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examples/legacy/iter_nn_local/share/train_input.yaml 0 → 100644
View file @ 47af8be9
model_args:
hidden_sizes: [100, 100, 100]
output_scale: 100
use_resnet: true
actv_fn: gelu
data_args:
batch_size: 16
group_batch: 1
e_name: l_e_delta
d_name: [dm_eig]
preprocess_args:
preshift: false
prescale: true
prescale_clip: 0.05
prefit_ridge: 1e1
prefit_trainable: false
train_args:
ckpt_file: model.pth
decay_rate: 0.96
decay_steps: 300
display_epoch: 100
n_epoch: 30000
start_lr: 0.0001
train_paths:
- train_paths.raw
test_paths:
- test_paths.raw
examples/legacy/iter_nn_new/extra.py 0 → 100644
View file @ 47af8be9
#!/usr/bin/env python
# coding: utf-8
import os
import sys
import numpy as np
# sys.path.append('/path/to/source')
import deepks
from deepks.scf.stats import collect_data_grouped
from deepks.task.task import PythonTask, BatchTask, GroupBatchTask
from deepks.task.workflow import Sequence, Iteration
nsys = 1
niter = 25
ntrain = 1000
train_idx = np.arange(ntrain)
# SCF
scf_cmd_tmpl = " ".join([
"python -u /path/to/source/deepks/scf/main.py",
"scf_input.yaml",
"-m model.pth",
"-s mol_files.raw",
"-d results"])
envs = {"PYSCF_MAX_MEMORY": 16000}
scf_res = {"cpus_per_task": 10,
"time_limit": "6:00:00",
"mem_limit": 16,
"envs": envs}
task_scf = GroupBatchTask(
[BatchTask(scf_cmd_tmpl.format(i=i),
workdir=".", #f'task.{i}',
share_folder='share',
link_share_files=['mol_files.raw',
('raw_scf_input.yaml', 'scf_input.yaml')])
for i in range(nsys)],
workdir='00.scf',
outlog='log.scf',
resources=scf_res,
link_prev_files=['model.pth'])
# labeling
task_data = PythonTask(
lambda: [collect_data_grouped(train_idx=train_idx,
append=True,
ene_ref=f"e_ref.npy",
force_ref=f"f_ref.npy",
sys_dir=f"results")
for i in range(nsys)],
outlog='log.data',
workdir='01.data',
link_prev_files=['model.pth'] + [f"results" for i in range(nsys)],
share_folder='share',
link_share_files=[f'e_ref.npy' for i in range(nsys)]
+[f'f_ref.npy' for i in range(nsys)])
# training
train_cmd = " ".join([
"python -u /path/to/source/deepks/train/main.py",
"train_input.yaml",
"--restart old_model.pth"])
train_res = {"time_limit": "24:00:00",
"mem_limit": 32,
"numb_gpu": 1}
task_train = BatchTask(cmds=train_cmd,
outlog='log.train',
workdir='02.train',
resources=train_res,
link_prev_files=[('model.pth', 'old_model.pth'),
'train_paths.raw', 'test_paths.raw'],
share_folder = 'share',
link_share_files=["train_input.yaml"])
# combine
seq = Sequence([task_scf, task_data, task_train])
iterate = Iteration(seq, niter, init_folder='share/init', record_file='RECORD')
if os.path.exists('RECORD'):
iterate.restart()
else:
iterate.run()
examples/legacy/iter_nn_new/init_train/input.yaml 0 → 100644
View file @ 47af8be9
model_args:
hidden_sizes: [100, 100, 100]
output_scale: 100
use_resnet: true
actv_fn: mygelu
data_args:
batch_size: 16
group_batch: 1
e_name: e_cc
d_name: [dm_eig]
preprocess_args:
preshift: true
prescale: true
prescale_clip: 0.05
prefit_ridge: 1e1
prefit_trainable: false
train_args:
ckpt_file: model.pth
decay_rate: 0.96
decay_steps: 300
display_epoch: 100
n_epoch: 30000
start_lr: 0.0001
train_paths:
- /scratch/gpfs/yixiaoc/yixiaoc/deep.qc/data/sGDML/malonaldehyde/proj_ccsd_dz/train.1000
test_paths:
- /scratch/gpfs/yixiaoc/yixiaoc/deep.qc/data/sGDML/malonaldehyde/proj_ccsd_dz/test.1000
examples/legacy/iter_nn_new/init_train/log.train 0 → 100644
View file @ 47af8be9
# using seed: 3069819358
# training with 1 system(s)
# testing with 1 system(s)
# working on device: cuda:0
# epoch trn_err tst_err lr trn_time tst_time
0 3.94e-03 3.91e-03 1.00e-04 0.00 0.60
100 4.59e-04 4.64e-04 1.00e-04 0.21 0.04
200 3.12e-04 3.44e-04 1.00e-04 0.21 0.04
300 2.65e-04 3.08e-04 9.60e-05 0.21 0.04
400 2.39e-04 3.25e-04 9.60e-05 0.21 0.04
500 2.20e-04 2.66e-04 9.60e-05 0.21 0.04
600 2.09e-04 2.83e-04 9.22e-05 0.21 0.04
700 1.95e-04 2.37e-04 9.22e-05 0.21 0.04
800 1.78e-04 2.24e-04 9.22e-05 0.21 0.04
900 1.81e-04 2.31e-04 8.85e-05 0.21 0.04
1000 1.88e-04 3.36e-04 8.85e-05 0.21 0.04
1100 1.77e-04 2.75e-04 8.85e-05 0.21 0.06
1200 1.51e-04 2.07e-04 8.49e-05 0.21 0.04
1300 1.47e-04 1.93e-04 8.49e-05 0.21 0.04
1400 1.48e-04 1.88e-04 8.49e-05 0.21 0.04
1500 1.58e-04 2.45e-04 8.15e-05 0.21 0.04
1600 1.50e-04 1.85e-04 8.15e-05 0.21 0.04
1700 1.63e-04 1.86e-04 8.15e-05 0.21 0.04
1800 1.55e-04 1.88e-04 7.83e-05 0.21 0.04
1900 1.35e-04 1.83e-04 7.83e-05 0.21 0.04
2000 1.31e-04 1.83e-04 7.83e-05 0.21 0.04
2100 1.29e-04 2.08e-04 7.51e-05 0.21 0.04
2200 1.36e-04 1.99e-04 7.51e-05 0.21 0.04
2300 1.23e-04 1.82e-04 7.51e-05 0.21 0.04
2400 1.25e-04 1.95e-04 7.21e-05 0.21 0.04
2500 1.29e-04 1.95e-04 7.21e-05 0.21 0.04
2600 1.20e-04 1.90e-04 7.21e-05 0.21 0.04
2700 1.27e-04 1.70e-04 6.93e-05 0.21 0.04
2800 1.27e-04 1.73e-04 6.93e-05 0.21 0.04
2900 1.26e-04 1.64e-04 6.93e-05 0.21 0.04
3000 1.23e-04 1.66e-04 6.65e-05 0.21 0.04
3100 1.21e-04 1.70e-04 6.65e-05 0.21 0.04
3200 1.17e-04 1.77e-04 6.65e-05 0.21 0.04
3300 1.20e-04 2.27e-04 6.38e-05 0.21 0.04
3400 1.11e-04 1.83e-04 6.38e-05 0.21 0.04
3500 1.23e-04 1.77e-04 6.38e-05 0.21 0.04
3600 1.13e-04 1.94e-04 6.13e-05 0.21 0.04
3700 1.19e-04 1.80e-04 6.13e-05 0.21 0.04
3800 1.12e-04 2.02e-04 6.13e-05 0.21 0.04
3900 1.01e-04 1.56e-04 5.88e-05 0.21 0.04
4000 1.16e-04 1.66e-04 5.88e-05 0.21 0.04
4100 1.09e-04 1.62e-04 5.88e-05 0.21 0.04
4200 1.03e-04 1.54e-04 5.65e-05 0.21 0.04
4300 1.10e-04 1.52e-04 5.65e-05 0.21 0.04
4400 1.11e-04 1.56e-04 5.65e-05 0.21 0.04
4500 1.02e-04 1.49e-04 5.42e-05 0.21 0.04
4600 1.07e-04 1.57e-04 5.42e-05 0.21 0.04
4700 1.03e-04 1.53e-04 5.42e-05 0.21 0.04
4800 9.97e-05 1.64e-04 5.20e-05 0.21 0.04
4900 9.55e-05 1.47e-04 5.20e-05 0.21 0.04
5000 1.10e-04 1.52e-04 5.20e-05 0.21 0.04
5100 9.37e-05 1.51e-04 5.00e-05 0.21 0.04
5200 9.09e-05 1.50e-04 5.00e-05 0.22 0.04
5300 1.03e-04 1.43e-04 5.00e-05 0.21 0.04
5400 9.42e-05 1.41e-04 4.80e-05 0.21 0.04
5500 9.41e-05 1.52e-04 4.80e-05 0.21 0.04
5600 1.01e-04 1.44e-04 4.80e-05 0.21 0.04
5700 1.02e-04 1.70e-04 4.60e-05 0.21 0.04
5800 9.13e-05 1.41e-04 4.60e-05 0.21 0.04
5900 9.38e-05 1.41e-04 4.60e-05 0.21 0.04
6000 9.92e-05 1.48e-04 4.42e-05 0.21 0.04
6100 8.69e-05 1.39e-04 4.42e-05 0.21 0.04
6200 9.75e-05 1.38e-04 4.42e-05 0.21 0.04
6300 9.74e-05 1.42e-04 4.24e-05 0.21 0.04
6400 8.69e-05 1.48e-04 4.24e-05 0.21 0.04
6500 9.64e-05 1.47e-04 4.24e-05 0.21 0.04
6600 8.79e-05 1.40e-04 4.07e-05 0.21 0.04
6700 8.68e-05 1.35e-04 4.07e-05 0.21 0.04
6800 8.55e-05 1.34e-04 4.07e-05 0.21 0.04
6900 8.38e-05 1.39e-04 3.91e-05 0.21 0.04
7000 8.55e-05 1.41e-04 3.91e-05 0.21 0.04
7100 8.43e-05 1.34e-04 3.91e-05 0.21 0.04
7200 8.28e-05 1.39e-04 3.75e-05 0.21 0.04
7300 8.31e-05 1.53e-04 3.75e-05 0.21 0.04
7400 8.25e-05 1.36e-04 3.75e-05 0.21 0.04
7500 7.95e-05 1.33e-04 3.60e-05 0.21 0.04
7600 8.62e-05 1.56e-04 3.60e-05 0.21 0.04
7700 8.58e-05 1.31e-04 3.60e-05 0.21 0.04
7800 7.54e-05 1.31e-04 3.46e-05 0.21 0.04
7900 8.21e-05 1.46e-04 3.46e-05 0.21 0.04
8000 7.86e-05 1.55e-04 3.46e-05 0.21 0.04
8100 7.94e-05 1.32e-04 3.32e-05 0.21 0.04
8200 7.79e-05 1.32e-04 3.32e-05 0.21 0.04
8300 9.21e-05 1.52e-04 3.32e-05 0.21 0.04
8400 7.84e-05 1.38e-04 3.19e-05 0.21 0.04
8500 7.27e-05 1.30e-04 3.19e-05 0.21 0.04
8600 7.56e-05 1.33e-04 3.19e-05 0.21 0.04
8700 7.62e-05 1.42e-04 3.06e-05 0.21 0.04
8800 8.27e-05 1.28e-04 3.06e-05 0.21 0.04
8900 7.32e-05 1.29e-04 3.06e-05 0.21 0.04
9000 8.31e-05 1.29e-04 2.94e-05 0.21 0.04
9100 7.73e-05 1.32e-04 2.94e-05 0.21 0.04
9200 7.38e-05 1.28e-04 2.94e-05 0.21 0.04
9300 7.79e-05 1.38e-04 2.82e-05 0.21 0.04
9400 7.70e-05 1.36e-04 2.82e-05 0.21 0.04
9500 7.61e-05 1.31e-04 2.82e-05 0.21 0.04
9600 7.57e-05 1.27e-04 2.71e-05 0.21 0.04
9700 7.28e-05 1.35e-04 2.71e-05 0.21 0.04
9800 7.84e-05 1.35e-04 2.71e-05 0.21 0.04
9900 7.03e-05 1.26e-04 2.60e-05 0.21 0.04
10000 6.95e-05 1.30e-04 2.60e-05 0.21 0.04
10100 7.55e-05 1.26e-04 2.60e-05 0.21 0.04
10200 7.29e-05 1.31e-04 2.50e-05 0.21 0.04
10300 7.42e-05 1.26e-04 2.50e-05 0.21 0.04
10400 8.53e-05 1.39e-04 2.50e-05 0.21 0.04
10500 7.07e-05 1.28e-04 2.40e-05 0.21 0.04
10600 7.35e-05 1.39e-04 2.40e-05 0.21 0.04
10700 7.23e-05 1.33e-04 2.40e-05 0.21 0.04
10800 7.01e-05 1.24e-04 2.30e-05 0.21 0.04
10900 7.55e-05 1.25e-04 2.30e-05 0.21 0.04
11000 7.25e-05 1.29e-04 2.30e-05 0.21 0.04
11100 6.83e-05 1.29e-04 2.21e-05 0.21 0.04
11200 7.14e-05 1.35e-04 2.21e-05 0.21 0.04
11300 7.04e-05 1.25e-04 2.21e-05 0.21 0.04
11400 6.78e-05 1.29e-04 2.12e-05 0.21 0.04
11500 7.03e-05 1.26e-04 2.12e-05 0.21 0.04
11600 6.56e-05 1.23e-04 2.12e-05 0.21 0.04
11700 7.07e-05 1.23e-04 2.04e-05 0.21 0.04
11800 7.06e-05 1.24e-04 2.04e-05 0.21 0.04
11900 6.46e-05 1.23e-04 2.04e-05 0.21 0.04
12000 6.55e-05 1.22e-04 1.95e-05 0.21 0.04
12100 7.08e-05 1.23e-04 1.95e-05 0.21 0.04
12200 7.03e-05 1.27e-04 1.95e-05 0.21 0.04
12300 6.88e-05 1.25e-04 1.88e-05 0.21 0.04
12400 6.83e-05 1.24e-04 1.88e-05 0.21 0.04
12500 7.12e-05 1.36e-04 1.88e-05 0.21 0.04
12600 6.63e-05 1.31e-04 1.80e-05 0.21 0.04
12700 6.51e-05 1.21e-04 1.80e-05 0.21 0.04
12800 6.82e-05 1.24e-04 1.80e-05 0.21 0.04
12900 6.50e-05 1.23e-04 1.73e-05 0.21 0.04
13000 6.55e-05 1.21e-04 1.73e-05 0.21 0.04
13100 6.65e-05 1.21e-04 1.73e-05 0.21 0.04
13200 6.39e-05 1.21e-04 1.66e-05 0.21 0.04
13300 6.73e-05 1.22e-04 1.66e-05 0.21 0.04
13400 6.79e-05 1.22e-04 1.66e-05 0.21 0.04
13500 6.34e-05 1.21e-04 1.59e-05 0.21 0.04
13600 6.14e-05 1.22e-04 1.59e-05 0.21 0.04
13700 6.83e-05 1.32e-04 1.59e-05 0.21 0.04
13800 6.71e-05 1.29e-04 1.53e-05 0.21 0.04
13900 6.20e-05 1.21e-04 1.53e-05 0.21 0.04
14000 6.18e-05 1.21e-04 1.53e-05 0.21 0.04
14100 6.24e-05 1.25e-04 1.47e-05 0.22 0.04
14200 6.25e-05 1.19e-04 1.47e-05 0.21 0.04
14300 6.42e-05 1.19e-04 1.47e-05 0.21 0.04
14400 6.24e-05 1.19e-04 1.41e-05 0.21 0.04
14500 6.14e-05 1.19e-04 1.41e-05 0.21 0.04
14600 6.21e-05 1.19e-04 1.41e-05 0.21 0.04
14700 6.19e-05 1.23e-04 1.35e-05 0.21 0.04
14800 6.17e-05 1.20e-04 1.35e-05 0.21 0.04
14900 6.00e-05 1.21e-04 1.35e-05 0.21 0.04
15000 6.55e-05 1.19e-04 1.30e-05 0.21 0.04
15100 6.22e-05 1.24e-04 1.30e-05 0.21 0.04
15200 6.31e-05 1.19e-04 1.30e-05 0.21 0.04
15300 6.35e-05 1.23e-04 1.25e-05 0.21 0.04
15400 6.00e-05 1.19e-04 1.25e-05 0.21 0.04
15500 5.95e-05 1.24e-04 1.25e-05 0.21 0.04
15600 6.15e-05 1.21e-04 1.20e-05 0.21 0.04
15700 6.31e-05 1.20e-04 1.20e-05 0.21 0.04
15800 6.00e-05 1.18e-04 1.20e-05 0.21 0.04
15900 6.46e-05 1.21e-04 1.15e-05 0.21 0.04
16000 6.32e-05 1.18e-04 1.15e-05 0.21 0.04
16100 5.89e-05 1.19e-04 1.15e-05 0.21 0.04
16200 6.12e-05 1.18e-04 1.10e-05 0.21 0.04
16300 6.20e-05 1.29e-04 1.10e-05 0.21 0.04
16400 6.12e-05 1.18e-04 1.10e-05 0.21 0.04
16500 6.21e-05 1.19e-04 1.06e-05 0.21 0.04
16600 6.14e-05 1.19e-04 1.06e-05 0.21 0.04
16700 6.00e-05 1.18e-04 1.06e-05 0.21 0.04
16800 6.12e-05 1.36e-04 1.02e-05 0.21 0.04
16900 6.28e-05 1.21e-04 1.02e-05 0.21 0.04
17000 6.07e-05 1.25e-04 1.02e-05 0.21 0.04
17100 6.09e-05 1.19e-04 9.76e-06 0.21 0.04
17200 6.55e-05 1.26e-04 9.76e-06 0.21 0.04
17300 5.92e-05 1.22e-04 9.76e-06 0.21 0.04
17400 5.91e-05 1.17e-04 9.37e-06 0.21 0.04
17500 5.96e-05 1.28e-04 9.37e-06 0.21 0.04
17600 6.02e-05 1.17e-04 9.37e-06 0.21 0.04
17700 5.86e-05 1.18e-04 9.00e-06 0.21 0.04
17800 6.04e-05 1.18e-04 9.00e-06 0.21 0.04
17900 5.91e-05 1.17e-04 9.00e-06 0.21 0.04
18000 6.03e-05 1.19e-04 8.64e-06 0.21 0.04
18100 6.07e-05 1.17e-04 8.64e-06 0.21 0.04
18200 5.94e-05 1.17e-04 8.64e-06 0.21 0.04
18300 5.88e-05 1.19e-04 8.29e-06 0.22 0.04
18400 5.64e-05 1.17e-04 8.29e-06 0.21 0.04
18500 5.84e-05 1.17e-04 8.29e-06 0.21 0.04
18600 6.17e-05 1.19e-04 7.96e-06 0.21 0.04
18700 5.67e-05 1.17e-04 7.96e-06 0.21 0.04
18800 5.93e-05 1.20e-04 7.96e-06 0.21 0.04
18900 5.92e-05 1.19e-04 7.64e-06 0.21 0.04
19000 5.91e-05 1.17e-04 7.64e-06 0.22 0.04
19100 5.88e-05 1.21e-04 7.64e-06 0.22 0.04
19200 5.74e-05 1.17e-04 7.33e-06 0.22 0.04
19300 5.92e-05 1.19e-04 7.33e-06 0.22 0.04
19400 5.89e-05 1.20e-04 7.33e-06 0.22 0.04
19500 5.62e-05 1.16e-04 7.04e-06 0.22 0.04
19600 5.81e-05 1.17e-04 7.04e-06 0.22 0.04
19700 5.97e-05 1.18e-04 7.04e-06 0.22 0.04
19800 5.93e-05 1.22e-04 6.76e-06 0.22 0.04
19900 5.86e-05 1.17e-04 6.76e-06 0.22 0.04
20000 5.81e-05 1.16e-04 6.76e-06 0.22 0.04
20100 5.66e-05 1.16e-04 6.49e-06 0.22 0.04
20200 5.84e-05 1.16e-04 6.49e-06 0.22 0.04
20300 5.85e-05 1.16e-04 6.49e-06 0.22 0.04
20400 6.14e-05 1.18e-04 6.23e-06 0.22 0.04
20500 5.64e-05 1.16e-04 6.23e-06 0.21 0.04
20600 5.55e-05 1.16e-04 6.23e-06 0.21 0.04
20700 5.58e-05 1.16e-04 5.98e-06 0.21 0.04
20800 5.76e-05 1.17e-04 5.98e-06 0.21 0.04
20900 5.94e-05 1.21e-04 5.98e-06 0.21 0.04
21000 5.79e-05 1.16e-04 5.74e-06 0.21 0.04
21100 5.44e-05 1.16e-04 5.74e-06 0.21 0.04
21200 5.81e-05 1.15e-04 5.74e-06 0.21 0.04
21300 5.54e-05 1.15e-04 5.51e-06 0.21 0.04
21400 5.75e-05 1.16e-04 5.51e-06 0.21 0.04
21500 5.51e-05 1.15e-04 5.51e-06 0.21 0.04
21600 5.67e-05 1.15e-04 5.29e-06 0.21 0.04
21700 5.51e-05 1.18e-04 5.29e-06 0.21 0.04
21800 5.41e-05 1.16e-04 5.29e-06 0.21 0.04
21900 5.63e-05 1.17e-04 5.08e-06 0.21 0.04
22000 5.58e-05 1.16e-04 5.08e-06 0.21 0.04
22100 5.73e-05 1.15e-04 5.08e-06 0.21 0.04
22200 5.53e-05 1.16e-04 4.88e-06 0.21 0.04
22300 5.61e-05 1.15e-04 4.88e-06 0.21 0.04
22400 5.80e-05 1.15e-04 4.88e-06 0.21 0.04
22500 5.79e-05 1.20e-04 4.68e-06 0.22 0.04
22600 5.62e-05 1.16e-04 4.68e-06 0.21 0.04
22700 5.65e-05 1.16e-04 4.68e-06 0.21 0.04
22800 5.62e-05 1.15e-04 4.49e-06 0.21 0.04
22900 5.50e-05 1.15e-04 4.49e-06 0.21 0.04
23000 5.51e-05 1.15e-04 4.49e-06 0.21 0.04
23100 5.54e-05 1.15e-04 4.31e-06 0.21 0.04
23200 5.42e-05 1.22e-04 4.31e-06 0.21 0.04
23300 5.46e-05 1.15e-04 4.31e-06 0.21 0.04
23400 5.60e-05 1.15e-04 4.14e-06 0.21 0.04
23500 5.73e-05 1.15e-04 4.14e-06 0.22 0.04
23600 5.69e-05 1.15e-04 4.14e-06 0.22 0.04
23700 5.69e-05 1.15e-04 3.98e-06 0.22 0.04
23800 5.50e-05 1.16e-04 3.98e-06 0.22 0.04
23900 5.67e-05 1.21e-04 3.98e-06 0.22 0.04
24000 5.44e-05 1.16e-04 3.82e-06 0.22 0.04
24100 5.67e-05 1.15e-04 3.82e-06 0.22 0.04
24200 5.58e-05 1.15e-04 3.82e-06 0.22 0.04
24300 5.52e-05 1.15e-04 3.66e-06 0.22 0.04
24400 5.48e-05 1.16e-04 3.66e-06 0.22 0.04
24500 5.75e-05 1.15e-04 3.66e-06 0.22 0.04
24600 5.54e-05 1.19e-04 3.52e-06 0.22 0.04
24700 5.55e-05 1.15e-04 3.52e-06 0.21 0.04
24800 5.45e-05 1.15e-04 3.52e-06 0.22 0.04
24900 5.42e-05 1.15e-04 3.38e-06 0.22 0.04
25000 5.45e-05 1.15e-04 3.38e-06 0.23 0.04
25100 5.40e-05 1.16e-04 3.38e-06 0.21 0.04
25200 5.64e-05 1.15e-04 3.24e-06 0.21 0.04
25300 5.37e-05 1.15e-04 3.24e-06 0.21 0.04
25400 5.55e-05 1.15e-04 3.24e-06 0.21 0.04
25500 5.18e-05 1.15e-04 3.11e-06 0.21 0.04
25600 5.32e-05 1.14e-04 3.11e-06 0.21 0.04
25700 5.46e-05 1.15e-04 3.11e-06 0.21 0.04
25800 5.41e-05 1.14e-04 2.99e-06 0.21 0.04
25900 5.58e-05 1.15e-04 2.99e-06 0.21 0.04
26000 5.38e-05 1.14e-04 2.99e-06 0.21 0.04
26100 5.36e-05 1.15e-04 2.87e-06 0.21 0.04
26200 5.42e-05 1.15e-04 2.87e-06 0.21 0.04
26300 5.32e-05 1.16e-04 2.87e-06 0.21 0.04
26400 5.40e-05 1.15e-04 2.75e-06 0.21 0.04
26500 5.41e-05 1.14e-04 2.75e-06 0.21 0.04
26600 5.37e-05 1.15e-04 2.75e-06 0.21 0.04
26700 5.40e-05 1.14e-04 2.64e-06 0.22 0.04
26800 5.32e-05 1.14e-04 2.64e-06 0.21 0.04
26900 5.34e-05 1.14e-04 2.64e-06 0.21 0.04
27000 5.40e-05 1.15e-04 2.54e-06 0.21 0.04
27100 5.45e-05 1.14e-04 2.54e-06 0.22 0.04
27200 5.43e-05 1.14e-04 2.54e-06 0.22 0.04
27300 5.34e-05 1.14e-04 2.44e-06 0.22 0.04
27400 5.38e-05 1.14e-04 2.44e-06 0.22 0.04
27500 5.40e-05 1.14e-04 2.44e-06 0.21 0.04
27600 5.45e-05 1.14e-04 2.34e-06 0.22 0.04
27700 5.39e-05 1.15e-04 2.34e-06 0.22 0.04
27800 5.33e-05 1.14e-04 2.34e-06 0.22 0.04
27900 5.40e-05 1.14e-04 2.25e-06 0.22 0.04
28000 5.42e-05 1.14e-04 2.25e-06 0.22 0.04
28100 5.32e-05 1.17e-04 2.25e-06 0.22 0.04
28200 5.33e-05 1.14e-04 2.16e-06 0.22 0.04
28300 5.30e-05 1.14e-04 2.16e-06 0.22 0.04
28400 5.39e-05 1.14e-04 2.16e-06 0.22 0.04
28500 5.34e-05 1.16e-04 2.07e-06 0.23 0.04
28600 5.34e-05 1.15e-04 2.07e-06 0.21 0.04
28700 5.42e-05 1.16e-04 2.07e-06 0.21 0.04
28800 5.33e-05 1.14e-04 1.99e-06 0.21 0.04
28900 5.30e-05 1.14e-04 1.99e-06 0.21 0.04
29000 5.25e-05 1.14e-04 1.99e-06 0.21 0.04
29100 5.28e-05 1.14e-04 1.91e-06 0.21 0.04
29200 5.34e-05 1.14e-04 1.91e-06 0.21 0.04
29300 5.33e-05 1.14e-04 1.91e-06 0.21 0.04
29400 5.30e-05 1.14e-04 1.83e-06 0.21 0.04
29500 5.30e-05 1.14e-04 1.83e-06 0.23 0.04
29600 5.31e-05 1.14e-04 1.83e-06 0.22 0.04
29700 5.26e-05 1.14e-04 1.76e-06 0.22 0.04
29800 5.45e-05 1.14e-04 1.76e-06 0.22 0.04
29900 5.27e-05 1.14e-04 1.76e-06 0.22 0.04
30000 5.50e-05 1.14e-04 1.69e-06 0.22 0.04
examples/legacy/iter_nn_new/run.py 0 → 100644
View file @ 47af8be9
#!/usr/bin/env python
# coding: utf-8
import os
import sys
import numpy as np
# sys.path.append('/path/to/source')
import deepks
from deepks.scf.stats import collect_data_grouped
from deepks.task.task import PythonTask, BatchTask, GroupBatchTask
from deepks.task.workflow import Sequence, Iteration
nsys = 1
niter = 10
ntrain = 1000
train_idx = np.arange(ntrain)
# SCF
scf_cmd_tmpl = " ".join([
"python -u /path/to/source/deepks/scf/main.py",
"scf_input.yaml",
"-m model.pth",
"-s mol_files.raw",
"-d results"])
envs = {"PYSCF_MAX_MEMORY": 16000}
scf_res = {"cpus_per_task": 10,
"time_limit": "6:00:00",
"mem_limit": 16,
"envs": envs}
task_scf = GroupBatchTask(
[BatchTask(scf_cmd_tmpl.format(i=i),
workdir=".", #f'task.{i}',
share_folder='share',
link_share_files=['mol_files.raw', 'scf_input.yaml'])
for i in range(nsys)],
workdir='00.scf',
outlog='log.scf',
resources=scf_res,
link_prev_files=['model.pth'])
# labeling
task_data = PythonTask(
lambda: [collect_data_grouped(train_idx=train_idx,
append=True,
ene_ref=f"e_ref.npy",
force_ref=f"f_ref.npy",
sys_dir=f"results")
for i in range(nsys)],
outlog='log.data',
workdir='01.data',
link_prev_files=['model.pth'] + [f"results" for i in range(nsys)],
share_folder='share',
link_share_files=[f'e_ref.npy' for i in range(nsys)]
+[f'f_ref.npy' for i in range(nsys)])
# training
train_cmd = " ".join([
"python -u /path/to/source/deepks/train/main.py",
"train_input.yaml",
"--restart old_model.pth"])
train_res = {"time_limit": "24:00:00",
"mem_limit": 32,
"numb_gpu": 1}
task_train = BatchTask(cmds=train_cmd,
outlog='log.train',
workdir='02.train',
resources=train_res,
link_prev_files=[('model.pth', 'old_model.pth'),
'train_paths.raw', 'test_paths.raw'],
share_folder = 'share',
link_share_files=["train_input.yaml"])
# combine
seq = Sequence([task_scf, task_data, task_train])
iterate = Iteration(seq, niter, init_folder='share/init', record_file='RECORD')
if os.path.exists('RECORD'):
iterate.restart()
else:
iterate.run()
examples/legacy/iter_nn_new/share/init/model.pth 0 → 120000
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../../init_train/model.pth
\ No newline at end of file
examples/legacy/iter_nn_new/share/mol_files.raw 0 → 100644
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examples/legacy/iter_nn_new/share/raw_scf_input.yaml 0 → 100644
View file @ 47af8be9
basis: ccpvdz
systems: [mol_files.raw]
model_file: model.pth
dump_dir: results
dump_fields: [e_base, e_tot, dm_eig, conv, f_base, f_tot, grad_vx]
group: true
verbose: 1
scf_args:
conv_tol: 1e-7
conv_tol_grad: 3e-3
level_shift: 0.1
diis_space: 20
conv_check: false
examples/legacy/iter_nn_new/share/scf_input.yaml 0 → 100644
View file @ 47af8be9
basis: ccpvdz
systems: [mol_files.raw]
model_file: model.pth
dump_dir: results
dump_fields: [e_base, e_tot, dm_eig, conv, f_base, f_tot, grad_vx]
group: true
verbose: 1
scf_args:
conv_tol: 1e-7
conv_tol_grad: 3e-3
level_shift: 0.1
diis_space: 20
conv_check: false
penalty_terms:
- type: coulomb
required_labels: [dm]
strength: 1
random: true
examples/legacy/iter_nn_new/share/train_input.yaml 0 → 100644
View file @ 47af8be9
model_args:
hidden_sizes: [100, 100, 100]
output_scale: 100
use_resnet: true
actv_fn: mygelu
data_args:
batch_size: 16
group_batch: 1
e_name: l_e_delta
f_name: l_f_delta
extra_label: true
preprocess_args:
preshift: false
prescale: false
prefit_ridge: 1e1
prefit_trainable: false
train_args:
ckpt_file: model.pth
decay_rate: 0.7
decay_steps: 1000
display_epoch: 100
force_factor: 0.1
n_epoch: 10000
start_lr: 0.0001
train_paths:
- train_paths.raw
test_paths:
- test_paths.raw
examples/legacy/iter_nn_slurm/run.py 0 → 100644
View file @ 47af8be9
#!/usr/bin/env python
# coding: utf-8
import os
import sys
import glob
import numpy as np
# sys.path.append('/path/to/source')
import deepks
from deepks.task.task import PythonTask
from deepks.task.task import ShellTask
from deepks.task.task import BatchTask
from deepks.task.task import GroupBatchTask
from deepks.task.workflow import Sequence
from deepks.task.workflow import Iteration
from deepks.scf.stats import collect_data
niter = 20
# Define Training
nmodel = 4
train_res = {"time_limit": "24:00:00",
"mem_limit": 32,
"numb_gpu": 1}
train_cmd = "python -u /path/to/source/deepks/train/main.py input.yaml"
batch_train = [BatchTask(cmds=train_cmd,
workdir=f'task.{i:02}',
share_folder="share",
link_share_files=["input.yaml"],
link_prev_files=['train_paths.raw', 'test_paths.raw'])
for i in range(nmodel)]
run_train = GroupBatchTask(batch_train,
resources=train_res,
outlog="log.train")
post_train = ShellTask("ln -s task.00/model.pth .")
clean_train = ShellTask("rm slurm-*.out")
train_flow = Sequence([run_train, post_train, clean_train], workdir='00.train')
# Define SCF
ngroup = 24
ntrain = 3000
mol_files = np.loadtxt('share/mol_files.raw', dtype=str)
group_files = [mol_files[i::ngroup] for i in range(ngroup)]
envs = {"PYSCF_MAX_MEMORY": 32000}
scf_res = {"cpus_per_task": 5,
"time_limit": "24:00:00",
"mem_limit": 32,
"envs": envs}
remote = {"work_path": '/home/yixiaoc/SCR/yixiaoc/tmp',
"hostname": "della",
"username": "yixiaoc",
"port": 22}
disp = {"context_type": 'ssh',
"batch_type": 'slurm',
"remote_profile": remote}
cmd_templ = " ".join([
"python -u /path/to/source/deepks/scf/main.py",
"{mol_files}",
"-m ../model.pth",
"-d ../results",
"-B ccpvdz",
"--verbose 1",
"--conv-tol 1e-6",
"--conv-tol-grad 3e-2"
])
batch_scf = [BatchTask(cmds=cmd_templ.format(mol_files=" ".join(gf)),
workdir=f'task.{i:02}',
backward_files=['log.scf', 'err'])
for i, gf in enumerate(group_files)]
run_scf = GroupBatchTask(batch_scf,
dispatcher=disp,
resources=scf_res,
outlog="log.scf",
link_prev_files=['model.pth'],
forward_files=['model.pth'],
backward_files=['results/*'])
all_idx = np.loadtxt('share/index.raw', dtype=int)
train_idx = all_idx[:ntrain]
test_idx = all_idx[ntrain:]
post_scf = PythonTask(collect_data, call_args=[train_idx, test_idx],
call_kwargs={"sys_dir": "results", "ene_ref": "e_ref.npy"},
outlog='log.data',
share_folder='share',
link_share_files=['e_ref.npy'])
clean_scf = ShellTask("rm slurm-*.out")
scf_flow = Sequence([run_scf, post_scf, clean_scf], workdir='01.scf')
# Group them together
per_iter = Sequence([train_flow, scf_flow])
iterate = Iteration(per_iter, niter, init_folder='share/init', record_file='RECORD')
if os.path.exists('RECORD'):
iterate.restart()
else:
iterate.run()
examples/legacy/iter_nn_slurm/run_res.py 0 → 100644
View file @ 47af8be9
#!/usr/bin/env python
# coding: utf-8
import os
import sys
import glob
import numpy as np
# sys.path.append('/path/to/source')
import deepks
from deepks.task.task import PythonTask
from deepks.task.task import ShellTask
from deepks.task.task import BatchTask
from deepks.task.task import GroupBatchTask
from deepks.task.workflow import Sequence
from deepks.task.workflow import Iteration
from deepks.scf.stats import collect_data
niter = 5
ntrain = 7000
# Define Training
nmodel = 4
train_res = {"time_limit": "6:00:00",
"mem_limit": 32,
"numb_gpu": 1}
train_cmd = "python -u /path/to/source/deepks/train/main.py input.yaml --restart ../old_model.pth"
batch_train = [BatchTask(cmds=train_cmd,
workdir=f'task.{i:02}',
share_folder="share",
link_share_files=["input.yaml"],
link_prev_files=['train_paths.raw', 'test_paths.raw'])
for i in range(nmodel)]
run_train = GroupBatchTask(batch_train,
resources=train_res,
outlog="log.train",
link_prev_files=[('model.pth', 'old_model.pth')])
post_train = ShellTask("ln -s task.00/model.pth .")
clean_train = ShellTask("rm slurm-*.out")
train_flow = Sequence([run_train, post_train, clean_train], workdir='00.train')
# Define SCF
ngroup = 12
mol_files = np.loadtxt('share/mol_files.raw', dtype=str)
group_files = [mol_files[i::ngroup] for i in range(ngroup)]
envs = {"PYSCF_MAX_MEMORY": 32000}
scf_res = {"cpus_per_task": 5,
"time_limit": "6:00:00",
"mem_limit": 32,
"envs": envs}
remote = {"work_path": '/home/yixiaoc/SCR/yixiaoc/tmp',
"hostname": "della",
"username": "yixiaoc",
"port": 22}
disp = {"context_type": 'ssh',
"batch_type": 'slurm',
"remote_profile": remote}
cmd_templ = " ".join([
"python -u /path/to/source/deepks/scf/main.py",
"{mol_files}",
"-m ../model.pth",
"-d ../results",
"-B ccpvdz",
"--verbose 1",
"--conv-tol 1e-6",
"--conv-tol-grad 3e-2"
])
batch_scf = [BatchTask(cmds=cmd_templ.format(mol_files=" ".join(gf)),
workdir=f'task.{i:02}',
backward_files=['log.scf', 'err'])
for i, gf in enumerate(group_files)]
run_scf = GroupBatchTask(batch_scf,
dispatcher=disp,
resources=scf_res,
outlog="log.scf",
link_prev_files=['model.pth'],
forward_files=['model.pth'],
backward_files=['results/*'])
all_idx = np.loadtxt('share/index.raw', dtype=int)
train_idx = all_idx[:ntrain]
test_idx = all_idx[ntrain:]
post_scf = PythonTask(collect_data, call_args=[train_idx, test_idx],
call_kwargs={"sys_dir": "results", "ene_ref": "e_ref.npy"},
outlog='log.data',
share_folder='share',
link_share_files=['e_ref.npy'])
clean_scf = ShellTask("rm slurm-*.out")
scf_flow = Sequence([run_scf, post_scf, clean_scf], workdir='01.scf')
# Group them together
per_iter = Sequence([train_flow, scf_flow])
iterate = Iteration(per_iter, niter, init_folder='share/init', record_file='RECORD')
if os.path.exists('RECORD'):
iterate.restart()
else:
iterate.run()
examples/legacy/iter_nn_slurm/share/index.raw 0 → 100644
View file @ 47af8be9
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3204
1382
2636
394
6730
56
3095
4372
3807
3378
4434
5991
3604
1226
3776
2862
6489
5045
2315
2983
1615
3916
6511
4359
1828
5527
2353
6403
230
5696
4436
1681
701
1538
477
2344
4192
7206
5722
5286
1555
1082
3359
3772
2397
730
2294
2216
6854
108
6412
1770
5046
3728
2923
2419
6205
2677
1150
1962
3179
2998
2991
1356
6689
2670
3818
6916
5780
3764
6649
5944
2662
6877
221
4764
5123
5955
4545
4400
6299
4497
2086
4184
745
1053
1876
3291
4677
5502
6885
4028
3528
4471
757
554
1798
6406
4215
5294
3980
16
6571
300
3545
1375
4326
5267
3870
1652
851
7088
5049
2037
2020
3488
2284
3323
251
5240
3626
7023
4826
6230
5663
844
7001
4863
580
5328
2097
6223
812
3753
3557
4648
5998
14
143
6377
6343
5968
2897
5231
2735
2979
3659
6051
3262
3773
193
2040
6450
3168
6566
4671
4937
6614
5531
3282
6587
2011
4227
1485
3537
2154
6636
3836
383
6119
3492
3909
2384
4942
3021
6557
4074
4935
5283
2346
6066
1540
870
2454
2968
4682
6298
6510
6627
4405
7158
4212
1221
6056
3471
68
1180
4975
2891
6209
1519
6277
4877
445
932
4330
1434
2522
109
2283
4727
2358
5582
872
2159
4256
6108
5536
1918
983
2104
5839
2769
878
3306
4321
5766
310
4066
3338
5796
2724
3748
6021
5015
1526
5651
881
5971
2235
1395
5010
3023
4352
5657
3708
3067
1022
2742
1039
5447
3930
7060
2017
6989
5482
6597
3832
3125
3268
5725
2388
3581
6171
5773
6064
6466
189
5525
5056
4152
3693
6151
454
6327
7161
2288
2819
6481
1797
6388
6778
4853
4964
4814
3238
33
5793
5863
5756
6592
3525
1481
3578
4563
2151
4301
1219
306
7114
35
3356
2863
6692
5610
5795
1559
1875
1374
4644
729
4133
5432
4477
3205
444
3918
4240
2777
4521
4081
6163
843
2696
3953
661
3441
6751
5514
1006
2075
3740
750
4550
6438
3440
986
791
6773
3646
6741
6467
421
2259
4446
4124
3484
514
376
1033
3508
5071
1096
5488
4172
2818
4270
5783
59
1780
4087
2730
4103
266
322
1217
1444
2722
1380
3030
2799
6255
4978
2440
138
1139
6955
4203
4312
1146
862
4488
1388
222
1327
4141
6005
2802
1452
5004
6800
360
5828
2789
5016
3965
2022
3401
1475
1630
3796
4068
1372
145
2415
4744
1636
7041
2910
5391
4776
6019
5451
4857
3141
1480
3873
540
4892
6256
4413
3081
6031
4198
5348
2945
2423
4380
1168
381
7131
3932
6793
6204
567
87
462
2497
3892
5871
2829
6283
4970
1848
3568
2134
538
1898
1581
6786
3320
2560
6822
6940
1897
3038
373
3215
2623
807
1337
2723
4166
6938
2570
6082
4609
5537
1648
1460
5962
3027
215
5129
6701
6180
6015
3462
5186
2814
4839
6144
458
6269
4457
6126
6952
4391
756
6835
6384
5530
2203
5468
1666
1829
1719
5892
4487
201
6273
6486
2718
6668
2994
5222
1093
1102
6036
922
6828
4347
5633
5626
1568
1632
7166
141
5803
1119
1433
2525
5280
5025
6312
6346
4569
1700
4980
3474
2721
1411
4115
4714
2648
5409
2173
1799
1669
6222
1544
297
1166
6541
2269
3791
2717
139
1943
4518
3826
3878
6672
77
1777
1920
6842
7137
4368
6302
441
7099
3374
1685
1708
2325
2138
1970
5747
6728
7026
2474
3419
5474
5606
2736
4843
680
6857
2226
2045
2387
2850
5621
4787
4077
5913
3265
5443
3250
4353
2186
5515
316
5014
3643
3330
2300
2422
54
1832
4479
3087
5183
6167
5319
1103
1129
1722
1264
5958
7072
5938
2567
5263
5236
938
2574
4766
2739
1562
5261
6270
2303
7157
2252
6849
422
3706
1560
5400
1579
7136
1314
4332
523
5424
4901
703
6770
5320
3959
1177
4611
5594
1824
5180
3949
840
5697
2460
3106
5160
2534
3529
5596
3473
3380
4375
1647
6190
359
596
2382
6834
2025
1470
5569
1116
1571
1858
3910
5588
2817
2554
385
3324
1189
3477
6149
6909
5239
1497
4548
3889
6646
6479
6944
4607
1599
2841
5057
238
6111
5276
3858
4492
763
2181
1332
6057
5363
1351
95
3760
3771
1343
5169
1960
2876
6872
1003
677
2966
6532
1631
6706
749
1988
1506
1002
759
4949
7147
6458
6521
7171
2864
235
3768
387
6289
2587
1859
6840
1042
1939
2276
1527
2532
3843
4448
1344
2482
3450
1023
1
4046
4819
903
5470
7080
849
2171
2622
4096
2486
4923
4168
1465
662
2405
98
1273
2046
5274
172
1621
2618
6804
3819
4464
2513
3096
6030
2692
3563
5130
1165
5699
4392
912
3499
5399
60
1269
413
6630
1469
2116
3128
1046
1689
4128
7062
3169
897
6211
7030
4799
2356
5549
1996
1012
4802
2256
5288
1541
2562
4056
483
2726
3956
3651
2847
2205
5615
5336
2042
4781
1956
3455
6258
683
5350
6139
6901
2016
6290
1602
2157
309
937
3434
6516
4494
4953
2674
5887
704
3955
3967
5914
3849
3506
3596
2553
2993
3437
5466
5059
1186
2714
6970
144
995
1642
6259
2826
4370
5302
964
1484
5844
3379
6069
1442
3850
1691
4328
723
1761
1276
3660
4827
4986
6568
2924
6095
6381
2695
592
6951
3072
1381
2054
4811
6426
882
3720
6517
2031
1267
2977
7124
7098
2832
3731
1225
4639
4371
6523
5187
6591
6339
2913
2265
3914
4932
5331
4630
6664
2757
6496
782
5273
3683
3386
5921
6495
3754
4248
3669
1419
3933
5630
2262
6883
1197
3247
5577
6199
64
255
4996
1224
7178
5144
3286
629
1156
6447
4905
531
1825
5473
3553
3407
99
760
5778
5000
2502
6575
6385
5114
3632
1029
561
6366
605
160
650
3515
2542
909
4649
4029
6396
183
6535
2463
3609
3412
1265
2446
5730
1414
3263
5387
5134
5242
2751
6529
3164
3763
6416
3481
5672
1849
2804
3052
1365
6759
1767
624
1768
1455
598
6679
1570
5875
3852
6468
5012
81
4561
5357
5147
2399
1008
6338
6735
171
3988
3297
27
1513
1998
2624
5813
6226
2237
1565
5418
5017
1298
1436
778
3789
4973
1644
2725
6736
544
1295
4939
7134
2971
3727
6869
3390
1229
1951
2078
3442
328
1255
1902
3050
4847
974
1518
1075
2115
2855
1245
4314
6655
5307
2427
6276
6332
105
6602
304
1874
6993
3893
3579
3697
2980
3719
6132
6293
6739
3138
3219
3495
2985
5175
4735
2313
4850
2868
4356
5857
3066
3950
5870
6695
6037
6528
5572
2756
5494
3607
4695
2830
3844
2150
3468
1963
4102
1977
5305
3154
4694
4691
2970
642
3670
4183
7129
6673
7042
446
1045
956
2323
2704
2917
6995
2658
4642
3915
6550
4858
428
4237
7141
5195
5318
5146
4603
234
1785
3972
5779
2716
2523
2436
1921
3902
2582
6999
6780
1523
775
2280
3726
12
6414
2199
6394
4439
449
6393
3876
4625
6551
5655
4810
5106
2293
7087
5040
3624
4596
2345
838
5504
4854
4016
2914
2270
7076
5412
5729
4722
2052
2070
6170
1007
4262
5681
4627
4591
3954
1533
1539
4662
5375
315
2357
7125
599
3997
4988
2215
4453
5967
2592
126
5082
2082
1379
4503
4572
607
1024
5807
3214
1249
214
1517
3616
5117
1864
2564
1754
4552
6824
516
4374
3117
5587
7018
3449
1489
345
552
1912
2685
5981
2112
1087
4005
4197
267
4277
4646
955
3026
2605
5544
6497
1329
6424
6434
437
6039
4363
7092
4995
1230
6819
3124
6832
3654
2449
2227
5026
5054
5310
1181
6110
5516
281
5831
5919
4817
2879
1949
2489
5676
1726
3514
1155
600
2470
3366
4173
3485
990
4417
6062
7050
4284
353
3337
5642
3165
5104
6843
4053
4149
150
4619
6997
3220
2263
1752
5182
476
3460
6892
6291
2081
3517
465
2312
6135
3037
1774
3435
3735
929
6182
1778
1586
7077
3365
3046
2027
5157
3107
186
5023
2894
5469
5472
2102
893
1407
610
4590
3042
5073
5644
4030
2733
5192
3392
1940
6572
6815
2612
3718
1068
1908
5241
2892
3957
4483
6774
1478
3344
3936
2435
6131
2374
4797
6325
3815
623
5858
3821
4761
7160
485
4026
2149
2494
6915
3823
997
1968
6908
5449
548
494
3600
1348
2275
563
4075
5819
1961
4945
9
5689
2919
6263
797
4777
311
4122
1001
2652
6400
2703
3929
4895
2807
4387
5029
5201
1546
5786
6498
996
2873
5935
6118
551
2720
941
2285
3701
5545
4693
5973
3699
1954
4519
1690
1285
3592
323
2790
1487
5551
6984
740
6352
6437
271
473
1610
5080
3716
6165
2583
5853
6324
5535
236
5430
6704
4510
179
815
5471
6586
1004
5784
3621
579
4426
5996
3883
2060
1535
714
6512
6943
5051
4900
2669
2620
5540
6798
6102
6321
3146
7054
3610
4734
7148
848
389
5677
6888
1701
6008
5330
651
1253
148
4210
4108
3845
5524
2330
7179
2737
1047
85
5816
6678
505
4443
7194
6220
6233
1639
855
5976
2650
1670
2392
2426
4557
6419
3814
5353
1623
5899
4048
2591
1074
3586
3700
3863
3031
6214
3234
6569
2909
1420
3947
4899
6621
5890
3384
7024
3195
1649
2681
5948
4094
3580
4711
93
6758
5761
2444
3043
3470
1106
3648
1254
4406
2535
3152
6596
4264
4412
678
2420
5396
4155
3684
2631
907
5980
4985
362
277
864
6239
6768
5420
1209
1658
5805
1884
5662
3313
771
5058
5571
5937
188
1901
3536
5743
1138
3520
2738
6702
4435
4687
4668
2182
1739
894
1997
1335
7181
3973
634
5356
702
6896
80
3315
4534
5532
1596
719
7111
4982
5708
6816
75
3824
915
2080
3516
4554
4707
6202
5818
4415
2834
181
4360
6936
6808
920
279
1203
6925
4533
4515
1425
6446
787
3427
1836
5480
1850
4506
1814
867
69
3009
6554
2689
6059
245
5736
4567
3298
1657
1536
2334
5127
6192
4904
1801
243
2430
1603
4654
2656
6409
5444
2761
3533
4047
5
3985
511
6756
5912
7152
4261
873
5903
4604
4873
4774
3808
2056
542
530
1306
4911
4575
1698
621
1500
4971
5311
2055
5442
5408
3235
3034
4098
1578
5371
326
6249
1822
3490
1755
1773
4578
2758
2424
15
4334
3839
992
4834
6733
478
3793
7204
832
1922
6235
1233
4632
1916
6369
2575
3548
3270
4829
2676
6146
5275
5268
1705
1748
62
4220
3903
252
6839
2257
3357
2627
6137
6787
302
5593
2786
1608
4608
430
200
5640
4422
2691
785
3724
3211
7170
3504
4573
3410
939
1232
4063
3086
7000
6544
3990
2109
2320
268
7150
4110
3208
4116
6133
1907
6674
6432
453
344
2503
3126
73
6296
1744
1198
1404
6996
1457
6415
2947
6542
1072
921
5706
4675
4036
7197
6115
180
4281
2383
5213
819
2125
2061
49
424
1819
2196
7045
3114
3429
3190
114
7101
5178
6342
4595
6848
3131
4287
2242
5450
3016
4089
6525
3233
4535
7121
1607
2026
6913
2376
4820
4823
5459
1715
4815
3637
1192
3518
4060
2812
1991
3404
3723
2683
1709
2155
5370
4250
6391
5210
3173
4927
1865
5490
2432
5860
4884
4710
124
6130
6792
3415
606
5771
2778
1995
6507
3787
5190
1260
132
2492
3350
5956
4291
1985
6578
7140
6089
883
2594
5024
212
3786
6945
1665
6574
6637
4419
4423
2429
1909
3502
4054
6665
4202
6547
165
834
5266
5434
5576
2152
3180
961
5713
822
2686
3475
5548
3388
4207
5421
5694
401
5905
3327
4322
6397
1514
1493
608
4918
5750
5206
1370
1456
4393
2558
4462
411
1989
5988
3647
1861
6623
320
4504
319
2277
2801
887
1882
4940
5754
5886
4785
5145
6858
5446
3254
611
5149
5352
6874
4888
380
1883
4343
3243
5661
3232
1064
6032
3400
6609
1756
283
1090
1934
5512
1981
6942
985
3851
5768
559
2487
4865
6971
4698
5257
3358
5519
6663
5027
4505
1105
6866
5338
3274
1354
4719
6767
4084
5165
1974
3452
1081
2816
5001
1979
3688
2404
6334
5609
275
3970
5052
3527
4186
3682
1275
4513
1507
4579
876
5043
6962
539
742
6248
419
7175
1583
5878
5069
2763
858
22
1525
6727
1931
6651
2859
313
7032
6667
527
5769
386
1393
5983
4484
2350
5915
482
979
6085
7188
1779
5717
1683
1747
6549
18
5440
4209
2105
1408
4531
2093
5142
2773
3193
4258
229
6802
6653
3711
6191
1268
5925
3329
5850
2521
3966
486
6462
576
1304
5414
4822
946
2381
6504
5832
3549
3896
3598
6871
688
800
4244
2207
154
2304
1628
5220
6797
1721
963
637
2053
2337
3231
6629
5439
1958
5198
3209
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3015
2671
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6257
2779
4320
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568
4871
1741
818
3083
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594
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3166
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157
2445
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2092
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1964
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817
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1983
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628
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968
3008
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1930
208
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433
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1992
204
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104
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2009
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354
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1428
292
5249
2529
1789
3222
3063
3136
660
5478
3428
6715
7053
1010
4523
2473
2015
329
6882
2886
4600
5601
3089
6684
43
859
3161
3939
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2101
5726
5107
2200
4478
7190
2925
616
3483
4195
2603
392
3402
6795
4023
6488
6990
520
5896
6956
1982
4538
1697
1448
3888
3456
5563
2403
2860
5055
5849
3501
2871
5629
4280
1688
6723
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1338
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82
7191
1164
4367
1764
2425
3241
999
1724
3606
5616
2409
4616
39
518
5333
4440
1966
187
2867
4043
1324
3103
6140
2003
6216
2287
2660
1261
5835
1687
352
4786
3738
3532
2903
892
2095
831
3308
6354
7052
5381
1035
877
286
6476
1787
5856
4549
5260
1399
549
3393
1140
47
4204
5741
6482
1994
3246
801
6075
6101
3543
2128
6454
5845
1062
4666
1305
4752
5232
3969
3964
4530
5534
6052
1011
6934
218
3017
4921
6757
3641
1806
51
5566
3019
1975
795
6322
34
1905
6227
4105
1532
3309
2665
287
6761
6624
720
3801
1854
6305
3236
4824
3255
1296
1971
5245
509
776
3055
1839
2996
2271
1847
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6459
2765
1231
2606
305
3367
2734
4401
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225
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7056
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4247
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280
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1965
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1941
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978
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3303
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810
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1967
246
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examples/legacy/iter_nn_slurm/share/init/test_paths.raw 0 → 100644
View file @ 47af8be9
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examples/legacy/iter_nn_slurm/share/init/train_paths.raw 0 → 100644
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examples/legacy/iter_nn_slurm/share/input.yaml 0 → 100644
View file @ 47af8be9
model_args:
hidden_sizes: [100, 100, 100]
output_scale: 100
use_resnet: true
actv_fn: gelu
data_args:
batch_size: 1
group_batch: 16
e_name: l_e_delta
d_name: [dm_eig]
preprocess_args:
preshift: false
prescale: false
prefit_ridge: 1e0
prefit_trainable: false
train_args:
ckpt_file: model.pth
decay_rate: 0.96
decay_steps: 500
display_epoch: 100
n_epoch: 30000
start_lr: 0.0001
train_paths:
- train_paths.raw
test_paths:
- test_paths.raw
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