worker.learner

learner_hook

Please Reference ding/worker/learner/learner_hook.py for usage

Hook

class ding.worker.learner.learner_hook.Hook(name: str, priority: float, **kwargs)

Overview:

Abstract class for hooks.

Interfaces:

__init__, __call__

Property:

name, priority

__init__(name: str, priority: float, **kwargs) → None

Overview:

Init method for hooks. Set name and priority.

Arguments:

• name (str): The name of hook

• priority (float): The priority used in call_hook’s calling sequence. Lower value means higher priority.

LearnerHook

class ding.worker.learner.learner_hook.LearnerHook(*args, position: str, **kwargs)

Overview:

Abstract class for hooks used in Learner.

Interfaces:

__init__

Property:

name, priority, position

Note

Subclass should implement self.__call__.

__init__(*args, position: str, **kwargs) → None

Overview:

Init LearnerHook.

Arguments:

• position (str): The position to call hook in learner. Must be in [‘before_run’, ‘after_run’, ‘before_iter’, ‘after_iter’].

LoadCkptHook

class ding.worker.learner.learner_hook.LoadCkptHook(*args, ext_args: easydict.EasyDict = {}, **kwargs)

Overview:

Hook to load checkpoint

Interfaces:

__init__, __call__

Property:

name, priority, position

__call__(engine: BaseLearner) → None

Overview:

Load checkpoint to learner. Checkpoint info includes policy state_dict and iter num.

Arguments:

• engine (BaseLearner): The BaseLearner to load checkpoint to.

__init__(*args, ext_args: easydict.EasyDict = {}, **kwargs) → None

Overview:

Init LoadCkptHook.

Arguments:

• ext_args (EasyDict): Extended arguments. Use ext_args.freq to set load_ckpt_freq.

SaveCkptHook

class ding.worker.learner.learner_hook.SaveCkptHook(*args, ext_args: easydict.EasyDict = {}, **kwargs)

Overview:

Hook to save checkpoint

Interfaces:

__init__, __call__

Property:

name, priority, position

__call__(engine: BaseLearner) → None

Overview:

Save checkpoint in corresponding path. Checkpoint info includes policy state_dict and iter num.

Arguments:

• engine (BaseLearner): the BaseLearner which needs to save checkpoint

__init__(*args, ext_args: easydict.EasyDict = {}, **kwargs) → None

Overview:

init SaveCkptHook

Arguments:

• ext_args (EasyDict): extended_args, use ext_args.freq to set save_ckpt_freq

LogShowHook

class ding.worker.learner.learner_hook.LogShowHook(*args, ext_args: easydict.EasyDict = {}, **kwargs)

Overview:

Hook to show log

Interfaces:

__init__, __call__

Property:

name, priority, position

__call__(engine: BaseLearner) → None

Overview:

Show log, update record and tb_logger if rank is 0 and at interval iterations, clear the log buffer for all learners regardless of rank

Arguments:

• engine (BaseLearner): the BaseLearner

__init__(*args, ext_args: easydict.EasyDict = {}, **kwargs) → None

Overview:

init LogShowHook

Arguments:

• ext_args (EasyDict): extended_args, use ext_args.freq to set freq

LogReduceHook

class ding.worker.learner.learner_hook.LogReduceHook(*args, ext_args: easydict.EasyDict = {}, **kwargs)

Overview:

Hook to reduce the distributed(multi-gpu) logs

Interfaces:

__init__, __call__

Property:

name, priority, position

__call__(engine: BaseLearner) → None

Overview:

reduce the logs from distributed(multi-gpu) learners

Arguments:

• engine (BaseLearner): the BaseLearner

__init__(*args, ext_args: easydict.EasyDict = {}, **kwargs) → None

Overview:

init LogReduceHook

Arguments:

• ext_args (EasyDict): extended_args, use ext_args.freq to set log_reduce_freq

register_learner_hook

Overview:

Add a new LearnerHook class to hook_mapping, so you can build one instance with build_learner_hook_by_cfg.

Arguments:

• name (str): name of the register hook

• hook_type (type): the register hook_type you implemented that realize LearnerHook

Examples:

>>> class HookToRegister(LearnerHook):
>>>     def __init__(*args, **kargs):
>>>         ...
>>>         ...
>>>     def __call__(*args, **kargs):
>>>         ...
>>>         ...
>>> ...
>>> register_learner_hook('name_of_hook', HookToRegister)
>>> ...
>>> hooks = build_learner_hook_by_cfg(cfg)

build_learner_hook_by_cfg

Overview:

Build the learner hooks in hook_mapping by config. This function is often used to initialize hooks according to cfg, while add_learner_hook() is often used to add an existing LearnerHook to hooks.

Arguments:

• cfg (EasyDict): Config dict. Should be like {‘hook’: xxx}.

Returns:

• hooks (Dict[str, List[Hook]): Keys should be in [‘before_run’, ‘after_run’, ‘before_iter’, ‘after_iter’], each value should be a list containing all hooks in this position.

Note:

Lower value means higher priority.

merge_hooks

Overview:

Merge two hooks dict, which have the same keys, and each value is sorted by hook priority with stable method.

Arguments:

• hooks1 (Dict[str, List[Hook]): hooks1 to be merged.

• hooks2 (Dict[str, List[Hook]): hooks2 to be merged.

Returns:

• new_hooks (Dict[str, List[Hook]): New merged hooks dict.

Note:

This merge function uses stable sort method without disturbing the same priority hook.

base_learner

Please Reference ding/worker/learner/base_learner.py for usage

BaseLearner

class ding.worker.learner.base_learner.BaseLearner(cfg: easydict.EasyDict, policy: collections.namedtuple = None, tb_logger: Optional[SummaryWriter] = None, dist_info: Tuple[int, int] = None, exp_name: Optional[str] = 'default_experiment', instance_name: Optional[str] = 'learner')

Overview:

Base class for policy learning.

Interface:

train, call_hook, register_hook, save_checkpoint, start, setup_dataloader, close

Property:

learn_info, priority_info, last_iter, train_iter, rank, world_size, policy monitor, log_buffer, logger, tb_logger, ckpt_name, exp_name, instance_name

__init__(cfg: easydict.EasyDict, policy: collections.namedtuple = None, tb_logger: Optional[SummaryWriter] = None, dist_info: Tuple[int, int] = None, exp_name: Optional[str] = 'default_experiment', instance_name: Optional[str] = 'learner') → None

Overview:

Initialization method, build common learner components according to cfg, such as hook, wrapper and so on.

Arguments:

• cfg (EasyDict): Learner config, you can refer cls.config for details.

• policy (namedtuple): A collection of policy function of learn mode. And policy can also be initialized when runtime.

• tb_logger (SummaryWriter): Tensorboard summary writer.

• dist_info (Tuple[int, int]): Multi-GPU distributed training information.

• exp_name (str): Experiment name, which is used to indicate output directory.

• instance_name (str): Instance name, which should be unique among different learners.

Notes:

If you want to debug in sync CUDA mode, please add the following code at the beginning of __init__.

os.environ['CUDA_LAUNCH_BLOCKING'] = "1"  # for debug async CUDA

_setup_hook() → None

Overview:

Setup hook for base_learner. Hook is the way to implement some functions at specific time point in base_learner. You can refer to learner_hook.py.

_setup_wrapper() → None

Overview:

Use _time_wrapper to get train_time.

Note:

data_time is wrapped in setup_dataloader.

call_hook(name: str) → None

Overview:

Call the corresponding hook plugins according to position name.

Arguments:

• name (str): Hooks in which position to call, should be in [‘before_run’, ‘after_run’, ‘before_iter’, ‘after_iter’].

close() → None

Overview:

[Only Used In Parallel Mode] Close the related resources, e.g. dataloader, tensorboard logger, etc.

register_hook(hook: ding.worker.learner.learner_hook.LearnerHook) → None

Overview:

Add a new learner hook.

Arguments:

• hook (LearnerHook): The hook to be addedr.

save_checkpoint(ckpt_name: Optional[str] = None) → None

Overview:

Directly call save_ckpt_after_run hook to save checkpoint.

Note:

Must guarantee that “save_ckpt_after_run” is registered in “after_run” hook. This method is called in:

• auto_checkpoint (torch_utils/checkpoint_helper.py), which is designed for saving checkpoint whenever an exception raises.

• serial_pipeline (entry/serial_entry.py). Used to save checkpoint when reaching new highest evaluation reward.

setup_dataloader() → None

Overview:

[Only Used In Parallel Mode] Setup learner’s dataloader.

Note

Only in parallel mode will we use attributes get_data and _dataloader to get data from file system; Instead, in serial version, we can fetch data from memory directly.

In parallel mode, get_data is set by LearnerCommHelper, and should be callable. Users don’t need to know the related details if not necessary.

train(data: dict, envstep: int = - 1) → None

Overview:

Given training data, implement network update for one iteration and update related variables. Learner’s API for serial entry. Also called in start for each iteration’s training.

Arguments:

• data (dict): Training data which is retrieved from repaly buffer.

Note

_policy must be set before calling this method.

_policy.forward method contains: forward, backward, grad sync(if in multi-gpu mode) and parameter update.

before_iter and after_iter hooks are called at the beginning and ending.

create_learner

Overview:

Given the key(learner_name), create a new learner instance if in learner_mapping’s values, or raise an KeyError. In other words, a derived learner must first register, then can call create_learner to get the instance.

Arguments:

• cfg (EasyDict): Learner config. Necessary keys: [learner.import_module, learner.learner_type].

Returns:

• learner (BaseLearner): The created new learner, should be an instance of one of learner_mapping’s values.