def main():
assert (len(sys.argv) == 4)
config_file = io.abspath2(sys.argv[1])
param_name = sys.argv[2]
sampler = sys.argv[3]
assert (io.is_safe_name(param_name))
config = io.load_config(config_file)
run_experiment(config, param_name, sampler)
print 'done'
def main():
num_args = len(sys.argv) - 1
if num_args < 1:
config_path = '../config.ini'
elif num_args > 1:
raise Exception('too many arguments: %d. %d expected' % (num_args, 1))
else:
config_path = sys.argv[1]
config_file = io.abspath2(config_path)
np.random.seed(3463)
config = io.load_config(config_file)
model_list = io.get_model_list(config['input_path'], config['pkl_ext'])
np.random.shuffle(
model_list) # In case we don't finish at least random subset
# model_list = model_list[:5] # TODO remove, test only
assert (all(io.is_safe_name(ss) for ss in model_list))
print 'using models:'
print model_list
# Sort for reprodicibility
sampler_list = sorted(BUILD_STEP_PM.keys() + BUILD_STEP_MC.keys())
print 'using samplers:'
print sampler_list
# Run n_chains in the outer loop since if process get killed we have less
# chains but with even distribution over models and samplers.
scheduled_jobs = set(queued_or_running_jobs())
for model_name in model_list:
# Get the exact samples
run_experiment(config, model_name, config['exact_name'])
# Get the sampler samples
for i in xrange(config['n_chains']):
# TODO could put ADVI init here to keep it fixed across samplers
for sampler in sampler_list:
t = time()
job_name = "slurm-%s-%s-%d" % (model_name, sampler, i)
cmd_line_args = (config_file, model_name, sampler)
if job_name in scheduled_jobs:
print '%s already in scheduled jobs, but running anyway' % job_name
options = "-c 1 --job-name=%s -t 45:00 --mem=32gb --output %s.out" % (
job_name, job_name)
end = "slurm_job_main.sh %s %s %s" % cmd_line_args
command = "sbatch %s %s" % (options, end)
print 'Executing:', command
os.system(command)
print 'wall time %fs' % (time() - t)
print 'done'
def main():
'''This program can be run in parallel across different MC_chain files
indep. This is a top level routine so I am not worried about needing a
verbosity setting.'''
assert (len(sys.argv) == 3
) # Print usage error instead to be user friendly
config_file = io.abspath2(sys.argv[1])
mc_chain_name = sys.argv[2]
assert (io.is_safe_name(mc_chain_name))
print 'config %s' % config_file
config = io.load_config(config_file)
run_experiment(config, mc_chain_name)
print 'done'
def main():
num_args = len(sys.argv) - 1
if num_args < 1:
config_path = '../config.ini'
elif num_args > 1:
raise Exception('too many arguments: %d. %d expected' % (num_args, 1))
else:
config_path = sys.argv[1]
config_file = io.abspath2(config_path)
config = io.load_config(config_file)
model_list = io.get_model_list(config['input_path'], config['pkl_ext'])
# model_list = model_list[:5] # TODO remove, test only
assert (all(io.is_safe_name(ss) for ss in model_list))
print 'using models:'
print model_list
# Sort for reprodicibility
sampler_list = sorted(BUILD_STEP_PM.keys() + BUILD_STEP_MC.keys())
print 'using samplers:'
print sampler_list
# Get the exact samples
for model_name in model_list:
run_experiment(config, model_name, config['exact_name'])
# Run n_chains in the outer loop since if process get killed we have less
# chains but with even distribution over models and samplers.
for model_name in model_list:
for _ in xrange(config['n_chains']):
# TODO could put ADVI init here to keep it fixed across samplers
for sampler in sampler_list:
t = time()
try:
run_experiment(config, model_name, sampler)
except Exception as err:
print '%s/%s failed' % (model_name, sampler)
print str(err)
print 'wall time %fs' % (time() - t)
print 'done'
def main():
num_args = len(sys.argv) - 1
if num_args < 1:
config_path = '../config.ini'
elif num_args > 1:
raise Exception('too many arguments: %d. %d expected' % (num_args, 1))
else:
config_path = sys.argv[1]
config_file = io.abspath2(config_path)
print 'config %s' % config_file
config = io.load_config(config_file)
print(config['input_path'])
chains = io.get_chains(config['input_path'], config['csv_ext'],
config['size_limit_bytes'])
print 'inputs chains:'
print chains
print 'Running njobs=%d in parallel' % config['njobs']
try_run_experiment_with_config = partial(try_run_experiment, config)
Parallel(n_jobs=config['njobs'])(map(
delayed(try_run_experiment_with_config), chains))
print 'done'