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results: jobs/20261007-214214-idea-slip-ekf-cpu/** index/episodes.jsonl leaderboard/*

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results/index/episodes.jsonl CHANGED
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"worktree:/Users/shay/PycharmProjects/AK-ideas/slip-ekf", "tree": "66d35160c9c8604be7fce35aad7f48b9c28b7ba28fe945671d72a6396331de2b"}, "params": {"pathfinder_node": {"mpc.num_samples": 4096}}, "notes": "slip-ekf on the numpy MPC path (highmem), compare with base-cpu"}, "scenario": {"type": "clip", "clip_id": "20260520_223739_00263"}, "seed": 3775292264, "tier": "smoke", "engine_version": "225628db320e4ffcdbcd30e90064b91c2478cc1ae33d7a9abc198730db2ea6a4"}}, {"key": "3543bf71c88847a015f2692433c1a9c81c7fefeb2100425c1690f2d99e106f28", "resolved": {"kart": {"name": "kart1", "chrono_dir": "chrono/kart", "tyres": {"grip": 1.0}, "frame": {"torsion_scale": 1.0}, "steering_motor": {"rate_dps": 70.0, "travel_deg": 70.0, "step_deg": 0.9, "slop_deg": 5.0, "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": "kart1", "condition": "nominal", "recipe": {"name": "idea-slip-ekf-cpu", "source": {"repo": "AutonomousKart", "ref": "worktree:/Users/shay/PycharmProjects/AK-ideas/slip-ekf", "tree": "66d35160c9c8604be7fce35aad7f48b9c28b7ba28fe945671d72a6396331de2b"}, "params": {"pathfinder_node": {"mpc.num_samples": 4096}}, "notes": "slip-ekf on the numpy MPC path (highmem), compare with base-cpu"}, "scenario": {"type": "clip", "clip_id": "20260520_223739_00263"}, "seed": 1416345111, "tier": "smoke", "engine_version": "225628db320e4ffcdbcd30e90064b91c2478cc1ae33d7a9abc198730db2ea6a4"}}, {"key": "f313e715f5329ea93709848196fafbfea155948cdc4380a91f416057d8183781", "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-slip-ekf-cpu", "source": {"repo": "AutonomousKart", "ref": "worktree:/Users/shay/PycharmProjects/AK-ideas/slip-ekf", "tree": "66d35160c9c8604be7fce35aad7f48b9c28b7ba28fe945671d72a6396331de2b"}, "params": {"pathfinder_node": {"mpc.num_samples": 4096}}, "notes": "slip-ekf on the numpy MPC path (highmem), compare with base-cpu"}, "scenario": {"type": "clip", "clip_id": "20260520_223739_00263"}, "seed": 130671560, "tier": "smoke", "engine_version": "225628db320e4ffcdbcd30e90064b91c2478cc1ae33d7a9abc198730db2ea6a4"}}]
results/jobs/20261007-214214-idea-slip-ekf-cpu/gautschi/0/gautschi_launch.py ADDED
@@ -0,0 +1,143 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ """Runs on a Gautschi node (stdlib only): episodes from a queue in parallel workers, one contained Apptainer per episode."""
2
+ import argparse
3
+ import json
4
+ import os
5
+ import queue
6
+ import shutil
7
+ import subprocess
8
+ import threading
9
+ import time
10
+
11
+ RUN = ("source /opt/ros/humble/setup.bash && source /ws/install/setup.bash"
12
+ " && python3 -m simnode.batch /batch.json /out")
13
+ CUDA_LIB = "/ws/mpc_cuda/libmpc_cuda.so" # built per stack by runner/gautschi.build_stack for gautschi-gpu
14
+ SAMPLE_S = 15
15
+ STAGGER_S = 0.25 # between worker starts: the dev job's one error was a stack starved in the 96-way start burst
16
+ EPISODE_TIMEOUT_S = 1200 # about 4x the slowest lap; a killed episode has no outcome and is retried by the host
17
+
18
+
19
+ def worker_cmd(i, work, out, ep, sif, engine, stack, gpu=None):
20
+ """Private /tmp, HOME, /dev/shm, IPC, PIDs and loopback per episode, plus a distinct ROS_DOMAIN_ID per worker;
21
+ gpu (a host CUDA device) adds the driver and the stack's CUDA MPC kernel."""
22
+ cuda = ["--nv", "--env", f"CUDA_VISIBLE_DEVICES={gpu}", "--env", f"MPC_CUDA_LIB={CUDA_LIB}"] if gpu is not None else []
23
+ return ["apptainer", "exec", *cuda, "--contain", "--cleanenv", "--ipc", "--pid", "--net", "--network", "none", "--workdir", work,
24
+ "--env", f"ROS_DOMAIN_ID={i + 1}", "--env", "ROS_HOME=/tmp/ros", "--env", "TMPDIR=/tmp",
25
+ "--bind", f"{engine}:/opt/sim:ro", "--bind", f"{stack}:/ws:ro", "--bind", f"{out}:/out",
26
+ "--bind", f"{ep}:/batch.json:ro", sif, "bash", "-lc", RUN]
27
+
28
+
29
+ def parse_smi(text):
30
+ """nvidia-smi 'utilization.gpu, memory.used' csv rows to per-GPU lists."""
31
+ rows = [[int(v) for v in ln.split(",")] for ln in text.strip().splitlines() if ln.strip()]
32
+ return {"gpu_util": [r[0] for r in rows], "gpu_mem_mib": [r[1] for r in rows]}
33
+
34
+
35
+ def _solver(logdir):
36
+ """pathfinder's 'MPC solver: ...' and CUDA fallback lines from the episode's ROS logs."""
37
+ lines = []
38
+ for dp, _, fns in os.walk(logdir):
39
+ for fn in fns:
40
+ try:
41
+ with open(os.path.join(dp, fn), errors="replace") as f:
42
+ lines += [ln.strip() for ln in f if "MPC solver:" in ln or "CUDA MPC unavailable" in ln]
43
+ except OSError:
44
+ pass
45
+ return "\n".join(dict.fromkeys(lines))
46
+
47
+
48
+ def main(argv=None):
49
+ p = argparse.ArgumentParser()
50
+ for a in ("episodes", "out", "sif", "engine", "stack", "work", "timeline", "done"):
51
+ p.add_argument("--" + a, required=True)
52
+ p.add_argument("--workers", type=int, required=True)
53
+ p.add_argument("--gpus", type=int, default=0)
54
+ a = p.parse_args(argv)
55
+ with open(a.episodes) as f:
56
+ q = queue.Queue()
57
+ for e in json.load(f): # the host already ordered them, longest first
58
+ q.put(e)
59
+ os.makedirs(a.out, exist_ok=True)
60
+ lock, t0 = threading.Lock(), time.time()
61
+ 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)]
62
+
63
+ def log(**ev):
64
+ with lock, open(a.timeline, "a") as f:
65
+ f.write(json.dumps(dict(ev, t=round(time.time() - t0, 3))) + "\n")
66
+
67
+ def worker(i):
68
+ time.sleep(i * STAGGER_S)
69
+ while True:
70
+ try:
71
+ e = q.get_nowait()
72
+ except queue.Empty:
73
+ return
74
+ key, work = e["key"], os.path.join(a.work, e["key"])
75
+ stage, ep = os.path.join(work, "stage"), os.path.join(work, "ep.json")
76
+ os.makedirs(stage)
77
+ with open(ep, "w") as f:
78
+ json.dump([e], f)
79
+ log(ev="start", key=key, worker=i)
80
+ gpu = devs[i % len(devs)] if devs and e["resolved"].get("recipe", {}).get("sim", {}).get("cuda") else None
81
+ limit = EPISODE_TIMEOUT_S * max(1, e["resolved"]["scenario"].get("laps", 1)) # per lap: 5-lap episodes run 5x longer
82
+ try:
83
+ rc = subprocess.run(worker_cmd(i, work, stage, ep, a.sif, a.engine, a.stack, gpu), stdout=subprocess.DEVNULL,
84
+ stderr=subprocess.DEVNULL, timeout=limit).returncode
85
+ except subprocess.TimeoutExpired:
86
+ rc = "timeout"
87
+ solver = _solver(os.path.join(work, "tmp", "ros", "log"))
88
+ if solver and os.path.isdir(os.path.join(stage, key)):
89
+ with open(os.path.join(stage, key, "solver.txt"), "w") as f:
90
+ f.write(solver + "\n")
91
+ if os.path.exists(os.path.join(stage, key, "outcome.json")):
92
+ tmp, dst = os.path.join(a.out, ".tmp-" + key), os.path.join(a.out, key)
93
+ shutil.rmtree(tmp, ignore_errors=True)
94
+ shutil.copytree(os.path.join(stage, key), tmp)
95
+ shutil.rmtree(dst, ignore_errors=True)
96
+ os.rename(tmp, dst) # the host's rsync sees a key only once its directory is complete
97
+ with lock, open(a.done, "a") as f:
98
+ f.write(key + "\n")
99
+ shutil.rmtree(work, ignore_errors=True)
100
+ log(ev="end", key=key, worker=i, rc=rc, gpu=gpu,
101
+ solver=solver.split("MPC solver: ")[-1] if "MPC solver: " in solver else solver or None)
102
+
103
+ def sampler(stop):
104
+ """Job CPU use from its cgroup, as busy cores, every SAMPLE_S."""
105
+ try:
106
+ with open("/proc/self/cgroup") as f:
107
+ path = "/sys/fs/cgroup" + f.read().strip().split(":")[-1] + "/cpu.stat"
108
+ prev = None
109
+ while not stop.wait(SAMPLE_S if prev else 0):
110
+ with open(path) as f:
111
+ use = next(int(ln.split()[1]) for ln in f if ln.startswith("usage_usec"))
112
+ now = time.time()
113
+ if prev:
114
+ log(ev="cpu", busy_cores=round((use - prev[0]) / 1e6 / (now - prev[1]), 2))
115
+ prev = (use, now)
116
+ except (OSError, StopIteration, ValueError):
117
+ pass
118
+
119
+ def gpu_sampler(stop):
120
+ """GPU utilisation and memory of the job's GPUs, every SAMPLE_S."""
121
+ while not stop.wait(SAMPLE_S):
122
+ try:
123
+ p = subprocess.Popen(["nvidia-smi", "--query-gpu=utilization.gpu,memory.used", "--format=csv,noheader,nounits"],
124
+ stdout=subprocess.PIPE, stderr=subprocess.DEVNULL, text=True)
125
+ log(ev="gpu", **parse_smi(p.communicate(timeout=30)[0]))
126
+ except Exception: # no driver or a hung query: skip this sample
127
+ pass
128
+
129
+ cores = len(os.sched_getaffinity(0)) if hasattr(os, "sched_getaffinity") else os.cpu_count()
130
+ log(ev="launch", workers=a.workers, cores=cores)
131
+ stop = threading.Event()
132
+ threads = [threading.Thread(target=worker, args=(i,)) for i in range(a.workers)]
133
+ samplers = [sampler] + ([gpu_sampler] if a.gpus else [])
134
+ for t in threads + [threading.Thread(target=f, args=(stop,), daemon=True) for f in samplers]:
135
+ t.start()
136
+ for t in threads:
137
+ t.join()
138
+ stop.set()
139
+ log(ev="done")
140
+
141
+
142
+ if __name__ == "__main__":
143
+ main()
results/jobs/20261007-214214-idea-slip-ekf-cpu/gautschi/0/sbatch.sh ADDED
@@ -0,0 +1,5 @@
 
 
 
 
 
 
1
+ #!/bin/bash
2
+ #SBATCH -p highmem -A lilly-rob1 -N 1 -n 1 -c 96 --time 34 -J AutoSim-smoke --comment 20261007-214214-idea-slip-ekf-cpu -o AutoSim/jobs/20261007-214214-idea-slip-ekf-cpu/0/slurm-%j.out
3
+ W=/dev/shm/AutoSim-$SLURM_JOB_ID
4
+ trap 'rm -rf "$W"' EXIT
5
+ python3 $HOME/AutoSim/jobs/20261007-214214-idea-slip-ekf-cpu/0/gautschi_launch.py --episodes $HOME/AutoSim/jobs/20261007-214214-idea-slip-ekf-cpu/0/episodes.json --out /scratch/gautschi/manors/AutoSim/out/20261007-214214-idea-slip-ekf-cpu --sif $HOME/AutoSim/images/AutoSim-99d206f40cb1.sif --engine $HOME/AutoSim/engine/225628db320e4ffcdbcd30e90064b91c2478cc1ae33d7a9abc198730db2ea6a4 --stack $HOME/AutoSim/stacks/66d35160c9c8604be7fce35aad7f48b9c28b7ba28fe945671d72a6396331de2b --workers 15 --work $W --timeline $HOME/AutoSim/jobs/20261007-214214-idea-slip-ekf-cpu/0/timeline.jsonl --done $HOME/AutoSim/jobs/20261007-214214-idea-slip-ekf-cpu/0/done.txt
results/jobs/20261007-214214-idea-slip-ekf-cpu/job.json ADDED
@@ -0,0 +1,35 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "id": "20261007-214214-idea-slip-ekf-cpu",
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+ "tier": "smoke",
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+ "tier_version": 2,
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+ "recipe": {
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+ "name": "idea-slip-ekf-cpu",
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+ "source": {
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+ "repo": "AutonomousKart",
9
+ "ref": "worktree:slip-ekf"
10
+ },
11
+ "params": {
12
+ "pathfinder_node": {
13
+ "mpc.num_samples": 4096
14
+ }
15
+ },
16
+ "notes": "slip-ekf on the numpy MPC path (highmem), compare with base-cpu"
17
+ },
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+ "user": "shay",
19
+ "notes": "slip-ekf-cpu smoke",
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+ "where": "gautschi",
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+ "status": "done",
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+ "submitted_at": "2026-10-08T01:42:14.550+00:00",
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+ "started_at": "2026-10-08T01:42:14.553+00:00",
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+ "finished_at": "2026-10-08T01:50:47.806+00:00",
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+ "total": 15,
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+ "done": 15,
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+ "errors": 0,
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+ "build_log": null,
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+ "workers": 15,
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+ "slurm_id": "16902321",
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+ "slurm_ids": [
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+ "16902321"
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+ ],
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+ "engine_version": "225628db320e4ffcdbcd30e90064b91c2478cc1ae33d7a9abc198730db2ea6a4"
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+ }
results/jobs/20261007-214214-idea-slip-ekf-cpu/logs/shard-0000.jsonl.gz ADDED
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