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Config

danling.runner.config

Config

Bases: Config

Config is a Config that contains all states of a Runner.

Config is designed to store all critical information of a Run so that you can resume a run from a state and corresponding weights or even restart a run from a state.

Config is also designed to be serialisable and hashable, so that you can save it to a file. Config is saved in checkpoint together with weights by default.

Since Config is a Config, you can access its attributes by state["key"] or state.key.

General:

Name Type Description
run_name str

Defaults to "DanLing".
run_id str

hex of self.run_uuid.
run_uuid (UUID, property)

uuid5(self.experiment_id, str(hash(self))).
experiment_name str

Defaults to "DanLing".
experiment_id str

git hash of the current HEAD. Defaults to "xxxxxxxxxxxxxxxx" if Runner not under a git repo or git/gitpython not installed.
experiment_uuid (UUID, property)

UUID of self.experiment_id. Defaults to UUID('78787878-7878-7878-7878-787878787878') if Runner not under a git repo or git/gitpython not installed.

Reproducibility:

Name Type Description
seed int

Defaults to randint(0, 2**32 - 1).
deterministic bool

Ensure deterministic operations. Defaults to False.

Progress:

Name Type Description
iter int

The number of data samples processed. equals to step when batch_size = 1.
step int

The number of step calls.
epoch int

The number of complete passes over the datasets.
iter_end int

End running iters. Note that step_end not initialised since this variable may not apply to some Runners.
step_end int

End running step. Note that step_end not initialised since this variable may not apply to some Runners.
epoch_end int

End running epoch. Note that epoch_end not initialised since this variable may not apply to some Runners.

In general you should only use one of iter_end, step_end, epoch_end to indicate the length of running.

IO:

Name Type Description
project_root str

The root directory for all experiments. Defaults to "experiments".

project_root is the root directory of all Experiments, and should be consistent across the Project.

dir is the directory of a certain Run.

There is no attributes/properties for Group and Experiment.

checkpoint_dir_name is relative to dir, and is passed to generate checkpoint_dir (checkpoint_dir = os.path.join(dir, checkpoint_dir_name)). In practice, checkpoint_dir_name is rarely called.

logging:

Name Type Description
log bool

Whether to log the outputs. Defaults to True.
tensorboard bool

Whether to use tensorboard. Defaults to False.
log_interval int

Interval of printing logs. Defaults to None, print logs every 1/10 of the longest split.
save_interval int

Interval of saving intermediate checkpoints. Defaults to None, never save checkpoints. If <= 0, save only the latest and the best checkpoints.
Notes

Config is a Config, so you can access its attributes by state["name"] or state.name.

See Also

BaseRunner: The base runner class.

Source code in danling/runner/config.py
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class Config(chanfig.Config):  # pylint: disable=too-many-instance-attributes
    r"""
    `Config` is a [`Config`][chanfig.Config] that contains all states of a `Runner`.

    `Config` is designed to store all critical information of a Run so that you can resume a run
    from a state and corresponding weights or even restart a run from a state.

    `Config` is also designed to be serialisable and hashable, so that you can save it to a file.
    `Config` is saved in checkpoint together with weights by default.

    Since `Config` is a [`Config`][chanfig.Config], you can access its attributes by
    `state["key"]` or `state.key`.

    Attributes: General:
        run_name (str): Defaults to `"DanLing"`.
        run_id (str): hex of `self.run_uuid`.
        run_uuid (UUID, property): `uuid5(self.experiment_id, str(hash(self)))`.
        experiment_name (str): Defaults to `"DanLing"`.
        experiment_id (str): git hash of the current HEAD.
            Defaults to `"xxxxxxxxxxxxxxxx"` if Runner not under a git repo or git/gitpython not installed.
        experiment_uuid (UUID, property): UUID of `self.experiment_id`.
            Defaults to `UUID('78787878-7878-7878-7878-787878787878')`
            if Runner not under a git repo or git/gitpython not installed.

    Attributes: Reproducibility:
        seed (int): Defaults to `randint(0, 2**32 - 1)`.
        deterministic (bool): Ensure [deterministic](https://pytorch.org/docs/stable/notes/randomness.html) operations.
            Defaults to `False`.

    Attributes: Progress:
        iter (int): The number of data samples processed.
            equals to `step` when `batch_size = 1`.
        step (int): The number of `step` calls.
        epoch (int): The number of complete passes over the datasets.
        iter_end (int): End running iters.
            Note that `step_end` not initialised since this variable may not apply to some Runners.
        step_end (int): End running step.
            Note that `step_end` not initialised since this variable may not apply to some Runners.
        epoch_end (int): End running epoch.
            Note that `epoch_end` not initialised since this variable may not apply to some Runners.

    In general you should only use one of `iter_end`, `step_end`, `epoch_end` to indicate the length of running.

    Attributes: IO:
        project_root (str): The root directory for all experiments.
            Defaults to `"experiments"`.

    `project_root` is the root directory of all **Experiments**, and should be consistent across the **Project**.

    `dir` is the directory of a certain **Run**.

    There is no attributes/properties for **Group** and **Experiment**.

    `checkpoint_dir_name` is relative to `dir`, and is passed to generate `checkpoint_dir`
    (`checkpoint_dir = os.path.join(dir, checkpoint_dir_name)`).
    In practice, `checkpoint_dir_name` is rarely called.

    Attributes: logging:
        log (bool): Whether to log the outputs.
            Defaults to `True`.
        tensorboard (bool): Whether to use `tensorboard`.
            Defaults to `False`.
        log_interval (int): Interval of printing logs.
            Defaults to `None`, print logs every 1/10 of the longest split.
        save_interval (int): Interval of saving intermediate checkpoints.
            Defaults to `None`, never save checkpoints.
            If <= 0, save only the latest and the best checkpoints.

    Notes:
        `Config` is a [`Config`][chanfig.Config], so you can access its attributes by `state["name"]` or `state.name`.

    See Also:
        [`BaseRunner`][danling.runner.BaseRunner]: The base runner class.
    """

    # DO NOT set default value in class, as they won't be stored in `__dict__`.

    run_name: str = defaults.RUN_NAME
    run_id: str
    experiment_name: str = defaults.EXPERIMENT_NAME
    experiment_id: str

    seed: Optional[int] = None
    deterministic: bool = False

    iter: int = 0
    step: int = 0
    epoch: int = 0
    iter_begin: int = 0
    step_begin: int = 0
    epoch_begin: int = 0
    iter_end: Optional[int] = None
    step_end: Optional[int] = None
    epoch_end: Optional[int] = None

    score_split: Optional[str] = None
    score_name: str = "loss"

    project_root: str = "experiments"
    checkpoint_dir_name: str = "checkpoints"
    log: bool = True
    tensorboard: bool = False
    log_interval: Optional[int] = None
    save_interval: Optional[int] = None

    def __post_init__(self):
        if "experiment_id" not in self:
            self.experiment_id = get_git_hash() or defaults.EXPERIMENT_ID
        if "run_id" not in self:
            self.run_id = self.run_uuid.hex
        self.setattr("ignored_keys_in_hash", defaults.IGNORED_CONFIG_IN_HASH)

    @property
    def experiment_uuid(self) -> UUID:
        r"""
        UUID of the experiment.
        """

        return UUID(bytes=bytes(self.experiment_id.ljust(16, "x")[:16], encoding="ascii"))

    @property
    def run_uuid(self) -> UUID:
        r"""
        UUID of the run.
        """

        ignored_keys_in_hash = self.getattr("ignored_keys_in_hash", defaults.IGNORED_CONFIG_IN_HASH)
        state: chanfig.Config = chanfig.Config({k: v for k, v in self.dict().items() if k not in ignored_keys_in_hash})
        return uuid5(self.experiment_uuid, state.yamls())

    def __hash__(self) -> int:
        return int(self.run_uuid.hex, 16)

experiment_uuid property

Python
1
experiment_uuid: UUID

UUID of the experiment.

run_uuid property

Python
1
run_uuid: UUID

UUID of the run.