
+        {
+            "rank": int(rank),
+            "run_kind": run_kind,
+            "seed": int(seed),
+            "compare_robust": bool(compare_robust),
+            "wandb_entity": str(env.get("WANDB_ENTITY", "")),
+            "wandb_project": str(env.get("WANDB_PROJECT", "")),
+            "command": " ".join(cmd),
+        }
+    )
     proc = subprocess.run(cmd, cwd=cwd, env=env)
     return int(proc.returncode)
 
 
 def main() -> None:
     parser = argparse.ArgumentParser(
-        description="Launch one train run per Ray TPU node"
+        description="Launch one train/benchmark run per Ray TPU node"
     )
-    parser.add_argument("--train-args", type=str, required=True)
+    parser.add_argument("--run-kind", choices=["train", "benchmark"], default="train")
+    parser.add_argument("--entry-args", type=str, default="")
+    parser.add_argument("--train-args", type=str, default="")
     parser.add_argument("--num-nodes", type=int, default=0)
     parser.add_argument("--tpu-per-task", type=float, default=8.0)
     parser.add_argument("--base-seed", type=int, default=42)
     parser.add_argument("--sync-jax", action="store_true")
     parser.add_argument("--coordinator-port", type=int, default=12355)
     parser.add_argument("--run-group", type=str, default="")
+    parser.add_argument("--compare-robust", action="store_true")
+    parser.add_argument("--output-root", type=str, default="")
+    parser.add_argument("--wandb-entity", type=str, default="")
+    parser.add_argument("--wandb-project", type=str, default="")
     args = parser.parse_args()
 
+    entry_args = str(args.entry_args or args.train_args).strip()
+    if not entry_args:
+        raise ValueError("--entry-args (or legacy --train-args) is required")
+
     ray.init(address="auto")
 
     node_ips = _alive_node_ips()
@@ -118,8 +170,11 @@ def main() -> None:
             "nodes": node_ips,
             "world_size": world_size,
             "coordinator": f"{coordinator_ip}:{int(args.coordinator_port)}",
-            "train_args": args.train_args,
+            "run_kind": str(args.run_kind),
+            "entry_args": entry_args,
             "run_group": run_group,
+            "compare_robust": bool(args.compare_robust),
+            "output_root": str(args.output_root),
         }
     )
 
@@ -130,14 +185,19 @@ def main() -> None:
         futures.append(
             _train_on_node.options(resources=resources).remote(
                 root=root,
-                train_args=args.train_args,
+                run_kind=str(args.run_kind),
+                entry_args=entry_args,
                 rank=rank,
                 world_size=world_size,
                 coordinator_ip=coordinator_ip,
                 coordinator_port=int(args.coordinator_port),
                 base_seed=int(args.base_seed),
                 run_group=run_group,
-                sync_jax=bool(args.sync_jax),
+                compare_robust=bool(args.compare_robust),
+                output_root=str(args.output_root),
+                wandb_entity=str(args.wandb_entity),
+                wandb_project=str(args.wandb_project),
+                sync_jax=bool(args.sync_jax and str(args.run_kind) == "train"),
             )
         )
 

submit_ray_job.sh

@@ -3,6 +3,7 @@
 # Modes:
 #   RAY_MODE=single       -> one run (default)
 #   RAY_MODE=distributed  -> one run per TPU node (experimental)
+#   RAY_MODE=benchmark    -> one benchmark run per TPU node (overnight)
 
 set -euo pipefail
 
@@ -27,6 +28,9 @@ env = dotenv_values(".env")
 # Filter out empty/None values
 env_vars = {k: v for k, v in env.items() if v}
 env_vars.setdefault("CLOUD_TPU_TASK_ID", os.getenv("CLOUD_TPU_TASK_ID", "0"))
+for k in ("WANDB_ENTITY", "WANDB_PROJECT", "PHANTOM_BENCHMARK_COMPARE_ROBUST"):
+    if os.getenv(k):
+        env_vars[k] = os.getenv(k)
 
 print(json.dumps({
     "pip": [
@@ -38,7 +42,8 @@ print(json.dumps({
         "pandas",
         "pydantic",
         "graphviz",
-        "huggingface_hub"
+        "huggingface_hub",
+        "matplotlib"
     ],
     "env_vars": env_vars
 }))
@@ -46,12 +51,22 @@ print(json.dumps({
 
 RAY_MODE="${RAY_MODE:-single}"
 TRAIN_ARGS="${TRAIN_ARGS:---algo ppo --total-timesteps 1000000}"
+BENCHMARK_ARGS="${BENCHMARK_ARGS:---project capstone_tpu --tiers static,surge,linear,qtable,ppo --alpha-values 0.0,0.1,0.25,0.4,0.6,0.8 --episodes 12 --total-timesteps 30000 --max-steps 100 --robust-radius 0.2 --robust-points 7 --robust-rollouts 1 --lambda-coi 0.2 --eta-ux 0.5 --reward-profit-weight 1.0 --device cpu}"
+
+SUBMIT_ARGS=()
+if [ "${RAY_NO_WAIT:-0}" = "1" ]; then
+  SUBMIT_ARGS+=(--no-wait)
+fi
+if [ -n "${SUBMISSION_ID:-}" ]; then
+  SUBMIT_ARGS+=(--submission-id "$SUBMISSION_ID")
+fi
 
 COMMON_ARGS=(
   job submit
   --address http://localhost:8265
   --working-dir "$ROOT"
   --runtime-env-json "$RUNTIME_ENV_JSON"
+  "${SUBMIT_ARGS[@]}"
   --
 )
 
@@ -77,5 +92,25 @@ if [ "$RAY_MODE" = "distributed" ]; then
   exit 0
 fi
 
-echo "Unsupported RAY_MODE='$RAY_MODE' (expected 'single' or 'distributed')." >&2
+if [ "$RAY_MODE" = "benchmark" ]; then
+  DIST_ARGS=(
+    python
+    scripts/ray_distributed_train.py
+    --run-kind benchmark
+    --entry-args "$BENCHMARK_ARGS"
+    --num-nodes "${NUM_NODES:-4}"
+    --tpu-per-task "${TPU_PER_TASK:-8}"
+    --base-seed "${BASE_SEED:-42}"
+    --output-root "${OUTPUT_ROOT:-engine/studies/results/overnight}"
+    --wandb-entity "${WANDB_ENTITY:-lusiana}"
+    --wandb-project "${WANDB_PROJECT:-capstone_tpu}"
+  )
+  if [ "${COMPARE_ROBUST:-1}" = "1" ]; then
+    DIST_ARGS+=(--compare-robust)
+  fi
+  "$RAY_BIN" "${COMMON_ARGS[@]}" "${DIST_ARGS[@]}"
+  exit 0
+fi
+
+echo "Unsupported RAY_MODE='$RAY_MODE' (expected 'single', 'distributed', or 'benchmark')." >&2
 exit 1