results: jobs/20261008-121953-idea-ilqr-tuned/** index/episodes.jsonl leaderboard/*

results/index/episodes.jsonl

@@ -33302,3 +33302,561 @@
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results/jobs/20261008-121953-idea-ilqr-tuned/breakdown.json

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+}

results/jobs/20261008-121953-idea-ilqr-tuned/episodes.jsonl

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"f58f687b302edfcc305593f9078a8edcf2333f661204d8390172ee2dbcef19ce", "resolved": {"kart": {"name": "kart3", "chrono_dir": "chrono/kart3", "tyres": {"grip": 1.0}, "frame": {"torsion_scale": 1.0}, "steering_motor": {"rate_dps": 71.0, "travel_deg": 70.0, "step_deg": 0.9, "slop_deg": 6.5, "center_offset_deg": 0.4, "position_uncertainty_deg": 1.0}, "actuator": {"latency_ms": 240.0, "drive_tau_s": 0.41, "steer_latency_ms": 60.0}, "sensors": {"gps": {"enabled": true, "fix_chain": "measured", "fixed_gm_sigma_m": 0.006, "fixed_gm_tau_s": 7.0, "fixed_white_m": 0.002, "error_scale": 1.0, "latency_ms": 60.0, "rate_hz": 20.0, "float_dwell_scale": 1.0, "outages": false, "wrong_fix": false, "heading_source": "vtg"}, "imu": {"enabled": true, "vibration_scale": 0.3, "dlpf_hz": 0}, "wheel": {"white_mps": 0.03, "scale_error": 0.01, "rate_hz": 100.0}, "steer_encoder": {"enabled": false, "resolution_deg": 0.35, "white_deg": 0.2, "rate_hz": 50.0}}, "system_frequency": 60}, "kart_name": "kart3", "condition": "nominal", "recipe": {"name": "idea-ilqr-tuned", "source": {"repo": "AutonomousKart", "ref": "worktree:/Users/shay/PycharmProjects/AK-ideas/ilqr-tuned", "tree": "6fdf37babcd086e56ce216e44552540cc0c70d4563961f1e91b8b50abab970be"}, "params": {"pathfinder_node": {"planner": "ilqr", "mpc.num_samples": 4096, "mpc.adaptive_gain.enabled": true}}, "sim": {}, "notes": "ilqr-v2 with the iLQR's own cost weights (ilqr.tuned_weights: w_delta_rate 1000, w_d and w_heading x2) sized for the 33.7 deg/s motor"}, "scenario": {"type": "clip", "clip_id": "20260520_223739_00263"}, "seed": 130671560, "tier": "smoke", "engine_version": "9468b6d2e43b440756c4f5e2ee7b6fcd221e3f1b7d9724496e70de28f1d0cde3"}}]

results/jobs/20261008-121953-idea-ilqr-tuned/gautschi/0/gautschi_launch.py

@@ -0,0 +1,143 @@
+"""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 []
+    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/20261008-121953-idea-ilqr-tuned/gautschi/0/sbatch.sh

@@ -0,0 +1,5 @@
+#!/bin/bash
+#SBATCH -p highmem -A lilly-rob1 -N 1 -n 1 -c 96 --time 34 -J AutoSim-smoke --comment 20261008-121953-idea-ilqr-tuned -o AutoSim/jobs/20261008-121953-idea-ilqr-tuned/0/slurm-%j.out
+W=/dev/shm/AutoSim-$SLURM_JOB_ID
+trap 'rm -rf "$W"' EXIT
+python3 $HOME/AutoSim/jobs/20261008-121953-idea-ilqr-tuned/0/gautschi_launch.py --episodes $HOME/AutoSim/jobs/20261008-121953-idea-ilqr-tuned/0/episodes.json --out /scratch/gautschi/manors/AutoSim/out/20261008-121953-idea-ilqr-tuned --sif $HOME/AutoSim/images/AutoSim-99d206f40cb1.sif --engine $HOME/AutoSim/engine/9468b6d2e43b440756c4f5e2ee7b6fcd221e3f1b7d9724496e70de28f1d0cde3 --stack $HOME/AutoSim/stacks/6fdf37babcd086e56ce216e44552540cc0c70d4563961f1e91b8b50abab970be --workers 15 --work $W --timeline $HOME/AutoSim/jobs/20261008-121953-idea-ilqr-tuned/0/timeline.jsonl --done $HOME/AutoSim/jobs/20261008-121953-idea-ilqr-tuned/0/done.txt

results/jobs/20261008-121953-idea-ilqr-tuned/job.json

@@ -0,0 +1,38 @@
+{
+ "id": "20261008-121953-idea-ilqr-tuned",
+ "tier": "smoke",
+ "tier_version": 2,
+ "recipe": {
+  "name": "idea-ilqr-tuned",
+  "source": {
+   "repo": "AutonomousKart",
+   "ref": "worktree:ilqr-tuned"
+  },
+  "params": {
+   "pathfinder_node": {
+    "planner": "ilqr",
+    "mpc.num_samples": 4096,
+    "mpc.adaptive_gain.enabled": true
+   }
+  },
+  "sim": {},
+  "notes": "ilqr-v2 with the iLQR's own cost weights (ilqr.tuned_weights: w_delta_rate 1000, w_d and w_heading x2) sized for the 33.7 deg/s motor"
+ },
+ "user": "shay",
+ "notes": "ilqr-tuned smoke",
+ "where": "gautschi",
+ "status": "done",
+ "submitted_at": "2026-10-08T16:19:53.010+00:00",
+ "started_at": "2026-10-08T16:19:53.010+00:00",
+ "finished_at": "2026-10-08T16:21:27.827+00:00",
+ "total": 15,
+ "done": 15,
+ "errors": 0,
+ "build_log": null,
+ "workers": 15,
+ "slurm_id": "16907053",
+ "slurm_ids": [
+  "16907053"
+ ],
+ "engine_version": "9468b6d2e43b440756c4f5e2ee7b6fcd221e3f1b7d9724496e70de28f1d0cde3"
+}

results/jobs/20261008-121953-idea-ilqr-tuned/logs/shard-0000.jsonl.gz

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:e587cbbf0427085d8574e2fc0a20af9ffb6e531f6ea45dfc47d3174940eb3390
+size 8288216

results/jobs/20261008-121953-idea-ilqr-tuned/summary.json

@@ -0,0 +1,156 @@
+{
+ "cte_p50": {
+  "value": 0.12583312911247727,
+  "worst_kart": null,
+  "per_kart": {
+   "kart1": 0.1968957950222492,
+   "kart2": 0.12677703079444735,
+   "kart3": 0.10104139725350297
+  }
+ },
+ "cte_p90": {
+  "value": 0.7461891437770143,
+  "worst_kart": null,
+  "per_kart": {
+   "kart1": 1.750381547936447,
+   "kart2": 0.5403805284778231,
+   "kart3": 0.51018476669934
+  }
+ },
+ "cte_p99": {
+  "value": 2.371512076412056,
+  "worst_kart": null,
+  "per_kart": {
+   "kart1": 2.549125061555492,
+   "kart2": 1.9406529339319245,
+   "kart3": 2.1217939341845944
+  }
+ },
+ "recovery_rate": {
+  "value": 0.5454545454545454,
+  "worst_kart": null,
+  "per_kart": {
+   "kart1": 0.5,
+   "kart2": 0.6666666666666666,
+   "kart3": 0.5
+  }
+ },
+ "recovery_s_median": {
+  "value": 1.2834999999999992,
+  "worst_kart": null,
+  "per_kart": {
+   "kart1": 0.6332499999999994,
+   "kart2": 3.5,
+   "kart3": 1.4417499999999999
+  }
+ },
+ "safety": {
+  "value": 0.5693016369170025,
+  "worst_kart": null,
+  "per_kart": {
+   "kart1": 0.43392226148409896,
+   "kart2": 0.5888675008020533,
+   "kart3": 0.6537342386032978
+  }
+ },
+ "unsafe_lat": {
+  "value": 0.009706342025170685,
+  "worst_kart": null,
+  "per_kart": {
+   "kart1": 0.02402826855123675,
+   "kart2": 0.0,
+   "kart3": 0.016165535079211122
+  }
+ },
+ "unsafe_rate": {
+  "value": 0.4206629925146007,
+  "worst_kart": null,
+  "per_kart": {
+   "kart1": 0.5554770318021202,
+   "kart2": 0.40680141161373123,
+   "kart3": 0.3252505657937278
+  }
+ },
+ "unsafe_travel": {
+  "value": 0.0,
+  "worst_kart": null,
+  "per_kart": {
+   "kart1": 0.0,
+   "kart2": 0.0,
+   "kart3": 0.0
+  }
+ },
+ "unsafe_spin": {
+  "value": 0.0006580570864522499,
+  "worst_kart": null,
+  "per_kart": {
+   "kart1": 0.0014134275618374558,
+   "kart2": 0.00016041065126724417,
+   "kart3": 0.0009699321047526673
+  }
+ },
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+  "per_kart": {
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+   "kart2": 0.03079884504331088,
+   "kart3": 0.049789848043970256
+  }
+ },
+ "osc_10": {
+  "value": 41.55717461123029,
+  "worst_kart": null,
+  "per_kart": {
+   "kart1": 32.706478289757456,
+   "kart2": 47.255833333893634,
+   "kart3": 38.16953546240547
+  }
+ },
+ "osc_20": {
+  "value": 23.82160864372083,
+  "worst_kart": null,
+  "per_kart": {
+   "kart1": 25.12385513448828,
+   "kart2": 27.467613831089317,
+   "kart3": 15.281500688037983
+  }
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+ "osc_30": {
+  "value": 17.009032495465732,
+  "worst_kart": null,
+  "per_kart": {
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+   "kart2": 16.763993268484505,
+   "kart3": 10.078100220188379
+  }
+ },
+ "laps_pct": {
+  "value": 33.333333333333336,
+  "worst_kart": null,
+  "per_kart": {
+   "kart1": 0.0,
+   "kart2": 100.0,
+   "kart3": 0.0
+  }
+ },
+ "failures": {
+  "value": 10,
+  "worst_kart": null,
+  "per_kart": {
+   "kart1": 4,
+   "kart2": 2,
+   "kart3": 4
+  }
+ },
+ "n": {
+  "value": 15,
+  "worst_kart": null,
+  "per_kart": {}
+ },
+ "errors": {
+  "value": 0,
+  "worst_kart": null,
+  "per_kart": {}
+ }
+}

results/leaderboard/2__753d10039f1b.json

@@ -44,5 +44,51 @@
    "n": 558,
    "errors": 0
   }
+ },
+ {
+  "job_id": "20261008-111027-idea-understeer",
+  "recipe": "idea-understeer",
+  "notes": "understeer dev (on top of combo-all)",
+  "user": "shay",
+  "ran_at": "2026-10-08T16:18:16.410+00:00",
+  "duration_s": 4068.481,
+  "cte_p50": 0.20300024133973654,
+  "cte_p90": 0.47988052080487176,
+  "cte_p99": 1.048898159703786,
+  "recovery_rate": 0.8982035928143712,
+  "recovery_s_median": 2.0832500000000005,
+  "safety": 0.8008152207019563,
+  "unsafe_lat": 0.00036911802524161006,
+  "unsafe_rate": 0.13288775749768952,
+  "unsafe_travel": 0.0,
+  "unsafe_spin": 1.4817405116746172e-05,
+  "unsafe_off": 0.08181545481695714,
+  "osc_10": 28.736276825843834,
+  "osc_20": 12.502998850221562,
+  "osc_30": 6.187356024558801,
+  "laps_pct": 85.96491228070175,
+  "failures": 50,
+  "n": 558,
+  "errors": 0,
+  "worst": {
+   "cte_p50": 0.20924339040218004,
+   "cte_p90": 0.49605135931535727,
+   "cte_p99": 1.1020159438831436,
+   "recovery_rate": 0.8962264150943396,
+   "recovery_s_median": 2.10825,
+   "safety": 0.7802748331370236,
+   "unsafe_lat": 0.0007333284107610052,
+   "unsafe_rate": 0.1609167648213589,
+   "unsafe_travel": 0.0,
+   "unsafe_spin": 2.4275699114847062e-05,
+   "unsafe_off": 0.09224924868337449,
+   "osc_10": 31.68415203442129,
+   "osc_20": 15.875481796353395,
+   "osc_30": 8.877155860337314,
+   "laps_pct": 78.94736842105263,
+   "failures": 21,
+   "n": 558,
+   "errors": 0
+  }
  }
 ]

results/leaderboard/smoke-2__9468b6d2e43b.json

@@ -0,0 +1,48 @@
+[
+ {
+  "job_id": "20261008-121953-idea-ilqr-tuned",
+  "recipe": "idea-ilqr-tuned",
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+]