rllib/agents/trainer_config.py
def debugging(
        self,
        *,
        logger_config: Optional[dict] = None,
        log_level: Optional[str] = None,
        log_sys_usage: Optional[bool] = None,
        fake_sampler: Optional[bool] = None,
        seed: Optional[int] = None,
    ) -> "TrainerConfig":
        """Sets the config's debugging settings.

        Args:
            logger_config: Define logger-specific configuration to be used inside Logger
                Default value None allows overwriting with nested dicts.
            log_level: Set the ray.rllib.* log level for the agent process and its
                workers. Should be one of DEBUG, INFO, WARN, or ERROR. The DEBUG level
                will also periodically print out summaries of relevant internal dataflow
                (this is also printed out once at startup at the INFO level). When using
                the `rllib train` command, you can also use the `-v` and `-vv` flags as
                shorthand for INFO and DEBUG.
            log_sys_usage: Log system resource metrics to results. This requires
                `psutil` to be installed for sys stats, and `gputil` for GPU metrics.
            fake_sampler: Use fake (infinite speed) sampler. For testing only.
            seed: This argument, in conjunction with worker_index, sets the random
                seed of each worker, so that identically configured trials will have
                identical results. This makes experiments reproducible.

        Returns:
            This updated TrainerConfig object.
        """
        if logger_config is not None:
            self.logger_config = logger_config
        if log_level is not None:
            self.log_level = log_level
        if log_sys_usage is not None:
            self.log_sys_usage = log_sys_usage
        if fake_sampler is not None:
            self.fake_sampler = fake_sampler
        if seed is not None:
            self.seed = seed

        return self
Sets the config's debugging settings.

        Args:
            logger_config: Define logger-specific configuration to be used inside Logger
                Default value None allows overwriting with nested dicts.
            log_level: Set the ray.rllib.* log level for the agent process and its
                workers. Should be one of DEBUG, INFO, WARN, or ERROR. The DEBUG level
                will also periodically print out summaries of relevant internal dataflow
                (this is also printed out once at startup at the INFO level). When using
                the `rllib train` command, you can also use the `-v` and `-vv` flags as
                shorthand for INFO and DEBUG.
            log_sys_usage: Log system resource metrics to results. This requires
                `psutil` to be installed for sys stats, and `gputil` for GPU metrics.
            fake_sampler: Use fake (infinite speed) sampler. For testing only.
            seed: This argument, in conjunction with worker_index, sets the random
                seed of each worker, so that identically configured trials will have
                identical results. This makes experiments reproducible.

        Returns:
            This updated TrainerConfig object.
        
Sets the config's debugging settings.
Sets the config ' s debugging settings .
def debugging(
        self,
        *,
        logger_config: Optional[dict] = None,
        log_level: Optional[str] = None,
        log_sys_usage: Optional[bool] = None,
        fake_sampler: Optional[bool] = None,
        seed: Optional[int] = None,
    ) -> "TrainerConfig":
        if logger_config is not None:
            self.logger_config = logger_config
        if log_level is not None:
            self.log_level = log_level
        if log_sys_usage is not None:
            self.log_sys_usage = log_sys_usage
        if fake_sampler is not None:
            self.fake_sampler = fake_sampler
        if seed is not None:
            self.seed = seed
        return self
def debugging ( self , * , logger_config : Optional [ dict ] = None , log_level : Optional [ str ] = None , log_sys_usage : Optional [ bool ] = None , fake_sampler : Optional [ bool ] = None , seed : Optional [ int ] = None , ) -> "TrainerConfig" : if logger_config is not None : self . logger_config = logger_config if log_level is not None : self . log_level = log_level if log_sys_usage is not None : self . log_sys_usage = log_sys_usage if fake_sampler is not None : self . fake_sampler = fake_sampler if seed is not None : self . seed = seed return self
Sets the config's debugging settings.
Sets the config ' s debugging settings .
"""Sets the config's debugging settings.

        Args:
            logger_config: Define logger-specific configuration to be used inside Logger
                Default value None allows overwriting with nested dicts.
            log_level: Set the ray.rllib.* log level for the agent process and its
                workers. Should be one of DEBUG, INFO, WARN, or ERROR. The DEBUG level
                will also periodically print out summaries of relevant internal dataflow
                (this is also printed out once at startup at the INFO level). When using
                the `rllib train` command, you can also use the `-v` and `-vv` flags as
                shorthand for INFO and DEBUG.
            log_sys_usage: Log system resource metrics to results. This requires
                `psutil` to be installed for sys stats, and `gputil` for GPU metrics.
            fake_sampler: Use fake (infinite speed) sampler. For testing only.
            seed: This argument, in conjunction with worker_index, sets the random
                seed of each worker, so that identically configured trials will have
                identical results. This makes experiments reproducible.

        Returns:
            This updated TrainerConfig object.
        """
This updated TrainerConfig object.
This updated TrainerConfig object .
self
logger_config
Optional[dict]
Define logger-specific configuration to be used inside Logger
Default value None allows overwriting with nested dicts.
Define logger - specific configuration to be used inside Logger Default value None allows overwriting with nested dicts .
log_level
Optional[str]
Set the ray.rllib.* log level for the agent process and its
workers. Should be one of DEBUG, INFO, WARN, or ERROR. The DEBUG level
will also periodically print out summaries of relevant internal dataflow
(this is also printed out once at startup at the INFO level). When using
the `rllib train` command, you can also use the `-v` and `-vv` flags as
shorthand for INFO and DEBUG.
Set the ray . rllib . * log level for the agent process and its workers . Should be one of DEBUG INFO WARN or ERROR . The DEBUG level will also periodically print out summaries of relevant internal dataflow ( this is also printed out once at startup at the INFO level ) . When using the ` rllib train ` command you can also use the ` - v ` and ` - vv ` flags as shorthand for INFO and DEBUG .
log_sys_usage
Optional[bool]
Log system resource metrics to results. This requires
`psutil` to be installed for sys stats, and `gputil` for GPU metrics.
Log system resource metrics to results . This requires ` psutil ` to be installed for sys stats and ` gputil ` for GPU metrics .
fake_sampler
Optional[bool]
Use fake (infinite speed) sampler. For testing only.
Use fake ( infinite speed ) sampler . For testing only .
seed
Optional[int]
This argument, in conjunction with worker_index, sets the random
seed of each worker, so that identically configured trials will have
identical results. This makes experiments reproducible.
This argument in conjunction with worker_index sets the random seed of each worker so that identically configured trials will have identical results . This makes experiments reproducible .