描述
我正在使用带有 A3C 模型的 PTAN 库，我正在尝试使用wandb 扫描，但我遇到了一些奇怪的问题，我不确定这是否是关于扫描的错误（因为如果我只想使用一个简单的模型没有任何涉及的线程将正常工作）或者我做错了什么。
如何重现
训练功能：
def train(conf):
batch = []
step_idx = 0
epoch = conf['epochs']
try:
with commune.RewardTracker(writer, stop_reward=conf['reward_bound']) as tracker:
with ptan.common.utils.TBMeanTracker(writer, batch_size=100) as tb_tracker:
while True:
if step_idx == epoch:
break
train_entry = train_queue.get()
if isinstance(train_entry, TotalReward):
if tracker.reward(train_entry.reward, step_idx):
break
continue
if isinstance(train_entry, TotalProfit):
tracker.profits(train_entry.total_profit, train_entry.curr_profit, step_idx)
continue
step_idx += 1
if step_idx % 100 == 0:
torch.save(net.state_dict(), os.path.join(SAVING_FOLDER, PROJECT_NAME))
batch.append(train_entry)
if len(batch) < conf['batch_size']:
continue
states_v, actions_t, vals_ref_v = commune.unpack_batch(batch, net,
last_val_gamma=conf['gamma'] ** conf['reward_steps'],
device=device)
batch.clear()
optimizer.zero_grad()
logits_v, value_v = net(states_v)
loss_value_v = F.mse_loss(value_v.squeeze(-1), vals_ref_v)
log_prob_v = F.log_softmax(logits_v, dim=1)
adv_v = vals_ref_v - value_v.detach()
log_prob_actions_v = adv_v * log_prob_v[range(conf['batch_size']), actions_t]
loss_policy_v = -log_prob_actions_v.mean()
prob_v = F.softmax(logits_v, dim=1)
entropy_loss_v = conf['entropy_beta'] * (prob_v * log_prob_v).sum(dim=1).mean()
loss_v = entropy_loss_v + loss_value_v + loss_policy_v
loss_v.backward()
nn_utils.clip_grad_norm_(net.parameters(), conf['clip_grad'])
optimizer.step()
tb_tracker.track("advantage", adv_v, step_idx)
tb_tracker.track("values", value_v, step_idx)
tb_tracker.track("batch_rewards", vals_ref_v, step_idx)
tb_tracker.track("loss_entropy", entropy_loss_v, step_idx)
tb_tracker.track("loss_policy", loss_policy_v, step_idx)
tb_tracker.track("loss_value", loss_value_v, step_idx)
tb_tracker.track("loss_total", loss_v, step_idx)
finally:
for p in data_proc_list:
p.terminate()
p.join()
主功能：
if __name__ == "__main__":
mp.set_start_method('fork')
device = torch.device("cuda:0" if use_cuda else "cpu")
with open(r'sweep_config.yaml') as file:
sweep_config = yaml.load(file, Loader=yaml.FullLoader)
logs_dir_name = "a3c_stock"
wandb.tensorboard.patch(root_logdir=logs_dir_name)
sweep_id = wandb.sweep(sweep_config, project="sweep_project", entity="vildnex")
wandb.init(config=config_default)
config = wandb.config
writer = SummaryWriter(comment=logs_dir_name)
env = make_env(config)
net = commune.AtariA2C(env.observation_space.shape, env.action_space.n).to(device)
net.share_memory()
if not os.path.isdir(SAVING_FOLDER):
os.mkdir(SAVING_FOLDER)
if os.path.isfile(os.path.join(SAVING_FOLDER, PROJECT_NAME)):
net.load_state_dict(torch.load(os.path.join(SAVING_FOLDER, PROJECT_NAME), map_location=device))
optimizer = optim.RMSprop(net.parameters(), lr=config.learning_rate, eps=1e-3)
train_queue = mp.Queue(maxsize=config.processes_count)
data_proc_list = []
dict_conf = dict(config)
for _ in range(config.processes_count):
data_proc = mp.Process(target=data_func, args=(net, device, train_queue, dict_conf))
data_proc.start()
data_proc_list.append(data_proc)
wandb.agent(sweep_id, lambda: train(dict_conf))
错误信息：
Exception in thread Thread-6:
Traceback (most recent call last):
File "<PATH>/venv/lib/python3.9/site-packages/wandb/agents/pyagent.py", line 303, in _run_job
self._function()
File "<PATH>/RL_TraningBot/EXPERIMENTS/A3C_TEST.py", line 191, in <lambda>
wandb.agent(sweep_id, lambda: train(dict_conf))
File "<PATH>/RL_TraningBot/EXPERIMENTS/A3C_TEST.py", line 105, in train
tracker.profits(train_entry.total_profit, train_entry.curr_profit, step_idx)
File "<PATH>/RL_TraningBot/EXPERIMENTS/commune.py", line 118, in profits
self.writer.add_scalar("total_profit", total_profit, frame)
File "<PATH>/venv/lib/python3.9/site-packages/torch/utils/tensorboard/writer.py", line 344, in add_scalar
self._get_file_writer().add_summary(
File "<PATH>/venv/lib/python3.9/site-packages/torch/utils/tensorboard/writer.py", line 250, in _get_file_writer
self.file_writer = FileWriter(self.log_dir, self.max_queue,
File "<PATH>/venv/lib/python3.9/site-packages/torch/utils/tensorboard/writer.py", line 60, in __init__
self.event_writer = EventFileWriter(
File "<PATH>/venv/lib/python3.9/site-packages/wandb/integration/tensorboard/monkeypatch.py", line 157, in __init__
_notify_tensorboard_logdir(logdir, save=save, root_logdir=root_logdir_arg)
File "<PATH>/venv/lib/python3.9/site-packages/wandb/integration/tensorboard/monkeypatch.py", line 167, in _notify_tensorboard_logdir
wandb.run._tensorboard_callback(logdir, save=save, root_logdir=root_logdir)
File "<PATH>/venv/lib/python3.9/site-packages/wandb/sdk/wandb_run.py", line 804, in _tensorboard_callback
self._backend.interface.publish_tbdata(logdir, save, root_logdir)
File "<PATH>/venv/lib/python3.9/site-packages/wandb/sdk/interface/interface.py", line 202, in publish_tbdata
self._publish(rec)
File "<PATH>/venv/lib/python3.9/site-packages/wandb/sdk/interface/interface.py", line 518, in _publish
raise Exception("The wandb backend process has shutdown")
Exception: The wandb backend process has shutdown
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/usr/lib/python3.9/threading.py", line 954, in _bootstrap_inner
self.run()
File "/usr/lib/python3.9/threading.py", line 892, in run
self._target(*self._args, **self._kwargs)
File "<PATH>/venv/lib/python3.9/site-packages/wandb/agents/pyagent.py", line 308, in _run_job
wandb.finish(exit_code=1)
File "<PATH>/venv/lib/python3.9/site-packages/wandb/sdk/wandb_run.py", line 2374, in finish
wandb.run.finish(exit_code=exit_code)
File "<PATH>/venv/lib/python3.9/site-packages/wandb/sdk/wandb_run.py", line 1144, in finish
if self._wl and len(self._wl._global_run_stack) > 0:
File "<PATH>/venv/lib/python3.9/site-packages/wandb/sdk/wandb_setup.py", line 234, in __getattr__
return getattr(self._instance, name)
AttributeError: 'NoneType' object has no attribute '_global_run_stack'
环境
- 操作系统：Manjaro 5.21.5
- 环境：PyCharm 本地
- Python版本：3.9