tests/test.sh exit code (0 → resolved); the classification below is post-hoc and cannot change it.cd /app && ls -la && echo "---TASK CONFIG---" && cat task_config.json && echo "---FEATURE SCHEMA---" && cat feature_schema.json
total 15
drwxrws--- 3 h2tagent h2tagent 138 Jun 19 18:00 .
drwxr-xr-x 1 root root 4096 Jun 20 07:18 ..
-rw-rw---- 1 h2tagent h2tagent 2710 Jun 19 17:59 candidate_model.py
drwxrws--- 5 h2tagent h2tagent 58 Jun 19 17:59 data
-rw-rw---- 1 h2tagent h2tagent 1195 Jun 19 17:59 dataset.py
-rw-rw---- 1 h2tagent h2tagent 238 Jun 19 17:59 feature_schema.json
-rw-rw---- 1 h2tagent h2tagent 633 Jun 19 17:59 task_config.json
-rw-rw---- 1 h2tagent h2tagent 3184 Jun 19 17:59 train.py
---TASK CONFIG---
{
"task_id": "ks-equation-1d-forecast",
"dataset_name": "ks-equation-1d-chaotic",
"feature_format": "trajectory-window-k-frames",
"label_format": "next-frame",
"grid_size": 128,
"in_channels": 4,
"out_channels": 1,
"rollout_horizon": 20,
"seed": 20260517,
"model": {
"width": 32,
"modes": 16,
"num_layers": 4
},
"training": {
"epochs": 10,
"batch_size": 64,
"lr": 0.001,
"weight_decay": 0.0001,
"loss": "MSE"
},
"evaluation": {
"primary_metric": "rollout_rel_l2_score",
"secondary_metrics": [
"rollout_rmse_score",
"final_step_rel_l2_score"
]
}
}
---FEATURE SCHEMA---
{
"domain": "1d-periodic",
"grid_size": 128,
"domain_length": 22.0,
"dt_effective": 0.25,
"input": {"channels": 4, "description": "last k=4 frames of u(x,t)"},
"output": {"channels": 1, "description": "next frame u(x,t+1)"}
}cd /app && echo "===== candidate_model.py =====" && cat candidate_model.py && echo "===== dataset.py =====" && cat dataset.py && echo "===== train.py =====" && cat train.py
===== candidate_model.py =====
from __future__ import annotations
from typing import Mapping
import torch
import torch.nn as nn
class SpectralConv1d(nn.Module):
def __init__(self, in_channels: int, out_channels: int, modes: int) -> None:
super().__init__()
self.in_channels = in_channels
self.out_channels = out_channels
self.modes = modes
scale = 1.0 / (in_channels * out_channels)
self.weight = nn.Parameter(
scale * torch.randn(in_channels, out_channels, modes, dtype=torch.cfloat)
)
@staticmethod
def _mul1d(inp: torch.Tensor, weight: torch.Tensor) -> torch.Tensor:
return torch.einsum("bix,iox->box", inp, weight)
def forward(self, x: torch.Tensor) -> torch.Tensor:
b, _, n = x.shape
x_ft = torch.fft.rfft(x, norm="ortho")
out_ft = torch.zeros(b, self.out_channels, n // 2 + 1, dtype=torch.cfloat, device=x.device)
m = min(self.modes, x_ft.shape[-1])
out_ft[:, :, :m] = self._mul1d(x_ft[:, :, :m], self.weight[:, :, :m])
return torch.fft.irfft(out_ft, n=n, norm="ortho")
class FNO1dBlock(nn.Module):
def __init__(self, width: int, modes: int) -> None:
super().__init__()
self.spectral = SpectralConv1d(width, width, modes)
self.bypass = nn.Conv1d(width, width, 1)
self.act = nn.GELU()
def forward(self, x: torch.Tensor) -> torch.Tensor:
return self.act(self.spectral(x) + self.bypass(x))
class FNO1d(nn.Module):
def __init__(self, in_channels: int, out_channels: int, width: int, modes: int, num_layers: int, grid_size: int) -> None:
super().__init__()
self.lift = nn.Conv1d(in_channels + 1, width, 1)
self.blocks = nn.ModuleList(FNO1dBlock(width, modes) for _ in range(num_layers))
self.proj = nn.Sequential(
nn.Conv1d(width, width, 1),
nn.GELU(),
nn.Conv1d(width, out_channels, 1),
)
self.register_buffer("_grid", torch.linspace(0.0, 1.0, grid_size + 1)[:grid_size].view(1, 1, grid_size), persistent=False)
def forward(self, x: torch.Tensor) -> torch.Tensor:
b = x.shape[0]
grid = self._grid.expand(b, -1, -1).to(x.device, dtype=x.dtype)
x = torch.cat([x, grid], dim=1)
x = self.lift(x)
for block in self.blocks:
x = block(x)
return self.proj(x)
def build_model(config: Mapping) -> FNO1d:
m = config["model"]
return FNO1d(
in_channels=int(config["in_channels"]),
out_channels=int(config["out_channels"]),
width=int(m["width"]),
modes=int(m["modes"]),
num_layers=int(m["num_layers"]),
grid_size=int(config["grid_size"]),
)
===== dataset.py =====
from __future__ import annotations
from pathlib import Path
import torch
from torch.utils.data import Dataset
class KSForecast(Dataset):
def __init__(self, split_dir: str | Path, k: int = 4) -> None:
self.dir = Path(split_dir)
self.k = int(k)
payload = torch.load(self.dir / "trajectories.pt", map_location="cpu", weights_only=True)
self.trajectories: torch.Tensor = payload["trajectories"].float()
n_traj, t_stored, _ = self.trajectories.shape
if t_stored <= self.k:
raise ValueError(f"trajectories too short for k={self.k}: t_stored={t_stored}")
self._index = [(i, t) for i in range(n_traj) for t in range(self.k, t_stored)]
def __len__(self) -> int:
return len(self._index)
def __getitem__(self, idx: int) -> dict:
i, t = self._index[idx]
return {
"window": self.trajectories[i, t - self.k : t],
"next_frame": self.trajectories[i, t : t + 1],
}
def compute_standardization(trajectories: torch.Tensor) -> tuple[float, float]:
mean = float(trajectories.mean().item())
std = float(trajectories.std().clamp_min(1e-6).item())
return mean, std
===== train.py =====
from __future__ impo
…[truncated]cd /app && python3 -c "
import torch, time
print('torch', torch.__version__)
print('cuda available:', torch.cuda.is_available())
if torch.cuda.is_available():
print('device:', torch.cuda.get_device_name(0))
import os
print('CPU count:', os.cpu_count())
" && echo "---" && nvidia-smi 2>/dev/null | head -15 || echo "no nvidia-smi"torch 2.5.1+cu124 cuda available: True device: NVIDIA H100 80GB HBM3 CPU count: 3 --- Sat Jun 20 07:18:36 2026 +-----------------------------------------------------------------------------------------+ | NVIDIA-SMI 580.82.07 Driver Version: 580.82.07 CUDA Version: 13.0 | +-----------------------------------------+------------------------+----------------------+ | GPU Name Persistence-M | Bus-Id Disp.A | Volatile Uncorr. ECC | | Fan Temp Perf Pwr:Usage/Cap | Memory-Usage | GPU-Util Compute M. | | | | MIG M. | |=========================================+========================+======================| | 0 NVIDIA H100 80GB HBM3 Off | N/A Off | 0 | | N/A 48C P0 312W / 700W | 7723MiB / 81559MiB | 46% Default | | | | Disabled | +-----------------------------------------+------------------------+----------------------+ +-----------------------------------------------------------------------------------------+ | Processes: |
cd /app && python3 -c "
import torch
for split in ['train','val','public_test']:
p = f'data/{split}/trajectories.pt'
payload = torch.load(p, map_location='cpu', weights_only=True)
print(split, 'keys:', list(payload.keys()))
t = payload['trajectories'].float()
print(' shape:', tuple(t.shape), 'dtype:', t.dtype)
print(' mean: %.5f std: %.5f min: %.3f max: %.3f' % (t.mean(), t.std(), t.min(), t.max()))
"train keys: ['trajectories'] shape: (200, 100, 128) dtype: torch.float32 mean: -0.00000 std: 1.15997 min: -3.245 max: 3.248 val keys: ['trajectories'] shape: (32, 100, 128) dtype: torch.float32 mean: -0.00000 std: 1.13391 min: -3.197 max: 3.210 public_test keys: ['trajectories'] shape: (16, 100, 128) dtype: torch.float32 mean: 0.00000 std: 1.22519 min: -3.172 max: 3.183
cd /app && python3 -c "
import torch
t = torch.load('data/train/trajectories.pt', weights_only=True)['trajectories'].float()
# relative L2 between consecutive frames
def rel_l2(a,b): return (((a-b)**2).sum(-1).sqrt() / (b**2).sum(-1).sqrt().clamp_min(1e-8)).mean()
print('consecutive frame rel_l2 (step 1):', rel_l2(t[:,1:],t[:,:-1]).item())
print('step 5 apart rel_l2:', rel_l2(t[:,5:],t[:,:-5]).item())
print('step 20 apart rel_l2:', rel_l2(t[:,20:],t[:,:-20]).item())
# persistence baseline over 20-step rollout: predict frame stays same
# naive: if model predicts identity (next=last), what's rollout error
# energy spectrum
uf = torch.fft.rfft(t[0,0])
print('num freq modes:', uf.shape)
import numpy as np
mag = t.abs().mean(0).mean(0)
print('mean |u| over x shape:', mag.shape)
# check per-traj mean/std
print('per-traj mean range:', t.mean(dim=(1,2)).min().item(), t.mean(dim=(1,2)).max().item())
print('per-traj std range:', t.std(dim=(1,2)).min().item(), t.std(dim=(1,2)).max().item())
"consecutive frame rel_l2 (step 1): 0.03802228718996048 step 5 apart rel_l2: 0.18893802165985107 step 20 apart rel_l2: 0.6709074378013611 num freq modes: torch.Size([65]) mean |u| over x shape: torch.Size([128]) per-traj mean range: -3.4809113458322827e-07 6.71148313813319e-07 per-traj std range: 0.9273907542228699 1.8977771997451782
cd /app && python3 -c "
import torch
from candidate_model import build_model
import json
cfg = json.load(open('task_config.json'))
m = build_model(cfg)
n = sum(p.numel() for p in m.parameters())
print('param count:', n)
x = torch.randn(64,4,128)
import time
m.eval()
with torch.no_grad():
for _ in range(3): m(x)
t0=time.time()
for _ in range(50): m(x)
print('cpu 50 fwd (bs64):', time.time()-t0)
"param count: 71041 cpu 50 fwd (bs64): 0.8072891235351562
cd /app && TASK_EPOCHS_OVERRIDE=2 python3 train.py 2>&1 | tail -10
[train] device=cuda epochs=2 pairs=19200 [train] epoch=1/2 mse=6.0238e-02 val_rel_l2=0.0136 [train] epoch=2/2 mse=1.6068e-04 val_rel_l2=0.0088 [train] wrote /app/outputs/model.pt
cd /app && cat > eval_rollout.py << 'EOF'
import torch, json, sys
from candidate_model import build_model
def load_model(path, cfg, device):
ckpt = torch.load(path, map_location=device, weights_only=True)
m = build_model(cfg).to(device)
m.load_state_dict(ckpt['state_dict'])
m.eval()
return m, ckpt['feat_mean'], ckpt['feat_std']
@torch.no_grad()
def rollout_eval(model, traj, mean, std, k=4, horizon=20, start=0, device='cuda'):
# traj: [N, T, X]; seed frames [start:start+k], predict next horizon frames
traj = traj.to(device)
N = traj.shape[0]
window = (traj[:, start:start+k] - mean) / std # [N,k,X] normalized
preds = []
for h in range(horizon):
out = model(window) # [N,1,X] normalized
preds.append(out)
window = torch.cat([window[:, 1:], out], dim=1)
pred = torch.cat(preds, dim=1) * std + mean # [N,horizon,X]
gt = traj[:, start+k:start+k+horizon] # [N,horizon,X]
# per-step rel L2 averaged over batch
num = (pred-gt).pow(2).sum(-1).sqrt()
den = gt.pow(2).sum(-1).sqrt().clamp_min(1e-8)
rel = (num/den) # [N,horizon]
return rel, pred, gt
if __name__ == '__main__':
cfg = json.load(open('task_config.json'))
device = 'cuda' if torch.cuda.is_available() else 'cpu'
path = sys.argv[1] if len(sys.argv)>1 else 'outputs/model.pt'
split = sys.argv[2] if len(sys.argv)>2 else 'val'
traj = torch.load(f'data/{split}/trajectories.pt', weights_only=True)['trajectories'].float()
model, mean, std = load_model(path, cfg, device)
rel, pred, gt = rollout_eval(model, traj, mean, std, start=0)
per_step = rel.mean(0)
print(f'[{split}] rollout mean rel_l2 over 20 steps: {rel.mean().item():.4f}')
print(f'[{split}] final-step (20) rel_l2: {per_step[-1].item():.4f}')
print('per-step rel_l2:', ' '.join(f'{v:.3f}' for v in per_step.tolist()))
# rmse
rmse = (pred-gt).pow(2).mean().sqrt().item()
print(f'[{split}] rollout rmse: {rmse:.4f}')
EOF
python3 eval_rollout.py outputs/model.pt val[val] rollout mean rel_l2 over 20 steps: 0.0311 [val] final-step (20) rel_l2: 0.0543 per-step rel_l2: 0.009 0.011 0.013 0.015 0.018 0.020 0.023 0.025 0.028 0.030 0.033 0.035 0.037 0.039 0.041 0.043 0.046 0.048 0.051 0.054 [val] rollout rmse: 0.0505
cd /app && python3 train.py 2>&1 | tail -12 && echo "=== ROLLOUT EVAL (start=0) ===" && python3 eval_rollout.py outputs/model.pt val
[train] device=cuda epochs=10 pairs=19200 [train] epoch=1/10 mse=6.0238e-02 val_rel_l2=0.0136 [train] epoch=2/10 mse=1.3373e-04 val_rel_l2=0.0076 [train] epoch=3/10 mse=6.1280e-05 val_rel_l2=0.0058 [train] epoch=4/10 mse=3.6445e-05 val_rel_l2=0.0046 [train] epoch=5/10 mse=2.4894e-05 val_rel_l2=0.0038 [train] epoch=6/10 mse=1.8479e-05 val_rel_l2=0.0034 [train] epoch=7/10 mse=1.4761e-05 val_rel_l2=0.0032 [train] epoch=8/10 mse=1.2601e-05 val_rel_l2=0.0030 [train] epoch=9/10 mse=1.1432e-05 val_rel_l2=0.0029 [train] epoch=10/10 mse=1.0889e-05 val_rel_l2=0.0028 [train] wrote /app/outputs/model.pt === ROLLOUT EVAL (start=0) === [val] rollout mean rel_l2 over 20 steps: 0.0099 [val] final-step (20) rel_l2: 0.0192 per-step rel_l2: 0.003 0.003 0.004 0.005 0.006 0.006 0.007 0.008 0.008 0.009 0.010 0.010 0.011 0.012 0.013 0.014 0.016 0.017 0.018 0.019 [val] rollout rmse: 0.0171
cd /app && python3 -c "
import torch, json
from eval_rollout import load_model, rollout_eval
cfg = json.load(open('task_config.json'))
device='cuda'
model, mean, std = load_model('outputs/model.pt', cfg, device)
for split in ['val','public_test']:
traj = torch.load(f'data/{split}/trajectories.pt', weights_only=True)['trajectories'].float()
for start in [0, 10, 40, 76]:
rel,_,_ = rollout_eval(model, traj, mean, std, start=start)
print(f'{split} start={start:3d} mean={rel.mean().item():.4f} final={rel.mean(0)[-1].item():.4f}')
"val start= 0 mean=0.0099 final=0.0192 val start= 10 mean=0.0098 final=0.0177 val start= 40 mean=0.0095 final=0.0183 val start= 76 mean=0.0093 final=0.0181 public_test start= 0 mean=0.0082 final=0.0149 public_test start= 10 mean=0.0077 final=0.0162 public_test start= 40 mean=0.0069 final=0.0125 public_test start= 76 mean=0.0071 final=0.0136
cd /app && python3 -c "
import torch
t = torch.load('data/train/trajectories.pt', weights_only=True)['trajectories'].float()
# Check zero spatial mean invariant
print('max abs spatial mean per frame:', t.mean(-1).abs().max().item())
# Translation invariance is structural (periodic grid). Check reflection symmetry statistically:
# If u(x,t) solution then -flip(u) should have same statistical distribution. Can't verify exactly w/o simulating,
# but we can verify the data distribution is symmetric under it by comparing moments.
u = t.reshape(-1,128)
refl = -torch.flip(u, dims=[-1])
print('orig skew (mean u^3):', (u**3).mean().item(), ' refl skew:', (refl**3).mean().item())
# spectrum preserved under flip+sign
import torch.fft as fft
p1 = fft.rfft(u,dim=-1).abs().mean(0)
p2 = fft.rfft(refl,dim=-1).abs().mean(0)
print('spectrum max abs diff orig vs refl:', (p1-p2).abs().max().item())
"max abs spatial mean per frame: 7.82310962677002e-07 orig skew (mean u^3): 0.01211780495941639 refl skew: -0.012117799371480942 spectrum max abs diff orig vs refl: 9.5367431640625e-07
command: create
path: /app/train_improved.py
file_text: from __future__ import annotations
import json
import os
import sys
import copy
import math
from pathlib import Path
import torch
import torch.nn.functional as F
APP_DIR = Path(os.environ.get("APP_DIR", "/app"))
sys.path.insert(0, str(Path(__file__).resolve().parent))
from candidate_model import build_model
from dataset import KSForecast, compute_standardization
def getenv_f(name, default):
return float(os.environ.get(name, default))
def getenv_i(name, default):
return int(os.environ.get(name, default))
# ----------------------- augmentation -----------------------
def augment(frames: torch.Tensor, do_shift: bool, do_reflect: bool) -> torch.Tensor:
"""frames: [B, F, X]. Apply per-sample KS symmetries consistently across the F axis.
- periodic cyclic shift along x (translation invariance)
- reflection u(x) -> -u(-x) implemented as -flip(x) (valid up to a translation)
"""
B, Fr, X = frames.shape
if do_shift:
shifts = torch.randint(0, X, (B,), device=frames.device)
ar = torch.arange(X, device=frames.device).view(1, X)
idx = (ar - shifts.view(B, 1)) % X # [B, X]
idx = idx.view(B, 1, X).expand(B, Fr, X)
frames = torch.gather(frames, 2, idx)
if do_reflect:
mask = (torch.rand(B, device=frames.device) < 0.5)
if mask.any():
refl = -torch.flip(frames, dims=[-1])
m = mask.view(B, 1, 1)
frames = torch.where(m, refl, frames)
return frames
# ----------------------- loss -----------------------
def rollout_rel_l2(pred: torch.Tensor, gt: torch.Tensor) -> torch.Tensor:
"""pred, gt: [B, R, X] in physical units. Mean over batch & steps of per-(b,step) rel L2."""
num = (pred - gt).pow(2).sum(-1).sqrt()
den = gt.pow(2).sum(-1).sqrt().clamp_min(1e-8)
return (num / den).mean()
@torch.no_grad()
def eval_rollout(model, traj, mean, std, k=4, horizon=20, starts=(0,), device="cuda"):
model.eval()
traj = traj.to(device)
rels = []
for start in starts:
window = (traj[:, start:start + k] - mean) / std
preds = []
for _ in range(horizon):
out = model(window)
preds.append(out)
window = torch.cat([window[:, 1:], out], dim=1)
pred = torch.cat(preds, dim=1) * std + mean
gt = traj[:, start + k:start + k + horizon]
num = (pred - gt).pow(2).sum(-1).sqrt()
den = gt.pow(2).sum(-1).sqrt().clamp_min(1e-8)
rels.append((num / den))
rel = torch.cat(rels, dim=0)
return rel.mean().item(), rel.mean(0)[-1].item()
class EMA:
def __init__(self, model, decay):
self.decay = decay
self.shadow = copy.deepcopy(model.state_dict())
def update(self, model):
d = self.decay
for k, v in model.state_dict().items():
s = self.shadow[k]
if v.dtype.is_floating_point or v.is_complex():
s.mul_(d).add_(v.detach(), alpha=1 - d)
else:
s.copy_(v)
def copy_to(self, model):
model.load_state_dict(self.shadow, strict=True)
def main():
with (APP_DIR / "task_config.json").open() as fh:
cfg = json.load(fh)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
seed = getenv_i("SEED", cfg["seed"])
torch.manual_seed(seed)
k = int(cfg["in_channels"])
train_ds = KSForecast(APP_DIR / "data" / "train", k=k)
val_ds = KSForecast(APP_DIR / "data" / "val", k=k)
feat_mean, feat_std = compute_standardization(train_ds.trajectories)
traj = train_ds.trajectories.to(device) # [N, T, X]
val_traj = val_ds.trajectories # keep cpu, moved in eval
N, T, X = traj.shape
# hyperparams
epochs = getenv_i("TASK_EPOCHS_OVERRIDE", getenv_i("EPOCHS", 60))
batch_size = getenv_i("BATCH", 128)
steps_per_epoch = getenv_i("STEPS_PER_EPOCH", 300)
lr = getenv_f("LR",
…[truncated]New file created successfully at /app/train_improved.py
cd /app && EPOCHS=4 WARM_EPOCHS=1 CURR_EPOCHS=2 RMAX=6 STEPS_PER_EPOCH=150 OUT=smoke.pt python3 train_improved.py 2>&1 | tail -8
[train] device=cuda epochs=4 Rmax=6 bs=128 spe=150 lr=0.002 noise=0.01 aug(shift=True,refl=True) loss=rel ema=0.999 [train] ep=1/4 R=1 loss=2.2282e-01 val_roll_mean=1.0116 val_final=1.0124 best=1.0116 lr=1.79e-03 [train] ep=2/4 R=4 loss=2.3128e-02 val_roll_mean=1.0073 val_final=1.0099 best=1.0073 lr=1.08e-03 [train] ep=3/4 R=6 loss=2.2657e-02 val_roll_mean=1.0017 val_final=1.0079 best=1.0017 lr=3.23e-04 [train] ep=4/4 R=6 loss=8.0519e-03 val_roll_mean=0.9945 val_final=1.0062 best=0.9945 lr=0.00e+00 [train] wrote /app/outputs/smoke.pt best_val_roll_mean=0.9945
command: str_replace
path: /app/train_improved.py
old_str: class EMA:
def __init__(self, model, decay):
self.decay = decay
self.shadow = copy.deepcopy(model.state_dict())
def update(self, model):
d = self.decay
for k, v in model.state_dict().items():
new_str: class EMA:
def __init__(self, model, decay):
self.decay = decay
self.shadow = copy.deepcopy(model.state_dict())
self.n = 0
def update(self, model):
self.n += 1
# EMA warmup: effective decay ramps up so early random weights don't dominate
d = min(self.decay, (1 + self.n) / (10 + self.n))
for k, v in model.state_dict().items():The file /app/train_improved.py has been edited successfully.
command: str_replace
path: /app/train_improved.py
old_str: # eval with EMA weights
backup = copy.deepcopy(model.state_dict())
ema.copy_to(model)
vmean, vfinal = eval_rollout(model, val_traj, feat_mean, feat_std,
k=k, horizon=20, starts=(0, 20, 40, 60), device=device)
if vmean < best:
best = vmean
best_state = copy.deepcopy(model.state_dict())
model.load_state_dict(backup)
print(f"[train] ep={ep+1}/{epochs} R={R} loss={running/steps_per_epoch:.4e} "
f"val_roll_mean={vmean:.4f} val_final={vfinal:.4f} best={best:.4f} "
f"lr={sched.get_last_lr()[0]:.2e}", flush=True)
new_str: # eval RAW weights
backup = copy.deepcopy(model.state_dict())
rmean, rfinal = eval_rollout(model, val_traj, feat_mean, feat_std,
k=k, horizon=20, starts=(0, 20, 40, 60), device=device)
tag = "raw"
if rmean < best:
best = rmean; best_state = copy.deepcopy(backup)
# eval EMA weights
ema.copy_to(model)
vmean, vfinal = eval_rollout(model, val_traj, feat_mean, feat_std,
k=k, horizon=20, starts=(0, 20, 40, 60), device=device)
if vmean < best:
best = vmean; best_state = copy.deepcopy(model.state_dict()); tag = "ema"
model.load_state_dict(backup)
print(f"[train] ep={ep+1}/{epochs} R={R} loss={running/steps_per_epoch:.4e} "
f"raw={rmean:.4f}/{rfinal:.4f} ema={vmean:.4f}/{vfinal:.4f} "
f"best={best:.4f}({tag}) lr={sched.get_last_lr()[0]:.2e}", flush=True)The file /app/train_improved.py has been edited successfully.
cd /app && EPOCHS=4 WARM_EPOCHS=1 CURR_EPOCHS=2 RMAX=6 STEPS_PER_EPOCH=150 OUT=smoke.pt python3 train_improved.py 2>&1 | tail -6
[train] device=cuda epochs=4 Rmax=6 bs=128 spe=150 lr=0.002 noise=0.01 aug(shift=True,refl=True) loss=rel ema=0.999 [train] ep=1/4 R=1 loss=2.2282e-01 raw=0.0749/0.1180 ema=0.0367/0.0628 best=0.0367(ema) lr=1.79e-03 [train] ep=2/4 R=4 loss=2.3128e-02 raw=0.0461/0.0842 ema=0.0206/0.0352 best=0.0206(ema) lr=1.08e-03 [train] ep=3/4 R=6 loss=2.2657e-02 raw=0.0274/0.0558 ema=0.0156/0.0267 best=0.0156(ema) lr=3.23e-04 [train] ep=4/4 R=6 loss=8.0519e-03 raw=0.0129/0.0221 ema=0.0132/0.0227 best=0.0129(raw) lr=0.00e+00 [train] wrote /app/outputs/smoke.pt best_val_roll_mean=0.0129
cd /app && EPOCHS=1 WARM_EPOCHS=0 CURR_EPOCHS=0 RMAX=12 STEPS_PER_EPOCH=300 OUT=timing.pt python3 -c "
import time, os
os.environ.setdefault('EPOCHS','1')
t0=time.time()
import train_improved
train_improved.main()
print('ELAPSED %.1fs'%(time.time()-t0))
" 2>&1 | tail -4[train] device=cuda epochs=1 Rmax=12 bs=128 spe=300 lr=0.002 noise=0.01 aug(shift=True,refl=True) loss=rel ema=0.999 [train] ep=1/1 R=12 loss=1.6127e-01 raw=0.0275/0.0434 ema=0.0280/0.0440 best=0.0275(raw) lr=0.00e+00 [train] wrote /app/outputs/timing.pt best_val_roll_mean=0.0275 ELAPSED 21.1s
cd /app && EPOCHS=60 STEPS_PER_EPOCH=300 RMAX=14 WARM_EPOCHS=3 CURR_EPOCHS=22 NOISE=0.01 LR=2e-3 EMA=0.999 OUT=run1.pt nohup python3 train_improved.py > run1.log 2>&1 & echo "launched PID $!"; sleep 60; echo "=== progress after 60s ==="; tail -6 run1.log
launched PID 259 === progress after 60s === [train] ep=4/60 R=2 loss=2.9098e-02 raw=0.2023/0.3856 ema=0.0297/0.0553 best=0.0186(raw) lr=2.00e-03 [train] ep=5/60 R=2 loss=2.0475e-02 raw=0.0450/0.0705 ema=0.0164/0.0298 best=0.0164(ema) lr=1.99e-03 [train] ep=6/60 R=3 loss=1.8003e-02 raw=0.0333/0.0545 ema=0.0130/0.0210 best=0.0130(ema) lr=1.99e-03 [train] ep=7/60 R=3 loss=1.8516e-02 raw=0.0771/0.1633 ema=0.0127/0.0215 best=0.0127(ema) lr=1.98e-03 [train] ep=8/60 R=4 loss=2.6168e-02 raw=0.0424/0.0701 ema=0.0145/0.0268 best=0.0127(raw) lr=1.96e-03 [train] ep=9/60 R=5 loss=1.6070e-02 raw=0.0293/0.0514 ema=0.0108/0.0181 best=0.0108(ema) lr=1.95e-03
cd /app && sleep 150; tail -10 run1.log
[train] ep=12/60 R=6 loss=2.1517e-02 raw=0.0298/0.0486 ema=0.0125/0.0235 best=0.0095(raw) lr=1.88e-03 [train] ep=13/60 R=7 loss=1.3949e-02 raw=0.0167/0.0258 ema=0.0102/0.0178 best=0.0095(raw) lr=1.85e-03 [train] ep=14/60 R=8 loss=1.3579e-02 raw=0.0214/0.0335 ema=0.0085/0.0139 best=0.0085(ema) lr=1.82e-03 [train] ep=15/60 R=8 loss=1.2994e-02 raw=0.0183/0.0318 ema=0.0075/0.0118 best=0.0075(ema) lr=1.79e-03 [train] ep=16/60 R=9 loss=1.3388e-02 raw=0.0189/0.0288 ema=0.0069/0.0106 best=0.0069(ema) lr=1.75e-03 [train] ep=17/60 R=9 loss=1.2695e-02 raw=0.0197/0.0321 ema=0.0065/0.0098 best=0.0065(ema) lr=1.72e-03 [train] ep=18/60 R=10 loss=2.3512e-02 raw=0.0250/0.0408 ema=0.0072/0.0113 best=0.0065(raw) lr=1.68e-03 [train] ep=19/60 R=10 loss=1.6571e-02 raw=0.0151/0.0243 ema=0.0075/0.0117 best=0.0065(raw) lr=1.64e-03 [train] ep=20/60 R=11 loss=2.1278e-02 raw=0.0713/0.1406 ema=0.0075/0.0119 best=0.0065(raw) lr=1.59e-03 [train] ep=21/60 R=12 loss=2.3453e-02 raw=0.0173/0.0310 ema=0.0082/0.0131 best=0.0065(raw) lr=1.55e-03
cd /app && sleep 240; tail -12 run1.log
[train] ep=23/60 R=13 loss=1.3515e-02 raw=0.0274/0.0545 ema=0.0070/0.0107 best=0.0065(raw) lr=1.45e-03 [train] ep=24/60 R=13 loss=3.4391e-02 raw=0.0525/0.1186 ema=0.0080/0.0127 best=0.0065(raw) lr=1.40e-03 [train] ep=25/60 R=14 loss=1.4808e-02 raw=0.0112/0.0173 ema=0.0081/0.0129 best=0.0065(raw) lr=1.35e-03 [train] ep=26/60 R=14 loss=1.0464e-02 raw=0.0109/0.0158 ema=0.0074/0.0116 best=0.0065(raw) lr=1.30e-03 [train] ep=27/60 R=14 loss=9.9992e-03 raw=0.0104/0.0158 ema=0.0067/0.0103 best=0.0065(raw) lr=1.25e-03 [train] ep=28/60 R=14 loss=9.7824e-03 raw=0.0129/0.0236 ema=0.0061/0.0092 best=0.0061(ema) lr=1.19e-03 [train] ep=29/60 R=14 loss=1.0119e-02 raw=0.0093/0.0147 ema=0.0054/0.0081 best=0.0054(ema) lr=1.14e-03 [train] ep=30/60 R=14 loss=9.3086e-03 raw=0.0098/0.0164 ema=0.0050/0.0073 best=0.0050(ema) lr=1.08e-03 [train] ep=31/60 R=14 loss=1.0463e-02 raw=0.0127/0.0244 ema=0.0046/0.0068 best=0.0046(ema) lr=1.03e-03 [train] ep=32/60 R=14 loss=8.7848e-03 raw=0.0099/0.0164 ema=0.0043/0.0063 best=0.0043(ema) lr=9.72e-04 [train] ep=33/60 R=14 loss=1.1561e-02 raw=0.0077/0.0128 ema=0.0041/0.0060 best=0.0041(ema) lr=9.17e-04 [train] ep=34/60 R=14 loss=7.4721e-03 raw=0.0074/0.0117 ema=0.0039/0.0056 best=0.0039(ema) lr=8.63e-04
cd /app && sleep 270; tail -14 run1.log
[train] ep=35/60 R=14 loss=7.3357e-03 raw=0.0095/0.0176 ema=0.0037/0.0053 best=0.0037(ema) lr=8.08e-04 [train] ep=36/60 R=14 loss=1.3481e-02 raw=0.0148/0.0296 ema=0.0036/0.0052 best=0.0036(ema) lr=7.55e-04 [train] ep=37/60 R=14 loss=9.3481e-03 raw=0.0063/0.0098 ema=0.0034/0.0050 best=0.0034(ema) lr=7.01e-04 [train] ep=38/60 R=14 loss=6.1752e-03 raw=0.0055/0.0081 ema=0.0033/0.0049 best=0.0033(ema) lr=6.49e-04 [train] ep=39/60 R=14 loss=5.8573e-03 raw=0.0051/0.0074 ema=0.0032/0.0046 best=0.0032(ema) lr=5.98e-04 [train] ep=40/60 R=14 loss=5.5851e-03 raw=0.0055/0.0086 ema=0.0030/0.0043 best=0.0030(ema) lr=5.48e-04 [train] ep=41/60 R=14 loss=5.3636e-03 raw=0.0048/0.0078 ema=0.0028/0.0041 best=0.0028(ema) lr=5.00e-04 [train] ep=42/60 R=14 loss=5.2129e-03 raw=0.0052/0.0091 ema=0.0027/0.0038 best=0.0027(ema) lr=4.53e-04 [train] ep=43/60 R=14 loss=6.6152e-03 raw=0.0047/0.0073 ema=0.0026/0.0036 best=0.0026(ema) lr=4.08e-04 [train] ep=44/60 R=14 loss=4.5526e-03 raw=0.0042/0.0067 ema=0.0024/0.0033 best=0.0024(ema) lr=3.64e-04 [train] ep=45/60 R=14 loss=4.3624e-03 raw=0.0037/0.0060 ema=0.0023/0.0031 best=0.0023(ema) lr=3.23e-04 [train] ep=46/60 R=14 loss=4.2090e-03 raw=0.0033/0.0051 ema=0.0022/0.0030 best=0.0022(ema) lr=2.83e-04 [train] ep=47/60 R=14 loss=4.0464e-03 raw=0.0037/0.0061 ema=0.0021/0.0029 best=0.0021(ema) lr=2.46e-04 [train] ep=48/60 R=14 loss=3.9151e-03 raw=0.0030/0.0049 ema=0.0020/0.0027 best=0.0020(ema) lr=2.11e-04
cd /app && sleep 220; tail -13 run1.log
[train] ep=47/60 R=14 loss=4.0464e-03 raw=0.0037/0.0061 ema=0.0021/0.0029 best=0.0021(ema) lr=2.46e-04 [train] ep=48/60 R=14 loss=3.9151e-03 raw=0.0030/0.0049 ema=0.0020/0.0027 best=0.0020(ema) lr=2.11e-04 [train] ep=49/60 R=14 loss=3.8450e-03 raw=0.0039/0.0070 ema=0.0019/0.0026 best=0.0019(ema) lr=1.78e-04 [train] ep=50/60 R=14 loss=3.9305e-03 raw=0.0025/0.0047 ema=0.0018/0.0025 best=0.0018(ema) lr=1.48e-04 [train] ep=51/60 R=14 loss=3.4196e-03 raw=0.0023/0.0036 ema=0.0017/0.0024 best=0.0017(ema) lr=1.21e-04 [train] ep=52/60 R=14 loss=3.3310e-03 raw=0.0021/0.0032 ema=0.0017/0.0023 best=0.0017(ema) lr=9.56e-05 [train] ep=53/60 R=14 loss=3.1554e-03 raw=0.0019/0.0029 ema=0.0017/0.0022 best=0.0017(ema) lr=7.35e-05 [train] ep=54/60 R=14 loss=3.1124e-03 raw=0.0017/0.0024 ema=0.0016/0.0022 best=0.0016(ema) lr=5.42e-05 [train] ep=55/60 R=14 loss=3.0735e-03 raw=0.0017/0.0023 ema=0.0016/0.0022 best=0.0016(ema) lr=3.77e-05 [train] ep=56/60 R=14 loss=3.0363e-03 raw=0.0016/0.0022 ema=0.0016/0.0021 best=0.0016(ema) lr=2.42e-05 [train] ep=57/60 R=14 loss=2.9998e-03 raw=0.0015/0.0022 ema=0.0015/0.0021 best=0.0015(ema) lr=1.36e-05 [train] ep=58/60 R=14 loss=2.9941e-03 raw=0.0015/0.0021 ema=0.0015/0.0020 best=0.0015(raw) lr=6.07e-06 [train] ep=59/60 R=14 loss=2.9788e-03 raw=0.0015/0.0020 ema=0.0015/0.0020 best=0.0015(raw) lr=1.52e-06
cd /app && sleep 10; tail -3 run1.log; echo "=== full rollout eval of run1.pt ==="; python3 eval_rollout.py outputs/run1.pt val; echo; python3 eval_rollout.py outputs/run1.pt public_test
[train] ep=59/60 R=14 loss=2.9788e-03 raw=0.0015/0.0020 ema=0.0015/0.0020 best=0.0015(raw) lr=1.52e-06 [train] ep=60/60 R=14 loss=2.9764e-03 raw=0.0015/0.0020 ema=0.0015/0.0020 best=0.0015(raw) lr=0.00e+00 [train] wrote /app/outputs/run1.pt best_val_roll_mean=0.0015 === full rollout eval of run1.pt === [val] rollout mean rel_l2 over 20 steps: 0.0016 [val] final-step (20) rel_l2: 0.0021 per-step rel_l2: 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.002 0.002 0.002 0.002 0.002 0.002 0.002 0.002 0.002 0.002 0.002 0.002 0.002 [val] rollout rmse: 0.0027 [public_test] rollout mean rel_l2 over 20 steps: 0.0016 [public_test] final-step (20) rel_l2: 0.0021 per-step rel_l2: 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.002 0.002 0.002 0.002 0.002 0.002 0.002 0.002 0.002 0.002 0.002 0.002 [public_test] rollout rmse: 0.0039
cd /app && python3 -c "
import torch, json
from eval_rollout import load_model, rollout_eval
cfg = json.load(open('task_config.json'))
device='cuda'
print('model | split | mean20 final20')
for name in ['model.pt','run1.pt']:
m, mean, std = load_model(f'outputs/{name}', cfg, device)
for split in ['val','public_test']:
traj = torch.load(f'data/{split}/trajectories.pt', weights_only=True)['trajectories'].float()
rel,_,_ = rollout_eval(m, traj, mean, std, start=0, horizon=20)
print(f'{name:15s} | {split:11s} | {rel.mean().item():.4f} {rel.mean(0)[-1].item():.4f}')
print()
# robustness of run1 across ALL valid starts, horizon 20
m, mean, std = load_model('outputs/run1.pt', cfg, device)
for split in ['val','public_test']:
traj = torch.load(f'data/{split}/trajectories.pt', weights_only=True)['trajectories'].float()
worst=0; allm=[]
for s in range(0, 100-4-20+1):
rel,_,_ = rollout_eval(m, traj, mean, std, start=s, horizon=20)
allm.append(rel.mean().item()); worst=max(worst, rel.mean(0)[-1].item())
import numpy as np
print(f'{split}: over {len(allm)} start pts mean20 avg={np.mean(allm):.4f} max={np.max(allm):.4f} worst final-step={worst:.4f}')
# longer horizon stability (40 steps) from start 0
for split in ['val','public_test']:
traj = torch.load(f'data/{split}/trajectories.pt', weights_only=True)['trajectories'].float()
rel,_,_ = rollout_eval(m, traj, mean, std, start=0, horizon=40)
print(f'{split} horizon40: mean={rel.mean().item():.4f} final(40)={rel.mean(0)[-1].item():.4f}')
"model | split | mean20 final20 model.pt | val | 0.0099 0.0192 model.pt | public_test | 0.0082 0.0149 run1.pt | val | 0.0016 0.0021 run1.pt | public_test | 0.0016 0.0021 val: over 77 start pts mean20 avg=0.0015 max=0.0016 worst final-step=0.0025 public_test: over 77 start pts mean20 avg=0.0012 max=0.0016 worst final-step=0.0024 val horizon40: mean=0.0025 final(40)=0.0048 public_test horizon40: mean=0.0025 final(40)=0.0047
cd /app && EPOCHS=64 STEPS_PER_EPOCH=350 RMAX=20 WARM_EPOCHS=2 CURR_EPOCHS=20 NOISE=0.007 LR=2e-3 EMA=0.9995 SEED=7 OUT=run2.pt nohup python3 train_improved.py > run2.log 2>&1 & echo "launched PID $!"; sleep 5; head -1 run2.log
launched PID 337
cd /app && python3 -c "
import torch, json
from candidate_model import build_model
cfg = json.load(open('task_config.json'))
# load EXACTLY as grader: weights_only=True
ckpt = torch.load('outputs/run1.pt', map_location='cpu', weights_only=True)
assert isinstance(ckpt, dict), 'ckpt must be dict'
print('keys:', sorted(ckpt.keys()))
for key in ['state_dict','feat_mean','feat_std']:
assert key in ckpt, f'missing {key}'
print('feat_mean:', ckpt['feat_mean'], type(ckpt['feat_mean']).__name__)
print('feat_std :', ckpt['feat_std'], type(ckpt['feat_std']).__name__)
assert isinstance(ckpt['feat_mean'], float) and isinstance(ckpt['feat_std'], float), 'must be float scalars'
# build fresh model from task_config and load state dict strictly
m = build_model(cfg)
missing, unexpected = m.load_state_dict(ckpt['state_dict'], strict=False)
print('missing keys:', missing)
print('unexpected keys:', unexpected)
assert not missing and not unexpected, 'state_dict mismatch with build_model(task_config)'
m.eval()
# forward with the documented shapes: input [B,4,128] -> output [B,1,128]
x = torch.randn(3,4,128)
with torch.no_grad():
y = m(x)
print('forward output shape:', tuple(y.shape))
assert tuple(y.shape)==(3,1,128)
print('CONTRACT OK: loads with weights_only=True, matches build_model, correct I/O shapes')
"keys: ['feat_mean', 'feat_std', 'state_dict'] feat_mean: -3.819819571759808e-09 float feat_std : 1.1599736213684082 float missing keys: [] unexpected keys: [] forward output shape: (3, 1, 128) CONTRACT OK: loads with weights_only=True, matches build_model, correct I/O shapes
cd /app && cp outputs/run1.pt outputs/model.pt && echo "model.pt <- run1.pt" && sleep 130 && tail -6 run2.log
model.pt <- run1.pt [train] ep=7/64 R=6 loss=3.2046e-02 raw=0.0721/0.1542 ema=0.0233/0.0421 best=0.0139(raw) lr=1.98e-03 [train] ep=8/64 R=7 loss=2.2066e-02 raw=0.0242/0.0382 ema=0.0180/0.0313 best=0.0139(raw) lr=1.97e-03 [train] ep=9/64 R=8 loss=1.4485e-02 raw=0.0199/0.0298 ema=0.0098/0.0167 best=0.0098(ema) lr=1.96e-03 [train] ep=10/64 R=9 loss=1.4676e-02 raw=0.0390/0.0802 ema=0.0078/0.0125 best=0.0078(ema) lr=1.94e-03 [train] ep=11/64 R=10 loss=1.5124e-02 raw=0.0190/0.0307 ema=0.0069/0.0108 best=0.0069(ema) lr=1.92e-03 [train] ep=12/64 R=10 loss=1.6013e-02 raw=0.0178/0.0273 ema=0.0062/0.0096 best=0.0062(ema) lr=1.90e-03
cd /app && sleep 280; tail -6 run2.log
[train] ep=19/64 R=17 loss=2.6873e-02 raw=0.0213/0.0452 ema=0.0063/0.0098 best=0.0059(raw) lr=1.68e-03 [train] ep=20/64 R=18 loss=1.9688e-02 raw=0.0125/0.0234 ema=0.0064/0.0098 best=0.0059(raw) lr=1.65e-03 [train] ep=21/64 R=19 loss=2.1525e-02 raw=0.0192/0.0371 ema=0.0064/0.0099 best=0.0059(raw) lr=1.61e-03 [train] ep=22/64 R=20 loss=1.5607e-02 raw=0.0157/0.0314 ema=0.0060/0.0092 best=0.0059(raw) lr=1.56e-03 [train] ep=23/64 R=20 loss=1.6456e-02 raw=0.0111/0.0194 ema=0.0057/0.0086 best=0.0057(ema) lr=1.52e-03 [train] ep=24/64 R=20 loss=1.2907e-02 raw=0.0108/0.0182 ema=0.0053/0.0077 best=0.0053(ema) lr=1.48e-03
cd /app && sleep 290; tail -7 run2.log
[train] ep=27/64 R=20 loss=2.2879e-02 raw=0.0196/0.0361 ema=0.0055/0.0084 best=0.0052(raw) lr=1.33e-03 [train] ep=28/64 R=20 loss=1.4365e-02 raw=0.0080/0.0124 ema=0.0055/0.0083 best=0.0052(raw) lr=1.29e-03 [train] ep=29/64 R=20 loss=9.9160e-03 raw=0.0085/0.0125 ema=0.0054/0.0081 best=0.0052(raw) lr=1.24e-03 [train] ep=30/64 R=20 loss=1.0384e-02 raw=0.0086/0.0129 ema=0.0049/0.0073 best=0.0049(ema) lr=1.18e-03 [train] ep=31/64 R=20 loss=1.4513e-02 raw=0.0083/0.0122 ema=0.0047/0.0068 best=0.0047(ema) lr=1.13e-03 [train] ep=32/64 R=20 loss=8.9591e-03 raw=0.0090/0.0137 ema=0.0043/0.0062 best=0.0043(ema) lr=1.08e-03 [train] ep=33/64 R=20 loss=8.3530e-03 raw=0.0072/0.0106 ema=0.0041/0.0058 best=0.0041(ema) lr=1.03e-03
cd /app && sleep 290; tail -7 run2.log
[train] ep=37/64 R=20 loss=8.7343e-03 raw=0.0058/0.0089 ema=0.0032/0.0045 best=0.0032(ema) lr=8.25e-04 [train] ep=38/64 R=20 loss=6.6816e-03 raw=0.0053/0.0080 ema=0.0031/0.0043 best=0.0031(ema) lr=7.75e-04 [train] ep=39/64 R=20 loss=7.6062e-03 raw=0.0053/0.0077 ema=0.0030/0.0041 best=0.0030(ema) lr=7.25e-04 [train] ep=40/64 R=20 loss=7.3026e-03 raw=0.0052/0.0082 ema=0.0028/0.0039 best=0.0028(ema) lr=6.75e-04 [train] ep=41/64 R=20 loss=5.7974e-03 raw=0.0051/0.0079 ema=0.0027/0.0036 best=0.0027(ema) lr=6.27e-04 [train] ep=42/64 R=20 loss=9.4703e-03 raw=0.0058/0.0099 ema=0.0027/0.0036 best=0.0027(ema) lr=5.79e-04 [train] ep=43/64 R=20 loss=5.5098e-03 raw=0.0043/0.0064 ema=0.0025/0.0034 best=0.0025(ema) lr=5.33e-04
cd /app && sleep 290; tail -8 run2.log
[train] ep=45/64 R=20 loss=7.2911e-03 raw=0.0036/0.0061 ema=0.0023/0.0031 best=0.0023(ema) lr=4.44e-04 [train] ep=46/64 R=20 loss=4.4284e-03 raw=0.0036/0.0053 ema=0.0022/0.0029 best=0.0022(ema) lr=4.02e-04 [train] ep=47/64 R=20 loss=5.5551e-03 raw=0.0036/0.0058 ema=0.0021/0.0028 best=0.0021(ema) lr=3.62e-04 [train] ep=48/64 R=20 loss=4.4656e-03 raw=0.0032/0.0050 ema=0.0021/0.0027 best=0.0021(ema) lr=3.23e-04 [train] ep=49/64 R=20 loss=3.7993e-03 raw=0.0029/0.0042 ema=0.0020/0.0026 best=0.0020(ema) lr=2.86e-04 [train] ep=50/64 R=20 loss=4.2032e-03 raw=0.0045/0.0081 ema=0.0019/0.0025 best=0.0019(ema) lr=2.50e-04 [train] ep=51/64 R=20 loss=4.4755e-03 raw=0.0023/0.0034 ema=0.0018/0.0024 best=0.0018(ema) lr=2.17e-04 [train] ep=52/64 R=20 loss=4.1238e-03 raw=0.0023/0.0036 ema=0.0018/0.0023 best=0.0018(ema) lr=1.86e-04
cd /app && sleep 200; tail -4 run2.log; echo "=== run2 full eval ==="; python3 eval_rollout.py outputs/run2.pt val; python3 eval_rollout.py outputs/run2.pt public_test
[train] ep=56/64 R=20 loss=2.9017e-03 raw=0.0017/0.0025 ema=0.0017/0.0022 best=0.0016(raw) lr=8.42e-05
[train] ep=57/64 R=20 loss=2.8069e-03 raw=0.0016/0.0024 ema=0.0016/0.0021 best=0.0016(raw) lr=6.47e-05
[train] ep=58/64 R=20 loss=2.7627e-03 raw=0.0016/0.0023 ema=0.0016/0.0021 best=0.0016(raw) lr=4.77e-05
[train] ep=59/64 R=20 loss=2.7217e-03 raw=0.0014/0.0018 ema=0.0016/0.0021 best=0.0014(raw) lr=3.32e-05
=== run2 full eval ===
[stderr]
Traceback (most recent call last):
File "/app/eval_rollout.py", line 36, in <module>
model, mean, std = load_model(path, cfg, device)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/app/eval_rollout.py", line 5, in load_model
ckpt = torch.load(path, map_location=device, weights_only=True)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/opt/conda/lib/python3.11/site-packages/torch/serialization.py", line 1319, in load
with _open_file_like(f, "rb") as opened_file:
^^^^^^^^^^^^^^^^^^^^^^^^
File "/opt/conda/lib/python3.11/site-packages/torch/serialization.py", line 659, in _open_file_like
return _open_file(name_or_buffer, mode)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/opt/conda/lib/python3.11/site-packages/torch/serialization.py", line 640, in __init__
super().__init__(open(name, mode))
^^^^^^^^^^^^^^^^
FileNotFoundError: [Errno 2] No such file or directory: 'outputs/run2.pt'
Traceback (most recent call last):
File "/app/eval_rollout.py", line 36, in <module>
model, mean, std = load_model(path, cfg, device)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/app/eval_rollout.py", line 5, in load_model
ckpt = torch.load(path, map_location=device, weights_only=True)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/opt/conda/lib/python3.11/site-packages/torch/serialization.py", line 1319, in load
with _open_file_like(f, "rb") as opened_file:
^^^^^^^^^^^^^^^^^^^^^^^^
File "/opt/conda/lib/python3.11/site-packages/torch/serialization.py", line 659, in _open_file_like
return _open_file(name_or_buffer, mode)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/opt/conda/lib/python3.11/site-packages/torch/serialization.py", line 640, in __init__
super().__init__(open(name, mode))
^^^^^^^^^^^^^^^^
FileNotFoundError: [Errno 2] No such file or directory: 'outputs/run2.pt'cd /app && while [ ! -f outputs/run2.pt ]; do sleep 5; done; tail -3 run2.log; echo "=== run2 full eval ==="; python3 eval_rollout.py outputs/run2.pt val; python3 eval_rollout.py outputs/run2.pt public_test
[train] ep=63/64 R=20 loss=2.6199e-03 raw=0.0013/0.0017 ema=0.0016/0.0021 best=0.0013(raw) lr=1.33e-06 [train] ep=64/64 R=20 loss=2.6108e-03 raw=0.0013/0.0016 ema=0.0015/0.0020 best=0.0013(raw) lr=0.00e+00 [train] wrote /app/outputs/run2.pt best_val_roll_mean=0.0013 === run2 full eval === [val] rollout mean rel_l2 over 20 steps: 0.0015 [val] final-step (20) rel_l2: 0.0018 per-step rel_l2: 0.002 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.002 0.002 0.002 0.002 0.002 0.002 0.002 0.002 0.002 0.002 [val] rollout rmse: 0.0027 [public_test] rollout mean rel_l2 over 20 steps: 0.0015 [public_test] final-step (20) rel_l2: 0.0016 per-step rel_l2: 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.002 0.002 0.002 0.002 0.002 0.002 0.002 0.002 0.002 0.002 0.002 0.002 0.002 [public_test] rollout rmse: 0.0041
cd /app && python3 -c "
import torch, json, numpy as np
from eval_rollout import load_model, rollout_eval
cfg = json.load(open('task_config.json')); device='cuda'
for name in ['run1.pt','run2.pt']:
m, mean, std = load_model(f'outputs/{name}', cfg, device)
print(f'=== {name} ===')
for split in ['val','public_test']:
traj = torch.load(f'data/{split}/trajectories.pt', weights_only=True)['trajectories'].float()
allm=[]; worstf=0
for s in range(0, 100-4-20+1):
rel,_,_ = rollout_eval(m, traj, mean, std, start=s, horizon=20)
allm.append(rel.mean().item()); worstf=max(worstf, rel.mean(0)[-1].item())
rel40,_,_ = rollout_eval(m, traj, mean, std, start=0, horizon=40)
print(f' {split:11s}: allstart mean20 avg={np.mean(allm):.4f} max={np.max(allm):.4f} worstFinal={worstf:.4f} | h40 mean={rel40.mean().item():.4f} final40={rel40.mean(0)[-1].item():.4f}')
"=== run1.pt === val : allstart mean20 avg=0.0015 max=0.0016 worstFinal=0.0025 | h40 mean=0.0025 final40=0.0048 public_test: allstart mean20 avg=0.0012 max=0.0016 worstFinal=0.0024 | h40 mean=0.0025 final40=0.0047 === run2.pt === val : allstart mean20 avg=0.0013 max=0.0015 worstFinal=0.0022 | h40 mean=0.0019 final40=0.0030 public_test: allstart mean20 avg=0.0010 max=0.0015 worstFinal=0.0018 | h40 mean=0.0016 final40=0.0026
cd /app && python3 -c "
import torch, json
from candidate_model import build_model
cfg = json.load(open('task_config.json'))
ckpt = torch.load('outputs/run2.pt', map_location='cpu', weights_only=True)
assert set(['state_dict','feat_mean','feat_std']).issubset(ckpt.keys())
assert isinstance(ckpt['feat_mean'], float) and isinstance(ckpt['feat_std'], float)
m = build_model(cfg)
miss,unexp = m.load_state_dict(ckpt['state_dict'], strict=True)
x=torch.randn(2,4,128)
with torch.no_grad(): y=m(x)
assert tuple(y.shape)==(2,1,128)
print('run2 contract OK; feat_mean=%.3e feat_std=%.4f'%(ckpt['feat_mean'],ckpt['feat_std']))
" && cp outputs/run2.pt outputs/model.pt && echo "PROMOTED run2.pt -> model.pt"run2 contract OK; feat_mean=-3.820e-09 feat_std=1.1600 PROMOTED run2.pt -> model.pt
cd /app && python3 -c "
import torch, json, numpy as np
from candidate_model import build_model
cfg = json.load(open('task_config.json')); device='cuda'
# ---- grader-style load & rollout ----
ckpt = torch.load('/app/outputs/model.pt', map_location=device, weights_only=True)
m = build_model(cfg).to(device); m.load_state_dict(ckpt['state_dict']); m.eval()
mean, std = ckpt['feat_mean'], ckpt['feat_std']
k, H = 4, 20
@torch.no_grad()
def grade(split, horizon=H):
traj = torch.load(f'/app/data/{split}/trajectories.pt', weights_only=True)['trajectories'].float().to(device)
w = (traj[:, :k]-mean)/std; preds=[]
for _ in range(horizon):
o=m(w); preds.append(o); w=torch.cat([w[:,1:],o],dim=1)
pred=torch.cat(preds,1)*std+mean; gt=traj[:,k:k+horizon]
rel=((pred-gt).pow(2).sum(-1).sqrt()/gt.pow(2).sum(-1).sqrt().clamp_min(1e-8))
rmse=(pred-gt).pow(2).mean().sqrt().item()
return rel.mean().item(), rel.mean(0)[-1].item(), rmse, torch.isfinite(pred).all().item()
for sp in ['val','public_test']:
mn,fn,rm,ok = grade(sp)
print(f'[GRADER-SIM {sp:11s}] rollout_rel_l2(mean20)={mn:.4f} final_step_rel_l2={fn:.4f} rmse={rm:.4f} finite={ok}')
# long-horizon stress (80 steps) for stability
for sp in ['val','public_test']:
mn,fn,rm,ok = grade(sp, horizon=80)
print(f'[STRESS h80 {sp:11s}] mean={mn:.4f} final80={fn:.4f} finite={ok}')
"[GRADER-SIM val ] rollout_rel_l2(mean20)=0.0015 final_step_rel_l2=0.0018 rmse=0.0027 finite=True [GRADER-SIM public_test] rollout_rel_l2(mean20)=0.0015 final_step_rel_l2=0.0016 rmse=0.0041 finite=True [STRESS h80 val ] mean=0.0041 final80=0.0114 finite=True [STRESS h80 public_test] mean=0.0029 final80=0.0058 finite=True
cd /app && rm -f outputs/smoke.pt outputs/timing.pt && echo "=== FINAL SUMMARY: baseline vs improved (start=0, horizon=20) ===" && python3 -c "
import torch, json
from eval_rollout import load_model, rollout_eval
cfg=json.load(open('task_config.json')); dev='cuda'
# retrain-free baseline numbers were measured earlier; reload run1-era baseline if present else note
print('%-10s %-12s %8s %8s'%('model','split','mean20','final20'))
for name in ['model.pt']:
m,mu,sd=load_model(f'outputs/{name}',cfg,dev)
for sp in ['val','public_test']:
tr=torch.load(f'data/{sp}/trajectories.pt',weights_only=True)['trajectories'].float()
rel,_,_=rollout_eval(m,mu,sd,start=0) if False else rollout_eval(m,tr,mu,sd,start=0)
print('%-10s %-12s %8.4f %8.4f'%('improved',sp,rel.mean().item(),rel.mean(0)[-1].item()))
print()
print('Reference baseline (train.py, 10 ep): val mean20=0.0099 final20=0.0192 | public_test mean20=0.0082 final20=0.0149')
" && echo && echo "=== outputs/ contents ===" && ls -la outputs/=== FINAL SUMMARY: baseline vs improved (start=0, horizon=20) === model split mean20 final20 improved val 0.0015 0.0018 improved public_test 0.0015 0.0016 Reference baseline (train.py, 10 ep): val mean20=0.0099 final20=0.0192 | public_test mean20=0.0082 final20=0.0149 === outputs/ contents === total 1625 drwxr-sr-x 2 h2tagent h2tagent 4096 Jun 20 08:09 . drwxrws--- 1 h2tagent h2tagent 4096 Jun 20 07:40 .. -rw-r--r-- 1 h2tagent h2tagent 551838 Jun 20 08:08 model.pt -rw-r--r-- 1 h2tagent h2tagent 551838 Jun 20 07:39 run1.pt -rw-r--r-- 1 h2tagent h2tagent 551838 Jun 20 08:08 run2.pt
cd /app && md5sum outputs/model.pt outputs/run2.pt && python3 -c "
import torch, json
from candidate_model import build_model
cfg = json.load(open('/app/task_config.json'))
ckpt = torch.load('/app/outputs/model.pt', weights_only=True) # exact grader load
assert isinstance(ckpt, dict)
assert set(['state_dict','feat_mean','feat_std']).issubset(ckpt), ckpt.keys()
assert type(ckpt['feat_mean']) is float and type(ckpt['feat_std']) is float
m = build_model(cfg)
m.load_state_dict(ckpt['state_dict'], strict=True) # exact architecture match
m.eval()
with torch.no_grad():
y = m((torch.randn(5,4,128)-ckpt['feat_mean'])/ckpt['feat_std'])
assert tuple(y.shape)==(5,1,128) and torch.isfinite(y).all()
print('FINAL CONTRACT OK -> /app/outputs/model.pt')
print(' keys:', sorted(ckpt), '| feat_mean=%.3e feat_std=%.5f'%(ckpt['feat_mean'],ckpt['feat_std']))
print(' state_dict tensors:', len(ckpt['state_dict']))
"5d21e5efa0337a4248aa336fcad4ad9f outputs/model.pt 5d21e5efa0337a4248aa336fcad4ad9f outputs/run2.pt FINAL CONTRACT OK -> /app/outputs/model.pt keys: ['feat_mean', 'feat_std', 'state_dict'] | feat_mean=-3.820e-09 feat_std=1.15997 state_dict tensors: 18
No file changes, the agent left the workspace untouched.
# SyncVals normalized verifier summary # task: ks-equation-1d-forecast # attempt: 10 status: completed raw_score: 0.5 # reward rule: continuous: score 0.5 >= 0.5 # => reward = 1.0 (PASS) # # Note: these tasks score via an external hidden grader; no pytest/verifier # block is embedded in the agent transcript. The block below is the last # sizable tool-output captured in the run (supporting evidence). ---------------------------------------------------------------------- 5d21e5efa0337a4248aa336fcad4ad9f outputs/model.pt 5d21e5efa0337a4248aa336fcad4ad9f outputs/run2.pt FINAL CONTRACT OK -> /app/outputs/model.pt keys: ['feat_mean', 'feat_std', 'state_dict'] | feat_mean=-3.820e-09 feat_std=1.15997 state_dict tensors: 18
Reproduce this trial: git checkout 2f94510 && PYTHONPATH=src python3 scripts/build_site.py , then open trial/trial_d7309438d7954f0a. Re-running the agent live requires EVAL_PLATFORM_ENABLE_OAUTH_SMOKE=1 and is non-deterministic.
Trial trial_d7309438d7954f0a · verifier authoritative; classifier explanatory.