def run_experiment_here(
experiment_function,
variant,
seed=None,
use_gpu=True,
gpu_id=0,
# Logger params:
snapshot_mode='last',
snapshot_gap=1,
force_randomize_seed=False,
log_dir=None,
):
"""
Run an experiment locally without any serialization.
:param experiment_function: Function. `variant` will be passed in as its
only argument.
:param exp_prefix: Experiment prefix for the save file.
:param variant: Dictionary passed in to `experiment_function`.
:param exp_id: Experiment ID. Should be unique across all
experiments. Note that one experiment may correspond to multiple seeds,.
:param seed: Seed used for this experiment.
:param use_gpu: Run with GPU. By default False.
:param script_name: Name of the running script
:param log_dir: If set, set the log directory to this. Otherwise,
the directory will be auto-generated based on the exp_prefix.
:return:
"""
torch.set_num_threads(1)
if force_randomize_seed or seed is None:
seed = random.randint(0, 100000)
variant['seed'] = str(seed)
log_dir = variant['log_dir']
# The logger's default mode is to
# append to the text file if the file already exists
# So this would not override and erase any existing
# log file in the same log dir.
logger.reset()
setup_logger(
snapshot_mode=snapshot_mode,
snapshot_gap=snapshot_gap,
log_dir=log_dir,
)
# Assume this file is at the top level of the repo
git_infos = get_git_infos([osp.dirname(__file__)])
run_experiment_here_kwargs = dict(
variant=variant,
seed=seed,
use_gpu=use_gpu,
snapshot_mode=snapshot_mode,
snapshot_gap=snapshot_gap,
git_infos=git_infos,
)
exp_setting = dict(
run_experiment_here_kwargs=run_experiment_here_kwargs
)
exp_setting_pkl_path = osp.join(log_dir, 'experiment.pkl')
# Check if existing result exists
prev_exp_state = None
if osp.isfile(exp_setting_pkl_path):
# Sanity check to make sure the experimental setting
# of the saved data and the current experiment run is the same
prev_exp_setting = load_pkl(exp_setting_pkl_path)
logger.log(f'Log dir is not empty: {os.listdir(log_dir)}')
if prev_exp_setting != exp_setting:
logger.log("""Previous experimental setting is not
the same as the current experimental setting.
Very risky to try to reload the previous state.
Exitting""")
logger.log(f'Previous: {prev_exp_setting}')
logger.log(f'Current: {exp_setting}')
exit(1)
try:
prev_exp_state = load_gzip_pickle(
osp.join(log_dir, 'params.zip_pkl'))
logger.log('Trying to restore the state of the experiment program')
except FileNotFoundError:
logger.log("""There is no previous experiment state available.
Do not try to restore.""")
prev_exp_state = None
# Log the variant
logger.log("Variant:")
logger.log(json.dumps(dict_to_safe_json(variant), indent=2))
variant_log_path = osp.join(log_dir, 'variant.json')
logger.log_variant(variant_log_path, variant)
# Save the current experimental setting
dump_pkl(exp_setting_pkl_path, exp_setting)
log_git_infos(git_infos, log_dir)
logger.log(f'Seed: {seed}')
set_seed(seed)
logger.log(f'Using GPU: {use_gpu}')
set_gpu_mode(use_gpu, gpu_id)
return experiment_function(variant, prev_exp_state)
def create_simple_exp_name():
"""
Create a unique experiment name with a timestamp
"""
now = datetime.datetime.now(dateutil.tz.tzlocal())
timestamp = now.strftime('%Y_%m_%d_%H_%M_%S')
return timestamp
std_threshold = 0.1
in_mdp_batch_size = 128
eval_statistics = OrderedDict()
logger.reset()
setup_logger(
log_dir=osp.join('./tune_threshold_loggings', create_simple_exp_name()))
filename = f'./goals/ant-dir-normal-goals.pkl'
train_goals, wd_goals, ood_goals = pickle.load(open(filename, 'rb'))
env = env_producer('ant-dir', 0, train_goals[0])
for epoch in range(200):
file_name = osp.join('./data_reward_predictions', f'params_{epoch}.pkl')
params = pickle.load(open(file_name, "rb"))
obs = params['obs']
actions = params['actions']