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+"""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() diff --git a/results/jobs/20261008-135505-best-stack/gautschi/0/sbatch.sh b/results/jobs/20261008-135505-best-stack/gautschi/0/sbatch.sh new file mode 100644 index 0000000000000000000000000000000000000000..9b724bb15359d41688658e02c732c83604e9a033 --- /dev/null +++ b/results/jobs/20261008-135505-best-stack/gautschi/0/sbatch.sh @@ -0,0 +1,5 @@ +#!/bin/bash +#SBATCH -p highmem -A lilly-rob1 -N 1 -n 1 -c 192 --time 517 -J AutoSim-large-best-stack-1 --comment 20261008-135505-best-stack -o AutoSim/jobs/20261008-135505-best-stack/0/slurm-%j.out +W=/dev/shm/AutoSim-$SLURM_JOB_ID +trap 'rm -rf "$W"' EXIT +python3 $HOME/AutoSim/jobs/20261008-135505-best-stack/0/gautschi_launch.py --episodes $HOME/AutoSim/jobs/20261008-135505-best-stack/0/episodes.json --out /scratch/gautschi/manors/AutoSim/out/20261008-135505-best-stack --sif $HOME/AutoSim/images/AutoSim-99d206f40cb1.sif --engine $HOME/AutoSim/engine/9468b6d2e43b440756c4f5e2ee7b6fcd221e3f1b7d9724496e70de28f1d0cde3 --stack $HOME/AutoSim/stacks/961dd8ca8ac6ba265501f0806e8c4afcf0f719c2f1a57eddccaa419ebbc53017 --workers 96 --work $W --timeline $HOME/AutoSim/jobs/20261008-135505-best-stack/0/timeline.jsonl --done $HOME/AutoSim/jobs/20261008-135505-best-stack/0/done.txt diff --git a/results/jobs/20261008-135505-best-stack/gautschi/1/done.txt b/results/jobs/20261008-135505-best-stack/gautschi/1/done.txt new file mode 100644 index 0000000000000000000000000000000000000000..0447a223900acf34695b116fc201c663af295814 --- /dev/null +++ b/results/jobs/20261008-135505-best-stack/gautschi/1/done.txt @@ -0,0 +1,5337 @@ +bc6e27691d922a589123e8eda5abdef21b293c72de336d62fd8b6beb517c08ae +167eb38d8841c8d497c3e19962f619701d2f6b7669fa87a30e70047b2122ef7a +0d7861305157468cf78f056f74f8114fe92b4f15638309de20ed07f766b28edf +330aed6f009d393ab6aa3ee3eb4d74888865c76727849c01855809138e93d1cf 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sha256:0104adcc4d115ab284e3f4f752fed548074a662cbcfbaea91658b77ea4b22493 +size 12317732 diff --git a/results/jobs/20261008-135505-best-stack/gautschi/1/gautschi_launch.py b/results/jobs/20261008-135505-best-stack/gautschi/1/gautschi_launch.py new file mode 100644 index 0000000000000000000000000000000000000000..bf8e0fad5f2b6ef416b2f1d3b27679a56f888d15 --- /dev/null +++ b/results/jobs/20261008-135505-best-stack/gautschi/1/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() diff --git a/results/jobs/20261008-135505-best-stack/gautschi/1/sbatch.sh b/results/jobs/20261008-135505-best-stack/gautschi/1/sbatch.sh new file mode 100644 index 0000000000000000000000000000000000000000..ebc3ae6e9cf28ee8dc2243ad84aef92e2ffea450 --- /dev/null +++ b/results/jobs/20261008-135505-best-stack/gautschi/1/sbatch.sh @@ -0,0 +1,5 @@ +#!/bin/bash +#SBATCH -p highmem -A lilly-rob1 -N 1 -n 1 -c 192 --time 516 -J AutoSim-large-best-stack-2 --comment 20261008-135505-best-stack -o AutoSim/jobs/20261008-135505-best-stack/1/slurm-%j.out +W=/dev/shm/AutoSim-$SLURM_JOB_ID +trap 'rm -rf "$W"' EXIT +python3 $HOME/AutoSim/jobs/20261008-135505-best-stack/1/gautschi_launch.py --episodes $HOME/AutoSim/jobs/20261008-135505-best-stack/1/episodes.json --out /scratch/gautschi/manors/AutoSim/out/20261008-135505-best-stack --sif $HOME/AutoSim/images/AutoSim-99d206f40cb1.sif --engine $HOME/AutoSim/engine/9468b6d2e43b440756c4f5e2ee7b6fcd221e3f1b7d9724496e70de28f1d0cde3 --stack $HOME/AutoSim/stacks/961dd8ca8ac6ba265501f0806e8c4afcf0f719c2f1a57eddccaa419ebbc53017 --workers 96 --work $W --timeline $HOME/AutoSim/jobs/20261008-135505-best-stack/1/timeline.jsonl --done $HOME/AutoSim/jobs/20261008-135505-best-stack/1/done.txt diff --git a/results/jobs/20261008-135505-best-stack/job.json b/results/jobs/20261008-135505-best-stack/job.json new file mode 100644 index 0000000000000000000000000000000000000000..4198743a39a241fe88d0bf5bbf2d44913ae664ea --- /dev/null +++ b/results/jobs/20261008-135505-best-stack/job.json @@ -0,0 +1,37 @@ +{ + "id": "20261008-135505-best-stack", + "tier": "large", + "tier_version": 2, + "recipe": { + "name": "best-stack", + "source": { + "repo": "AutonomousKart", + "ref": "worktree:understeer" + }, + "params": { + "pathfinder_node": { + "mpc.num_samples": 4096 + } + }, + "sim": {}, + "notes": "Best stack from the 2026-10-07/08 idea study (dev 491/557 vs baseline 378): stack main + sim-support with (1) speed profile: friction-limited forward-backward speed profile from line curvature and the existing a_lat/a_max/a_min limits, command ramps from measured speed at handover; (2) adaptive steering: online RLS of the steering-to-yaw gain from IMU yaw rate, fed to the MPC steer map; (3) understeer: online understeer gradient K_us, rollout uses wheel/(1+K_us v^2/L); (4) traction control: slip-ratio cap of the speed command (11.5% knee, friction-ellipse budget) and wheel-speed gating in the EKF; (5) slip-aware EKF: [x,y,yaw,v,beta] with IMU lateral accel, VTG course modelled as antenna velocity direction. Sampling MPC at 4096 samples on numpy (highmem, no GPU)." + }, + "user": "shay", + "notes": "Best stack (speed profile + adaptive steering + understeer + traction control + slip-aware EKF), numpy MPPI 4096, large tier", + "where": "gautschi", + "status": "done", + "submitted_at": "2026-10-08T17:55:05.819+00:00", + "started_at": "2026-10-08T17:55:05.819+00:00", + "finished_at": "2026-10-08T23:38:44.632+00:00", + "total": 10692, + "done": 10692, + "errors": 19, + "build_log": null, + "workers": 96, + "slurm_id": "16907983", + "slurm_ids": [ + "16907983", + "16907984" + ], + "engine_version": "9468b6d2e43b440756c4f5e2ee7b6fcd221e3f1b7d9724496e70de28f1d0cde3" +} \ No newline at end of file diff --git a/results/jobs/20261008-135505-best-stack/logs/shard-0000.jsonl.gz b/results/jobs/20261008-135505-best-stack/logs/shard-0000.jsonl.gz new file mode 100644 index 0000000000000000000000000000000000000000..defd42d595deb357353d11c03e93b9f98358cc94 --- /dev/null +++ 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