{"action":"created","issue":{"url":"https://api.github.com/repos/ray-project/ray/issues/2411","repository_url":"https://api.github.com/repos/ray-project/ray","labels_url":"https://api.github.com/repos/ray-project/ray/issues/2411/labels{/name}","comments_url":"https://api.github.com/repos/ray-project/ray/issues/2411/comments","events_url":"https://api.github.com/repos/ray-project/ray/issues/2411/events","html_url":"https://github.com/ray-project/ray/issues/2411","id":341753368,"node_id":"MDU6SXNzdWUzNDE3NTMzNjg=","number":2411,"title":"In A3C, the speed for processing samples does not necessarily increase when increasing num_workers and available cpus","user":{"login":"luochao1024","id":38442610,"node_id":"MDQ6VXNlcjM4NDQyNjEw","avatar_url":"https://avatars1.githubusercontent.com/u/38442610?v=4","gravatar_id":"","url":"https://api.github.com/users/luochao1024","html_url":"https://github.com/luochao1024","followers_url":"https://api.github.com/users/luochao1024/followers","following_url":"https://api.github.com/users/luochao1024/following{/other_user}","gists_url":"https://api.github.com/users/luochao1024/gists{/gist_id}","starred_url":"https://api.github.com/users/luochao1024/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/luochao1024/subscriptions","organizations_url":"https://api.github.com/users/luochao1024/orgs","repos_url":"https://api.github.com/users/luochao1024/repos","events_url":"https://api.github.com/users/luochao1024/events{/privacy}","received_events_url":"https://api.github.com/users/luochao1024/received_events","type":"User","site_admin":false},"labels":[],"state":"open","locked":false,"assignee":null,"assignees":[],"milestone":null,"comments":0,"created_at":"2018-07-17T03:32:57Z","updated_at":"2018-07-17T06:22:49Z","closed_at":null,"author_association":"NONE","body":"<!--\r\nGeneral questions should be asked on the mailing list ray-dev@googlegroups.com.\r\n\r\nBefore submitting an issue, please fill out the following form.\r\n-->\r\n\r\n### System information\r\n- **OS Platform and Distribution (e.g., Linux Ubuntu 16.04)**: Linux Ubuntu 16.04\r\n- **Ray installed from (source or binary)**: source\r\n- **Ray version**: 0.5.0\r\n- **Python version**: 3.5.1\r\n- **Exact command to reproduce**:\r\n\r\n<!--\r\nYou can obtain the Ray version with\r\n\r\npython -c \"import ray; print(ray.__version__)\"\r\n-->\r\n\r\n### Describe the problem\r\n<!-- Describe the problem clearly here. -->\r\nI try to increase num_workers to make a3c algorithm process samples faster. When I have 8 cpus available and set `num_workers = 7`, I saw `time_this_iter_s = 8.141056299209595`, Here is the command I use\r\n```\r\ntaskset -c 0-7 python3 train.py --run A3C --env=PongDeterministic-v4 --config='{\"nu\r\nm_workers\":7, \"sample_batch_size\": 20}'\r\n```\r\nHere is the result printed:\r\n```\r\nTrainingResult for A3C_PongDeterministic-v4_0:\r\n  date: 2018-07-17_03-17-25\r\n  episode_len_mean: 955.3333333333334\r\n  episode_reward_max: -18.0\r\n  episode_reward_mean: -20.0\r\n  episode_reward_min: -21.0\r\n  episodes_total: 6\r\n  experiment_id: 42648b4933ab416488accf930c055759\r\n  hostname: cpu-32\r\n  info:\r\n    apply_time_ms: 13.457\r\n    dispatch_time_ms: 19.801\r\n    num_steps_sampled: 10000\r\n    num_steps_trained: 10000\r\n    wait_time_ms: 52.587\r\n  node_ip: 10.128.0.5\r\n  pid: 21411\r\n  policy_reward_mean:\r\n    default: -20.0\r\n  time_this_iter_s: 8.141056299209595\r\n  time_total_s: 102.94127798080444\r\n  timestamp: 1531797445\r\n  timesteps_this_iter: 1000\r\n  timesteps_total: 10000\r\n  training_iteration: 10\r\n  \r\n== Status ==\r\nUsing FIFO scheduling algorithm.\r\nResources requested: 8/32 CPUs, 0/0 GPUs\r\nResult logdir: /home/wangluochao93/ray_results/default\r\nRUNNING trials:\r\n - A3C_PongDeterministic-v4_0:  RUNNING [pid=21411], 102 s, 10000 ts, -20 rew\r\n```\r\n\r\nHere is the cpu usage when I use the `top` command:\r\n```\r\ntop - 03:16:31 up 44 min,  2 users,  load average: 12.98, 15.85, 14.67\r\nTasks: 377 total,   1 running, 376 sleeping,   0 stopped,   0 zombie\r\n%Cpu0  : 83.7 us,  9.6 sy,  0.0 ni,  6.6 id,  0.0 wa,  0.0 hi,  0.0 si,  0.0 st\r\n%Cpu1  : 79.8 us,  6.0 sy,  0.0 ni, 12.9 id,  0.0 wa,  0.0 hi,  1.3 si,  0.0 st\r\n%Cpu2  : 81.2 us,  8.9 sy,  0.0 ni,  9.9 id,  0.0 wa,  0.0 hi,  0.0 si,  0.0 st\r\n%Cpu3  : 83.8 us,  5.9 sy,  0.0 ni,  9.9 id,  0.0 wa,  0.0 hi,  0.3 si,  0.0 st\r\n%Cpu4  : 80.9 us,  6.7 sy,  0.0 ni, 11.7 id,  0.0 wa,  0.0 hi,  0.7 si,  0.0 st\r\n%Cpu5  : 68.4 us, 10.3 sy,  0.0 ni, 18.1 id,  0.0 wa,  0.0 hi,  3.2 si,  0.0 st\r\n%Cpu6  : 80.7 us, 11.1 sy,  0.0 ni,  8.2 id,  0.0 wa,  0.0 hi,  0.0 si,  0.0 st\r\n%Cpu7  : 81.9 us, 10.0 sy,  0.0 ni,  8.0 id,  0.0 wa,  0.0 hi,  0.0 si,  0.0 st\r\n%Cpu8  :  0.0 us,  0.0 sy,  0.0 ni, 98.0 id,  0.0 wa,  0.0 hi,  2.0 si,  0.0 st\r\n%Cpu9  :  0.0 us,  0.0 sy,  0.0 ni,100.0 id,  0.0 wa,  0.0 hi,  0.0 si,  0.0 st\r\n%Cpu10 :  0.0 us,  0.0 sy,  0.0 ni,100.0 id,  0.0 wa,  0.0 hi,  0.0 si,  0.0 st\r\n%Cpu11 :  0.0 us,  0.0 sy,  0.0 ni,100.0 id,  0.0 wa,  0.0 hi,  0.0 si,  0.0 st\r\n%Cpu12 :  0.0 us,  0.0 sy,  0.0 ni,100.0 id,  0.0 wa,  0.0 hi,  0.0 si,  0.0 st\r\n%Cpu13 :  0.0 us,  0.0 sy,  0.0 ni,100.0 id,  0.0 wa,  0.0 hi,  0.0 si,  0.0 st\r\n%Cpu14 :  0.0 us,  0.0 sy,  0.0 ni,100.0 id,  0.0 wa,  0.0 hi,  0.0 si,  0.0 st\r\n%Cpu15 :  0.0 us,  0.0 sy,  0.0 ni,100.0 id,  0.0 wa,  0.0 hi,  0.0 si,  0.0 st\r\n%Cpu16 :  0.0 us,  0.0 sy,  0.0 ni,100.0 id,  0.0 wa,  0.0 hi,  0.0 si,  0.0 st\r\n%Cpu17 :  0.0 us,  0.0 sy,  0.0 ni,100.0 id,  0.0 wa,  0.0 hi,  0.0 si,  0.0 st\r\n%Cpu18 :  0.0 us,  0.0 sy,  0.0 ni,100.0 id,  0.0 wa,  0.0 hi,  0.0 si,  0.0 st\r\n%Cpu19 :  0.0 us,  0.0 sy,  0.0 ni,100.0 id,  0.0 wa,  0.0 hi,  0.0 si,  0.0 st\r\n%Cpu20 :  0.0 us,  0.0 sy,  0.0 ni,100.0 id,  0.0 wa,  0.0 hi,  0.0 si,  0.0 st\r\n%Cpu21 :  0.0 us,  0.0 sy,  0.0 ni,100.0 id,  0.0 wa,  0.0 hi,  0.0 si,  0.0 st\r\n%Cpu22 :  0.0 us,  0.0 sy,  0.0 ni,100.0 id,  0.0 wa,  0.0 hi,  0.0 si,  0.0 st\r\n%Cpu23 :  0.0 us,  0.0 sy,  0.0 ni,100.0 id,  0.0 wa,  0.0 hi,  0.0 si,  0.0 st\r\n%Cpu24 :  0.0 us,  0.0 sy,  0.0 ni,100.0 id,  0.0 wa,  0.0 hi,  0.0 si,  0.0 st\r\n%Cpu25 :  0.0 us,  0.0 sy,  0.0 ni,100.0 id,  0.0 wa,  0.0 hi,  0.0 si,  0.0 st\r\n%Cpu26 :  0.0 us,  0.0 sy,  0.0 ni,100.0 id,  0.0 wa,  0.0 hi,  0.0 si,  0.0 st\r\n%Cpu27 :  0.0 us,  0.0 sy,  0.0 ni,100.0 id,  0.0 wa,  0.0 hi,  0.0 si,  0.0 st\r\n%Cpu28 :  0.0 us,  0.0 sy,  0.0 ni,100.0 id,  0.0 wa,  0.0 hi,  0.0 si,  0.0 st\r\n%Cpu29 :  0.0 us,  0.0 sy,  0.0 ni,100.0 id,  0.0 wa,  0.0 hi,  0.0 si,  0.0 st\r\n%Cpu30 :  0.0 us,  0.0 sy,  0.0 ni,100.0 id,  0.0 wa,  0.0 hi,  0.0 si,  0.0 st\r\n%Cpu31 :  0.0 us,  0.0 sy,  0.0 ni,100.0 id,  0.0 wa,  0.0 hi,  0.0 si,  0.0 st\r\nKiB Mem : 12376860+total, 11223540+free,  5318656 used,  6214532 buff/cache\r\nKiB Swap:        0 total,        0 free,        0 used. 11176814+\r\n```\r\n\r\n\r\nWhen I have 16 cpus available and set `num_workers = 15`, I saw `time_this_iter_s = 5.766817331314087` which is less than `8.141056299209595`. This means increasing the num_workers increases the speed. Here is the command I use\r\n```\r\ntaskset -c 0-15 python3 train.py --run A3C --env=PongDeterministic-v4 --config='{\"nu\r\nm_workers\":15, \"sample_batch_size\": 20}'\r\n```\r\nHere is the result printed\r\n```\r\nTrainingResult for A3C_PongDeterministic-v4_0:\r\n  date: 2018-07-17_03-12-40\r\n  episode_len_mean: 926.6363636363636\r\n  episode_reward_max: -20.0\r\n  episode_reward_mean: -20.545454545454547\r\n  episode_reward_min: -21.0\r\n  episodes_total: 11\r\n  experiment_id: 2597b35ee4e744c2a8e9b4fd2549b42c\r\n  hostname: cpu-32\r\n  info:\r\n    apply_time_ms: 14.291\r\n    dispatch_time_ms: 23.486\r\n    num_steps_sampled: 4000\r\n    num_steps_trained: 4000\r\n    wait_time_ms: 29.482\r\n  node_ip: 10.128.0.5\r\n  pid: 18777\r\n  policy_reward_mean:\r\n    default: -20.545454545454547\r\n  time_this_iter_s: 5.766817331314087\r\n  time_total_s: 33.41109848022461\r\n  timestamp: 1531797160\r\n  timesteps_this_iter: 1000\r\n  timesteps_total: 4000\r\n  training_iteration: 4\r\n  \r\n== Status ==\r\nUsing FIFO scheduling algorithm.\r\nResources requested: 16/32 CPUs, 0/0 GPUs\r\nResult logdir: /home/wangluochao93/ray_results/default\r\nRUNNING trials:\r\n - A3C_PongDeterministic-v4_0:  RUNNING [pid=18777], 33 s, 4000 ts, -20.5 rew\r\n```\r\n\r\nHere is the cpu usage:\r\n```\r\ntop - 03:14:07 up 41 min,  2 users,  load average: 17.77, 18.39, 15.17\r\nTasks: 377 total,   3 running, 374 sleeping,   0 stopped,   0 zombie\r\n%Cpu0  : 90.4 us,  5.9 sy,  0.0 ni,  3.6 id,  0.0 wa,  0.0 hi,  0.0 si,  0.0 st\r\n%Cpu1  : 91.3 us,  7.3 sy,  0.0 ni,  1.3 id,  0.0 wa,  0.0 hi,  0.0 si,  0.0 st\r\n%Cpu2  : 91.7 us,  7.0 sy,  0.0 ni,  1.3 id,  0.0 wa,  0.0 hi,  0.0 si,  0.0 st\r\n%Cpu3  : 91.9 us,  5.4 sy,  0.0 ni,  2.7 id,  0.0 wa,  0.0 hi,  0.0 si,  0.0 st\r\n%Cpu4  : 92.4 us,  6.3 sy,  0.0 ni,  1.0 id,  0.0 wa,  0.0 hi,  0.3 si,  0.0 st\r\n%Cpu5  : 92.4 us,  6.3 sy,  0.0 ni,  1.3 id,  0.0 wa,  0.0 hi,  0.0 si,  0.0 st\r\n%Cpu6  : 88.7 us,  8.0 sy,  0.0 ni,  3.0 id,  0.0 wa,  0.0 hi,  0.3 si,  0.0 st\r\n%Cpu7  : 87.1 us, 10.2 sy,  0.0 ni,  2.6 id,  0.0 wa,  0.0 hi,  0.0 si,  0.0 st\r\n%Cpu8  : 90.0 us,  8.0 sy,  0.0 ni,  2.0 id,  0.0 wa,  0.0 hi,  0.0 si,  0.0 st\r\n%Cpu9  : 90.7 us,  7.0 sy,  0.0 ni,  2.3 id,  0.0 wa,  0.0 hi,  0.0 si,  0.0 st\r\n%Cpu10 : 91.3 us,  6.0 sy,  0.0 ni,  2.7 id,  0.0 wa,  0.0 hi,  0.0 si,  0.0 st\r\n%Cpu11 : 92.0 us,  6.3 sy,  0.0 ni,  1.7 id,  0.0 wa,  0.0 hi,  0.0 si,  0.0 st\r\n%Cpu12 : 90.6 us,  7.4 sy,  0.0 ni,  2.0 id,  0.0 wa,  0.0 hi,  0.0 si,  0.0 st\r\n%Cpu13 : 90.1 us,  6.6 sy,  0.0 ni,  3.3 id,  0.0 wa,  0.0 hi,  0.0 si,  0.0 st\r\n%Cpu14 : 86.2 us, 10.4 sy,  0.0 ni,  3.4 id,  0.0 wa,  0.0 hi,  0.0 si,  0.0 st\r\n%Cpu15 : 79.0 us, 16.0 sy,  0.0 ni,  4.7 id,  0.0 wa,  0.0 hi,  0.3 si,  0.0 st\r\n%Cpu16 :  0.0 us,  0.0 sy,  0.0 ni, 98.0 id,  0.0 wa,  0.0 hi,  2.0 si,  0.0 st\r\n%Cpu17 :  0.0 us,  0.0 sy,  0.0 ni,100.0 id,  0.0 wa,  0.0 hi,  0.0 si,  0.0 st\r\n%Cpu18 :  0.0 us,  0.0 sy,  0.0 ni,100.0 id,  0.0 wa,  0.0 hi,  0.0 si,  0.0 st\r\n%Cpu19 :  0.0 us,  0.0 sy,  0.0 ni,100.0 id,  0.0 wa,  0.0 hi,  0.0 si,  0.0 st\r\n%Cpu20 :  0.0 us,  0.3 sy,  0.0 ni, 99.7 id,  0.0 wa,  0.0 hi,  0.0 si,  0.0 st\r\n%Cpu21 :  0.0 us,  0.0 sy,  0.0 ni,100.0 id,  0.0 wa,  0.0 hi,  0.0 si,  0.0 st\r\n%Cpu22 :  0.0 us,  0.0 sy,  0.0 ni,100.0 id,  0.0 wa,  0.0 hi,  0.0 si,  0.0 st\r\n%Cpu23 :  0.0 us,  0.0 sy,  0.0 ni,100.0 id,  0.0 wa,  0.0 hi,  0.0 si,  0.0 st\r\n%Cpu24 :  0.0 us,  0.0 sy,  0.0 ni,100.0 id,  0.0 wa,  0.0 hi,  0.0 si,  0.0 st\r\n%Cpu25 :  0.0 us,  0.0 sy,  0.0 ni,100.0 id,  0.0 wa,  0.0 hi,  0.0 si,  0.0 st\r\n%Cpu26 :  0.0 us,  0.0 sy,  0.0 ni,100.0 id,  0.0 wa,  0.0 hi,  0.0 si,  0.0 st\r\n%Cpu27 :  0.0 us,  0.0 sy,  0.0 ni,100.0 id,  0.0 wa,  0.0 hi,  0.0 si,  0.0 st\r\n%Cpu28 :  0.0 us,  0.0 sy,  0.0 ni,100.0 id,  0.0 wa,  0.0 hi,  0.0 si,  0.0 st\r\n%Cpu29 :  0.0 us,  0.0 sy,  0.0 ni,100.0 id,  0.0 wa,  0.0 hi,  0.0 si,  0.0 st\r\n%Cpu30 :  0.0 us,  0.0 sy,  0.0 ni,100.0 id,  0.0 wa,  0.0 hi,  0.0 si,  0.0 st\r\n%Cpu31 :  0.0 us,  0.0 sy,  0.0 ni,100.0 id,  0.0 wa,  0.0 hi,  0.0 si,  0.0 st\r\nKiB Mem : 12376860+total, 87142112 free,  5920932 used, 30705556 buff/cache\r\nKiB Swap:        0 total,        0 free,        0 used. 86701776 \r\n```\r\n\r\nThe problem is when I have 32 cpus and 31 workers, I saw `time_this_iter_s = 9.538779020309448`. This is wired. I expected to see` time_this_iter_s < 5.7`. Here is the command I use\r\n```\r\ntaskset -c 0-31 python3 train.py --run A3C --env=PongDeterministic-v4 --config='{\"nu\r\nm_workers\":31, \"sample_batch_size\": 20}'\r\n```\r\nTrainingResult for A3C_PongDeterministic-v4_0:\r\n  date: 2018-07-17_03-08-34\r\n  episode_len_mean: 830.6470588235294\r\n  episode_reward_max: -20.0\r\n  episode_reward_mean: -20.176470588235293\r\n  episode_reward_min: -21.0\r\n  episodes_total: 17\r\n  experiment_id: 65e2aea265b1457cb94e75028fa5a503\r\n  hostname: cpu-32\r\n  info:\r\n    apply_time_ms: 15.905\r\n    dispatch_time_ms: 49.127\r\n    num_steps_sampled: 13000\r\n    num_steps_trained: 13000\r\n    wait_time_ms: 22.446\r\n  node_ip: 10.128.0.5\r\n  pid: 14874\r\n  policy_reward_mean:\r\n    default: -20.176470588235293\r\n  time_this_iter_s: 9.538779020309448\r\n  time_total_s: 139.73909282684326\r\n  timestamp: 1531796914\r\n  timesteps_this_iter: 1000\r\n  timesteps_total: 13000\r\n  training_iteration: 13\r\n  \r\n== Status ==\r\nUsing FIFO scheduling algorithm.\r\nResources requested: 32/32 CPUs, 0/0 GPUs\r\nResult logdir: /home/wangluochao93/ray_results/default\r\nRUNNING trials:\r\n - A3C_PongDeterministic-v4_0:  RUNNING [pid=14874], 139 s, 13000 ts, -20.2 rew\r\n```\r\nHere is the cpu usage\r\n```\r\ntop - 03:10:11 up 37 min,  2 users,  load average: 22.74, 17.59, 13.88\r\nTasks: 377 total,   3 running, 374 sleeping,   0 stopped,   0 zombie\r\n%Cpu0  : 89.3 us,  4.0 sy,  0.0 ni,  6.7 id,  0.0 wa,  0.0 hi,  0.0 si,  0.0 st\r\n%Cpu1  : 87.8 us,  4.6 sy,  0.0 ni,  7.6 id,  0.0 wa,  0.0 hi,  0.0 si,  0.0 st\r\n%Cpu2  : 77.9 us,  5.5 sy,  0.0 ni, 15.0 id,  0.0 wa,  0.0 hi,  1.6 si,  0.0 st\r\n%Cpu3  : 61.9 us, 10.6 sy,  0.0 ni, 27.5 id,  0.0 wa,  0.0 hi,  0.0 si,  0.0 st\r\n%Cpu4  : 81.1 us,  4.7 sy,  0.0 ni, 14.3 id,  0.0 wa,  0.0 hi,  0.0 si,  0.0 st\r\n%Cpu5  : 78.1 us,  3.0 sy,  0.0 ni, 18.9 id,  0.0 wa,  0.0 hi,  0.0 si,  0.0 st\r\n%Cpu6  : 74.8 us,  2.3 sy,  0.0 ni, 22.8 id,  0.0 wa,  0.0 hi,  0.0 si,  0.0 st\r\n%Cpu7  : 54.8 us,  6.9 sy,  0.0 ni, 38.0 id,  0.0 wa,  0.0 hi,  0.3 si,  0.0 st\r\n%Cpu8  : 88.0 us,  3.3 sy,  0.0 ni,  8.7 id,  0.0 wa,  0.0 hi,  0.0 si,  0.0 st\r\n%Cpu9  : 67.9 us,  3.6 sy,  0.0 ni, 28.5 id,  0.0 wa,  0.0 hi,  0.0 si,  0.0 st\r\n%Cpu10 : 70.8 us,  5.3 sy,  0.0 ni, 23.9 id,  0.0 wa,  0.0 hi,  0.0 si,  0.0 st\r\n%Cpu11 : 80.1 us,  3.0 sy,  0.0 ni, 16.9 id,  0.0 wa,  0.0 hi,  0.0 si,  0.0 st\r\n%Cpu12 : 75.6 us,  3.3 sy,  0.0 ni, 21.1 id,  0.0 wa,  0.0 hi,  0.0 si,  0.0 st\r\n%Cpu13 : 77.9 us,  4.3 sy,  0.0 ni, 17.8 id,  0.0 wa,  0.0 hi,  0.0 si,  0.0 st\r\n%Cpu14 : 74.3 us,  7.1 sy,  0.0 ni, 18.6 id,  0.0 wa,  0.0 hi,  0.0 si,  0.0 st\r\n%Cpu15 : 89.0 us,  3.3 sy,  0.0 ni,  7.7 id,  0.0 wa,  0.0 hi,  0.0 si,  0.0 st\r\n%Cpu16 : 58.3 us,  6.3 sy,  0.0 ni, 35.3 id,  0.0 wa,  0.0 hi,  0.0 si,  0.0 st\r\n%Cpu17 : 61.8 us,  6.3 sy,  0.0 ni, 31.9 id,  0.0 wa,  0.0 hi,  0.0 si,  0.0 st\r\n%Cpu18 : 64.1 us,  4.7 sy,  0.0 ni, 30.9 id,  0.0 wa,  0.0 hi,  0.3 si,  0.0 st\r\n%Cpu19 : 91.7 us,  3.6 sy,  0.0 ni,  4.6 id,  0.0 wa,  0.0 hi,  0.0 si,  0.0 st\r\n%Cpu20 : 84.8 us,  3.0 sy,  0.0 ni, 12.3 id,  0.0 wa,  0.0 hi,  0.0 si,  0.0 st\r\n%Cpu21 : 81.1 us,  2.0 sy,  0.0 ni, 16.6 id,  0.0 wa,  0.0 hi,  0.3 si,  0.0 st\r\n%Cpu22 : 77.2 us,  5.0 sy,  0.0 ni, 17.9 id,  0.0 wa,  0.0 hi,  0.0 si,  0.0 st\r\n%Cpu23 : 80.5 us,  5.3 sy,  0.0 ni, 13.9 id,  0.0 wa,  0.0 hi,  0.3 si,  0.0 st\r\n%Cpu24 : 61.9 us,  5.2 sy,  0.0 ni, 32.6 id,  0.0 wa,  0.0 hi,  0.3 si,  0.0 st\r\n%Cpu25 : 96.3 us,  1.7 sy,  0.0 ni,  2.0 id,  0.0 wa,  0.0 hi,  0.0 si,  0.0 st\r\n%Cpu26 : 76.8 us,  4.0 sy,  0.0 ni, 19.2 id,  0.0 wa,  0.0 hi,  0.0 si,  0.0 st\r\n%Cpu27 : 79.0 us,  6.0 sy,  0.0 ni, 15.0 id,  0.0 wa,  0.0 hi,  0.0 si,  0.0 st\r\n%Cpu28 : 81.7 us,  7.0 sy,  0.0 ni, 11.3 id,  0.0 wa,  0.0 hi,  0.0 si,  0.0 st\r\n%Cpu29 : 85.1 us,  5.3 sy,  0.0 ni,  9.6 id,  0.0 wa,  0.0 hi,  0.0 si,  0.0 st\r\n%Cpu30 : 82.7 us,  3.3 sy,  0.0 ni, 14.0 id,  0.0 wa,  0.0 hi,  0.0 si,  0.0 st\r\n%Cpu31 : 81.1 us,  3.0 sy,  0.0 ni, 15.9 id,  0.0 wa,  0.0 hi,  0.0 si,  0.0 st\r\nKiB Mem : 12376860+total, 82327936 free,  7073908 used, 34366760 buff/cache\r\nKiB Swap:        0 total,        0 free,        0 used. 81890888 \r\n```"},"comment":{"url":"https://api.github.com/repos/ray-project/ray/issues/comments/405473018","html_url":"https://github.com/ray-project/ray/issues/2411#issuecomment-405473018","issue_url":"https://api.github.com/repos/ray-project/ray/issues/2411","id":405473018,"node_id":"MDEyOklzc3VlQ29tbWVudDQwNTQ3MzAxOA==","user":{"login":"ericl","id":14922,"node_id":"MDQ6VXNlcjE0OTIy","avatar_url":"https://avatars3.githubusercontent.com/u/14922?v=4","gravatar_id":"","url":"https://api.github.com/users/ericl","html_url":"https://github.com/ericl","followers_url":"https://api.github.com/users/ericl/followers","following_url":"https://api.github.com/users/ericl/following{/other_user}","gists_url":"https://api.github.com/users/ericl/gists{/gist_id}","starred_url":"https://api.github.com/users/ericl/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/ericl/subscriptions","organizations_url":"https://api.github.com/users/ericl/orgs","repos_url":"https://api.github.com/users/ericl/repos","events_url":"https://api.github.com/users/ericl/events{/privacy}","received_events_url":"https://api.github.com/users/ericl/received_events","type":"User","site_admin":false},"created_at":"2018-07-17T06:22:49Z","updated_at":"2018-07-17T06:22:49Z","author_association":"CONTRIBUTOR","body":"It could be that at 32 processes you're running into contention between sibling hyperthreads on the same core, since that machine probably only has 16 physical cores. So I wouldn't necessarily expect a gain to 32.\r\n\r\nIt's also possible the driver is starting to become a bottleneck, though your workers still seem busy so maybe it's not. Cc @richardliaw who has worked with a3c a lot more"}}
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