def main():
'''
spinup.ppo(env_fn, actor_critic=<function mlp_actor_critic>, ac_kwargs={}, seed=0,
steps_per_epoch=4000, epochs=50, gamma=0.99, clip_ratio=0.2, pi_lr=0.0003, vf_lr=0.001,
train_pi_iters=80, train_v_iters=80, lam=0.97, max_ep_len=1000, target_kl=0.01, logger_kwargs={}, save_freq=10)
'''
parser = base_argparser()
parser.add_argument("--pi_lr", type=float, default=0.0003)
parser.add_argument("--vf_lr", type=float, default=0.001)
parser.add_argument("--train_iters", type=int, default=80)
parser.add_argument("--clip_ratio", type=float, default=0.2)
parser.add_argument("--lam", type=float, default=0.97)
parser.add_argument("--target_kl", type=float, default=0.01)
args = parser.parse_args()
# scale_hypers(args)
args.start_steps = 0
if args.test:
test(args)
else:
if args.remote:
name = '-'.join([*args.exp_name.split('_'), str(args.seed)])
meta.call(
backend=args.backend,
fn=train,
kwargs=dict(args=args),
log_relpath=name,
job_name=name,
update=args.update,
num_gpu=0,
)
else:
train(args)
def main():
parser = argparse.ArgumentParser()
parser.add_argument('backend')
parser.add_argument('--cluster',
type=str,
default='ibis',
help='name of cluster')
args = parser.parse_args()
kwargs_dict = vars(args)
if args.backend == 'interactive':
run(**kwargs_dict)
else:
meta.call(
backend='kube',
cluster=args.cluster,
fn=run,
kwargs=kwargs_dict,
job_name='cs231n-cifar10',
log_relpath=f'hp-{datetime.now().strftime("%Y%m%d%H%M")}',
num_cpu='1',
num_gpu='8',
mpi_machines=1,
mpi_proc_per_machine='num_gpu',
)
def main():
'''
spinup.sac(env_fn, actor_critic=<function mlp_actor_critic>, ac_kwargs={}, seed=0,
steps_per_epoch=5000, epochs=100, replay_size=1000000, gamma=0.99, polyak=0.995,
lr=0.001, alpha=0.2, batch_size=100, start_steps=10000, max_ep_len=1000, logger_kwargs={}, save_freq=1)
'''
parser = base_argparser()
parser.add_argument("--polyak", type=float, default=0.995)
parser.add_argument("--alpha", type=float, default=0.2)
args = parser.parse_args()
scale_hypers(args)
if args.test:
test(args)
else:
if args.remote:
name = '-'.join([*args.exp_name.split('_')])
meta.call(
backend=args.backend,
fn=train,
kwargs=dict(args=args),
log_relpath=name,
job_name=name,
update=args.update,
num_gpu=0,
num_cpu=args.ncpu,
)
else:
train(args)
# log_relpath='atlas_%s' % datetime.datetime.now().strftime('%Y_%m_%d_%H_%M_%s'),
# num_cpu='auto',
# mpi_proc_per_machine=8,
# mpi_machines=1,
# num_gpu=8)
for op_x in ["mixed3a", "mixed3b", "mixed4a", "mixed4b", "mixed4c", "mixed4d", "mixed5a", "mixed5b"]:
for op_y in ["mixed3a", "mixed3b", "mixed4a", "mixed4b", "mixed4c", "mixed4d", "mixed5a", "mixed5b"]:
import json
import hashlib
ops = [op_x, op_y]
identifier = hashlib.md5(json.dumps((model, ops)).encode('utf-8')).hexdigest()
meta.call(
backend=args.backend,
fn=run,
args = [ identifier ],
log_relpath='atlas_%s' % datetime.datetime.now().strftime('%Y_%m_%d_%H_%M_%s'),
num_cpu='auto',
mpi_proc_per_machine=8,
mpi_machines=1,
num_gpu=8)
# D = load("gs://clarity-public/ggoh/diff/temp1/means.json")
# coordinates = D['coordinates']
# canvas = np.ones((40*A.shape[1], 40*A.shape[1],3))
# A = load("gs://clarity-public/ggoh/diff/temp1/ry.npy")
# def slice_xy(x,y,img):
# s = A.shape[1]
# log_relpath='atlas_%s' % datetime.datetime.now().strftime('%Y_%m_%d_%H_%M_%s'),
# num_cpu='auto',
# mpi_proc_per_machine=1,
# mpi_machines=1,
# num_gpu=1)
model = ("InceptionV1", "InceptionV1")
if args.backend == "interactive":
ops = ("mixed3a", "mixed4c")
run(model, ops)
else:
for op_x in [
"mixed3a", "mixed3b", "mixed4a", "mixed4b", "mixed4c",
"mixed4d", "mixed5a", "mixed5b", "head0_bottleneck",
"head1_bottleneck"
]:
for op_y in [
"mixed3a", "mixed3b", "mixed4a", "mixed4b", "mixed4c",
"mixed4d", "mixed5a", "mixed5b", "head0_bottleneck",
"head1_bottleneck"
]:
meta.call(
backend=args.backend,
fn=run,
args=[model, [op_x, op_y]],
log_relpath='atlas_%s' %
datetime.datetime.now().strftime('%Y_%m_%d_%H_%M_%s'),
num_cpu='auto',
num_gpu=1)