def test_hyper_time_limited(test_config):
config = DEFAULT_CONFIG.copy()
config.update(a2c.config)
config.update(simple_addition.config)
config.update(test_config)
config['max_steps'] = 100000
config['checkpoint_step'] = 43
distributions = dict(n_train=2**np.array([5, 6, 7]))
n_repeats = 2
session = build_and_submit(
name="test_hyper", config=config, distributions=distributions,
n_repeats=n_repeats, kind='parallel', host_pool=':', wall_time='1min',
cleanup_time='10seconds', slack_time='5seconds', ppn=2, load_avg_threshold=1e6)
path = session.job_path
files = os.listdir(path)
assert set(files) == set(
['orig.zip', 'experiments', 'os_environ.txt', 'results.zip', 'pip_freeze.txt',
'dps_git_summary.txt', 'nodefile.txt', 'results.txt', 'job_log.txt', 'uname.txt', 'lscpu.txt']
)
experiments = os.listdir(os.path.join(path, 'experiments'))
for exp in experiments:
assert exp.startswith('exp_')
assert os.path.isdir(os.path.join(path, 'experiments', exp))
assert len(experiments) == n_repeats * len(distributions['n_train'])
with open(os.path.join(path, 'results.txt'), 'r') as f:
results = f.read()
assert "n_ops: 6" in results
assert "n_completed_ops: 6" in results
assert "n_partially_completed_ops: 0" in results
assert "n_ready_incomplete_ops: 0" in results
assert "n_not_ready_incomplete_ops: 0" in results
def run_experiment(name,
config,
readme,
distributions=None,
durations=None,
alg=None,
task="grid",
name_variables=None,
env_kwargs=None):
name = sanitize(name)
durations = durations or {}
parser = argparse.ArgumentParser()
parser.add_argument("duration", choices=list(durations.keys()) + ["local"])
args, _ = parser.parse_known_args()
_config = DEFAULT_CONFIG.copy()
env_kwargs = env_kwargs or {}
env_kwargs['task'] = task
env_config = get_env_config(**env_kwargs)
_config.update(env_config)
if alg:
alg_config = getattr(alg_module, "{}_config".format(alg))
_config.update(alg_config)
alg_name = sanitize(alg_config.alg_name)
else:
alg_name = ""
_config.update(config)
_config.update_from_command_line()
_config.env_name = "{}_env={}".format(name, sanitize(env_config.env_name))
if args.duration == "local":
_config.exp_name = "alg={}".format(alg_name)
with _config:
return training_loop()
else:
run_kwargs = Config(
kind="slurm",
pmem=5000,
ignore_gpu=False,
)
duration_args = durations[args.duration]
if 'config' in duration_args:
_config.update(duration_args['config'])
del duration_args['config']
run_kwargs.update(durations[args.duration])
run_kwargs.update_from_command_line()
if name_variables is not None:
name_variables_str = "_".join("{}={}".format(
sanitize(str(k)), sanitize(str(getattr(_config, k))))
for k in name_variables.split(","))
_config.env_name = "{}_{}".format(_config.env_name, name_variables_str)
exp_name = "{}_alg={}_duration={}".format(_config.env_name, alg_name,
args.duration)
build_and_submit(name=exp_name,
config=_config,
distributions=distributions,
**run_kwargs)