results: jobs/20261105-024128-A26_MPPI___Adaptive_Gain/** index/episodes.jsonl leaderboard/*
Browse files- results/jobs/20261105-024128-A26_MPPI___Adaptive_Gain/breakdown.json +1884 -0
- results/jobs/20261105-024128-A26_MPPI___Adaptive_Gain/episodes.jsonl +0 -0
- results/jobs/20261105-024128-A26_MPPI___Adaptive_Gain/gautschi/0/done.txt +51 -0
- results/jobs/20261105-024128-A26_MPPI___Adaptive_Gain/gautschi/0/episodes.json +0 -0
- results/jobs/20261105-024128-A26_MPPI___Adaptive_Gain/gautschi/0/gautschi_launch.py +145 -0
- results/jobs/20261105-024128-A26_MPPI___Adaptive_Gain/gautschi/1/episodes.json +0 -0
- results/jobs/20261105-024128-A26_MPPI___Adaptive_Gain/gautschi/1/gautschi_launch.py +145 -0
- results/jobs/20261105-024128-A26_MPPI___Adaptive_Gain/job.json +45 -0
- results/jobs/20261105-024128-A26_MPPI___Adaptive_Gain/summary.json +292 -0
- results/leaderboard/2__bbd85e0dce44.json +94 -0
results/jobs/20261105-024128-A26_MPPI___Adaptive_Gain/breakdown.json
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The diff for this file is too large to render.
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results/jobs/20261105-024128-A26_MPPI___Adaptive_Gain/gautschi/0/done.txt
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| 20 |
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18c49338d7df9d45556a0d404280ff40d4c473314259f43903b6c3910717b4e1
|
| 21 |
+
a88a71cee92b7a90d661babb28c8c2e2d5d2a3bba3ce5ad31e60ee55cf84f38f
|
| 22 |
+
4a8b907bdb795ad6d8d677f7049b794c29ba51a8cd2fd6775773ee05c1ee50d1
|
| 23 |
+
ebd8e57c8dceb56ce497ee1891b413c7d01ace4aa6dc690277c804fd4e52d24e
|
| 24 |
+
4f913c36b59b7b65f2dc7b4d0195e1c67d79f56ba9dd0dd76fef3ab8b0f0198a
|
| 25 |
+
3dbc5ea4799f11f17e39607b4d2b4aa3d22b3476f58d07aa33486151cd4f550c
|
| 26 |
+
89cda71f6f7547ed8c405d2e58acb8c3146e59b7cd2ca8b4e4ea6687cc161c06
|
| 27 |
+
94681f21fe0db7f5c5c4060551f85006084772fa9fee4b3a6945bf02992d68c3
|
| 28 |
+
fa13f9b586658b2f4f6289ec2a22b6c97b15f60a05eb4ef4c30822cbc1d2f3f8
|
| 29 |
+
860a251198ee0e1d13de17a12ff927830eea1b8557f5da8cae66846ff161b2e5
|
| 30 |
+
4ea1894adbf3a56dbbb9a69da972fa64f73b18c3e01749e179ef611384c1d25f
|
| 31 |
+
502e8d011fe90e0c36066259193574c9175651a27cb339bd4477c3a2470b22a0
|
| 32 |
+
cbb2b5d28017fffbd633706ae63bf47e7dcaf8706481bb931e4f47474ed27608
|
| 33 |
+
b7b63d2e5a0a18be4de93ca40a1e1c6676e66562c4ddbe96cdff62d0080229c9
|
| 34 |
+
9059e9bd4f66b9276285214ebb8ec24875b267ebcd597d6a36fae87bba6a057e
|
| 35 |
+
adf709c86acef603d365972d61f1e54041d7cece233b33582b94307c96ae43dd
|
| 36 |
+
5e2324750efa643302d0da5760e91659478beb8e5590929c155b7d91b4516103
|
| 37 |
+
073347bada7076a93b2c7d5b85d653f3c9dc556d4e5e21d89d978d8db545b61f
|
| 38 |
+
fed7f4e7d6623d93dfa7c2f0c902db71e3f25c4c83e1a06a5aa75fb00a28dc86
|
| 39 |
+
256a54de2f495ca2b5e23d719be09bc933aca13187f3b769134b08a149101fb5
|
| 40 |
+
418b36ec7288ced24163de8f1d2df6a4f4ad53d248dc40350b219459455b4bbf
|
| 41 |
+
57aee415acd0a7de13fda9fdc4d64cc3ff2bbf15dd9be1cb2d3a7ae96d5b7f00
|
| 42 |
+
404fb77359e6fb242a085ed8d72a95b409eb857ce2453054c0fe4d66449d4ad4
|
| 43 |
+
06a39db90665643a733c4c584a32ad64139696698321b0ae7522dd2889a5dded
|
| 44 |
+
1a81aae7b3f8f5780b3dbef4fda9eaeccd03779451e3b160b9b4f2710dbe2213
|
| 45 |
+
994dfa68ebc55dead2a7067984e3cae12b08351bfca717b5eafbd62999347f12
|
| 46 |
+
8039b021545922057e8bf3dc099e37669e46e40c4ff54439d1cfb1937effb624
|
| 47 |
+
59f561f3bf345345e3ad4d7a417bb6525d22ee8c1c13d7a9b6adfb1682cc45a9
|
| 48 |
+
dd0b052da78a72f9acd3b43343122b410f702b805948b722a2e5557b35418221
|
| 49 |
+
f885eedcef57496895c3fe2d17e3740baefe1f522d4777018a3dce45d36987c3
|
| 50 |
+
ad4865896405d6c66a1d008419114adc0426a73c7c67fb2f461d96a94880a7d2
|
| 51 |
+
4e1911fcb7e5762dd28f16ccde196a2406e19f6a08ddd26713c8ceb08f8e8a8a
|
results/jobs/20261105-024128-A26_MPPI___Adaptive_Gain/gautschi/0/episodes.json
ADDED
|
The diff for this file is too large to render.
See raw diff
|
|
|
results/jobs/20261105-024128-A26_MPPI___Adaptive_Gain/gautschi/0/gautschi_launch.py
ADDED
|
@@ -0,0 +1,145 @@
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|
| 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 |
+
if gpu is None and os.path.exists(os.path.join(stack, "mpc_cuda", "libmpc_cuda.so")):
|
| 24 |
+
cuda = ["--env", f"MPC_CUDA_LIB={CUDA_LIB}"] # CPU node: the library's native iLQR solve still loads; its MPPI falls back to numpy
|
| 25 |
+
return ["apptainer", "exec", *cuda, "--contain", "--cleanenv", "--ipc", "--pid", "--net", "--network", "none", "--workdir", work,
|
| 26 |
+
"--env", f"ROS_DOMAIN_ID={i + 1}", "--env", "ROS_HOME=/tmp/ros", "--env", "TMPDIR=/tmp",
|
| 27 |
+
"--bind", f"{engine}:/opt/sim:ro", "--bind", f"{stack}:/ws:ro", "--bind", f"{out}:/out",
|
| 28 |
+
"--bind", f"{ep}:/batch.json:ro", sif, "bash", "-lc", RUN]
|
| 29 |
+
|
| 30 |
+
|
| 31 |
+
def parse_smi(text):
|
| 32 |
+
"""nvidia-smi 'utilization.gpu, memory.used' csv rows to per-GPU lists."""
|
| 33 |
+
rows = [[int(v) for v in ln.split(",")] for ln in text.strip().splitlines() if ln.strip()]
|
| 34 |
+
return {"gpu_util": [r[0] for r in rows], "gpu_mem_mib": [r[1] for r in rows]}
|
| 35 |
+
|
| 36 |
+
|
| 37 |
+
def _solver(logdir):
|
| 38 |
+
"""pathfinder's 'MPC solver: ...' and CUDA fallback lines from the episode's ROS logs."""
|
| 39 |
+
lines = []
|
| 40 |
+
for dp, _, fns in os.walk(logdir):
|
| 41 |
+
for fn in fns:
|
| 42 |
+
try:
|
| 43 |
+
with open(os.path.join(dp, fn), errors="replace") as f:
|
| 44 |
+
lines += [ln.strip() for ln in f if "MPC solver:" in ln or "CUDA MPC unavailable" in ln]
|
| 45 |
+
except OSError:
|
| 46 |
+
pass
|
| 47 |
+
return "\n".join(dict.fromkeys(lines))
|
| 48 |
+
|
| 49 |
+
|
| 50 |
+
def main(argv=None):
|
| 51 |
+
p = argparse.ArgumentParser()
|
| 52 |
+
for a in ("episodes", "out", "sif", "engine", "stack", "work", "timeline", "done"):
|
| 53 |
+
p.add_argument("--" + a, required=True)
|
| 54 |
+
p.add_argument("--workers", type=int, required=True)
|
| 55 |
+
p.add_argument("--gpus", type=int, default=0)
|
| 56 |
+
a = p.parse_args(argv)
|
| 57 |
+
with open(a.episodes) as f:
|
| 58 |
+
q = queue.Queue()
|
| 59 |
+
for e in json.load(f): # the host already ordered them, longest first
|
| 60 |
+
q.put(e)
|
| 61 |
+
os.makedirs(a.out, exist_ok=True)
|
| 62 |
+
lock, t0 = threading.Lock(), time.time()
|
| 63 |
+
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)]
|
| 64 |
+
|
| 65 |
+
def log(**ev):
|
| 66 |
+
with lock, open(a.timeline, "a") as f:
|
| 67 |
+
f.write(json.dumps(dict(ev, t=round(time.time() - t0, 3))) + "\n")
|
| 68 |
+
|
| 69 |
+
def worker(i):
|
| 70 |
+
time.sleep(i * STAGGER_S)
|
| 71 |
+
while True:
|
| 72 |
+
try:
|
| 73 |
+
e = q.get_nowait()
|
| 74 |
+
except queue.Empty:
|
| 75 |
+
return
|
| 76 |
+
key, work = e["key"], os.path.join(a.work, e["key"])
|
| 77 |
+
stage, ep = os.path.join(work, "stage"), os.path.join(work, "ep.json")
|
| 78 |
+
os.makedirs(stage)
|
| 79 |
+
with open(ep, "w") as f:
|
| 80 |
+
json.dump([e], f)
|
| 81 |
+
log(ev="start", key=key, worker=i)
|
| 82 |
+
gpu = devs[i % len(devs)] if devs and e["resolved"].get("recipe", {}).get("sim", {}).get("cuda") else None
|
| 83 |
+
limit = EPISODE_TIMEOUT_S * max(1, e["resolved"]["scenario"].get("laps", 1)) # per lap: 5-lap episodes run 5x longer
|
| 84 |
+
try:
|
| 85 |
+
rc = subprocess.run(worker_cmd(i, work, stage, ep, a.sif, a.engine, a.stack, gpu), stdout=subprocess.DEVNULL,
|
| 86 |
+
stderr=subprocess.DEVNULL, timeout=limit).returncode
|
| 87 |
+
except subprocess.TimeoutExpired:
|
| 88 |
+
rc = "timeout"
|
| 89 |
+
solver = _solver(os.path.join(work, "tmp", "ros", "log"))
|
| 90 |
+
if solver and os.path.isdir(os.path.join(stage, key)):
|
| 91 |
+
with open(os.path.join(stage, key, "solver.txt"), "w") as f:
|
| 92 |
+
f.write(solver + "\n")
|
| 93 |
+
if os.path.exists(os.path.join(stage, key, "outcome.json")):
|
| 94 |
+
tmp, dst = os.path.join(a.out, ".tmp-" + key), os.path.join(a.out, key)
|
| 95 |
+
shutil.rmtree(tmp, ignore_errors=True)
|
| 96 |
+
shutil.copytree(os.path.join(stage, key), tmp)
|
| 97 |
+
shutil.rmtree(dst, ignore_errors=True)
|
| 98 |
+
os.rename(tmp, dst) # the host's rsync sees a key only once its directory is complete
|
| 99 |
+
with lock, open(a.done, "a") as f:
|
| 100 |
+
f.write(key + "\n")
|
| 101 |
+
shutil.rmtree(work, ignore_errors=True)
|
| 102 |
+
log(ev="end", key=key, worker=i, rc=rc, gpu=gpu,
|
| 103 |
+
solver=solver.split("MPC solver: ")[-1] if "MPC solver: " in solver else solver or None)
|
| 104 |
+
|
| 105 |
+
def sampler(stop):
|
| 106 |
+
"""Job CPU use from its cgroup, as busy cores, every SAMPLE_S."""
|
| 107 |
+
try:
|
| 108 |
+
with open("/proc/self/cgroup") as f:
|
| 109 |
+
path = "/sys/fs/cgroup" + f.read().strip().split(":")[-1] + "/cpu.stat"
|
| 110 |
+
prev = None
|
| 111 |
+
while not stop.wait(SAMPLE_S if prev else 0):
|
| 112 |
+
with open(path) as f:
|
| 113 |
+
use = next(int(ln.split()[1]) for ln in f if ln.startswith("usage_usec"))
|
| 114 |
+
now = time.time()
|
| 115 |
+
if prev:
|
| 116 |
+
log(ev="cpu", busy_cores=round((use - prev[0]) / 1e6 / (now - prev[1]), 2))
|
| 117 |
+
prev = (use, now)
|
| 118 |
+
except (OSError, StopIteration, ValueError):
|
| 119 |
+
pass
|
| 120 |
+
|
| 121 |
+
def gpu_sampler(stop):
|
| 122 |
+
"""GPU utilisation and memory of the job's GPUs, every SAMPLE_S."""
|
| 123 |
+
while not stop.wait(SAMPLE_S):
|
| 124 |
+
try:
|
| 125 |
+
p = subprocess.Popen(["nvidia-smi", "--query-gpu=utilization.gpu,memory.used", "--format=csv,noheader,nounits"],
|
| 126 |
+
stdout=subprocess.PIPE, stderr=subprocess.DEVNULL, text=True)
|
| 127 |
+
log(ev="gpu", **parse_smi(p.communicate(timeout=30)[0]))
|
| 128 |
+
except Exception: # no driver or a hung query: skip this sample
|
| 129 |
+
pass
|
| 130 |
+
|
| 131 |
+
cores = len(os.sched_getaffinity(0)) if hasattr(os, "sched_getaffinity") else os.cpu_count()
|
| 132 |
+
log(ev="launch", workers=a.workers, cores=cores)
|
| 133 |
+
stop = threading.Event()
|
| 134 |
+
threads = [threading.Thread(target=worker, args=(i,)) for i in range(a.workers)]
|
| 135 |
+
samplers = [sampler] + ([gpu_sampler] if a.gpus else [])
|
| 136 |
+
for t in threads + [threading.Thread(target=f, args=(stop,), daemon=True) for f in samplers]:
|
| 137 |
+
t.start()
|
| 138 |
+
for t in threads:
|
| 139 |
+
t.join()
|
| 140 |
+
stop.set()
|
| 141 |
+
log(ev="done")
|
| 142 |
+
|
| 143 |
+
|
| 144 |
+
if __name__ == "__main__":
|
| 145 |
+
main()
|
results/jobs/20261105-024128-A26_MPPI___Adaptive_Gain/gautschi/1/episodes.json
ADDED
|
The diff for this file is too large to render.
See raw diff
|
|
|
results/jobs/20261105-024128-A26_MPPI___Adaptive_Gain/gautschi/1/gautschi_launch.py
ADDED
|
@@ -0,0 +1,145 @@
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 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 |
+
if gpu is None and os.path.exists(os.path.join(stack, "mpc_cuda", "libmpc_cuda.so")):
|
| 24 |
+
cuda = ["--env", f"MPC_CUDA_LIB={CUDA_LIB}"] # CPU node: the library's native iLQR solve still loads; its MPPI falls back to numpy
|
| 25 |
+
return ["apptainer", "exec", *cuda, "--contain", "--cleanenv", "--ipc", "--pid", "--net", "--network", "none", "--workdir", work,
|
| 26 |
+
"--env", f"ROS_DOMAIN_ID={i + 1}", "--env", "ROS_HOME=/tmp/ros", "--env", "TMPDIR=/tmp",
|
| 27 |
+
"--bind", f"{engine}:/opt/sim:ro", "--bind", f"{stack}:/ws:ro", "--bind", f"{out}:/out",
|
| 28 |
+
"--bind", f"{ep}:/batch.json:ro", sif, "bash", "-lc", RUN]
|
| 29 |
+
|
| 30 |
+
|
| 31 |
+
def parse_smi(text):
|
| 32 |
+
"""nvidia-smi 'utilization.gpu, memory.used' csv rows to per-GPU lists."""
|
| 33 |
+
rows = [[int(v) for v in ln.split(",")] for ln in text.strip().splitlines() if ln.strip()]
|
| 34 |
+
return {"gpu_util": [r[0] for r in rows], "gpu_mem_mib": [r[1] for r in rows]}
|
| 35 |
+
|
| 36 |
+
|
| 37 |
+
def _solver(logdir):
|
| 38 |
+
"""pathfinder's 'MPC solver: ...' and CUDA fallback lines from the episode's ROS logs."""
|
| 39 |
+
lines = []
|
| 40 |
+
for dp, _, fns in os.walk(logdir):
|
| 41 |
+
for fn in fns:
|
| 42 |
+
try:
|
| 43 |
+
with open(os.path.join(dp, fn), errors="replace") as f:
|
| 44 |
+
lines += [ln.strip() for ln in f if "MPC solver:" in ln or "CUDA MPC unavailable" in ln]
|
| 45 |
+
except OSError:
|
| 46 |
+
pass
|
| 47 |
+
return "\n".join(dict.fromkeys(lines))
|
| 48 |
+
|
| 49 |
+
|
| 50 |
+
def main(argv=None):
|
| 51 |
+
p = argparse.ArgumentParser()
|
| 52 |
+
for a in ("episodes", "out", "sif", "engine", "stack", "work", "timeline", "done"):
|
| 53 |
+
p.add_argument("--" + a, required=True)
|
| 54 |
+
p.add_argument("--workers", type=int, required=True)
|
| 55 |
+
p.add_argument("--gpus", type=int, default=0)
|
| 56 |
+
a = p.parse_args(argv)
|
| 57 |
+
with open(a.episodes) as f:
|
| 58 |
+
q = queue.Queue()
|
| 59 |
+
for e in json.load(f): # the host already ordered them, longest first
|
| 60 |
+
q.put(e)
|
| 61 |
+
os.makedirs(a.out, exist_ok=True)
|
| 62 |
+
lock, t0 = threading.Lock(), time.time()
|
| 63 |
+
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)]
|
| 64 |
+
|
| 65 |
+
def log(**ev):
|
| 66 |
+
with lock, open(a.timeline, "a") as f:
|
| 67 |
+
f.write(json.dumps(dict(ev, t=round(time.time() - t0, 3))) + "\n")
|
| 68 |
+
|
| 69 |
+
def worker(i):
|
| 70 |
+
time.sleep(i * STAGGER_S)
|
| 71 |
+
while True:
|
| 72 |
+
try:
|
| 73 |
+
e = q.get_nowait()
|
| 74 |
+
except queue.Empty:
|
| 75 |
+
return
|
| 76 |
+
key, work = e["key"], os.path.join(a.work, e["key"])
|
| 77 |
+
stage, ep = os.path.join(work, "stage"), os.path.join(work, "ep.json")
|
| 78 |
+
os.makedirs(stage)
|
| 79 |
+
with open(ep, "w") as f:
|
| 80 |
+
json.dump([e], f)
|
| 81 |
+
log(ev="start", key=key, worker=i)
|
| 82 |
+
gpu = devs[i % len(devs)] if devs and e["resolved"].get("recipe", {}).get("sim", {}).get("cuda") else None
|
| 83 |
+
limit = EPISODE_TIMEOUT_S * max(1, e["resolved"]["scenario"].get("laps", 1)) # per lap: 5-lap episodes run 5x longer
|
| 84 |
+
try:
|
| 85 |
+
rc = subprocess.run(worker_cmd(i, work, stage, ep, a.sif, a.engine, a.stack, gpu), stdout=subprocess.DEVNULL,
|
| 86 |
+
stderr=subprocess.DEVNULL, timeout=limit).returncode
|
| 87 |
+
except subprocess.TimeoutExpired:
|
| 88 |
+
rc = "timeout"
|
| 89 |
+
solver = _solver(os.path.join(work, "tmp", "ros", "log"))
|
| 90 |
+
if solver and os.path.isdir(os.path.join(stage, key)):
|
| 91 |
+
with open(os.path.join(stage, key, "solver.txt"), "w") as f:
|
| 92 |
+
f.write(solver + "\n")
|
| 93 |
+
if os.path.exists(os.path.join(stage, key, "outcome.json")):
|
| 94 |
+
tmp, dst = os.path.join(a.out, ".tmp-" + key), os.path.join(a.out, key)
|
| 95 |
+
shutil.rmtree(tmp, ignore_errors=True)
|
| 96 |
+
shutil.copytree(os.path.join(stage, key), tmp)
|
| 97 |
+
shutil.rmtree(dst, ignore_errors=True)
|
| 98 |
+
os.rename(tmp, dst) # the host's rsync sees a key only once its directory is complete
|
| 99 |
+
with lock, open(a.done, "a") as f:
|
| 100 |
+
f.write(key + "\n")
|
| 101 |
+
shutil.rmtree(work, ignore_errors=True)
|
| 102 |
+
log(ev="end", key=key, worker=i, rc=rc, gpu=gpu,
|
| 103 |
+
solver=solver.split("MPC solver: ")[-1] if "MPC solver: " in solver else solver or None)
|
| 104 |
+
|
| 105 |
+
def sampler(stop):
|
| 106 |
+
"""Job CPU use from its cgroup, as busy cores, every SAMPLE_S."""
|
| 107 |
+
try:
|
| 108 |
+
with open("/proc/self/cgroup") as f:
|
| 109 |
+
path = "/sys/fs/cgroup" + f.read().strip().split(":")[-1] + "/cpu.stat"
|
| 110 |
+
prev = None
|
| 111 |
+
while not stop.wait(SAMPLE_S if prev else 0):
|
| 112 |
+
with open(path) as f:
|
| 113 |
+
use = next(int(ln.split()[1]) for ln in f if ln.startswith("usage_usec"))
|
| 114 |
+
now = time.time()
|
| 115 |
+
if prev:
|
| 116 |
+
log(ev="cpu", busy_cores=round((use - prev[0]) / 1e6 / (now - prev[1]), 2))
|
| 117 |
+
prev = (use, now)
|
| 118 |
+
except (OSError, StopIteration, ValueError):
|
| 119 |
+
pass
|
| 120 |
+
|
| 121 |
+
def gpu_sampler(stop):
|
| 122 |
+
"""GPU utilisation and memory of the job's GPUs, every SAMPLE_S."""
|
| 123 |
+
while not stop.wait(SAMPLE_S):
|
| 124 |
+
try:
|
| 125 |
+
p = subprocess.Popen(["nvidia-smi", "--query-gpu=utilization.gpu,memory.used", "--format=csv,noheader,nounits"],
|
| 126 |
+
stdout=subprocess.PIPE, stderr=subprocess.DEVNULL, text=True)
|
| 127 |
+
log(ev="gpu", **parse_smi(p.communicate(timeout=30)[0]))
|
| 128 |
+
except Exception: # no driver or a hung query: skip this sample
|
| 129 |
+
pass
|
| 130 |
+
|
| 131 |
+
cores = len(os.sched_getaffinity(0)) if hasattr(os, "sched_getaffinity") else os.cpu_count()
|
| 132 |
+
log(ev="launch", workers=a.workers, cores=cores)
|
| 133 |
+
stop = threading.Event()
|
| 134 |
+
threads = [threading.Thread(target=worker, args=(i,)) for i in range(a.workers)]
|
| 135 |
+
samplers = [sampler] + ([gpu_sampler] if a.gpus else [])
|
| 136 |
+
for t in threads + [threading.Thread(target=f, args=(stop,), daemon=True) for f in samplers]:
|
| 137 |
+
t.start()
|
| 138 |
+
for t in threads:
|
| 139 |
+
t.join()
|
| 140 |
+
stop.set()
|
| 141 |
+
log(ev="done")
|
| 142 |
+
|
| 143 |
+
|
| 144 |
+
if __name__ == "__main__":
|
| 145 |
+
main()
|
results/jobs/20261105-024128-A26_MPPI___Adaptive_Gain/job.json
ADDED
|
@@ -0,0 +1,45 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"id": "20261105-024128-A26_MPPI___Adaptive_Gain",
|
| 3 |
+
"tier": "dev",
|
| 4 |
+
"tier_version": 2,
|
| 5 |
+
"recipe": {
|
| 6 |
+
"name": "A26 MPPI + Adaptive Gain",
|
| 7 |
+
"source": {
|
| 8 |
+
"repo": "AutonomousKart",
|
| 9 |
+
"ref": "worktree:AutonomousKart-sim"
|
| 10 |
+
},
|
| 11 |
+
"params": {
|
| 12 |
+
"pathfinder_node": {
|
| 13 |
+
"planner": "mpc",
|
| 14 |
+
"mpc.num_samples": 32768,
|
| 15 |
+
"mpc.adaptive_gain.enabled": true
|
| 16 |
+
}
|
| 17 |
+
},
|
| 18 |
+
"sim": {
|
| 19 |
+
"cuda": true,
|
| 20 |
+
"active_imu": "back",
|
| 21 |
+
"use_camera": false,
|
| 22 |
+
"controller_type": "mppi",
|
| 23 |
+
"baseline": true
|
| 24 |
+
},
|
| 25 |
+
"notes": "A26 MPPI sampling rollout stack with adaptive steering gain (SteerGainRLS, 32768 samples, back IMU imu_2, no camera)"
|
| 26 |
+
},
|
| 27 |
+
"user": "shay",
|
| 28 |
+
"notes": "",
|
| 29 |
+
"where": "ideas",
|
| 30 |
+
"status": "done",
|
| 31 |
+
"submitted_at": "2026-11-05T07:41:28.254+00:00",
|
| 32 |
+
"started_at": "2026-11-05T07:41:28.256+00:00",
|
| 33 |
+
"finished_at": "2026-11-05T08:37:57.322+00:00",
|
| 34 |
+
"total": 186,
|
| 35 |
+
"done": 186,
|
| 36 |
+
"errors": 147,
|
| 37 |
+
"build_log": null,
|
| 38 |
+
"workers": 10,
|
| 39 |
+
"slurm_id": "661",
|
| 40 |
+
"slurm_ids": [
|
| 41 |
+
"4624",
|
| 42 |
+
"661"
|
| 43 |
+
],
|
| 44 |
+
"engine_version": "bbd85e0dce441d49ae06ba82dddb973a04f86a9fdd0705c67c5e131c98efe8b5"
|
| 45 |
+
}
|
results/jobs/20261105-024128-A26_MPPI___Adaptive_Gain/summary.json
ADDED
|
@@ -0,0 +1,292 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
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|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
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results/leaderboard/2__bbd85e0dce44.json
CHANGED
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@@ -186,5 +186,99 @@
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| 187 |
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| 190 |
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| 189 |
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| 190 |
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{
|
| 191 |
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|
| 192 |
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|
| 193 |
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|
| 194 |
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| 195 |
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| 273 |
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| 280 |
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| 281 |
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| 282 |
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}
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| 283 |
}
|
| 284 |
]
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