BLOCK_DEBUG = """
python3 evaluate.py \\
--problem={problem} \\
--directory=results/{policy}/{base}/{flags} \\
--repeat=1 \\
--debug=True
"""
BASE_RUNNER = (
"sbatch {queue}-N 1 -n 1 -o logs/{policy}-{base}-{flags}.log -t {time} "
"{allocation}-J {shortname}{base}{flags} "
"scripts/{policy}-{base}-{flags}.sh")
args = ArgParser(sys.argv[1:])
flags = args.pop_get("--flags", "test").split(",")
ctx = {
"presets":
args.pop_get("--presets", "conv_train"),
"policy":
args.pop_get("--policy", "rnnprop"),
"strategy":
args.pop_get("--strategy", "repeat"),
"problem":
args.pop_get("--problem", "conv_train,conv_deeper_pool,conv_cifar10_pool"),
"base":
args.pop_get("--base", "test"),
"time":
args.pop_get("--time", "24:00:00"),
}
node = args.pop_get("--node", "maverick2")
python baseline.py directory --vgpu=1
```
"""
import os
import sys
import numpy as np
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
import tensorflow as tf
import l2o
from config import ArgParser, get_eval_problem
from gpu_setup import create_distribute
args = ArgParser(sys.argv[1:])
vgpus = args.pop_get("--vgpu", default=1, dtype=int)
cpu = args.pop_get("--cpu", default=False, dtype=bool)
gpus = args.pop_get("--gpus", default=None)
use_keras = args.pop_get("--keras", default=True, dtype=bool)
distribute = create_distribute(vgpus=vgpus, do_cpu=cpu, gpus=gpus)
problem = args.pop_get("--problem", "conv_train")
target = args.pop_get("--optimizer", "adam")
target_cfg = {
"adam": {
"class_name": "Adam",
"config": {
"learning_rate": 0.005,
"beta_1": 0.9,
"beta_2": 0.999
checkpoint is used.
--periods : int
Periods to evaluate.
--stages : int
Stages to evaluate. Only use if strategy=curriculum.
(all other args) : float
Passed as overrides to strategy/policy building.
"""
if len(sys.argv) < 2:
print(HELP)
exit(0)
# Distribute args
args = ArgParser(sys.argv[1:])
vgpus = args.pop_get("--vgpu", default=1, dtype=int)
gpus = args.pop_get("--gpus", default=None)
do_cpu = args.pop_get("--cpu", default=False, dtype=bool)
distribute = create_distribute(vgpus=vgpus, do_cpu=do_cpu, gpus=gpus)
# Core args
problems = args.pop_get("--problem", "conv_train").split(",")
targets = args.pop_get("--directory", "weights").split(",")
repeat = args.pop_get("--repeat", default=10, dtype=int)
debug = args.pop_get("--debug", default=False, dtype=bool)
show_info = args.pop_get("--info", default=False, dtype=bool)
recursive = args.pop_check("--recursive")
# Unpack recursive
if recursive:
targets = [os.path.join(t, d) for t in targets for d in os.listdir(t)]
"""Resume Training.
Run with
```
python resume.py directory --vgpu=1
```
"""
import os
import sys
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
import tensorflow as tf
import l2o
from config import ArgParser
from gpu_setup import create_distribute
args = ArgParser(sys.argv[2:])
vgpus = args.pop_get("--vgpu", default=1, dtype=int)
distribute = create_distribute(vgpus=vgpus)
with distribute.scope():
strategy = l2o.strategy.build_from_config(sys.argv[1])
strategy.train()
```
"""
import os
import sys
import numpy as np
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
import tensorflow as tf
import l2o
from config import ArgParser, get_eval_problem
from gpu_setup import create_distribute
args = ArgParser(sys.argv[1:])
vgpus = args.pop_get("--vgpu", default=1, dtype=int)
distribute = create_distribute(vgpus=vgpus)
problem = args.pop_get("--problem", "conv_train")
output = args.pop_get("--out", "eval")
repeat = args.pop_get("--repeat", 10, dtype=int)
policy = l2o.policies.AdamOptimizer(
learning_rate=0.001, beta_1=0.9, beta_2=0.999, epsilon=1e-07)
opt = policy.architecture(policy)
with distribute.scope():
results = []
for i in range(repeat):
print("Evaluation Training {}/{}".format(i + 1, repeat))
"""Hyperparameter grid search."""
import os
import sys
import numpy as np
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
import tensorflow as tf
import l2o
from config import ArgParser, get_eval_problem
from gpu_setup import create_distribute
args = ArgParser(sys.argv[1:])
vgpus = args.pop_get("--vgpu", default=1, dtype=int)
distribute = create_distribute(vgpus=vgpus)
# policy_name = args.pop_get("--optimizer", "adam")
problem_name = args.pop_get("--problem", "conv_train")
learning_rates = [0.0005, 0.001, 0.002, 0.005, 0.01, 0.02, 0.05, 0.1, 0.2, 0.5]
policy_names = ["adam", "rmsprop", "sgd", "momentum", "addsign", "powersign"]
for policy_name in policy_names:
dst = "gridsearch/{}/{}".format(problem_name, policy_name)
os.makedirs(dst, exist_ok=True)
for lr in learning_rates:
if policy_name == "adam":
policy = l2o.policies.AdamOptimizer(learning_rate=lr,
import os
import sys
from config import get_default, get_preset, ArgParser
args = ArgParser(sys.argv[1:])
# Finally ready to import tensorflow
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
import tensorflow as tf
import l2o
from gpu_setup import create_distribute
# Directory
directory = args.pop_get("--directory", default="weights")
# Distribute
vgpus = int(args.pop_get("--vgpu", default=1))
memory_limit = int(args.pop_get("--vram", default=12000))
gpus = args.pop_get("--gpus", default=None)
distribute = create_distribute(
vgpus=vgpus, memory_limit=memory_limit, gpus=gpus)
# Pick up flags first
initialize_only = args.pop_check("--initialize")
# Default params
strategy = args.pop_get("--strategy", "repeat")
policy = args.pop_get("--policy", "rnnprop")
default = get_default(strategy=strategy, policy=policy)