"{: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))
