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Edit docstrings (#1013)

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pollfly 2023-05-28 08:48:49 +03:00 committed by GitHub
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5 changed files with 145 additions and 137 deletions
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14
clearml/automation/controller.py
View File

@ -470,7 +470,7 @@ class PipelineController(object):
pass
:param post_execute_callback: Callback function, called when a step (Task) is completed
and it other jobs are executed. Allows a user to modify the Task status after completion.
and other jobs are executed. Allows a user to modify the Task status after completion.
.. code-block:: py
@ -738,7 +738,7 @@ class PipelineController(object):
pass
:param post_execute_callback: Callback function, called when a step (Task) is completed
and it other jobs are executed. Allows a user to modify the Task status after completion.
and other jobs are executed. Allows a user to modify the Task status after completion.
.. code-block:: py
@ -862,7 +862,7 @@ class PipelineController(object):
pass
:param Callable step_task_completed_callback: Callback function, called when a step (Task) is completed
and it other jobs are executed. Allows a user to modify the Task status after completion.
and other jobs are executed. Allows a user to modify the Task status after completion.
.. code-block:: py
@ -951,7 +951,7 @@ class PipelineController(object):
def connect_configuration(self, configuration, name=None, description=None):
# type: (Union[Mapping, list, Path, str], Optional[str], Optional[str]) -> Union[dict, Path, str]
"""
Connect a configuration dictionary or configuration file (pathlib.Path / str) to a the PipelineController object.
Connect a configuration dictionary or configuration file (pathlib.Path / str) to the PipelineController object.
This method should be called before reading the configuration file.
For example, a local file:
@ -1373,7 +1373,7 @@ class PipelineController(object):
pass
:param Callable step_task_completed_callback: Callback function, called when a step (Task) is completed
and it other jobs are executed. Allows a user to modify the Task status after completion.
and other jobs are executed. Allows a user to modify the Task status after completion.
.. code-block:: py
@ -1895,7 +1895,7 @@ class PipelineController(object):
pass
:param post_execute_callback: Callback function, called when a step (Task) is completed
and it other jobs are executed. Allows a user to modify the Task status after completion.
and other jobs are executed. Allows a user to modify the Task status after completion.
.. code-block:: py
@ -3644,7 +3644,7 @@ class PipelineDecorator(PipelineController):
pass
:param post_execute_callback: Callback function, called when a step (Task) is completed
and it other jobs are executed. Allows a user to modify the Task status after completion.
and other jobs are executed. Allows a user to modify the Task status after completion.
.. code-block:: py

1
clearml/automation/hpbandster/bandster.py
View File

@ -229,6 +229,7 @@ class OptimizerBOHB(SearchStrategy, RandomSeed):
year = {2018},
}
:param eta: float (3)
In each iteration, a complete run of sequential halving is executed. In it,
after evaluating each configuration on the same subset size, only a fraction of

40
clearml/automation/optimization.py
View File

@ -432,7 +432,7 @@ class SearchStrategy(object):
Helper function, Implementation is not required. Default use in process_step default implementation.
Check if the job needs to be aborted or already completed.
If returns ``False``, the job was aborted / completed, and should be taken off the current job list
If returns ``False``, the job was aborted / completed, and should be taken off the current job list.
If there is a budget limitation, this call should update
``self.budget.compute_time.update`` / ``self.budget.iterations.update``
@ -534,6 +534,8 @@ class SearchStrategy(object):
where index 0 is the best performing Task.
Example w/ all_metrics=False:
.. code-block:: py
[
('0593b76dc7234c65a13a301f731958fa',
{
@ -550,6 +552,8 @@ class SearchStrategy(object):
Example w/ all_metrics=True:
.. code-block:: py
[
('0593b76dc7234c65a13a301f731958fa',
{
@ -599,9 +603,8 @@ class SearchStrategy(object):
# type: (int, bool, bool, bool) -> Sequence[(str, dict)]
"""
Return a list of dictionaries of the top performing experiments.
Example: [
{'task_id': Task-ID, 'metrics': scalar-metric-dict, 'hyper_parameters': Hyper-Parameters},
]
Example: ``[{'task_id': Task-ID, 'metrics': scalar-metric-dict, 'hyper_parameters': Hyper-Parameters},]``
Order is based on the controller ``Objective`` object.
:param int top_k: The number of Tasks (experiments) to return.
@ -614,6 +617,8 @@ class SearchStrategy(object):
where index 0 is the best performing Task.
Example w/ all_metrics=False:
.. code-block:: py
[
{
task_id: '0593b76dc7234c65a13a301f731958fa',
@ -632,6 +637,8 @@ class SearchStrategy(object):
Example w/ all_metrics=True:
.. code-block:: py
[
{
task_id: '0593b76dc7234c65a13a301f731958fa',
@ -761,7 +768,8 @@ class SearchStrategy(object):
"""
Set the function used to name a newly created job.
:param callable naming_function:
:param callable naming_function: Callable function for naming a newly created job.
Use the following format:
.. code-block:: py
@ -1072,7 +1080,7 @@ class RandomSearch(SearchStrategy):
class HyperParameterOptimizer(object):
"""
Hyper-parameter search controller. Clones the base experiment, changes arguments and tries to maximize/minimize
Hyperparameter search controller. Clones the base experiment, changes arguments and tries to maximize/minimize
the defined objective.
"""
_tag = 'optimization'
@ -1105,7 +1113,6 @@ class HyperParameterOptimizer(object):
``validation``).
:param str objective_metric_series: The Objective metric series to maximize / minimize (for example, ``loss``).
:param str objective_metric_sign: The objective to maximize / minimize.
The values are:
- ``min`` - Minimize the last reported value for the specified title/series scalar.
@ -1121,23 +1128,20 @@ class HyperParameterOptimizer(object):
default is ``None``, indicating no time limit.
:param float compute_time_limit: The maximum compute time in minutes. When time limit is exceeded,
all jobs aborted. (Optional)
:param bool auto_connect_task: Store optimization arguments and configuration in the Task
:param bool auto_connect_task: Store optimization arguments and configuration in the Task.
The values are:
- ``True`` - The optimization argument and configuration will be stored in the Task. All arguments will
be under the hyper-parameter section ``opt``, and the optimization hyper_parameters space will
be under the hyperparameter section ``opt``, and the optimization hyper_parameters space will be
stored in the Task configuration object section.
- ``False`` - Do not store with Task.
- ``Task`` - A specific Task object to connect the optimization process with.
:param bool always_create_task: Always create a new Task
:param bool always_create_task: Always create a new Task.
The values are:
- ``True`` - No current Task initialized. Create a new task named ``optimization`` in the ``base_task_id``
project.
- ``False`` - Use the :py:meth:`task.Task.current_task` (if exists) to report statistics.
:param str spawn_project: If project name is specified, create all optimization Jobs (Tasks) in the
@ -1505,9 +1509,8 @@ class HyperParameterOptimizer(object):
# type: (int, bool, bool, bool) -> Sequence[(str, dict)]
"""
Return a list of dictionaries of the top performing experiments.
Example: [
{'task_id': Task-ID, 'metrics': scalar-metric-dict, 'hyper_parameters': Hyper-Parameters},
]
Example: ``[{'task_id': Task-ID, 'metrics': scalar-metric-dict, 'hyper_parameters': Hyper-Parameters},]``
Order is based on the controller ``Objective`` object.
:param int top_k: The number of Tasks (experiments) to return.
@ -1520,6 +1523,8 @@ class HyperParameterOptimizer(object):
where index 0 is the best performing Task.
Example w/ all_metrics=False:
.. code-block:: py
[
{
task_id: '0593b76dc7234c65a13a301f731958fa',
@ -1538,6 +1543,8 @@ class HyperParameterOptimizer(object):
Example w/ all_metrics=True:
.. code-block:: py
[
{
task_id: '0593b76dc7234c65a13a301f731958fa',
@ -1615,7 +1622,6 @@ class HyperParameterOptimizer(object):
``validation``).
:param str objective_metric_series: The Objective metric series to maximize / minimize (for example, ``loss``).
:param str objective_metric_sign: The objective to maximize / minimize.
The values are:
- ``min`` - Minimize the last reported value for the specified title/series scalar.

10
clearml/automation/parameters.py
View File

@ -110,7 +110,7 @@ class Parameter(RandomSeed):
class UniformParameterRange(Parameter):
"""
Uniform randomly sampled hyper-parameter object.
Uniform randomly sampled hyperparameter object.
"""
def __init__(
@ -129,8 +129,7 @@ class UniformParameterRange(Parameter):
:param float min_value: The minimum sample to use for uniform random sampling.
:param float max_value: The maximum sample to use for uniform random sampling.
:param float step_size: If not ``None``, set step size (quantization) for value sampling.
:param bool include_max_value: Range includes the ``max_value``
:param bool include_max_value: Range includes the ``max_value``.
The values are:
- ``True`` - The range includes the ``max_value`` (Default)
@ -221,7 +220,7 @@ class LogUniformParameterRange(UniformParameterRange):
class UniformIntegerParameterRange(Parameter):
"""
Uniform randomly sampled integer Hyper-Parameter object.
Uniform randomly sampled integer Hyperparameter object.
"""
def __init__(self, name, min_value, max_value, step_size=1, include_max_value=True):
@ -233,8 +232,7 @@ class UniformIntegerParameterRange(Parameter):
:param int min_value: The minimum sample to use for uniform random sampling.
:param int max_value: The maximum sample to use for uniform random sampling.
:param int step_size: The default step size is ``1``.
:param bool include_max_value: Range includes the ``max_value``
:param bool include_max_value: Range includes the ``max_value``.
The values are:
- ``True`` - Includes the ``max_value`` (Default)

5
clearml/datasets/dataset.py
View File

@ -324,6 +324,7 @@ class Dataset(object):
# type: () -> Mapping[str, LinkEntry]
"""
Notice this call returns an internal representation, do not modify!
:return: dict with relative file path as key, and LinkEntry as value
"""
return self._dataset_link_entries
@ -643,6 +644,7 @@ class Dataset(object):
If -1 is provided, use a single zip artifact for the entire dataset change-set (old behaviour)
:param max_workers: Numbers of threads to be spawned when zipping and uploading the files.
If None (default) it will be set to:
- 1: if the upload destination is a cloud provider ('s3', 'gs', 'azure')
- number of logical cores: otherwise
:param int retries: Number of retries before failing to upload each zip. If 0, the upload is not retried.
@ -839,7 +841,7 @@ class Dataset(object):
# type: (Union[numpy.array, pd.DataFrame, Dict[str, Any]], str, bool) -> () # noqa: F821
"""
Attach a user-defined metadata to the dataset. Check `Task.upload_artifact` for supported types.
If type is Optionally make it visible as a table in the UI.
If type is Pandas Dataframes, optionally make it visible as a table in the UI.
"""
if metadata_name.startswith(self.__data_entry_name_prefix):
raise ValueError("metadata_name can not start with '{}'".format(self.__data_entry_name_prefix))
@ -1786,6 +1788,7 @@ class Dataset(object):
"""
Return a Logger object for the Dataset, allowing users to report statistics metrics
and debug samples on the Dataset itself
:return: Logger object
"""
return self._task.get_logger()