API Reference

This is the class and function reference of ablator. Please refer to the tutorials for further details, as the class and function raw specifications may not be enough to give full guidelines on their uses.

ablator.analysis: Analysis module

Plot module

analysis.plot.main.PlotAnalysis(results[, ...])	Class for plotting experiment results.
analysis.plot.cat_plot.Categorical(*args, ...)	This class is for preparing the results that are associated with each categorical attribute to be studied (e.g., grouping metric results with each of the attributes).
analysis.plot.cat_plot.ViolinPlot(*args, ...)	Class for constructing violinplots.
analysis.plot.num_plot.Numerical(metric, ...)	Base class for numerical plots
analysis.plot.num_plot.LinearPlot(metric, ...)	Class for generating linear plots
analysis.plot.utils.parse_name_remap([...])	Returns a dictionary mapping input attribute names to output attribute names, with optional remapping based on name_map.

Analysis Main classes

analysis.main.Analysis(results[, ...])

A class that stores and processes the attributes, metrics, and other data for the plotting of the experiment result.

Analysis Results classes

analysis.results.Results(config, experiment_dir)	Class for processing experiment results.
analysis.results.read_result(config_type, ...)	Read the results of an experiment and return them as a pandas DataFrame.

ablator.config: Config module

Base Config classes

config.main.ConfigBase(*args[, debug])	This class is the building block for all configuration objects within ablator.
config.main.configclass(cls)	Decorator for ConfigBase subclasses, adds the config_class attribute to the class.

Prototype Config classes

config.proto.ModelConfig(*args[, debug])	A base class for model configuration.
config.proto.RunConfig(*args[, debug])	The base run configuration that defines the setting of an experiment (experiment main directory, number of checkpoints to maintain, hardware device to use, etc.).
config.proto.TrainConfig(*args[, debug])	Training configuration that defines the training setting, e.g., batch size, number of epochs, the optimizer to use, etc.
config.proto.Optim(value)	Type of optimization direction.

Config Type classes

config.types.Derived()

Derived is used for attributes that are derived during the experiment (after launching the experiment trainer.launch()).

HPO Config classes

config.hpo.FieldType(value)	Type of search space.
config.hpo.SearchSpace(*args, **kwargs)	Search space configuration, required in ParallelConfig, is used to define the search space for a hyperparameter.
config.hpo.SubConfiguration(**kwargs)	Subconfiguration for a SearchSpace.

Parallel Config classes

config.mp.ParallelConfig(*args[, debug])	Parallel training configuration, extending from RunConfig, defines the settings of a parallel experiment (number of trials to run for, number of concurrent trials, search space for hyperparameter search, etc.).
config.mp.SearchAlgo(value)	Type of search algorithm.

Config Utils functions

config.utils.dict_hash(*dictionaries[, hash_len])	Calculates the MD5 hash of one or more dictionaries.
config.utils.flatten_nested_dict(dict_[, ...])	Flattens a nested dictionary, expanding lists and tuples if specified.

ablator.main: Main module

HPO Sampler module

main.hpo.base.BaseSampler()
main.hpo.grid.GridSampler(search_space[, ...])	GridSampler, expands the grid-space into evenly spaced intervals.
main.hpo.optuna.OptunaSampler(search_algo, ...)	OptunaSampler this class serves as an interface for Optuna based samplers.

Main Model module

main.model.main.ModelBase(model_class)	Base class that removes training boiler-plate code with extensible support for multiple use-cases.
main.model.wrapper.ModelWrapper(model_class)	A wrapper around model_class that removes training boiler-plate code.

Experiment State module

main.state.state.ExperimentState(...[, ...])	Initializes the ExperimentState.
main.state.store.TrialState(value)	An enumeration of possible states for a trial with more pruned states.

Prototype Trainer classes

main.proto.ProtoTrainer(wrapper, run_config)

Manages resources for Prototyping.

Multi-process Trainer classes

main.mp.ParallelTrainer(wrapper, run_config)

A class for parallelizing multiple training processes of models of different configurations with ray.

ablator.modules: Extra modules

Metrics module

modules.metrics.main.Metrics(*args[, ...])	Stores and manages predictions and calculates metrics given some custom evaluation functions.
modules.metrics.stores.ArrayStore([...])	Base class for manipulations (storing, getting, resetting) of batches of values.
modules.metrics.stores.MovingAverage([...])	This class is used to store moving average metrics
modules.metrics.stores.PredictionStore([...])	A class for storing prediction scores.

Storage module

modules.storage.remote.RemoteConfig(*args[, ...])	Configuration for a remote storage.
modules.storage.remote.run_cmd_wait(cmd[, ...])	Run a command and wait for it to finish.

Optimizer classes

modules.optimizer.OptimizerArgs(*args[, debug])	A base class for optimizer arguments, here we define learning rate lr.
modules.optimizer.OptimizerConfig(name, ...)	Configuration for an optimizer, including optimizer name and arguments (these arguments are specific to a certain type of optimizer like SGD, Adam, AdamW).
modules.optimizer.SGDConfig(*args[, debug])	Configuration for an SGD optimizer.
modules.optimizer.AdamWConfig(*args[, debug])	Configuration for an AdamW optimizer.
modules.optimizer.AdamConfig(*args[, debug])	Configuration for an Adam optimizer.
modules.optimizer.get_optim_parameters(model)	Get model parameters to be optimized.

Scheduler classes

modules.scheduler.SchedulerArgs(*args[, debug])	Abstract base class for defining arguments to initialize a learning rate scheduler.
modules.scheduler.SchedulerConfig(name, ...)	A class that defines a configuration for a learning rate scheduler.
modules.scheduler.StepLRConfig(*args[, debug])	Configuration class for StepLR scheduler.
modules.scheduler.OneCycleConfig(*args[, debug])	Configuration class for the OneCycleLR scheduler.
modules.scheduler.PlateuaConfig(*args[, debug])	Configuration class for ReduceLROnPlateau scheduler.

ablator.utils: Utilities

Base utilities

utils.base.iter_to_numpy(iterable)	Convert elements of the input iterable to NumPy arrays if they are torch.Tensor objects.
utils.base.iter_to_device(data_dict, device)	Moving torch.Tensor elements to the specified device.
utils.base.apply_lambda_to_iter(iterable, fn)	Applies a given function fn to each element of an iterable data structure.
utils.base.set_seed(seed)	Set the random seed.
utils.base.get_lr(optimizer)	Get the learning rate from an optimizer.
utils.base.debugger_is_active()	Check if the debugger is currently active.
utils.base.get_latest_chkpts(checkpoint_dir)	Get a list of all checkpoint files in a directory, sorted from the latest to the earliest.
utils.base.parse_device(device)	Parse a device string, an integer, or a list of device strings or integers.
utils.base.is_oom_exception(err)	is_oom_exception checks whether the exception is caused by CUDA out of memory errors.
utils.base.num_format(value[, width])	Format number to be no larger than width by converting to scientific notation when the value exceeds width either by informative decimal places or size.

File utilities

utils.file.clean_checkpoints(...)	Remove all but the n latest checkpoints from the given directory.
utils.file.default_val_parser(val)	Converts the input value to a JSON compatible format.
utils.file.dict_to_json(dict_)	Convert a dictionary into a JSON string.
utils.file.json_to_dict(json_)	Convert a JSON string into a dictionary.
utils.file.make_sub_dirs(parent, *dir_names)	Create subdirectories under the given parent directory.
utils.file.nested_set(dict_, keys, value)	Set a value in a nested dictionary.
utils.file.save_checkpoint(state[, filename])	Save a checkpoint of the given state.