results: jobs/20261011-001459-large-ilqr-steer-map/** index/episodes.jsonl leaderboard/*

results/jobs/20261011-001459-large-ilqr-steer-map/breakdown.json

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

results/jobs/20261011-001459-large-ilqr-steer-map/episodes.jsonl

@@ -0,0 +1,45 @@
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+{"key": "cc77aee5955de6ea602c1406d58652be34ce88c43cfdd70eec7f781e3b657536", "kart_name": "kart3", "condition": "nominal", "scenario": {"type": "clip", "clip_id": "20260520_223739_00263", "speed_cap": 8}, "status": "ok", "cause": null, "context_valid": true, "laps_completed": 0, "laps_attempted": 0, "wall_s": 29.022819555015303, "sim_s": 24.267, "t_end": 24.267, "handoff_t": 9.067, "auto_s": 15.04800000000057, "cte_hist": [3, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 3, 0, 0, 1, 0, 1, 1, 0, 3, 0, 1, 0, 1, 1, 1, 1, 1, 2, 2, 11, 10, 3, 4, 3, 8, 7, 6, 8, 8, 3, 7, 9, 8, 6, 10, 15, 29, 15, 8, 6, 11, 11, 8, 9, 8, 11, 17, 7, 4, 10, 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": 0.47568723327258317, "off_track_pct": 7.236842105263158, "safety": 0.8048245614035088, "events": 21, "osc_10": 47.05882352941177, "osc_20": 17.647058823529413, "osc_30": 5.882352941176471, "peak_cte_after_m": 1.2111392725530723, "unsafe_lat": 0.0, "unsafe_rate": 0.12280701754385964, "unsafe_travel": 0.0, "unsafe_spin": 0.0, "unsafe_off": 0.07236842105263158, "cte_p50": 0.4395892179005155, "cte_p90": 0.9398655773525273, "cte_p95": 1.0211716393299206, "cte_p99": 1.2012306594201105, "recovered": true, "recovery_s": 0.016500000000000625, "loc_n": 912, "loc_ss": 182.10019032067896}
+{"key": "ee303766526f36c388dfac322040005c31e462e0965a67d62c1dce9bb2c933fb", "kart_name": "kart1", "condition": "nominal", "scenario": {"type": "clip", "clip_id": "20260520_223739_00263", "speed_cap": 10}, "status": "ok", "cause": null, "context_valid": true, "laps_completed": 0, "laps_attempted": 0, "wall_s": 31.576253060018644, "sim_s": 24.367, "t_end": 24.367, "handoff_t": 9.167, "auto_s": 15.04800000000057, "cte_hist": [3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 2, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 2, 1, 0, 0, 3, 0, 2, 0, 2, 1, 2, 10, 11, 8, 6, 14, 14, 11, 22, 15, 4, 6, 5, 7, 6, 12, 15, 6, 9, 8, 10, 7, 8, 13, 17, 8, 15, 4, 6, 6, 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": 0.42288932003345825, "off_track_pct": 15.241228070175438, "safety": 0.7510964912280702, "events": 11, "osc_10": 35.294117647058826, "osc_20": 23.529411764705884, "osc_30": 0.0, "peak_cte_after_m": 1.0791880762018702, "unsafe_lat": 0.0, "unsafe_rate": 0.15350877192982457, "unsafe_travel": 0.0, "unsafe_spin": 0.0, "unsafe_off": 0.1524122807017544, "cte_p50": 0.3484770395017024, "cte_p90": 0.8516814400062088, "cte_p95": 0.914160201498096, "cte_p99": 1.068672048338566, "recovered": true, "recovery_s": 0.016500000000000625, "loc_n": 912, "loc_ss": 204.973023755897}
+{"key": "6fe015d77d7873bf3ff4c1fa82f438d17c9a344f943f49b5007acc1f00b66d38", "kart_name": "kart3", "condition": "nominal", "scenario": {"type": "clip", "clip_id": "20260520_000232_00015", "speed_cap": 8}, "status": "error", "cause": "state_frame", "context_valid": null, "laps_completed": 0, "laps_attempted": 0, "wall_s": 6.878034972003661, "sim_s": 0.0, "t_end": 0.0, "handoff_t": 0.0}

results/jobs/20261011-001459-large-ilqr-steer-map/gautschi/0/done.txt

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results/jobs/20261011-001459-large-ilqr-steer-map/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/20261011-001459-large-ilqr-steer-map/job.json

@@ -0,0 +1,131 @@
+{
+ "id": "20261011-001459-large-ilqr-steer-map",
+ "tier": "smoke",
+ "tier_version": 3,
+ "recipe": {
+  "name": "large-ilqr-steer-map",
+  "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": false
+   }
+  },
+  "sim": {
+   "cuda": true
+  },
+  "notes": "large-ilqr-fast with a predefined steer map per kart design: cmd -> kinematic wheel angle fitted to the baseline's steady steer_low_kart_low_sensors_low laps (results/ideas/steer-map/fit_maps.py), the same map in every condition, adaptive gain off",
+  "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,
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+ "user": "shay",
+ "notes": "smoke: large-ilqr-steer-map (pre-large check)",
+ "where": "ideas",
+ "status": "done",
+ "submitted_at": "2026-10-11T07:14:59.885+00:00",
+ "started_at": "2026-10-11T07:14:59.886+00:00",
+ "finished_at": "2026-10-11T07:19:49.325+00:00",
+ "total": 45,
+ "done": 45,
+ "errors": 1,
+ "build_log": null,
+ "workers": 10,
+ "slurm_id": "3844816",
+ "slurm_ids": [
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+ ],
+ "engine_version": "b37fb519b32e564d2d8a8ea1259cbf2e695ba1a28d93504f20e4c2970a4f6f00"
+}

results/jobs/20261011-001459-large-ilqr-steer-map/summary.json

@@ -0,0 +1,156 @@
+{
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+ }
+}

results/leaderboard/smoke-3__b37fb519b32e.json

@@ -44,5 +44,51 @@
    "n": 45,
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 ]