results: jobs/20261105-013942-A26_PP___Adaptive_Gain/** index/episodes.jsonl leaderboard/*

results/jobs/20261105-013942-A26_PP___Adaptive_Gain/breakdown.json

@@ -0,0 +1,1912 @@
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results/jobs/20261105-013942-A26_PP___Adaptive_Gain/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/20261105-013942-A26_PP___Adaptive_Gain/job.json

@@ -0,0 +1,44 @@
+{
+ "id": "20261105-013942-A26_PP___Adaptive_Gain",
+ "tier": "dev",
+ "tier_version": 2,
+ "recipe": {
+  "name": "A26 PP + Adaptive Gain",
+  "source": {
+   "repo": "AutonomousKart",
+   "ref": "worktree:AutonomousKart-sim"
+  },
+  "params": {
+   "pathfinder_node": {
+    "planner": "pure_pursuit",
+    "mpc.num_samples": 128,
+    "mpc.adaptive_gain.enabled": true
+   }
+  },
+  "sim": {
+   "active_imu": "back",
+   "use_camera": false,
+   "controller_type": "pp",
+   "baseline": true,
+   "cuda": true
+  },
+  "notes": "A26 Pure Pursuit stack with adaptive steering gain (SteerGainRLS, back IMU imu_2, no camera)"
+ },
+ "user": "shay",
+ "notes": "",
+ "where": "ideas",
+ "status": "done",
+ "submitted_at": "2026-11-05T06:39:42.713+00:00",
+ "started_at": "2026-11-05T06:39:42.715+00:00",
+ "finished_at": "2026-11-05T07:29:26.516+00:00",
+ "total": 186,
+ "done": 186,
+ "errors": 20,
+ "build_log": null,
+ "workers": 10,
+ "slurm_id": "4556",
+ "slurm_ids": [
+  "4556"
+ ],
+ "engine_version": "bbd85e0dce441d49ae06ba82dddb973a04f86a9fdd0705c67c5e131c98efe8b5"
+}

results/jobs/20261105-013942-A26_PP___Adaptive_Gain/summary.json

@@ -0,0 +1,292 @@
+{
+ "cte_p50": {
+  "value": 0.20771314269140198,
+  "worst_kart": null,
+  "per_kart": {
+   "kart1": 0.20771314269140198
+  }
+ },
+ "cte_p90": {
+  "value": 0.7864734250774548,
+  "worst_kart": null,
+  "per_kart": {
+   "kart1": 0.7864734250774548
+  }
+ },
+ "cte_p99": {
+  "value": 1.8286483649367904,
+  "worst_kart": null,
+  "per_kart": {
+   "kart1": 1.8286483649367904
+  }
+ },
+ "recovery_rate": {
+  "value": 0.8297872340425532,
+  "worst_kart": null,
+  "per_kart": {
+   "kart1": 0.8297872340425532
+  }
+ },
+ "recovery_s_median": {
+  "value": 1.64175,
+  "worst_kart": null,
+  "per_kart": {
+   "kart1": 1.64175
+  }
+ },
+ "safety": {
+  "value": 0.6777603446604974,
+  "worst_kart": null,
+  "per_kart": {
+   "kart1": 0.6777603446604974
+  }
+ },
+ "unsafe_lat": {
+  "value": 0.0001692879723349085,
+  "worst_kart": null,
+  "per_kart": {
+   "kart1": 0.0001692879723349085
+  }
+ },
+ "unsafe_rate": {
+  "value": 0.29070915712990697,
+  "worst_kart": null,
+  "per_kart": {
+   "kart1": 0.29070915712990697
+  }
+ },
+ "unsafe_travel": {
+  "value": 0.0,
+  "worst_kart": null,
+  "per_kart": {
+   "kart1": 0.0
+  }
+ },
+ "unsafe_spin": {
+  "value": 2.5761213181398996e-05,
+  "worst_kart": null,
+  "per_kart": {
+   "kart1": 2.5761213181398996e-05
+  }
+ },
+ "unsafe_off": {
+  "value": 0.052357825704538355,
+  "worst_kart": null,
+  "per_kart": {
+   "kart1": 0.052357825704538355
+  }
+ },
+ "osc_10": {
+  "value": 25.579423038615722,
+  "worst_kart": null,
+  "per_kart": {
+   "kart1": 25.579423038615722
+  }
+ },
+ "osc_20": {
+  "value": 9.012276798818716,
+  "worst_kart": null,
+  "per_kart": {
+   "kart1": 9.012276798818716
+  }
+ },
+ "osc_30": {
+  "value": 4.552675924968688,
+  "worst_kart": null,
+  "per_kart": {
+   "kart1": 4.552675924968688
+  }
+ },
+ "laps_pct": {
+  "value": 54.716981132075475,
+  "worst_kart": null,
+  "per_kart": {
+   "kart1": 54.716981132075475
+  }
+ },
+ "failures": {
+  "value": 39,
+  "worst_kart": null,
+  "per_kart": {
+   "kart1": 39
+  }
+ },
+ "loc_rms": {
+  "value": 0.345,
+  "worst_kart": null,
+  "per_kart": {
+   "kart1": 0.345
+  }
+ },
+ "loc_p50": {
+  "value": 0.336,
+  "worst_kart": null,
+  "per_kart": {
+   "kart1": 0.336
+  }
+ },
+ "loc_p95": {
+  "value": 0.367,
+  "worst_kart": null,
+  "per_kart": {
+   "kart1": 0.367
+  }
+ },
+ "loc_p99": {
+  "value": 0.387,
+  "worst_kart": null,
+  "per_kart": {
+   "kart1": 0.387
+  }
+ },
+ "best_lap_s": {
+  "value": 73.466,
+  "worst_kart": null,
+  "per_kart": {
+   "kart1": 73.466
+  }
+ },
+ "lap_time_90": {
+  "value": 99.477,
+  "worst_kart": null,
+  "per_kart": {
+   "kart1": 99.477
+  }
+ },
+ "lap_time_99": {
+  "value": 106.116,
+  "worst_kart": null,
+  "per_kart": {
+   "kart1": 106.116
+  }
+ },
+ "lap_time_999": {
+  "value": 106.78,
+  "worst_kart": null,
+  "per_kart": {
+   "kart1": 106.78
+  }
+ },
+ "learned_to_slide_rate": {
+  "value": 0.0,
+  "worst_kart": null,
+  "per_kart": {
+   "kart1": 0.0
+  }
+ },
+ "laps_to_slide": {
+  "value": null,
+  "worst_kart": null,
+  "per_kart": {
+   "kart1": null
+  }
+ },
+ "slide_pct_p95": {
+  "value": 49.81,
+  "worst_kart": null,
+  "per_kart": {
+   "kart1": 49.81
+  }
+ },
+ "lap_improvement_s": {
+  "value": 0.0,
+  "worst_kart": null,
+  "per_kart": {
+   "kart1": 0.0
+  }
+ },
+ "overtake_rate": {
+  "value": null,
+  "worst_kart": null,
+  "per_kart": {
+   "kart1": null
+  }
+ },
+ "overtake_s_median": {
+  "value": null,
+  "worst_kart": null,
+  "per_kart": {
+   "kart1": null
+  }
+ },
+ "min_clearance_m": {
+  "value": null,
+  "worst_kart": null,
+  "per_kart": {
+   "kart1": null
+  }
+ },
+ "collisions": {
+  "value": 0,
+  "worst_kart": null,
+  "per_kart": {
+   "kart1": 0
+  }
+ },
+ "collision_rate": {
+  "value": null,
+  "worst_kart": null,
+  "per_kart": {
+   "kart1": null
+  }
+ },
+ "score_control": {
+  "value": 42.2,
+  "worst_kart": null,
+  "per_kart": {
+   "kart1": 42.2
+  }
+ },
+ "score_localization": {
+  "value": 0.0,
+  "worst_kart": null,
+  "per_kart": {
+   "kart1": 0.0
+  }
+ },
+ "score_overtake": {
+  "value": null,
+  "worst_kart": null,
+  "per_kart": {
+   "kart1": null
+  }
+ },
+ "score_slide": {
+  "value": null,
+  "worst_kart": null,
+  "per_kart": {
+   "kart1": null
+  }
+ },
+ "score_endurance": {
+  "value": null,
+  "worst_kart": null,
+  "per_kart": {
+   "kart1": null
+  }
+ },
+ "score_clips": {
+  "value": 80.9,
+  "worst_kart": null,
+  "per_kart": {
+   "kart1": 80.9
+  }
+ },
+ "composite_score": {
+  "value": 53.5,
+  "worst_kart": null,
+  "per_kart": {
+   "kart1": 53.5
+  }
+ },
+ "n": {
+  "value": 186,
+  "worst_kart": null,
+  "per_kart": {}
+ },
+ "errors": {
+  "value": 20,
+  "worst_kart": null,
+  "per_kart": {}
+ }
+}

results/leaderboard/2__bbd85e0dce44.json

@@ -92,5 +92,99 @@
    "n": 186,
    "errors": 21
   }
+ },
+ {
+  "job_id": "20261105-013942-A26_PP___Adaptive_Gain",
+  "recipe": "A26 PP + Adaptive Gain",
+  "notes": "",
+  "user": "shay",
+  "ran_at": "2026-11-05T07:29:26.516+00:00",
+  "duration_s": 2983.801,
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+  "unsafe_rate": 0.29070915712990697,
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+  "unsafe_off": 0.052357825704538355,
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+  "score_clips": 80.9,
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+  }
  }
 ]