吞吐量输出为0

Question

吞吐量输出为0

Closed this issue 5 months ago · 7 comments

环境：
python=3.8
tensorflow=1.15.5+cu113
numpy=1.18.5
ws4py=0.5.1
pyarrow=5.0.0
pyzmq=22.3.0

启动命令为：

python actor_n/actor.py
python learner/learner.py

actor.py参数设置：

parser = ArgumentParser()
parser.add_argument('--ip', type=str, default='127.0.0.1',
help='IP address of learner server')
parser.add_argument('--data_port', type=int, default=5000,
help='Learner server port to send training data')
parser.add_argument('--param_port', type=int, default=5001,
help='Learner server port to subscribe model parameters')
parser.add_argument('--exp_path', type=str, default='/mnt/workspace/guandan_mcc/Clients',
help='Directory to save logging data, model parameters and config file')
parser.add_argument('--num_saved_ckpt', type=int, default=4,
help='Number of recent checkpoint files to be saved')
parser.add_argument('--observation_space', type=int, default=(567,),
help='The YAML configuration file')
parser.add_argument('--action_space', type=int, default=(5, 216),
help='The YAML configuration file')
parser.add_argument('--epsilon', type=float, default=0.01,
help='Epsilon')

learner.py 参数设置：

parser = ArgumentParser()
parser.add_argument('--alg', type=str, default='MC', help='The RL algorithm')
parser.add_argument('--env', type=str, default='GuanDan', help='The game environment')
parser.add_argument('--data_port', type=int, default=5000, help='Learner server port to receive training data')
parser.add_argument('--param_port', type=int, default=5001, help='Learner server to publish model parameters')
parser.add_argument('--model', type=str, default='guandan_model', help='Training model')
parser.add_argument('--pool_size', type=int, default=65536, help='The max length of data pool')
parser.add_argument('--batch_size', type=int, default=32768, help='The batch size for training')
parser.add_argument('--training_freq', type=int, default=250,
help='How many receptions of new data are between each training, '
'which can be fractional to represent more than one training per reception')
parser.add_argument('--keep_training', type=bool, default=False,
help="No matter whether new data is received recently, keep training as long as the data is enough "
"and ignore --training_freq")
parser.add_argument('--config', type=str, default=None, help='Directory to config file')
parser.add_argument('--exp_path', type=str, default=None, help='Directory to save logging data and config file')
parser.add_argument('--record_throughput_interval', type=int, default=60,
help='The time interval between each throughput record')
parser.add_argument('--num_envs', type=int, default=1, help='The number of environment copies')
parser.add_argument('--ckpt_save_freq', type=int, default=3000, help='The number of updates between each weights saving')
parser.add_argument('--ckpt_save_type', type=str, default='weight', help='Type of checkpoint file will be recorded : weight(smaller) or checkpoint(bigger')
parser.add_argument('--observation_space', type=int, default=(567,),
help='The YAML configuration file')
parser.add_argument('--action_space', type=int, default=(5, 216),
help='The YAML configuration file')
parser.add_argument('--epsilon', type=float, default=0.01,
help='Epsilon')

端口检测：netstat - nlt

Proto Recv-Q Send-Q Local Address Foreign Address State
tcp 0 0 127.0.0.1:5000 0.0.0.0:* LISTEN
tcp 0 0 127.0.0.1:5001 0.0.0.0:* LISTEN
tcp 0 0 10.224.128.51:10250 0.0.0.0:* LISTEN
tcp 0 0 127.0.0.1:59083 0.0.0.0:* LISTEN
tcp 0 0 127.0.0.1:111 0.0.0.0:* LISTEN
tcp 0 0 127.0.0.1:6000 0.0.0.0:* LISTEN
tcp 0 0 127.0.0.1:6001 0.0.0.0:* LISTEN
tcp 0 0 127.0.0.1:46609 0.0.0.0:* LISTEN
tcp 0 0 127.0.0.1:34449 0.0.0.0:* LISTEN
tcp 0 0 127.0.0.1:6002 0.0.0.0:* LISTEN
tcp 0 0 0.0.0.0:8082 0.0.0.0:* LISTEN
tcp 0 0 127.0.0.1:6003 0.0.0.0:* LISTEN
tcp 0 0 127.0.0.1:35253 0.0.0.0:* LISTEN
tcp 0 0 127.0.0.1:8086 0.0.0.0:* LISTEN
tcp 0 0 127.0.0.1:46423 0.0.0.0:* LISTEN
tcp 0 0 0.0.0.0:8088 0.0.0.0:* LISTEN
tcp 0 0 0.0.0.0:8888 0.0.0.0:* LISTEN
tcp 0 0 10.224.128.51:8889 0.0.0.0:* LISTEN
tcp 0 0 127.0.0.1:43713 0.0.0.0:* LISTEN
tcp6 0 0 ::1:111 :::* LISTEN
tcp6 0 0 :::3011 :::* LISTEN
本地的5000，5001，6000，6001端口均已启动

启动learner.py后显示：
Logging to LEARNER-2023-11-09-17-05-31/log
Data socket has been bound to port 5000
Receiving FPS: 0.00, Consuming FPS: 0.00
Receiving FPS: 0.00, Consuming FPS: 0.00
Receiving FPS: 0.00, Consuming FPS: 0.00
Receiving FPS: 0.00, Consuming FPS: 0.00
Receiving FPS: 0.00, Consuming FPS: 0.00
Receiving FPS: 0.00, Consuming FPS: 0.00
Receiving FPS: 0.00, Consuming FPS: 0.00
Receiving FPS: 0.00, Consuming FPS: 0.00
Receiving FPS: 0.00, Consuming FPS: 0.00
Receiving FPS: 0.00, Consuming FPS: 0.00
Receiving FPS: 0.00, Consuming FPS: 0.00
Receiving FPS: 0.00, Consuming FPS: 0.00
Receiving FPS: 0.00, Consuming FPS: 0.00
Receiving FPS: 0.00, Consuming FPS: 0.00
Receiving FPS: 0.00, Consuming FPS: 0.00

总结

依赖环境均按照项目给出的版本安装，没有对源码结构进行修改。learner.py里的参数配置均为默认值（修改过--pool_size,--batch_size的数值吞吐量打印还是为0）

Answer 1 · 2023-11-10T02:11:18.000Z

按你给出的条件来看没什么问题，可以尝试检查以下部分：两个程序是否可以通过 127.0.0.1:5000 正常通信；actor.py 是否正常采集数据并发送

Answer 2 · 2023-11-10T04:21:16.000Z

请问是用docker运行的吗，我们的代码是在linux服务器上运行，actor和learner分别在不同docker里，这边设本地端口我不确定能不能运行。因为gamecore和actor交互我记得也是用的本地端口，您这样设置感觉可能是端口被占用了

Answer 3 · 2023-11-10T07:17:21.000Z

请问是用docker运行的吗，我们的代码是在linux服务器上运行，actor和learner分别在不同docker里，这边设本地端口我不确定能不能运行。因为gamecore和actor交互我记得也是用的本地端口，您这样设置感觉可能是端口被占用了

还没有使用docker,使用的是linux云服务器部署，分别启用了4个端口：5000，5001，6000，6001，ip地址换了成服务器的局域网，但是运行的时候吞吐量还是为0.

Answer 4 · 2023-11-10T07:20:36.000Z

按你给出的条件来看没什么问题，可以尝试检查以下部分：两个程序是否可以通过 127.0.0.1:5000 正常通信；actor.py 是否正常采集数据并发送

好的，我试一下

Answer 5 · 2023-12-18T11:57:10.000Z

环境： python=3.8 tensorflow=1.15.5+cu113 numpy=1.18.5 ws4py=0.5.1 pyarrow=5.0.0 pyzmq=22.3.0

启动命令为：

python actor_n/actor.py python learner/learner.py

actor.py参数设置：

parser = ArgumentParser() parser.add_argument('--ip', type=str, default='127.0.0.1', help='IP address of learner server') parser.add_argument('--data_port', type=int, default=5000, help='Learner server port to send training data') parser.add_argument('--param_port', type=int, default=5001, help='Learner server port to subscribe model parameters') parser.add_argument('--exp_path', type=str, default='/mnt/workspace/guandan_mcc/Clients', help='Directory to save logging data, model parameters and config file') parser.add_argument('--num_saved_ckpt', type=int, default=4, help='Number of recent checkpoint files to be saved') parser.add_argument('--observation_space', type=int, default=(567,), help='The YAML configuration file') parser.add_argument('--action_space', type=int, default=(5, 216), help='The YAML configuration file') parser.add_argument('--epsilon', type=float, default=0.01, help='Epsilon')

learner.py 参数设置：

parser = ArgumentParser() parser.add_argument('--alg', type=str, default='MC', help='The RL algorithm') parser.add_argument('--env', type=str, default='GuanDan', help='The game environment') parser.add_argument('--data_port', type=int, default=5000, help='Learner server port to receive training data') parser.add_argument('--param_port', type=int, default=5001, help='Learner server to publish model parameters') parser.add_argument('--model', type=str, default='guandan_model', help='Training model') parser.add_argument('--pool_size', type=int, default=65536, help='The max length of data pool') parser.add_argument('--batch_size', type=int, default=32768, help='The batch size for training') parser.add_argument('--training_freq', type=int, default=250, help='How many receptions of new data are between each training, ' 'which can be fractional to represent more than one training per reception') parser.add_argument('--keep_training', type=bool, default=False, help="No matter whether new data is received recently, keep training as long as the data is enough " "and ignore --training_freq") parser.add_argument('--config', type=str, default=None, help='Directory to config file') parser.add_argument('--exp_path', type=str, default=None, help='Directory to save logging data and config file') parser.add_argument('--record_throughput_interval', type=int, default=60, help='The time interval between each throughput record') parser.add_argument('--num_envs', type=int, default=1, help='The number of environment copies') parser.add_argument('--ckpt_save_freq', type=int, default=3000, help='The number of updates between each weights saving') parser.add_argument('--ckpt_save_type', type=str, default='weight', help='Type of checkpoint file will be recorded : weight(smaller) or checkpoint(bigger') parser.add_argument('--observation_space', type=int, default=(567,), help='The YAML configuration file') parser.add_argument('--action_space', type=int, default=(5, 216), help='The YAML configuration file') parser.add_argument('--epsilon', type=float, default=0.01, help='Epsilon')

端口检测：netstat - nlt

Proto Recv-Q Send-Q Local Address Foreign Address State tcp 0 0 127.0.0.1:5000 0.0.0.0:* LISTEN tcp 0 0 127.0.0.1:5001 0.0.0.0:* LISTEN tcp 0 0 10.224.128.51:10250 0.0.0.0:* LISTEN tcp 0 0 127.0.0.1:59083 0.0.0.0:* LISTEN tcp 0 0 127.0.0.1:111 0.0.0.0:* LISTEN tcp 0 0 127.0.0.1:6000 0.0.0.0:* LISTEN tcp 0 0 127.0.0.1:6001 0.0.0.0:* LISTEN tcp 0 0 127.0.0.1:46609 0.0.0.0:* LISTEN tcp 0 0 127.0.0.1:34449 0.0.0.0:* LISTEN tcp 0 0 127.0.0.1:6002 0.0.0.0:* LISTEN tcp 0 0 0.0.0.0:8082 0.0.0.0:* LISTEN tcp 0 0 127.0.0.1:6003 0.0.0.0:* LISTEN tcp 0 0 127.0.0.1:35253 0.0.0.0:* LISTEN tcp 0 0 127.0.0.1:8086 0.0.0.0:* LISTEN tcp 0 0 127.0.0.1:46423 0.0.0.0:* LISTEN tcp 0 0 0.0.0.0:8088 0.0.0.0:* LISTEN tcp 0 0 0.0.0.0:8888 0.0.0.0:* LISTEN tcp 0 0 10.224.128.51:8889 0.0.0.0:* LISTEN tcp 0 0 127.0.0.1:43713 0.0.0.0:* LISTEN tcp6 0 0 ::1:111 :::* LISTEN tcp6 0 0 :::3011 :::* LISTEN 本地的5000，5001，6000，6001端口均已启动

启动learner.py后显示： Logging to LEARNER-2023-11-09-17-05-31/log Data socket has been bound to port 5000 Receiving FPS: 0.00, Consuming FPS: 0.00 Receiving FPS: 0.00, Consuming FPS: 0.00 Receiving FPS: 0.00, Consuming FPS: 0.00 Receiving FPS: 0.00, Consuming FPS: 0.00 Receiving FPS: 0.00, Consuming FPS: 0.00 Receiving FPS: 0.00, Consuming FPS: 0.00 Receiving FPS: 0.00, Consuming FPS: 0.00 Receiving FPS: 0.00, Consuming FPS: 0.00 Receiving FPS: 0.00, Consuming FPS: 0.00 Receiving FPS: 0.00, Consuming FPS: 0.00 Receiving FPS: 0.00, Consuming FPS: 0.00 Receiving FPS: 0.00, Consuming FPS: 0.00 Receiving FPS: 0.00, Consuming FPS: 0.00 Receiving FPS: 0.00, Consuming FPS: 0.00 Receiving FPS: 0.00, Consuming FPS: 0.00

总结

依赖环境均按照项目给出的版本安装，没有对源码结构进行修改。learner.py里的参数配置均为默认值（修改过--pool_size,--batch_size的数值吞吐量打印还是为0）

我遇到了同样的问题，请问你解决了吗？谢谢

Answer 6 · 2023-12-22T07:26:15.000Z

环境： python=3.8 tensorflow=1.15.5+cu113 numpy=1.18.5 ws4py=0.5.1 pyarrow=5.0.0 pyzmq=22.3.0

启动命令为：

python actor_n/actor.py python learner/learner.py

actor.py参数设置：

parser = ArgumentParser() parser.add_argument('--ip', type=str, default='127.0.0.1', help='IP address of learner server') parser.add_argument('--data_port', type=int, default=5000, help='Learner server port to send training data') parser.add_argument('--param_port', type=int, default=5001, help='Learner server port to subscribe model parameters') parser.add_argument('--exp_path', type=str, default='/mnt/workspace/guandan_mcc/Clients', help='Directory to save logging data, model parameters and config file') parser.add_argument('--num_saved_ckpt', type=int, default=4, help='Number of recent checkpoint files to be saved') parser.add_argument('--observation_space', type=int, default=(567,), help='The YAML configuration file') parser.add_argument('--action_space', type=int, default=(5, 216), help='The YAML configuration file') parser.add_argument('--epsilon', type=float, default=0.01, help='Epsilon')

learner.py 参数设置：

parser = ArgumentParser() parser.add_argument('--alg', type=str, default='MC', help='The RL algorithm') parser.add_argument('--env', type=str, default='GuanDan', help='The game environment') parser.add_argument('--data_port', type=int, default=5000, help='Learner server port to receive training data') parser.add_argument('--param_port', type=int, default=5001, help='Learner server to publish model parameters') parser.add_argument('--model', type=str, default='guandan_model', help='Training model') parser.add_argument('--pool_size', type=int, default=65536, help='The max length of data pool') parser.add_argument('--batch_size', type=int, default=32768, help='The batch size for training') parser.add_argument('--training_freq', type=int, default=250, help='How many receptions of new data are between each training, ' 'which can be fractional to represent more than one training per reception') parser.add_argument('--keep_training', type=bool, default=False, help="No matter whether new data is received recently, keep training as long as the data is enough " "and ignore --training_freq") parser.add_argument('--config', type=str, default=None, help='Directory to config file') parser.add_argument('--exp_path', type=str, default=None, help='Directory to save logging data and config file') parser.add_argument('--record_throughput_interval', type=int, default=60, help='The time interval between each throughput record') parser.add_argument('--num_envs', type=int, default=1, help='The number of environment copies') parser.add_argument('--ckpt_save_freq', type=int, default=3000, help='The number of updates between each weights saving') parser.add_argument('--ckpt_save_type', type=str, default='weight', help='Type of checkpoint file will be recorded : weight(smaller) or checkpoint(bigger') parser.add_argument('--observation_space', type=int, default=(567,), help='The YAML configuration file') parser.add_argument('--action_space', type=int, default=(5, 216), help='The YAML configuration file') parser.add_argument('--epsilon', type=float, default=0.01, help='Epsilon')

端口检测：netstat - nlt

Proto Recv-Q Send-Q Local Address Foreign Address State tcp 0 0 127.0.0.1:5000 0.0.0.0:* LISTEN tcp 0 0 127.0.0.1:5001 0.0.0.0:* LISTEN tcp 0 0 10.224.128.51:10250 0.0.0.0:* LISTEN tcp 0 0 127.0.0.1:59083 0.0.0.0:* LISTEN tcp 0 0 127.0.0.1:111 0.0.0.0:* LISTEN tcp 0 0 127.0.0.1:6000 0.0.0.0:* LISTEN tcp 0 0 127.0.0.1:6001 0.0.0.0:* LISTEN tcp 0 0 127.0.0.1:46609 0.0.0.0:* LISTEN tcp 0 0 127.0.0.1:34449 0.0.0.0:* LISTEN tcp 0 0 127.0.0.1:6002 0.0.0.0:* LISTEN tcp 0 0 0.0.0.0:8082 0.0.0.0:* LISTEN tcp 0 0 127.0.0.1:6003 0.0.0.0:* LISTEN tcp 0 0 127.0.0.1:35253 0.0.0.0:* LISTEN tcp 0 0 127.0.0.1:8086 0.0.0.0:* LISTEN tcp 0 0 127.0.0.1:46423 0.0.0.0:* LISTEN tcp 0 0 0.0.0.0:8088 0.0.0.0:* LISTEN tcp 0 0 0.0.0.0:8888 0.0.0.0:* LISTEN tcp 0 0 10.224.128.51:8889 0.0.0.0:* LISTEN tcp 0 0 127.0.0.1:43713 0.0.0.0:* LISTEN tcp6 0 0 ::1:111 :::* LISTEN tcp6 0 0 :::3011 :::* LISTEN 本地的5000，5001，6000，6001端口均已启动
启动learner.py后显示： Logging to LEARNER-2023-11-09-17-05-31/log Data socket has been bound to port 5000 Receiving FPS: 0.00, Consuming FPS: 0.00 Receiving FPS: 0.00, Consuming FPS: 0.00 Receiving FPS: 0.00, Consuming FPS: 0.00 Receiving FPS: 0.00, Consuming FPS: 0.00 Receiving FPS: 0.00, Consuming FPS: 0.00 Receiving FPS: 0.00, Consuming FPS: 0.00 Receiving FPS: 0.00, Consuming FPS: 0.00 Receiving FPS: 0.00, Consuming FPS: 0.00 Receiving FPS: 0.00, Consuming FPS: 0.00 Receiving FPS: 0.00, Consuming FPS: 0.00 Receiving FPS: 0.00, Consuming FPS: 0.00 Receiving FPS: 0.00, Consuming FPS: 0.00 Receiving FPS: 0.00, Consuming FPS: 0.00 Receiving FPS: 0.00, Consuming FPS: 0.00 Receiving FPS: 0.00, Consuming FPS: 0.00

总结

依赖环境均按照项目给出的版本安装，没有对源码结构进行修改。learner.py里的参数配置均为默认值（修改过--pool_size,--batch_size的数值吞吐量打印还是为0）

我遇到了同样的问题，请问你解决了吗？谢谢

还没解决，我感觉是docker的问题，应该要从docker入手，如果你有解决的办法请回复我一下，谢谢

Answer 7 · 2024-01-02T04:05:00.000Z

环境： python=3.8 tensorflow=1.15.5+cu113 numpy=1.18.5 ws4py=0.5.1 pyarrow=5.0.0 pyzmq=22.3.0

启动命令为：

python actor_n/actor.py python learner/learner.py

actor.py参数设置：

parser = ArgumentParser() parser.add_argument('--ip', type=str, default='127.0.0.1', help='IP address of learner server') parser.add_argument('--data_port', type=int, default=5000, help='Learner server port to send training data') parser.add_argument('--param_port', type=int, default=5001, help='Learner server port to subscribe model parameters') parser.add_argument('--exp_path', type=str, default='/mnt/workspace/guandan_mcc/Clients', help='Directory to save logging data, model parameters and config file') parser.add_argument('--num_saved_ckpt', type=int, default=4, help='Number of recent checkpoint files to be saved') parser.add_argument('--observation_space', type=int, default=(567,), help='The YAML configuration file') parser.add_argument('--action_space', type=int, default=(5, 216), help='The YAML configuration file') parser.add_argument('--epsilon', type=float, default=0.01, help='Epsilon')

learner.py 参数设置：

parser = ArgumentParser() parser.add_argument('--alg', type=str, default='MC', help='The RL algorithm') parser.add_argument('--env', type=str, default='GuanDan', help='The game environment') parser.add_argument('--data_port', type=int, default=5000, help='Learner server port to receive training data') parser.add_argument('--param_port', type=int, default=5001, help='Learner server to publish model parameters') parser.add_argument('--model', type=str, default='guandan_model', help='Training model') parser.add_argument('--pool_size', type=int, default=65536, help='The max length of data pool') parser.add_argument('--batch_size', type=int, default=32768, help='The batch size for training') parser.add_argument('--training_freq', type=int, default=250, help='How many receptions of new data are between each training, ' 'which can be fractional to represent more than one training per reception') parser.add_argument('--keep_training', type=bool, default=False, help="No matter whether new data is received recently, keep training as long as the data is enough " "and ignore --training_freq") parser.add_argument('--config', type=str, default=None, help='Directory to config file') parser.add_argument('--exp_path', type=str, default=None, help='Directory to save logging data and config file') parser.add_argument('--record_throughput_interval', type=int, default=60, help='The time interval between each throughput record') parser.add_argument('--num_envs', type=int, default=1, help='The number of environment copies') parser.add_argument('--ckpt_save_freq', type=int, default=3000, help='The number of updates between each weights saving') parser.add_argument('--ckpt_save_type', type=str, default='weight', help='Type of checkpoint file will be recorded : weight(smaller) or checkpoint(bigger') parser.add_argument('--observation_space', type=int, default=(567,), help='The YAML configuration file') parser.add_argument('--action_space', type=int, default=(5, 216), help='The YAML configuration file') parser.add_argument('--epsilon', type=float, default=0.01, help='Epsilon')

端口检测：netstat - nlt

Proto Recv-Q Send-Q Local Address Foreign Address State tcp 0 0 127.0.0.1:5000 0.0.0.0:* LISTEN tcp 0 0 127.0.0.1:5001 0.0.0.0:* LISTEN tcp 0 0 10.224.128.51:10250 0.0.0.0:* LISTEN tcp 0 0 127.0.0.1:59083 0.0.0.0:* LISTEN tcp 0 0 127.0.0.1:111 0.0.0.0:* LISTEN tcp 0 0 127.0.0.1:6000 0.0.0.0:* LISTEN tcp 0 0 127.0.0.1:6001 0.0.0.0:* LISTEN tcp 0 0 127.0.0.1:46609 0.0.0.0:* LISTEN tcp 0 0 127.0.0.1:34449 0.0.0.0:* LISTEN tcp 0 0 127.0.0.1:6002 0.0.0.0:* LISTEN tcp 0 0 0.0.0.0:8082 0.0.0.0:* LISTEN tcp 0 0 127.0.0.1:6003 0.0.0.0:* LISTEN tcp 0 0 127.0.0.1:35253 0.0.0.0:* LISTEN tcp 0 0 127.0.0.1:8086 0.0.0.0:* LISTEN tcp 0 0 127.0.0.1:46423 0.0.0.0:* LISTEN tcp 0 0 0.0.0.0:8088 0.0.0.0:* LISTEN tcp 0 0 0.0.0.0:8888 0.0.0.0:* LISTEN tcp 0 0 10.224.128.51:8889 0.0.0.0:* LISTEN tcp 0 0 127.0.0.1:43713 0.0.0.0:* LISTEN tcp6 0 0 ::1:111 :::* LISTEN tcp6 0 0 :::3011 :::* LISTEN 本地的5000，5001，6000，6001端口均已启动
启动learner.py后显示： Logging to LEARNER-2023-11-09-17-05-31/log Data socket has been bound to port 5000 Receiving FPS: 0.00, Consuming FPS: 0.00 Receiving FPS: 0.00, Consuming FPS: 0.00 Receiving FPS: 0.00, Consuming FPS: 0.00 Receiving FPS: 0.00, Consuming FPS: 0.00 Receiving FPS: 0.00, Consuming FPS: 0.00 Receiving FPS: 0.00, Consuming FPS: 0.00 Receiving FPS: 0.00, Consuming FPS: 0.00 Receiving FPS: 0.00, Consuming FPS: 0.00 Receiving FPS: 0.00, Consuming FPS: 0.00 Receiving FPS: 0.00, Consuming FPS: 0.00 Receiving FPS: 0.00, Consuming FPS: 0.00 Receiving FPS: 0.00, Consuming FPS: 0.00 Receiving FPS: 0.00, Consuming FPS: 0.00 Receiving FPS: 0.00, Consuming FPS: 0.00 Receiving FPS: 0.00, Consuming FPS: 0.00

总结

依赖环境均按照项目给出的版本安装，没有对源码结构进行修改。learner.py里的参数配置均为默认值（修改过--pool_size,--batch_size的数值吞吐量打印还是为0）

我遇到了同样的问题，请问你解决了吗？谢谢

还没解决，我感觉是docker的问题，应该要从docker入手，如果你有解决的办法请回复我一下，谢谢

按打印出的 log 来看仍需要检查两个程序（actor 和 learner）是否可以通过 127.0.0.1:5000 正常通信，可以使用 zmq 的范例程序进行简单测试一下；actor.py 是否正常采集数据并发送，可以在 actor.py 中采集数据的循环中加 print 看看是否在正常采集对局数据？