def main(argv):
from experiments.util import AsyncExecutor, generate_launch_commands
import experiments.meta_overfitting_v2.neural_processes_overfitting_base
command_list = []
for dataset in FLAGS.datasets.split(','):
if dataset == 'sin':
n_context_samples = [5]
elif dataset == 'cauchy':
n_context_samples = [20]
else:
raise AssertionError('dataset must be either of [sin, cauchy]')
exp_config = {
'exp_name': ['meta-overfitting-v2-nps-%s' % dataset],
'dataset': [dataset],
'n_threads': [N_THREADS],
'seed': list(range(30, 55)),
'data_seed': [28],
'weight_decay': [0.0, 1e-3, 1e-2, 1e-1, 2e-1, 4e-1],
'r_dim': [256, 512],
'n_iter_fit': [30000],
'lr': [1e-3],
'lr_decay': [0.97],
'n_train_tasks': [2, 4, 8, 16, 32, 64, 128, 256, 512],
'n_test_tasks': [200],
'n_context_samples': n_context_samples,
'n_test_samples': [100],
}
command_list.extend(
generate_launch_commands(
experiments.meta_overfitting_v2.
neural_processes_overfitting_base, exp_config))
if FLAGS.cluster:
cluster_cmds = []
for python_cmd in command_list:
cmd_hash = hashlib.md5(str.encode(python_cmd)).hexdigest()
bsub_cmd = 'bsub -oo /cluster/project/infk/krause/rojonas/stdout/gp-priors/meta-overfitting/%s.out' \
' -W 03:59'\
' -R "rusage[mem=1048]"' \
' -n %i '% (cmd_hash, N_THREADS)
cluster_cmds.append(bsub_cmd + ' ' + python_cmd)
answer = input(
"About to submit %i compute jobs to the cluster. Proceed? [yes/no]\n"
% len(cluster_cmds))
if answer == 'yes':
for cmd in cluster_cmds:
os.system(cmd)
else:
answer = input("About to run %i compute jobs locally on %i workers. "
"Proceed? [yes/no]\n" %
(len(command_list), FLAGS.n_workers))
if answer == 'yes':
exec_fn = lambda cmd: os.system(cmd)
executor = AsyncExecutor(n_jobs=FLAGS.n_workers)
executor.run(exec_fn, command_list)
def main(argv):
hparam_search_modules = [algo_map_dict[algo_str] for algo_str in FLAGS.algos.split(',')]
command_list = []
for hparam_search_module in hparam_search_modules:
exp_config = {
'dataset': ['cauchy_20', 'sin_20', 'physionet_0', 'physionet_2', 'swissfel'],
'covar_module': ['NN'],
'num_cpus': [2 * FLAGS.n_cpus],
'metric': [FLAGS.metric]
}
if FLAGS.load_analysis:
exp_config['load_analysis'] = [True]
if FLAGS.resume:
exp_config['resume'] = [True]
command_list += generate_launch_commands(hparam_search_module, exp_config, check_flags=False)
print(command_list)
if FLAGS.cluster:
cluster_cmds = []
for python_cmd in command_list:
bsub_cmd = 'bsub' \
' -W %i:59'%(3 if FLAGS.load_analysis else 23) + \
' -R "rusage[mem=6000]"' + \
' -R "rusage[ngpus_excl_p=%i]"'%FLAGS.n_gpus + \
' -R "span[hosts=1]"' \
' -n %i '% (FLAGS.n_cpus)
cluster_cmds.append(bsub_cmd + ' ' + python_cmd)
answer = input("About to submit %i compute jobs to the cluster. Proceed? [yes/no]\n"%len(cluster_cmds))
if answer == 'yes':
for cmd in cluster_cmds:
if FLAGS.dry:
print(cmd)
else:
os.system(cmd)
else:
answer = input("About to run %i compute jobs in a for loop. Proceed? [yes/no]\n"%len(command_list))
if answer == 'yes':
for cmd in command_list:
if FLAGS.dry:
print(cmd)
else:
os.system(cmd)
N_THREADS = 1
exp_config = {
'exp_name': ['meta-overfitting-swissfel'],
'dataset': ['swissfel'],
'n_threads': [N_THREADS],
'seed': [31, 32, 33, 34, 35],
'weight_decay': list(np.arange(0.05, 1.1, step=0.05)),
'covar_module': ['NN'],
'mean_module': ['NN'],
'num_layers': [4],
'layer_size': [32],
'n_iter_fit': [30000],
}
command_list = generate_launch_commands(experiments.meta_GPR_mll_base_exp, exp_config)
if cluster :
cluster_cmds = []
for python_cmd in command_list:
cmd_hash = hashlib.md5(str.encode(python_cmd)).hexdigest()
bsub_cmd = 'bsub -oo /cluster/project/infk/krause/rojonas/stdout/gp-priors/meta-overfitting/%s.out' \
' -W 3:59'\
' -R "rusage[mem=8048]"' \
' -n %i '% (cmd_hash, N_THREADS)
cluster_cmds.append(bsub_cmd + ' ' + python_cmd)
answer = input("About to submit %i compute jobs to the cluster. Proceed? [yes/no]\n"%len(cluster_cmds))
if answer == 'yes':
for cmd in cluster_cmds:
os.system(cmd)
NUM_CPUS = 20
command_list = []
for hparam_search_module in [
meta_mll_hparam, meta_svgd_hparam, meta_vi_hparam
]:
exp_config = {
'dataset':
['cauchy_20', 'sin_20', 'physionet_0', 'physionet_2', 'swissfel'],
'covar_module': ['NN'],
'num_cpus': [NUM_CPUS]
}
command_list += generate_launch_commands(hparam_search_module,
exp_config,
check_flags=False)
print(command_list)
if CLUSTER:
cluster_cmds = []
for python_cmd in command_list:
bsub_cmd = 'bsub' \
' -W 23:00'\
' -R "rusage[mem=4500]"' \
' -R "rusage[ngpus_excl_p=1]"' \
' -n %i '% (NUM_CPUS)
cluster_cmds.append(bsub_cmd + ' ' + python_cmd)
answer = input(