"{:d}".format(steps), "--sacred", "--sacred-name", name, "--dedup" ] my_env = os.environ.copy() my_env["CUDA_VISIBLE_DEVICES"] = str(gpu) return subprocess.Popen(s, env=my_env) num_runs = 10 num_objects = [5] in_ep_probs = [0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0] learning_rate = 0.0005 name = "210608_space_full_probs" executor = discovery_utils.setup_mock_executor([1, 2, 3], 1) jobs = [] for run_idx in range(num_runs): for objects in num_objects: for in_ep_prob in in_ep_probs: tmp_name = "{:s}_{:d}_{:d}_{:f}".format(name, run_idx, objects, in_ep_prob) seed = 1000 * run_idx jobs.append( executor.submit(run_train, "data/spaceinvaders_full_train_eps_0_5.h5",
"python", "eval_ids.py", "--dataset", dataset, "--save-folder", "checkpoints/{:s}".format(tmp_name), "--num-steps", "{:d}".format(steps), "--sacred", "--sacred-name", name ] my_env = os.environ.copy() my_env["CUDA_VISIBLE_DEVICES"] = str(gpu) return subprocess.Popen(s, env=my_env) num_runs = 20 num_objects = [3, 5] name = "211121_space_cswm" executor = discovery_utils.setup_mock_executor([0, 1, 2], 1) jobs = [] for run_idx in range(num_runs): for objects in num_objects: for baseline in [True, False]: tmp_name = "{:s}_{:d}_{:d}_{:s}".format(name, run_idx, objects, str(baseline)) seed = 1000 * run_idx jobs.append( executor.submit(run_train, "data/spaceinvaders_train.h5", objects, baseline, tmp_name, seed))