results: jobs/20261011-091732-diag-hs-map-adapt/** index/episodes.jsonl leaderboard/*

results/jobs/20261011-091732-diag-hs-map-adapt/breakdown.json

@@ -0,0 +1,1535 @@
+{
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+     "cte_p90": 0.938330392884898,
+     "status": "failed",
+     "cause": "spin",
+     "key": "0e7992ec49549efaf3397b80dabacb30077950c5a444685dffb68225c130acb1"
+    },
+    "line3_less_aggressive@8": {
+     "cte_p90": 0.9513643264264248,
+     "status": "failed",
+     "cause": "spin",
+     "key": "80f40e6d96376660f2a5f9ad31623f16b7962305f6e7423317c11adf125e9b04"
+    }
+   },
+   "kart3": {
+    "center@6": {
+     "cte_p90": 2.709159185837132,
+     "status": "failed",
+     "cause": "off_track",
+     "key": "aba4e24d2db2927dd516746d89ce40a5aefc77d259f4909f7387e0c81d694618"
+    },
+    "center@8": {
+     "cte_p90": 1.1685418830878658,
+     "status": "failed",
+     "cause": "spin",
+     "key": "b1a7c0e486887cb1dbde56331087056c009f0ce428c8d27d7343b6c59028a6f7"
+    },
+    "center@10": {
+     "cte_p90": 0.9398728956165414,
+     "status": "failed",
+     "cause": "spin",
+     "key": "28768e834a04c209aa87387436c123d81ceeb17e275d4247a886ac9fb3d97636"
+    },
+    "line1@6": {
+     "cte_p90": 2.312612353230487,
+     "status": "failed",
+     "cause": "off_track",
+     "key": "552eee90801b7f953ec66425a7708aca2f7254da062eb18225dc7fee794958cd"
+    },
+    "line1@8": {
+     "cte_p90": 1.9904333805943695,
+     "status": "failed",
+     "cause": "off_track",
+     "key": "7fc9b0b2fd65bfb1e34c32aafefead5aafc5b3839d2ce3e4054055efdbde566d"
+    },
+    "line1@10": {
+     "cte_p90": 0.8681197385433992,
+     "status": "failed",
+     "cause": "spin",
+     "key": "03f7f202d45b9bec4fc0adeda939e8715ef004248d3644dbaa91dba7066f42ac"
+    },
+    "line3_less_aggressive@8": {
+     "cte_p90": 0.9938877520269251,
+     "status": "failed",
+     "cause": "off_track",
+     "key": "782ec5cc8e42f64ff36cd64cb4dd3e13aed6a39275330eea90b2cfb19ac8b256"
+    },
+    "line3_less_aggressive@6": {
+     "cte_p90": 2.62084877447034,
+     "status": "failed",
+     "cause": "off_track",
+     "key": "e95c323c6dbf09c11bfa1bf658c37d75d7bcbe0ffe719434fa2e85edae950dd1"
+    },
+    "line3_less_aggressive@10": {
+     "cte_p90": 0.9167574028228151,
+     "status": "failed",
+     "cause": "spin",
+     "key": "cfe9a16357d00566815433e8f56fddfb676dda4d43a105f10c29d015bfa1757d"
+    }
+   },
+   "kart2": {
+    "center@6": {
+     "cte_p90": 1.089695659095186,
+     "status": "failed",
+     "cause": "off_track",
+     "key": "1edb1bf2b032448e2ae2b4152caadc5863c2988e9a08a4059d88e590f7f7c7b4"
+    },
+    "center@8": {
+     "cte_p90": 1.6525680198769048,
+     "status": "failed",
+     "cause": "off_track",
+     "key": "dafc560e348dc487b30f341a7b8d5b268f916a85dbacde6902519ef06624c24f"
+    },
+    "center@10": {
+     "cte_p90": 2.5135051050430763,
+     "status": "failed",
+     "cause": "off_track",
+     "key": "0cb7338e3418e8e32fb1311a3409122df74ff4fd08ae7c4523ad6876f670c682"
+    },
+    "line1@8": {
+     "cte_p90": 3.0314942913958483,
+     "status": "failed",
+     "cause": "off_track",
+     "key": "004d5f73bc08f5f67bd6357a5e39009358d9c730ed244aaae1359fca8eb76330"
+    },
+    "line1@6": {
+     "cte_p90": 1.93650798919398,
+     "status": "failed",
+     "cause": "off_track",
+     "key": "c52900c28605616ce8d13bfd43649b9e5fcf60485556e15b65852cd6e7727df6"
+    },
+    "line1@10": {
+     "cte_p90": 4.295233399191074,
+     "status": "failed",
+     "cause": "off_track",
+     "key": "d57f425e7a9cd265648a1a1788721c65c7c85c5432bf63dadca68654e08a9d84"
+    },
+    "line3_less_aggressive@8": {
+     "cte_p90": 0.49919116753067094,
+     "status": "failed",
+     "cause": "off_track",
+     "key": "a04181f22d265d1d3a0e101c91aaba81cf695a055653d85f36a3b1247fad9f86"
+    },
+    "line3_less_aggressive@10": {
+     "cte_p90": 0.52655850011474,
+     "status": "failed",
+     "cause": "off_track",
+     "key": "377ef98fde5903191162624a9c3f0c8ef31f0fc012155ac311f2522ccc92ad3b"
+    },
+    "line3_less_aggressive@6": {
+     "cte_p90": 0.38703868399131797,
+     "status": "failed",
+     "cause": "off_track",
+     "key": "de33ef087aafbb7a31dcbf0fcc34e6e71a36d6e6cf09b252bef964803cb7b084"
+    }
+   }
+  }
+ },
+ "clips": {}
+}

results/jobs/20261011-091732-diag-hs-map-adapt/error.txt

@@ -0,0 +1 @@
+reaped: owning server 22966 is gone

results/jobs/20261011-091732-diag-hs-map-adapt/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/20261011-091732-diag-hs-map-adapt/gautschi/0/sbatch.sh

@@ -0,0 +1,5 @@
+#!/bin/bash
+#SBATCH -p smallgpu -A lilly-rob1 -N 1 -n 1 -c 64 --gres=gpu:1 --time 168 -J AutoSim-large-gpu --comment 20261011-091732-diag-hs-map-adapt -o AutoSim/jobs/20261011-091732-diag-hs-map-adapt/0/slurm-%j.out
+W=/dev/shm/AutoSim-$SLURM_JOB_ID
+trap 'rm -rf "$W"' EXIT
+python3 $HOME/AutoSim/jobs/20261011-091732-diag-hs-map-adapt/0/gautschi_launch.py --episodes $HOME/AutoSim/jobs/20261011-091732-diag-hs-map-adapt/0/episodes.json --out /scratch/gautschi/manors/AutoSim/out/20261011-091732-diag-hs-map-adapt --sif $HOME/AutoSim/images/AutoSim-99d206f40cb1.sif --engine $HOME/AutoSim/engine/b37fb519b32e564d2d8a8ea1259cbf2e695ba1a28d93504f20e4c2970a4f6f00 --stack $HOME/AutoSim/stacks/fa5430cccaad0afa1f40c9fbe94903bea54f199790e9eb137f15f8a8c3765372 --workers 32 --work $W --timeline $HOME/AutoSim/jobs/20261011-091732-diag-hs-map-adapt/0/timeline.jsonl --done $HOME/AutoSim/jobs/20261011-091732-diag-hs-map-adapt/0/done.txt --gpus 1

results/jobs/20261011-091732-diag-hs-map-adapt/gautschi/1/done.txt

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results/jobs/20261011-091732-diag-hs-map-adapt/gautschi/1/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/20261011-091732-diag-hs-map-adapt/job.json

@@ -0,0 +1,130 @@
+{
+ "id": "20261011-091732-diag-hs-map-adapt",
+ "tier": "large",
+ "tier_version": 3,
+ "recipe": {
+  "name": "diag-hs-map-adapt",
+  "source": {
+   "repo": "AutonomousKart",
+   "ref": "worktree:ilqr-steer-map"
+  },
+  "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.enabled": true
+   }
+  },
+  "sim": {},
+  "notes": "RCA diagnostic only: predefined map with the online steer gain on",
+  "params_by_kart": {
+   "kart1": {
+    "pathfinder_node": {
+     "mpc.steer_map_wheel_deg": [
+      -20.1519,
+      -20.1519,
+      -12.5298,
+      -10.6243,
+      -8.7187,
+      -5.2384,
+      -2.8452,
+      -1.6118,
+      0.0,
+      1.3773,
+      3.6202,
+      7.0877,
+      10.4902,
+      12.3958,
+      14.3013,
+      21.9234,
+      21.9234
+     ]
+    }
+   },
+   "kart2": {
+    "pathfinder_node": {
+     "mpc.steer_map_wheel_deg": [
+      -13.1619,
+      -13.1619,
+      -8.8806,
+      -7.1977,
+      -4.9993,
+      -3.1573,
+      -2.0998,
+      -0.9566,
+      0.0,
+      1.0131,
+      2.0406,
+      3.2715,
+      4.8893,
+      7.0851,
+      9.1998,
+      13.4811,
+      13.4811
+     ]
+    }
+   },
+   "kart3": {
+    "pathfinder_node": {
+     "mpc.steer_map_wheel_deg": [
+      -18.7967,
+      -18.7967,
+      -12.3583,
+      -10.7487,
+      -9.139,
+      -5.4234,
+      -2.7691,
+      -1.3685,
+      0.0,
+      1.3738,
+      2.8156,
+      5.0623,
+      9.2253,
+      10.8349,
+      12.4445,
+      18.883,
+      18.883
+     ]
+    }
+   }
+  }
+ },
+ "user": "shay",
+ "notes": "RCA high steering noise",
+ "where": "gautschi-gpu",
+ "status": "done",
+ "submitted_at": "2026-10-11T16:17:32.492+00:00",
+ "started_at": "2026-10-11T18:01:24.009+00:00",
+ "finished_at": "2026-10-11T18:26:57.428+00:00",
+ "total": 243,
+ "done": 243,
+ "errors": 0,
+ "build_log": "jobs/20261011-091732-diag-hs-map-adapt/error.txt",
+ "workers": 9,
+ "slurm_id": "3977777",
+ "slurm_ids": [
+  "16950020",
+  "3977777"
+ ],
+ "engine_version": "b37fb519b32e564d2d8a8ea1259cbf2e695ba1a28d93504f20e4c2970a4f6f00"
+}

results/jobs/20261011-091732-diag-hs-map-adapt/summary.json

@@ -0,0 +1,156 @@
+{
+ "cte_p50": {
+  "value": 0.34034350609379393,
+  "worst_kart": null,
+  "per_kart": {
+   "kart1": 0.31638965531597396,
+   "kart2": 0.2720001909571856,
+   "kart3": 0.6242303142926777
+  }
+ },
+ "cte_p90": {
+  "value": 2.6546975090906364,
+  "worst_kart": null,
+  "per_kart": {
+   "kart1": 2.797593489343016,
+   "kart2": 1.1061669801065532,
+   "kart3": 3.9007299083487745
+  }
+ },
+ "cte_p99": {
+  "value": 5.1022015573980255,
+  "worst_kart": null,
+  "per_kart": {
+   "kart1": 5.15800461825473,
+   "kart2": 3.3277496614949156,
+   "kart3": 5.296151232337932
+  }
+ },
+ "recovery_rate": {
+  "value": null,
+  "worst_kart": null,
+  "per_kart": {
+   "kart1": null,
+   "kart2": null,
+   "kart3": null
+  }
+ },
+ "recovery_s_median": {
+  "value": null,
+  "worst_kart": null,
+  "per_kart": {
+   "kart1": null,
+   "kart2": null,
+   "kart3": null
+  }
+ },
+ "safety": {
+  "value": 0.5549231540627633,
+  "worst_kart": null,
+  "per_kart": {
+   "kart1": 0.5880431863458349,
+   "kart2": 0.5168307452890707,
+   "kart3": 0.5489905900706313
+  }
+ },
+ "unsafe_lat": {
+  "value": 0.00310108864800319,
+  "worst_kart": null,
+  "per_kart": {
+   "kart1": 0.003300304236462894,
+   "kart2": 0.0004036256040722048,
+   "kart3": 0.006046277166449769
+  }
+ },
+ "unsafe_rate": {
+  "value": 0.20411667291486044,
+  "worst_kart": null,
+  "per_kart": {
+   "kart1": 0.1585717320634864,
+   "kart2": 0.2472999242714266,
+   "kart3": 0.22338446956247032
+  }
+ },
+ "unsafe_travel": {
+  "value": 0.0,
+  "worst_kart": null,
+  "per_kart": {
+   "kart1": 0.0,
+   "kart2": 0.0,
+   "kart3": 0.0
+  }
+ },
+ "unsafe_spin": {
+  "value": 0.0001785392047123183,
+  "worst_kart": null,
+  "per_kart": {
+   "kart1": 0.00023809810122778395,
+   "kart2": 1.717555762009382e-05,
+   "kart3": 0.0002800162667137972
+  }
+ },
+ "unsafe_off": {
+  "value": 0.29836281630154876,
+  "worst_kart": null,
+  "per_kart": {
+   "kart1": 0.3049016542607076,
+   "kart2": 0.29505604176262434,
+   "kart3": 0.2921028498804789
+  }
+ },
+ "osc_10": {
+  "value": 19.019073302823394,
+  "worst_kart": null,
+  "per_kart": {
+   "kart1": 21.21722642073853,
+   "kart2": 18.47208453982412,
+   "kart3": 16.2327776955123
+  }
+ },
+ "osc_20": {
+  "value": 11.589701518545253,
+  "worst_kart": null,
+  "per_kart": {
+   "kart1": 13.081622752890256,
+   "kart2": 10.740456670473888,
+   "kart3": 10.27584603230827
+  }
+ },
+ "osc_30": {
+  "value": 7.142173641030418,
+  "worst_kart": null,
+  "per_kart": {
+   "kart1": 7.554936595920102,
+   "kart2": 7.402676993825351,
+   "kart3": 6.180336403510342
+  }
+ },
+ "laps_pct": {
+  "value": 1.5637860082304527,
+  "worst_kart": null,
+  "per_kart": {
+   "kart1": 1.728395061728395,
+   "kart2": 2.4691358024691357,
+   "kart3": 0.49382716049382713
+  }
+ },
+ "failures": {
+  "value": 242,
+  "worst_kart": null,
+  "per_kart": {
+   "kart1": 81,
+   "kart2": 80,
+   "kart3": 81
+  }
+ },
+ "n": {
+  "value": 243,
+  "worst_kart": null,
+  "per_kart": {}
+ },
+ "errors": {
+  "value": 0,
+  "worst_kart": null,
+  "per_kart": {}
+ }
+}

results/leaderboard/large-3__b37fb519b32e.json

@@ -136,5 +136,51 @@
    "n": 243,
    "errors": 0
   }
+ },
+ {
+  "job_id": "20261011-091732-diag-hs-map-adapt",
+  "recipe": "diag-hs-map-adapt",
+  "notes": "RCA high steering noise",
+  "user": "shay",
+  "ran_at": "2026-10-11T18:26:57.428+00:00",
+  "duration_s": 1533.419,
+  "cte_p50": 0.34034350609379393,
+  "cte_p90": 2.6546975090906364,
+  "cte_p99": 5.1022015573980255,
+  "recovery_rate": null,
+  "recovery_s_median": null,
+  "safety": 0.5549231540627633,
+  "unsafe_lat": 0.00310108864800319,
+  "unsafe_rate": 0.20411667291486044,
+  "unsafe_travel": 0.0,
+  "unsafe_spin": 0.0001785392047123183,
+  "unsafe_off": 0.29836281630154876,
+  "osc_10": 19.019073302823394,
+  "osc_20": 11.589701518545253,
+  "osc_30": 7.142173641030418,
+  "laps_pct": 1.5637860082304527,
+  "failures": 242,
+  "n": 243,
+  "errors": 0,
+  "worst": {
+   "cte_p50": 0.6242303142926777,
+   "cte_p90": 3.9007299083487745,
+   "cte_p99": 5.296151232337932,
+   "recovery_rate": null,
+   "recovery_s_median": null,
+   "safety": 0.5168307452890707,
+   "unsafe_lat": 0.006046277166449769,
+   "unsafe_rate": 0.2472999242714266,
+   "unsafe_travel": 0.0,
+   "unsafe_spin": 0.0002800162667137972,
+   "unsafe_off": 0.3049016542607076,
+   "osc_10": 21.21722642073853,
+   "osc_20": 13.081622752890256,
+   "osc_30": 7.554936595920102,
+   "laps_pct": 0.49382716049382713,
+   "failures": 81,
+   "n": 243,
+   "errors": 0
+  }
  }
 ]