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wandb: Tracking run with wandb version 0.16.5
wandb: Run data is saved locally in /home/user/zhangyang/PycharmProjects/Nips2024-ITPC-v2/Nips2024-ITPC-v2/onpolicy/scripts/results/MPE/simple_tag_tr/rmappotrsyn/exp_train_continue_tag_base_klcp_s2r2_v1/wandb/run-20240402_151614-zpfxdvd4
wandb: Run `wandb offline` to turn off syncing.
wandb: Syncing run MPE_55555
wandb: ⭐️ View project at https://wandb.ai/804703098/Continue_Tag_Base_v1
wandb: 🚀 View run at https://wandb.ai/804703098/Continue_Tag_Base_v1/runs/zpfxdvd4/workspace
choose to use gpu...
idv policy and team policy use same initial params!

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_klcp_s2r2_v1 updates 0/10 episodes, total num timesteps 200/2000, FPS 261.

team_policy eval average step individual rewards of agent0: -0.10793839903622392
team_policy eval average team episode rewards of agent0: 0.0
team_policy eval idv catch total num of agent0: 0
team_policy eval team catch total num: 0
team_policy eval average step individual rewards of agent1: 0.0022010023428877634
team_policy eval average team episode rewards of agent1: 0.0
team_policy eval idv catch total num of agent1: 4
team_policy eval team catch total num: 0
team_policy eval average step individual rewards of agent2: -0.1006318114337646
team_policy eval average team episode rewards of agent2: 0.0
team_policy eval idv catch total num of agent2: 0
team_policy eval team catch total num: 0
team_policy eval average step individual rewards of agent3: -0.09897310052907823
team_policy eval average team episode rewards of agent3: 0.0
team_policy eval idv catch total num of agent3: 0
team_policy eval team catch total num: 0
team_policy eval average step individual rewards of agent4: 0.020112416189865034
team_policy eval average team episode rewards of agent4: 0.0
team_policy eval idv catch total num of agent4: 4
team_policy eval team catch total num: 0
idv_policy eval average step individual rewards of agent0: -0.07727321734633781
idv_policy eval average team episode rewards of agent0: 0.0
idv_policy eval idv catch total num of agent0: 0
idv_policy eval team catch total num: 0
idv_policy eval average step individual rewards of agent1: -0.040486463478491164
idv_policy eval average team episode rewards of agent1: 0.0
idv_policy eval idv catch total num of agent1: 2
idv_policy eval team catch total num: 0
idv_policy eval average step individual rewards of agent2: -0.08459470888588971
idv_policy eval average team episode rewards of agent2: 0.0
idv_policy eval idv catch total num of agent2: 0
idv_policy eval team catch total num: 0
idv_policy eval average step individual rewards of agent3: -0.05815304121016016
idv_policy eval average team episode rewards of agent3: 0.0
idv_policy eval idv catch total num of agent3: 1
idv_policy eval team catch total num: 0
idv_policy eval average step individual rewards of agent4: -0.00637528738804098
idv_policy eval average team episode rewards of agent4: 0.0
idv_policy eval idv catch total num of agent4: 3
idv_policy eval team catch total num: 0

 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_klcp_s2r2_v1 updates 1/10 episodes, total num timesteps 400/2000, FPS 254.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_klcp_s2r2_v1 updates 2/10 episodes, total num timesteps 600/2000, FPS 274.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_klcp_s2r2_v1 updates 3/10 episodes, total num timesteps 800/2000, FPS 279.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_klcp_s2r2_v1 updates 4/10 episodes, total num timesteps 1000/2000, FPS 286.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_klcp_s2r2_v1 updates 5/10 episodes, total num timesteps 1200/2000, FPS 292.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_klcp_s2r2_v1 updates 6/10 episodes, total num timesteps 1400/2000, FPS 295.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_klcp_s2r2_v1 updates 7/10 episodes, total num timesteps 1600/2000, FPS 295.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_klcp_s2r2_v1 updates 8/10 episodes, total num timesteps 1800/2000, FPS 298.


 Scenario simple_tag_tr Algo rmappotrsyn Exp exp_train_continue_tag_base_klcp_s2r2_v1 updates 9/10 episodes, total num timesteps 2000/2000, FPS 301.

wandb: - 0.006 MB of 0.006 MB uploaded
wandb: \ 0.631 MB of 0.650 MB uploaded
wandb: | 0.650 MB of 0.650 MB uploaded
wandb: 
wandb: Run history:
wandb:                                       Aa_idv_actor_loss ▇█▇▆▇▄▂▁▁▅
wandb:                                          Ab_policy_loss ▄▇▅▅▅▅▁▄▃█
wandb:                                     Ac_idv_ppo_loss_abs ▁▇▇▇▆▇██▇▃
wandb:                                         Ad_idv_ppo_prop ▁▇█▇▆▇▇▆▆▂
wandb:                                                  Ae_eta ▃▅▆▆▇▆▁▁█▆
wandb:                                    Af_noclip_proportion ▆█▇▇▆▆▄▅▁▂
wandb:                                    Ag_update_proportion ▄█▂▄▅▅▄▅▄▁
wandb:                                          Ah_update_loss ▁▆█▇▆▇▇▇▆▂
wandb:                                         Ai_idv_epsilon' █▇▆▆▅▄▃▃▂▁
wandb:                                            Aj_idv_sigma ▁▃▄▃▃▃▇▆▅█
wandb:              Ak_idv_clip(sigma, 1-epislon', 1+epislon') ▁▃▄▃▄▃▅▆▅█
wandb:                                Al_idv_noclip_proportion █▇███▇▂▃▆▁
wandb:                       Am_idv_(sigma*A)update_proportion ▆▁▇▅▄▅▅▄▄█
wandb:                             An_idv_(sigma*A)update_loss █▁▂▂▃▂▂▁▂█
wandb:                                     Ao_idv_entropy_prop █▂▁▂▃▂▂▃▃▇
wandb:                                         Ap_dist_entropy ▁▁▁▃▁▄▅█▇▅
wandb:                                          Aq_idv_kl_prop ▁▁▁▁▁▁▁▁▁▁
wandb:                                          Ar_idv_kl_coef ▁▁▁▁▁▁▁▁▁▁
wandb:                                          As_idv_kl_loss ▁▃▄▄▄▄▆▆██
wandb:                                    At_idv_cross_entropy ▁▁▁▁▁▁▁▁▁▁
wandb:                                           Au_value_loss ▃█▁▁▁▁▁▁▁▁
wandb:                                           Av_advantages ▂▃▁▆██▅▅▂▆
wandb:                                       Aw_idv_actor_norm ▄▄▂▃▃▃▅█▅▁
wandb:                                      Ax_idv_critic_norm █▃▁▁▁▁▁▁▁▁
wandb:                                     Ba_idv_org_min_prop ▆▄▃▃▄▄▂▄█▁
wandb:                                     Bb_idv_org_max_prop ▂█▂▅▅▅▆▆▁▄
wandb:                                     Bc_idv_org_org_prop ▁▁▁▁▁▁▁▁▁▁
wandb:                                     Bd_idv_new_min_prop ▁▄▇▅▅▄▅▅▄█
wandb:                                     Be_idv_new_max_prop █▁▃▃▂▄▃▂▃▂
wandb:                                      Ta_team_actor_loss ▆▆█▅▇▅▄▁▃▆
wandb:                                     Tb_team_policy_loss ▁▁▅▂▃▄▄▄█▆
wandb:                                    Tc_team_ppo_loss_abs ▆▄▄▇▃█▄▄▁▄
wandb:                                        Td_team_ppo_prop ██▇▇▇▇▅▃▁▅
wandb:                                        Te_team_epsilon^ ▁▁▁▁▁▁▁▁▁▁
wandb:                                          Tf_team_sigma^ ▅▅▄▅▆▅▄▃█▁
wandb:          Tg_team_clip(sigma^, 1-epislon^', 1+epislon^') █▆▆█▇█▅▃▂▁
wandb:                               Th_team_noclip_proportion █▅▄▆▅▅▂▃▃▁
wandb:                     Ti_team_(sigma^*A)update_proportion █▅▄▅▅▅▂▂▃▁
wandb:                           Tj_team_(sigma^*A)update_loss █▆▆▅▄▄▇█▁▄
wandb:                                    Tk_team_entropy_prop ▁▁▂▂▂▂▄▆█▄
wandb:                                    Tl_team_dist_entropy ▁▁▁▂▁▄▅▇█▅
wandb:                                         Tm_team_kl_prop ▁▁▁▁▁▁▁▁▁▁
wandb:                                         Tn_team_kl_coef ▁▁▁▁▁▁▁▁▁▁
wandb:                                         To_team_kl_loss ▁▄▄▃▃▄▆▆▆█
wandb:                                   Tp_team_cross_entropy ▁▁▁▁▁▁▁▁▁▁
wandb:                                      Tq_team_value_loss ▃█▁▁▁▁▁▁▁▁
wandb:                                      Tr_team_advantages ▁▂▂▄▅▃▂▃█▆
wandb:                                      Ts_team_actor_norm ▁▅▅▃▅▅▄▇▅█
wandb:                                     Tt_team_critic_norm █▂▁▁▁▁▁▁▁▁
wandb:                     agent0/average_episode_team_rewards ▁▁▁▁▁▁▁▁▁█
wandb:                  agent0/average_step_individual_rewards █▆▂▃▁▅▄▁▅▆
wandb:     agent0/idv_policy_eval_average_episode_team_rewards ▁
wandb:  agent0/idv_policy_eval_average_step_individual_rewards ▁
wandb:              agent0/idv_policy_eval_idv_catch_total_num ▁
wandb:             agent0/idv_policy_eval_team_catch_total_num ▁
wandb:    agent0/team_policy_eval_average_episode_team_rewards ▁
wandb: agent0/team_policy_eval_average_step_individual_rewards ▁
wandb:             agent0/team_policy_eval_idv_catch_total_num ▁
wandb:            agent0/team_policy_eval_team_catch_total_num ▁
wandb:                     agent1/average_episode_team_rewards ▁▁▁▁▁▁▁▁▁█
wandb:                  agent1/average_step_individual_rewards ▁▄▅▄▃▆▁▂▄█
wandb:     agent1/idv_policy_eval_average_episode_team_rewards ▁
wandb:  agent1/idv_policy_eval_average_step_individual_rewards ▁
wandb:              agent1/idv_policy_eval_idv_catch_total_num ▁
wandb:             agent1/idv_policy_eval_team_catch_total_num ▁
wandb:    agent1/team_policy_eval_average_episode_team_rewards ▁
wandb: agent1/team_policy_eval_average_step_individual_rewards ▁
wandb:             agent1/team_policy_eval_idv_catch_total_num ▁
wandb:            agent1/team_policy_eval_team_catch_total_num ▁
wandb:                     agent2/average_episode_team_rewards ▁▁▁▁▁▁▁▁▁█
wandb:                  agent2/average_step_individual_rewards ▃▆▂▇▃▂█▁▁█
wandb:     agent2/idv_policy_eval_average_episode_team_rewards ▁
wandb:  agent2/idv_policy_eval_average_step_individual_rewards ▁
wandb:              agent2/idv_policy_eval_idv_catch_total_num ▁
wandb:             agent2/idv_policy_eval_team_catch_total_num ▁
wandb:    agent2/team_policy_eval_average_episode_team_rewards ▁
wandb: agent2/team_policy_eval_average_step_individual_rewards ▁
wandb:             agent2/team_policy_eval_idv_catch_total_num ▁
wandb:            agent2/team_policy_eval_team_catch_total_num ▁
wandb:                     agent3/average_episode_team_rewards ▁▁▁▁▁▁▁▁▁█
wandb:                  agent3/average_step_individual_rewards ▄█▂▃▅▃▂▁▃▅
wandb:     agent3/idv_policy_eval_average_episode_team_rewards ▁
wandb:  agent3/idv_policy_eval_average_step_individual_rewards ▁
wandb:              agent3/idv_policy_eval_idv_catch_total_num ▁
wandb:             agent3/idv_policy_eval_team_catch_total_num ▁
wandb:    agent3/team_policy_eval_average_episode_team_rewards ▁
wandb: agent3/team_policy_eval_average_step_individual_rewards ▁
wandb:             agent3/team_policy_eval_idv_catch_total_num ▁
wandb:            agent3/team_policy_eval_team_catch_total_num ▁
wandb:                     agent4/average_episode_team_rewards ▁▁▁▁▁▁▁▁▁█
wandb:                  agent4/average_step_individual_rewards ▄▅▄▂▁▂▂▁█▃
wandb:     agent4/idv_policy_eval_average_episode_team_rewards ▁
wandb:  agent4/idv_policy_eval_average_step_individual_rewards ▁
wandb:              agent4/idv_policy_eval_idv_catch_total_num ▁
wandb:             agent4/idv_policy_eval_team_catch_total_num ▁
wandb:    agent4/team_policy_eval_average_episode_team_rewards ▁
wandb: agent4/team_policy_eval_average_step_individual_rewards ▁
wandb:             agent4/team_policy_eval_idv_catch_total_num ▁
wandb:            agent4/team_policy_eval_team_catch_total_num ▁
wandb: 
wandb: Run summary:
wandb:                                       Aa_idv_actor_loss -0.31321
wandb:                                          Ab_policy_loss 0.00205
wandb:                                     Ac_idv_ppo_loss_abs 0.65068
wandb:                                         Ad_idv_ppo_prop 0.67363
wandb:                                                  Ae_eta 1.00137
wandb:                                    Af_noclip_proportion 0.947
wandb:                                    Ag_update_proportion 0.4154
wandb:                                          Ah_update_loss 0.09811
wandb:                                         Ai_idv_epsilon' 2.99775
wandb:                                            Aj_idv_sigma 1.11649
wandb:              Ak_idv_clip(sigma, 1-epislon', 1+epislon') 1.10443
wandb:                                Al_idv_noclip_proportion 0.9916
wandb:                       Am_idv_(sigma*A)update_proportion 0.5643
wandb:                             An_idv_(sigma*A)update_loss -0.01527
wandb:                                     Ao_idv_entropy_prop 0.32637
wandb:                                         Ap_dist_entropy 3.15581
wandb:                                          Aq_idv_kl_prop 0.0
wandb:                                          Ar_idv_kl_coef 0.0
wandb:                                          As_idv_kl_loss 0.02292
wandb:                                    At_idv_cross_entropy 0.0
wandb:                                           Au_value_loss 0.14068
wandb:                                           Av_advantages 0.0
wandb:                                       Aw_idv_actor_norm 0.68231
wandb:                                      Ax_idv_critic_norm 1.71333
wandb:                                     Ba_idv_org_min_prop 0.1929
wandb:                                     Bb_idv_org_max_prop 0.2225
wandb:                                     Bc_idv_org_org_prop 0.0
wandb:                                     Bd_idv_new_min_prop 0.3141
wandb:                                     Be_idv_new_max_prop 0.2502
wandb:                                      Ta_team_actor_loss -0.29493
wandb:                                     Tb_team_policy_loss 0.01493
wandb:                                    Tc_team_ppo_loss_abs 0.79813
wandb:                                        Td_team_ppo_prop 0.72035
wandb:                                        Te_team_epsilon^ 0.2
wandb:                                          Tf_team_sigma^ 0.98546
wandb:          Tg_team_clip(sigma^, 1-epislon^', 1+epislon^') 0.98098
wandb:                               Th_team_noclip_proportion 0.7388
wandb:                     Ti_team_(sigma^*A)update_proportion 0.8709
wandb:                           Tj_team_(sigma^*A)update_loss -0.02555
wandb:                                    Tk_team_entropy_prop 0.27965
wandb:                                    Tl_team_dist_entropy 3.10168
wandb:                                         Tm_team_kl_prop 0.0
wandb:                                         Tn_team_kl_coef 1.0
wandb:                                         To_team_kl_loss 0.01567
wandb:                                   Tp_team_cross_entropy 0.0
wandb:                                      Tq_team_value_loss 0.03192
wandb:                                      Tr_team_advantages 0.0
wandb:                                      Ts_team_actor_norm 0.78068
wandb:                                     Tt_team_critic_norm 0.57522
wandb:                     agent0/average_episode_team_rewards 2.5
wandb:                  agent0/average_step_individual_rewards 0.01373
wandb:     agent0/idv_policy_eval_average_episode_team_rewards 0.0
wandb:  agent0/idv_policy_eval_average_step_individual_rewards -0.07727
wandb:              agent0/idv_policy_eval_idv_catch_total_num 0
wandb:             agent0/idv_policy_eval_team_catch_total_num 0
wandb:    agent0/team_policy_eval_average_episode_team_rewards 0.0
wandb: agent0/team_policy_eval_average_step_individual_rewards -0.10794
wandb:             agent0/team_policy_eval_idv_catch_total_num 0
wandb:            agent0/team_policy_eval_team_catch_total_num 0
wandb:                     agent1/average_episode_team_rewards 2.5
wandb:                  agent1/average_step_individual_rewards 0.07555
wandb:     agent1/idv_policy_eval_average_episode_team_rewards 0.0
wandb:  agent1/idv_policy_eval_average_step_individual_rewards -0.04049
wandb:              agent1/idv_policy_eval_idv_catch_total_num 2
wandb:             agent1/idv_policy_eval_team_catch_total_num 0
wandb:    agent1/team_policy_eval_average_episode_team_rewards 0.0
wandb: agent1/team_policy_eval_average_step_individual_rewards 0.0022
wandb:             agent1/team_policy_eval_idv_catch_total_num 4
wandb:            agent1/team_policy_eval_team_catch_total_num 0
wandb:                     agent2/average_episode_team_rewards 2.5
wandb:                  agent2/average_step_individual_rewards 0.08205
wandb:     agent2/idv_policy_eval_average_episode_team_rewards 0.0
wandb:  agent2/idv_policy_eval_average_step_individual_rewards -0.08459
wandb:              agent2/idv_policy_eval_idv_catch_total_num 0
wandb:             agent2/idv_policy_eval_team_catch_total_num 0
wandb:    agent2/team_policy_eval_average_episode_team_rewards 0.0
wandb: agent2/team_policy_eval_average_step_individual_rewards -0.10063
wandb:             agent2/team_policy_eval_idv_catch_total_num 0
wandb:            agent2/team_policy_eval_team_catch_total_num 0
wandb:                     agent3/average_episode_team_rewards 2.5
wandb:                  agent3/average_step_individual_rewards -0.02472
wandb:     agent3/idv_policy_eval_average_episode_team_rewards 0.0
wandb:  agent3/idv_policy_eval_average_step_individual_rewards -0.05815
wandb:              agent3/idv_policy_eval_idv_catch_total_num 1
wandb:             agent3/idv_policy_eval_team_catch_total_num 0
wandb:    agent3/team_policy_eval_average_episode_team_rewards 0.0
wandb: agent3/team_policy_eval_average_step_individual_rewards -0.09897
wandb:             agent3/team_policy_eval_idv_catch_total_num 0
wandb:            agent3/team_policy_eval_team_catch_total_num 0
wandb:                     agent4/average_episode_team_rewards 2.5
wandb:                  agent4/average_step_individual_rewards -0.03469
wandb:     agent4/idv_policy_eval_average_episode_team_rewards 0.0
wandb:  agent4/idv_policy_eval_average_step_individual_rewards -0.00638
wandb:              agent4/idv_policy_eval_idv_catch_total_num 3
wandb:             agent4/idv_policy_eval_team_catch_total_num 0
wandb:    agent4/team_policy_eval_average_episode_team_rewards 0.0
wandb: agent4/team_policy_eval_average_step_individual_rewards 0.02011
wandb:             agent4/team_policy_eval_idv_catch_total_num 4
wandb:            agent4/team_policy_eval_team_catch_total_num 0
wandb: 
wandb: 🚀 View run MPE_55555 at: https://wandb.ai/804703098/Continue_Tag_Base_v1/runs/zpfxdvd4/workspace
wandb: Synced 6 W&B file(s), 0 media file(s), 0 artifact file(s) and 4 other file(s)
wandb: Find logs at: ./results/MPE/simple_tag_tr/rmappotrsyn/exp_train_continue_tag_base_klcp_s2r2_v1/wandb/run-20240402_151614-zpfxdvd4/logs
Traceback (most recent call last):
  File "train/train_mpe_trsyn.py", line 244, in <module>
    main(sys.argv[1:])
  File "train/train_mpe_trsyn.py", line 229, in main
    runner.run()
  File "/home/user/zhangyang/PycharmProjects/Nips2024-ITPC-v2/Nips2024-ITPC-v2/onpolicy/runner/shared/mpe_runner_trsyn.py", line 118, in run
    d = {"average_team_rewards_" + str(self.all_args.seed) + "_KL_loss_" + self.all_args.idv_use_kl_loss: average_team_rewards}
TypeError: can only concatenate str (not "bool") to str
Exception in thread ChkStopThr:
Traceback (most recent call last):
  File "/home/user/anaconda3/envs/zypython38/lib/python3.8/threading.py", line 932, in _bootstrap_inner
    self.run()
  File "/home/user/anaconda3/envs/zypython38/lib/python3.8/threading.py", line 870, in run
    self._target(*self._args, **self._kwargs)
  File "/home/user/anaconda3/envs/zypython38/lib/python3.8/site-packages/wandb/sdk/wandb_run.py", line 286, in check_stop_status
    Exception in thread self._loop_check_status(IntMsgThr
:
  File "/home/user/anaconda3/envs/zypython38/lib/python3.8/site-packages/wandb/sdk/wandb_run.py", line 224, in _loop_check_status
Traceback (most recent call last):
  File "/home/user/anaconda3/envs/zypython38/lib/python3.8/threading.py", line 932, in _bootstrap_inner
    local_handle = request()
  File "/home/user/anaconda3/envs/zypython38/lib/python3.8/site-packages/wandb/sdk/interface/interface.py", line 828, in deliver_stop_status
    self.run()
  File "/home/user/anaconda3/envs/zypython38/lib/python3.8/threading.py", line 870, in run
    return self._deliver_stop_status(status)
  File "/home/user/anaconda3/envs/zypython38/lib/python3.8/site-packages/wandb/sdk/interface/interface_shared.py", line 494, in _deliver_stop_status
    self._target(*self._args, **self._kwargs)
  File "/home/user/anaconda3/envs/zypython38/lib/python3.8/site-packages/wandb/sdk/wandb_run.py", line 300, in check_internal_messages
    return self._deliver_record(record)
  File "/home/user/anaconda3/envs/zypython38/lib/python3.8/site-packages/wandb/sdk/interface/interface_shared.py", line 459, in _deliver_record
    self._loop_check_status(
  File "/home/user/anaconda3/envs/zypython38/lib/python3.8/site-packages/wandb/sdk/wandb_run.py", line 224, in _loop_check_status
    handle = mailbox._deliver_record(record, interface=self)
  File "/home/user/anaconda3/envs/zypython38/lib/python3.8/site-packages/wandb/sdk/lib/mailbox.py", line 455, in _deliver_record
    local_handle = request()
  File "/home/user/anaconda3/envs/zypython38/lib/python3.8/site-packages/wandb/sdk/interface/interface.py", line 844, in deliver_internal_messages
    interface._publish(record)
  File "/home/user/anaconda3/envs/zypython38/lib/python3.8/site-packages/wandb/sdk/interface/interface_sock.py", line 51, in _publish
    return self._deliver_internal_messages(internal_message)
  File "/home/user/anaconda3/envs/zypython38/lib/python3.8/site-packages/wandb/sdk/interface/interface_shared.py", line 516, in _deliver_internal_messages
    self._sock_client.send_record_publish(record)
  File "/home/user/anaconda3/envs/zypython38/lib/python3.8/site-packages/wandb/sdk/lib/sock_client.py", line 221, in send_record_publish
    return self._deliver_record(record)
  File "/home/user/anaconda3/envs/zypython38/lib/python3.8/site-packages/wandb/sdk/interface/interface_shared.py", line 459, in _deliver_record
    self.send_server_request(server_req)
  File "/home/user/anaconda3/envs/zypython38/lib/python3.8/site-packages/wandb/sdk/lib/sock_client.py", line 155, in send_server_request
    handle = mailbox._deliver_record(record, interface=self)
  File "/home/user/anaconda3/envs/zypython38/lib/python3.8/site-packages/wandb/sdk/lib/mailbox.py", line 455, in _deliver_record
    self._send_message(msg)
  File "/home/user/anaconda3/envs/zypython38/lib/python3.8/site-packages/wandb/sdk/lib/sock_client.py", line 152, in _send_message
    interface._publish(record)
  File "/home/user/anaconda3/envs/zypython38/lib/python3.8/site-packages/wandb/sdk/interface/interface_sock.py", line 51, in _publish
    self._sendall_with_error_handle(header + data)
  File "/home/user/anaconda3/envs/zypython38/lib/python3.8/site-packages/wandb/sdk/lib/sock_client.py", line 130, in _sendall_with_error_handle
    self._sock_client.send_record_publish(record)
  File "/home/user/anaconda3/envs/zypython38/lib/python3.8/site-packages/wandb/sdk/lib/sock_client.py", line 221, in send_record_publish
    sent = self._sock.send(data)
BrokenPipeError: [Errno 32] Broken pipe
    self.send_server_request(server_req)
  File "/home/user/anaconda3/envs/zypython38/lib/python3.8/site-packages/wandb/sdk/lib/sock_client.py", line 155, in send_server_request
    self._send_message(msg)
  File "/home/user/anaconda3/envs/zypython38/lib/python3.8/site-packages/wandb/sdk/lib/sock_client.py", line 152, in _send_message
    self._sendall_with_error_handle(header + data)
  File "/home/user/anaconda3/envs/zypython38/lib/python3.8/site-packages/wandb/sdk/lib/sock_client.py", line 130, in _sendall_with_error_handle
    sent = self._sock.send(data)
BrokenPipeError: [Errno 32] Broken pipe
