results: jobs/20261011-001500-large-ilqr-steer-learn/** index/episodes.jsonl leaderboard/*

results/jobs/20261011-001500-large-ilqr-steer-learn/breakdown.json

@@ -0,0 +1,460 @@
+{
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+ }
+}

results/jobs/20261011-001500-large-ilqr-steer-learn/episodes.jsonl

@@ -0,0 +1,45 @@
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+{"key": "5ee5375da81951e48b1d5d3c8595585667b43a3fb3e8d9ec7757f1a07dc6eeb0", "kart_name": "kart1", "condition": "nominal", "scenario": {"type": "lap", "line": "line1", "laps": 1}, "status": "failed", "cause": "spin", "context_valid": null, "laps_completed": 0, "laps_attempted": 1, "wall_s": 50.512540720868856, "sim_s": 46.517, "t_end": 46.517, "handoff_t": 8.1, "auto_s": 38.03250000000144, "cte_hist": [6, 0, 0, 1, 0, 0, 2, 0, 0, 2, 1, 1, 2, 2, 1, 1, 1, 1, 2, 0, 3, 3, 4, 1, 1, 3, 3, 1, 2, 3, 2, 7, 3, 4, 6, 13, 10, 7, 8, 10, 10, 13, 29, 29, 29, 70, 41, 29, 26, 23, 56, 35, 55, 56, 50, 45, 25, 17, 11, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], "cte_mean": 0.32873023698375486, "off_track_pct": 19.652928416485903, "safety": 0.6008676789587852, "events": 46, "osc_10": 59.245960502692995, "osc_20": 37.70197486535009, "osc_30": 19.748653500897664, "peak_cte_after_m": 0.686815149825328, "unsafe_lat": 0.006073752711496746, "unsafe_rate": 0.3023861171366594, "unsafe_travel": 0.0, "unsafe_spin": 0.0004338394793926247, "unsafe_off": 0.196529284164859, "cte_p50": 0.3227743389094037, "cte_p90": 0.5256442594584227, "cte_p95": 0.5719624398902039, "cte_p99": 0.6396852871978411, "recovered": null, "recovery_s": null, "loc_n": 2305, "loc_ss": 515.205578082123}
+{"key": "a10fd1ec1c508ba95d6da3ee5f71333efde62b9b31da3ba02351ddb00c720417", "kart_name": "kart2", "condition": "nominal", "scenario": {"type": "lap", "line": "line1", "laps": 1}, "status": "ok", "cause": null, "context_valid": null, "laps_completed": 1, "laps_attempted": 1, "wall_s": 61.07767768809572, "sim_s": 60.033500000000004, "t_end": 60.033500000000004, "handoff_t": 8.1, "auto_s": 51.41400000000195, "cte_hist": [11, 2, 1, 1, 2, 1, 2, 4, 3, 1, 4, 4, 5, 6, 2, 6, 2, 7, 5, 6, 6, 6, 5, 13, 7, 6, 12, 8, 9, 7, 15, 11, 10, 14, 15, 16, 18, 19, 28, 25, 39, 45, 31, 38, 28, 28, 34, 40, 36, 28, 21, 26, 25, 22, 42, 30, 43, 39, 43, 34, 21, 12, 9, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], "cte_mean": 0.3100491409527234, "off_track_pct": 18.16431322207959, "safety": 0.57798459563543, "events": 94, "osc_10": 51.15089514066496, "osc_20": 35.80562659846547, "osc_30": 12.78772378516624, "peak_cte_after_m": 0.896737118772126, "unsafe_lat": 0.0, "unsafe_rate": 0.34050064184852374, "unsafe_travel": 0.0, "unsafe_spin": 0.0, "unsafe_off": 0.1816431322207959, "cte_p50": 0.2527441122430565, "cte_p90": 0.6414279865889406, "cte_p95": 0.7148041894121603, "cte_p99": 0.8221995597382904, "recovered": null, "recovery_s": null, "loc_n": 3116, "loc_ss": 721.387721142487}
+{"key": "b2d0ff8ce2495af0e8d1676fc6c439bed06a072ea44dd4496bf496fbbb0a7222", "kart_name": "kart3", "condition": "nominal", "scenario": {"type": "lap", "line": "line1", "laps": 1}, "status": "ok", "cause": null, "context_valid": null, "laps_completed": 1, "laps_attempted": 1, "wall_s": 63.57310122298077, "sim_s": 60.917, "t_end": 60.917, "handoff_t": 8.1, "auto_s": 52.28850000000198, "cte_hist": [17, 0, 2, 0, 0, 2, 0, 2, 3, 2, 3, 2, 2, 3, 3, 1, 7, 5, 3, 6, 3, 6, 9, 5, 10, 7, 5, 4, 11, 7, 6, 10, 9, 10, 12, 11, 15, 12, 17, 22, 21, 26, 34, 25, 30, 30, 29, 30, 36, 59, 53, 71, 56, 46, 27, 30, 45, 46, 31, 6, 11, 11, 10, 11, 4, 9, 2, 3, 3, 7, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], "cte_mean": 0.356428967989858, "off_track_pct": 18.901861786052383, "safety": 0.5673714105396024, "events": 63, "osc_10": 61.50627615062762, "osc_20": 42.67782426778243, "osc_30": 37.65690376569038, "peak_cte_after_m": 1.5163282604184547, "unsafe_lat": 0.0, "unsafe_rate": 0.36415272956768696, "unsafe_travel": 0.0, "unsafe_spin": 0.0, "unsafe_off": 0.18901861786052382, "cte_p50": 0.3346544622287302, "cte_p90": 0.6350852318199005, "cte_p95": 0.831782892460099, "cte_p99": 1.3208319687744026, "recovered": null, "recovery_s": null, "loc_n": 3169, "loc_ss": 715.67859112645}

results/jobs/20261011-001500-large-ilqr-steer-learn/gautschi/0/done.txt

@@ -0,0 +1,45 @@
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results/jobs/20261011-001500-large-ilqr-steer-learn/gautschi/0/gautschi_launch.py

@@ -0,0 +1,145 @@
+"""Runs on a Gautschi node (stdlib only): episodes from a queue in parallel workers, one contained Apptainer per episode."""
+import argparse
+import json
+import os
+import queue
+import shutil
+import subprocess
+import threading
+import time
+
+RUN = ("source /opt/ros/humble/setup.bash && source /ws/install/setup.bash"
+       " && python3 -m simnode.batch /batch.json /out")
+CUDA_LIB = "/ws/mpc_cuda/libmpc_cuda.so"   # built per stack by runner/gautschi.build_stack for gautschi-gpu
+SAMPLE_S = 15
+STAGGER_S = 0.25   # between worker starts: the dev job's one error was a stack starved in the 96-way start burst
+EPISODE_TIMEOUT_S = 1200   # about 4x the slowest lap; a killed episode has no outcome and is retried by the host
+
+
+def worker_cmd(i, work, out, ep, sif, engine, stack, gpu=None):
+    """Private /tmp, HOME, /dev/shm, IPC, PIDs and loopback per episode, plus a distinct ROS_DOMAIN_ID per worker;
+    gpu (a host CUDA device) adds the driver and the stack's CUDA MPC kernel."""
+    cuda = ["--nv", "--env", f"CUDA_VISIBLE_DEVICES={gpu}", "--env", f"MPC_CUDA_LIB={CUDA_LIB}"] if gpu is not None else []
+    if gpu is None and os.path.exists(os.path.join(stack, "mpc_cuda", "libmpc_cuda.so")):
+        cuda = ["--env", f"MPC_CUDA_LIB={CUDA_LIB}"]   # CPU node: the library's native iLQR solve still loads; its MPPI falls back to numpy
+    return ["apptainer", "exec", *cuda, "--contain", "--cleanenv", "--ipc", "--pid", "--net", "--network", "none", "--workdir", work,
+            "--env", f"ROS_DOMAIN_ID={i + 1}", "--env", "ROS_HOME=/tmp/ros", "--env", "TMPDIR=/tmp",
+            "--bind", f"{engine}:/opt/sim:ro", "--bind", f"{stack}:/ws:ro", "--bind", f"{out}:/out",
+            "--bind", f"{ep}:/batch.json:ro", sif, "bash", "-lc", RUN]
+
+
+def parse_smi(text):
+    """nvidia-smi 'utilization.gpu, memory.used' csv rows to per-GPU lists."""
+    rows = [[int(v) for v in ln.split(",")] for ln in text.strip().splitlines() if ln.strip()]
+    return {"gpu_util": [r[0] for r in rows], "gpu_mem_mib": [r[1] for r in rows]}
+
+
+def _solver(logdir):
+    """pathfinder's 'MPC solver: ...' and CUDA fallback lines from the episode's ROS logs."""
+    lines = []
+    for dp, _, fns in os.walk(logdir):
+        for fn in fns:
+            try:
+                with open(os.path.join(dp, fn), errors="replace") as f:
+                    lines += [ln.strip() for ln in f if "MPC solver:" in ln or "CUDA MPC unavailable" in ln]
+            except OSError:
+                pass
+    return "\n".join(dict.fromkeys(lines))
+
+
+def main(argv=None):
+    p = argparse.ArgumentParser()
+    for a in ("episodes", "out", "sif", "engine", "stack", "work", "timeline", "done"):
+        p.add_argument("--" + a, required=True)
+    p.add_argument("--workers", type=int, required=True)
+    p.add_argument("--gpus", type=int, default=0)
+    a = p.parse_args(argv)
+    with open(a.episodes) as f:
+        q = queue.Queue()
+        for e in json.load(f):   # the host already ordered them, longest first
+            q.put(e)
+    os.makedirs(a.out, exist_ok=True)
+    lock, t0 = threading.Lock(), time.time()
+    devs = [d for d in os.environ.get("CUDA_VISIBLE_DEVICES", "").split(",") if d][:a.gpus] or [str(n) for n in range(a.gpus)]
+
+    def log(**ev):
+        with lock, open(a.timeline, "a") as f:
+            f.write(json.dumps(dict(ev, t=round(time.time() - t0, 3))) + "\n")
+
+    def worker(i):
+        time.sleep(i * STAGGER_S)
+        while True:
+            try:
+                e = q.get_nowait()
+            except queue.Empty:
+                return
+            key, work = e["key"], os.path.join(a.work, e["key"])
+            stage, ep = os.path.join(work, "stage"), os.path.join(work, "ep.json")
+            os.makedirs(stage)
+            with open(ep, "w") as f:
+                json.dump([e], f)
+            log(ev="start", key=key, worker=i)
+            gpu = devs[i % len(devs)] if devs and e["resolved"].get("recipe", {}).get("sim", {}).get("cuda") else None
+            limit = EPISODE_TIMEOUT_S * max(1, e["resolved"]["scenario"].get("laps", 1))   # per lap: 5-lap episodes run 5x longer
+            try:
+                rc = subprocess.run(worker_cmd(i, work, stage, ep, a.sif, a.engine, a.stack, gpu), stdout=subprocess.DEVNULL,
+                                    stderr=subprocess.DEVNULL, timeout=limit).returncode
+            except subprocess.TimeoutExpired:
+                rc = "timeout"
+            solver = _solver(os.path.join(work, "tmp", "ros", "log"))
+            if solver and os.path.isdir(os.path.join(stage, key)):
+                with open(os.path.join(stage, key, "solver.txt"), "w") as f:
+                    f.write(solver + "\n")
+            if os.path.exists(os.path.join(stage, key, "outcome.json")):
+                tmp, dst = os.path.join(a.out, ".tmp-" + key), os.path.join(a.out, key)
+                shutil.rmtree(tmp, ignore_errors=True)
+                shutil.copytree(os.path.join(stage, key), tmp)
+                shutil.rmtree(dst, ignore_errors=True)
+                os.rename(tmp, dst)   # the host's rsync sees a key only once its directory is complete
+                with lock, open(a.done, "a") as f:
+                    f.write(key + "\n")
+            shutil.rmtree(work, ignore_errors=True)
+            log(ev="end", key=key, worker=i, rc=rc, gpu=gpu,
+                solver=solver.split("MPC solver: ")[-1] if "MPC solver: " in solver else solver or None)
+
+    def sampler(stop):
+        """Job CPU use from its cgroup, as busy cores, every SAMPLE_S."""
+        try:
+            with open("/proc/self/cgroup") as f:
+                path = "/sys/fs/cgroup" + f.read().strip().split(":")[-1] + "/cpu.stat"
+            prev = None
+            while not stop.wait(SAMPLE_S if prev else 0):
+                with open(path) as f:
+                    use = next(int(ln.split()[1]) for ln in f if ln.startswith("usage_usec"))
+                now = time.time()
+                if prev:
+                    log(ev="cpu", busy_cores=round((use - prev[0]) / 1e6 / (now - prev[1]), 2))
+                prev = (use, now)
+        except (OSError, StopIteration, ValueError):
+            pass
+
+    def gpu_sampler(stop):
+        """GPU utilisation and memory of the job's GPUs, every SAMPLE_S."""
+        while not stop.wait(SAMPLE_S):
+            try:
+                p = subprocess.Popen(["nvidia-smi", "--query-gpu=utilization.gpu,memory.used", "--format=csv,noheader,nounits"],
+                                     stdout=subprocess.PIPE, stderr=subprocess.DEVNULL, text=True)
+                log(ev="gpu", **parse_smi(p.communicate(timeout=30)[0]))
+            except Exception:   # no driver or a hung query: skip this sample
+                pass
+
+    cores = len(os.sched_getaffinity(0)) if hasattr(os, "sched_getaffinity") else os.cpu_count()
+    log(ev="launch", workers=a.workers, cores=cores)
+    stop = threading.Event()
+    threads = [threading.Thread(target=worker, args=(i,)) for i in range(a.workers)]
+    samplers = [sampler] + ([gpu_sampler] if a.gpus else [])
+    for t in threads + [threading.Thread(target=f, args=(stop,), daemon=True) for f in samplers]:
+        t.start()
+    for t in threads:
+        t.join()
+    stop.set()
+    log(ev="done")
+
+
+if __name__ == "__main__":
+    main()

results/jobs/20261011-001500-large-ilqr-steer-learn/gautschi/0/sbatch.sh

@@ -0,0 +1,5 @@
+#!/bin/bash
+#SBATCH -p highmem -A lilly-rob1 -N 1 -n 1 -c 96 --time 11 -J AutoSim-smoke --comment 20261011-001500-large-ilqr-steer-learn -o AutoSim/jobs/20261011-001500-large-ilqr-steer-learn/0/slurm-%j.out
+W=/dev/shm/AutoSim-$SLURM_JOB_ID
+trap 'rm -rf "$W"' EXIT
+python3 $HOME/AutoSim/jobs/20261011-001500-large-ilqr-steer-learn/0/gautschi_launch.py --episodes $HOME/AutoSim/jobs/20261011-001500-large-ilqr-steer-learn/0/episodes.json --out /scratch/gautschi/manors/AutoSim/out/20261011-001500-large-ilqr-steer-learn --sif $HOME/AutoSim/images/AutoSim-99d206f40cb1.sif --engine $HOME/AutoSim/engine/b37fb519b32e564d2d8a8ea1259cbf2e695ba1a28d93504f20e4c2970a4f6f00 --stack $HOME/AutoSim/stacks/09f6048d97a12a43c7f0539e9db22b0251c622d6cafda2946841254a27f32824 --workers 45 --work $W --timeline $HOME/AutoSim/jobs/20261011-001500-large-ilqr-steer-learn/0/timeline.jsonl --done $HOME/AutoSim/jobs/20261011-001500-large-ilqr-steer-learn/0/done.txt

results/jobs/20261011-001500-large-ilqr-steer-learn/job.json

@@ -0,0 +1,129 @@
+{
+ "id": "20261011-001500-large-ilqr-steer-learn",
+ "tier": "smoke",
+ "tier_version": 3,
+ "recipe": {
+  "name": "large-ilqr-steer-learn",
+  "source": {
+   "repo": "AutonomousKart",
+   "ref": "worktree:ilqr-steer-learn"
+  },
+  "params": {
+   "pathfinder_node": {
+    "planner": "ilqr",
+    "mpc.num_samples": 16,
+    "mpc.use_steer_map": true,
+    "mpc.steer_map_cmd_deg": [
+     -60.0,
+     -30.0,
+     -18.0,
+     -15.0,
+     -12.0,
+     -9.0,
+     -6.0,
+     -3.0,
+     0.0,
+     3.0,
+     6.0,
+     9.0,
+     12.0,
+     15.0,
+     18.0,
+     30.0,
+     60.0
+    ],
+    "mpc.adaptive_gain.map": true
+   }
+  },
+  "sim": {},
+  "notes": "large-ilqr-fast with a learned steer map: the rollout starts from each kart design's scalar gain (small-angle slope of the steer-map fit: 0.635, 0.357, 0.537) and RLS on IMU yaw rate learns the 17 knots, used as learned; understeer K_us estimated jointly as in the baseline",
+  "params_by_kart": {
+   "kart1": {
+    "pathfinder_node": {
+     "mpc.steer_map_wheel_deg": [
+      -38.112,
+      -19.056,
+      -11.4336,
+      -9.528,
+      -7.6224,
+      -5.7168,
+      -3.8112,
+      -1.9056,
+      0.0,
+      1.9056,
+      3.8112,
+      5.7168,
+      7.6224,
+      9.528,
+      11.4336,
+      19.056,
+      38.112
+     ]
+    }
+   },
+   "kart2": {
+    "pathfinder_node": {
+     "mpc.steer_map_wheel_deg": [
+      -21.408,
+      -10.704,
+      -6.4224,
+      -5.352,
+      -4.2816,
+      -3.2112,
+      -2.1408,
+      -1.0704,
+      0.0,
+      1.0704,
+      2.1408,
+      3.2112,
+      4.2816,
+      5.352,
+      6.4224,
+      10.704,
+      21.408
+     ]
+    }
+   },
+   "kart3": {
+    "pathfinder_node": {
+     "mpc.steer_map_wheel_deg": [
+      -32.19,
+      -16.095,
+      -9.657,
+      -8.0475,
+      -6.438,
+      -4.8285,
+      -3.219,
+      -1.6095,
+      0.0,
+      1.6095,
+      3.219,
+      4.8285,
+      6.438,
+      8.0475,
+      9.657,
+      16.095,
+      32.19
+     ]
+    }
+   }
+  }
+ },
+ "user": "shay",
+ "notes": "smoke: large-ilqr-steer-learn (pre-large check)",
+ "where": "gautschi",
+ "status": "done",
+ "submitted_at": "2026-10-11T07:15:00.849+00:00",
+ "started_at": "2026-10-11T07:15:00.850+00:00",
+ "finished_at": "2026-10-11T07:17:06.311+00:00",
+ "total": 45,
+ "done": 45,
+ "errors": 0,
+ "build_log": null,
+ "workers": 45,
+ "slurm_id": "16948050",
+ "slurm_ids": [
+  "16948050"
+ ],
+ "engine_version": "b37fb519b32e564d2d8a8ea1259cbf2e695ba1a28d93504f20e4c2970a4f6f00"
+}

results/jobs/20261011-001500-large-ilqr-steer-learn/summary.json

@@ -0,0 +1,156 @@
+{
+ "cte_p50": {
+  "value": 0.25273829378319984,
+  "worst_kart": null,
+  "per_kart": {
+   "kart1": 0.26657334376092234,
+   "kart2": 0.23633340396032954,
+   "kart3": 0.25250201779543663
+  }
+ },
+ "cte_p90": {
+  "value": 0.5883013241806034,
+  "worst_kart": null,
+  "per_kart": {
+   "kart1": 0.5709292342767373,
+   "kart2": 0.6017772095575997,
+   "kart3": 0.5915077561994585
+  }
+ },
+ "cte_p99": {
+  "value": 1.61783519595897,
+  "worst_kart": null,
+  "per_kart": {
+   "kart1": 1.5984261547417689,
+   "kart2": 1.6682736685107382,
+   "kart3": 1.609715086878302
+  }
+ },
+ "recovery_rate": {
+  "value": 1.0,
+  "worst_kart": null,
+  "per_kart": {
+   "kart1": 1.0,
+   "kart2": 1.0,
+   "kart3": 1.0
+  }
+ },
+ "recovery_s_median": {
+  "value": 5.58325,
+  "worst_kart": null,
+  "per_kart": {
+   "kart1": 5.3835,
+   "kart2": 5.5585,
+   "kart3": 5.83325
+  }
+ },
+ "safety": {
+  "value": 0.7265821020351269,
+  "worst_kart": null,
+  "per_kart": {
+   "kart1": 0.738800752890562,
+   "kart2": 0.7253586541438745,
+   "kart3": 0.7161027859313044
+  }
+ },
+ "unsafe_lat": {
+  "value": 0.00024393643713409535,
+  "worst_kart": null,
+  "per_kart": {
+   "kart1": 0.0007528905619790266,
+   "kart2": 0.0,
+   "kart3": 0.0
+  }
+ },
+ "unsafe_rate": {
+  "value": 0.18898104265402846,
+  "worst_kart": null,
+  "per_kart": {
+   "kart1": 0.15902124226942727,
+   "kart2": 0.20858705748787285,
+   "kart3": 0.1981049487615222
+  }
+ },
+ "unsafe_travel": {
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+  "per_kart": {
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+   "kart2": 0.0,
+   "kart3": 0.0
+  }
+ },
+ "unsafe_spin": {
+  "value": 1.7424031223863953e-05,
+  "worst_kart": null,
+  "per_kart": {
+   "kart1": 5.3777897284216184e-05,
+   "kart2": 0.0,
+   "kart3": 0.0
+  }
+ },
+ "unsafe_off": {
+  "value": 0.1273173961527739,
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+   "kart2": 0.10393229435442254,
+   "kart3": 0.13249909882074257
+  }
+ },
+ "osc_10": {
+  "value": 43.20111041636124,
+  "worst_kart": null,
+  "per_kart": {
+   "kart1": 45.00203392255203,
+   "kart2": 37.528842364441324,
+   "kart3": 47.13689689931536
+  }
+ },
+ "osc_20": {
+  "value": 20.66069414639829,
+  "worst_kart": null,
+  "per_kart": {
+   "kart1": 20.102753075649538,
+   "kart2": 14.851712554179093,
+   "kart3": 26.991677178716976
+  }
+ },
+ "osc_30": {
+  "value": 9.531799498852362,
+  "worst_kart": null,
+  "per_kart": {
+   "kart1": 8.276535287300367,
+   "kart2": 5.158097966082472,
+   "kart3": 15.098266686443084
+  }
+ },
+ "laps_pct": {
+  "value": 55.55555555555556,
+  "worst_kart": null,
+  "per_kart": {
+   "kart1": 33.333333333333336,
+   "kart2": 66.66666666666667,
+   "kart3": 66.66666666666667
+  }
+ },
+ "failures": {
+  "value": 4,
+  "worst_kart": null,
+  "per_kart": {
+   "kart1": 2,
+   "kart2": 1,
+   "kart3": 1
+  }
+ },
+ "n": {
+  "value": 45,
+  "worst_kart": null,
+  "per_kart": {}
+ },
+ "errors": {
+  "value": 0,
+  "worst_kart": null,
+  "per_kart": {}
+ }
+}

results/leaderboard/smoke-3__b37fb519b32e.json

@@ -0,0 +1,48 @@
+[
+ {
+  "job_id": "20261011-001500-large-ilqr-steer-learn",
+  "recipe": "large-ilqr-steer-learn",
+  "notes": "smoke: large-ilqr-steer-learn (pre-large check)",
+  "user": "shay",
+  "ran_at": "2026-10-11T07:17:06.311+00:00",
+  "duration_s": 125.461,
+  "cte_p50": 0.25273829378319984,
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+  "recovery_s_median": 5.58325,
+  "safety": 0.7265821020351269,
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+  "unsafe_rate": 0.18898104265402846,
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+  "errors": 0,
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+   "failures": 2,
+   "n": 45,
+   "errors": 0
+  }
+ }
+]