results: jobs/20261105-053401-A27_RL__Large_256x256_/** index/episodes.jsonl leaderboard/*

results/jobs/20261105-053401-A27_RL__Large_256x256_/breakdown.json

@@ -0,0 +1,350 @@
+{
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+ }
+}

results/jobs/20261105-053401-A27_RL__Large_256x256_/episodes.jsonl

@@ -0,0 +1,6 @@
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"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, "name": "imu_2", "location": "back", "parent_body": "chassis_rear", "pos_m": [0.1852, -0.0117, 0.072], "vibration_scale": 0.3, "dlpf_hz": 0}, "imu2": {"enabled": true, "name": "imu_1", "location": "front", "parent_body": "chassis_front", "pos_m": [0.9476, 0.0033, 0.0782], "vibration_scale": 0.25, "dlpf_hz": 44}, "camera": {"enabled": false, "parent_body": "gps_tower", "pos_m": [0.6717, -0.1085, 0.6276], "pitch_deg": -14.0, "yaw_deg": 0.0, "fov_v_deg": 58.0, "fov_h_deg": 86.0}, "wheel": {"white_mps": 0.03, "scale_error": 0.01, "rate_hz": 100.0}, "slam": {"enabled": false, "rate_hz": 30.0, "latency_ms": 20.0, "pos_sigma_m": 0.015, "yaw_sigma_deg": 0.25, "drift_mps": 0.004}, "gs3d": {"enabled": false, "rate_hz": 15.0, "latency_ms": 35.0, "inlier_prob": 0.92, "inlier_sigma_m": 0.018, "deadband_min_m": 0.08, "deadband_max_m": 0.85, "yaw_sigma_deg": 0.2}, "steer_encoder": {"enabled": false, "resolution_deg": 0.35, "white_deg": 0.2, "rate_hz": 50.0}}, "system_frequency": 60}, "kart_name": "kart1", "condition": "noise_medium_tire_low", "recipe": {"name": "A27 RL (Large 256x256)", "source": {"repo": "AutonomousKart", "ref": "worktree:/Users/shay/PycharmProjects/AutonomousKart-sim", "tree": "c1b6861526c569bb7725e5a82b8726fd37f6ccb9f4c44ccf7d403c322f5ce35d"}, "params": {"pathfinder_node": {"planner": "rl", "rl.model_size": "large", "mpc.adaptive_gain.enabled": false}}, "sim": {"cuda": true, "active_imu": "back", "use_camera": false, "controller_type": "rl", "rl": {"enabled": true, "model_size": "large", "world_model_arch": "mlp_256x256_z64", "alpha_schedule": "lap_progressive_1_to_0", "safety_shield": true}}, "notes": "A27 online alpha-conditioned TD3+BC RL controller (Large: 256x256 MLP, 64D latent world model) learning off Pure Pursuit and transitioning via alpha parameter"}, "scenario": {"type": "lap", "line": "line3_less_aggressive", "laps": 15, "test": "slide"}, "seed": 3677877041, "tier": "slide", "engine_version": "bbd85e0dce441d49ae06ba82dddb973a04f86a9fdd0705c67c5e131c98efe8b5"}}]

results/jobs/20261105-053401-A27_RL__Large_256x256_/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/20261105-053401-A27_RL__Large_256x256_/job.json

@@ -0,0 +1,50 @@
+{
+ "id": "20261105-053401-A27_RL__Large_256x256_",
+ "tier": "slide",
+ "tier_version": 1,
+ "recipe": {
+  "name": "A27 RL (Large 256x256)",
+  "source": {
+   "repo": "AutonomousKart",
+   "ref": "worktree:AutonomousKart-sim"
+  },
+  "params": {
+   "pathfinder_node": {
+    "planner": "rl",
+    "rl.model_size": "large",
+    "mpc.adaptive_gain.enabled": false
+   }
+  },
+  "sim": {
+   "cuda": true,
+   "active_imu": "back",
+   "use_camera": false,
+   "controller_type": "rl",
+   "rl": {
+    "enabled": true,
+    "model_size": "large",
+    "world_model_arch": "mlp_256x256_z64",
+    "alpha_schedule": "lap_progressive_1_to_0",
+    "safety_shield": true
+   }
+  },
+  "notes": "A27 online alpha-conditioned TD3+BC RL controller (Large: 256x256 MLP, 64D latent world model) learning off Pure Pursuit and transitioning via alpha parameter"
+ },
+ "user": "shay",
+ "notes": "",
+ "where": "ideas",
+ "status": "done",
+ "submitted_at": "2026-11-05T10:34:01.832+00:00",
+ "started_at": "2026-11-05T10:34:01.833+00:00",
+ "finished_at": "2026-11-05T10:38:53.316+00:00",
+ "total": 6,
+ "done": 6,
+ "errors": 1,
+ "build_log": null,
+ "workers": 6,
+ "slurm_id": "8153",
+ "slurm_ids": [
+  "8153"
+ ],
+ "engine_version": "bbd85e0dce441d49ae06ba82dddb973a04f86a9fdd0705c67c5e131c98efe8b5"
+}

results/jobs/20261105-053401-A27_RL__Large_256x256_/summary.json

@@ -0,0 +1,292 @@
+{
+ "cte_p50": {
+  "value": 0.15546112404268703,
+  "worst_kart": null,
+  "per_kart": {
+   "kart1": 0.15546112404268703
+  }
+ },
+ "cte_p90": {
+  "value": 0.4773493592713366,
+  "worst_kart": null,
+  "per_kart": {
+   "kart1": 0.4773493592713366
+  }
+ },
+ "cte_p99": {
+  "value": 1.757220806155775,
+  "worst_kart": null,
+  "per_kart": {
+   "kart1": 1.757220806155775
+  }
+ },
+ "recovery_rate": {
+  "value": null,
+  "worst_kart": null,
+  "per_kart": {
+   "kart1": null
+  }
+ },
+ "recovery_s_median": {
+  "value": null,
+  "worst_kart": null,
+  "per_kart": {
+   "kart1": null
+  }
+ },
+ "safety": {
+  "value": 0.4276782261183447,
+  "worst_kart": null,
+  "per_kart": {
+   "kart1": 0.4276782261183447
+  }
+ },
+ "unsafe_lat": {
+  "value": 0.0011602423617377853,
+  "worst_kart": null,
+  "per_kart": {
+   "kart1": 0.0011602423617377853
+  }
+ },
+ "unsafe_rate": {
+  "value": 0.4599716385200464,
+  "worst_kart": null,
+  "per_kart": {
+   "kart1": 0.4599716385200464
+  }
+ },
+ "unsafe_travel": {
+  "value": 0.0,
+  "worst_kart": null,
+  "per_kart": {
+   "kart1": 0.0
+  }
+ },
+ "unsafe_spin": {
+  "value": 0.00012891581797086504,
+  "worst_kart": null,
+  "per_kart": {
+   "kart1": 0.00012891581797086504
+  }
+ },
+ "unsafe_off": {
+  "value": 0.22734304499162047,
+  "worst_kart": null,
+  "per_kart": {
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+  }
+ },
+ "osc_10": {
+  "value": 42.01863352893476,
+  "worst_kart": null,
+  "per_kart": {
+   "kart1": 42.01863352893476
+  }
+ },
+ "osc_20": {
+  "value": 23.96995190261535,
+  "worst_kart": null,
+  "per_kart": {
+   "kart1": 23.96995190261535
+  }
+ },
+ "osc_30": {
+  "value": 15.80031088227222,
+  "worst_kart": null,
+  "per_kart": {
+   "kart1": 15.80031088227222
+  }
+ },
+ "laps_pct": {
+  "value": 2.6666666666666665,
+  "worst_kart": null,
+  "per_kart": {
+   "kart1": 2.6666666666666665
+  }
+ },
+ "failures": {
+  "value": 5,
+  "worst_kart": null,
+  "per_kart": {
+   "kart1": 5
+  }
+ },
+ "loc_rms": {
+  "value": 0.358,
+  "worst_kart": null,
+  "per_kart": {
+   "kart1": 0.358
+  }
+ },
+ "loc_p50": {
+  "value": 0.366,
+  "worst_kart": null,
+  "per_kart": {
+   "kart1": 0.366
+  }
+ },
+ "loc_p95": {
+  "value": 0.381,
+  "worst_kart": null,
+  "per_kart": {
+   "kart1": 0.381
+  }
+ },
+ "loc_p99": {
+  "value": 0.385,
+  "worst_kart": null,
+  "per_kart": {
+   "kart1": 0.385
+  }
+ },
+ "best_lap_s": {
+  "value": 67.191,
+  "worst_kart": null,
+  "per_kart": {
+   "kart1": 67.191
+  }
+ },
+ "lap_time_90": {
+  "value": 127.407,
+  "worst_kart": null,
+  "per_kart": {
+   "kart1": 127.407
+  }
+ },
+ "lap_time_99": {
+  "value": 133.89,
+  "worst_kart": null,
+  "per_kart": {
+   "kart1": 133.89
+  }
+ },
+ "lap_time_999": {
+  "value": 134.541,
+  "worst_kart": null,
+  "per_kart": {
+   "kart1": 134.541
+  }
+ },
+ "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": 54.77,
+  "worst_kart": null,
+  "per_kart": {
+   "kart1": 54.77
+  }
+ },
+ "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": 26.1,
+  "worst_kart": null,
+  "per_kart": {
+   "kart1": 26.1
+  }
+ },
+ "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": 1.8,
+  "worst_kart": null,
+  "per_kart": {
+   "kart1": 1.8
+  }
+ },
+ "score_endurance": {
+  "value": null,
+  "worst_kart": null,
+  "per_kart": {
+   "kart1": null
+  }
+ },
+ "score_clips": {
+  "value": null,
+  "worst_kart": null,
+  "per_kart": {
+   "kart1": null
+  }
+ },
+ "composite_score": {
+  "value": 15.1,
+  "worst_kart": null,
+  "per_kart": {
+   "kart1": 15.1
+  }
+ },
+ "n": {
+  "value": 6,
+  "worst_kart": null,
+  "per_kart": {}
+ },
+ "errors": {
+  "value": 1,
+  "worst_kart": null,
+  "per_kart": {}
+ }
+}

results/leaderboard/slide-1__bbd85e0dce44.json

@@ -186,5 +186,99 @@
    "n": 6,
    "errors": 1
   }
+ },
+ {
+  "job_id": "20261105-053401-A27_RL__Large_256x256_",
+  "recipe": "A27 RL (Large 256x256)",
+  "notes": "",
+  "user": "shay",
+  "ran_at": "2026-11-05T10:38:53.316+00:00",
+  "duration_s": 291.483,
+  "cte_p50": 0.15546112404268703,
+  "cte_p90": 0.4773493592713366,
+  "cte_p99": 1.757220806155775,
+  "recovery_rate": null,
+  "recovery_s_median": null,
+  "safety": 0.4276782261183447,
+  "unsafe_lat": 0.0011602423617377853,
+  "unsafe_rate": 0.4599716385200464,
+  "unsafe_travel": 0.0,
+  "unsafe_spin": 0.00012891581797086504,
+  "unsafe_off": 0.22734304499162047,
+  "osc_10": 42.01863352893476,
+  "osc_20": 23.96995190261535,
+  "osc_30": 15.80031088227222,
+  "laps_pct": 2.6666666666666665,
+  "failures": 5,
+  "loc_rms": 0.358,
+  "loc_p50": 0.366,
+  "loc_p95": 0.381,
+  "loc_p99": 0.385,
+  "best_lap_s": 67.191,
+  "lap_time_90": 127.407,
+  "lap_time_99": 133.89,
+  "lap_time_999": 134.541,
+  "learned_to_slide_rate": 0.0,
+  "laps_to_slide": null,
+  "slide_pct_p95": 54.77,
+  "lap_improvement_s": 0.0,
+  "overtake_rate": null,
+  "overtake_s_median": null,
+  "min_clearance_m": null,
+  "collisions": 0,
+  "collision_rate": null,
+  "score_control": 26.1,
+  "score_localization": 0.0,
+  "score_overtake": null,
+  "score_slide": 1.8,
+  "score_endurance": null,
+  "score_clips": null,
+  "composite_score": 15.1,
+  "n": 6,
+  "errors": 1,
+  "worst": {
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+   "cte_p90": 0.4773493592713366,
+   "cte_p99": 1.757220806155775,
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+   "safety": 0.4276782261183447,
+   "unsafe_lat": 0.0011602423617377853,
+   "unsafe_rate": 0.4599716385200464,
+   "unsafe_travel": 0.0,
+   "unsafe_spin": 0.00012891581797086504,
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+   "osc_10": 42.01863352893476,
+   "osc_20": 23.96995190261535,
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+   "best_lap_s": 67.191,
+   "lap_time_90": 127.407,
+   "lap_time_99": 133.89,
+   "lap_time_999": 134.541,
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+   "overtake_rate": null,
+   "overtake_s_median": null,
+   "min_clearance_m": null,
+   "collisions": 0,
+   "collision_rate": null,
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+   "score_localization": 0.0,
+   "score_overtake": null,
+   "score_slide": 1.8,
+   "score_endurance": null,
+   "score_clips": null,
+   "composite_score": 15.1,
+   "n": 6,
+   "errors": 1
+  }
  }
 ]