hpcflow.app.Workflow

class hpcflow.app.Workflow(workflow_ref, store_fmt=None, fs_kwargs=None, **kwargs)

Bases: Workflow

A concrete workflow.

Parameters:

• workflow_ref (str | Path | int) – Either the path to a persistent workflow, or an integer that will interpreted as the local ID of a workflow submission, as reported by the app show command.

• store_fmt (str | None) – The format of persistent store to use. Used to select the store manager class.

• fs_kwargs (dict[str, Any] | None) – Additional arguments to pass when resolving a virtual workflow reference.

• kwargs – For compatibility during pre-stable development phase.

Methods

abort_run	Abort the currently running action-run of the specified task/element.
add_loop	Add a loop to a subset of workflow tasks.
add_submission	Add a job submission to this workflow.
add_task	Add a task to this workflow.
add_task_after	Add a new task after the specified task.
add_task_before	Add a new task before the specified task.
batch_update	A context manager that batches up structural changes to the workflow and commits them to disk all together when the context manager exits.
cancel	Cancel any running jobscripts.
check_loop_termination	Check if a loop should terminate, given the specified completed run, and if so, set downstream iteration runs to be skipped.
check_parameters_exist	Check if all the parameters exist.
copy	Copy the workflow to a new path and return the copied workflow path.
delete	Delete the persistent data.
elements	Get the elements of the workflow's tasks.
from_JSON_file	Generate from a JSON file.
from_JSON_string	Generate from a JSON string.
from_YAML_file	Generate from a YAML file.
from_YAML_string	Generate from a YAML string.
from_file	Generate from either a YAML or JSON file, depending on the file extension.
from_template	Generate from a WorkflowTemplate object.
from_template_data	Generate from the data associated with a WorkflowTemplate object.
get_EAR_IDs_of_tasks	Get EAR IDs belonging to multiple tasks.
get_EAR_skipped	Check if an EAR is to be skipped.
get_EARs_from_IDs	Get element action run objects from a list of IDs.
get_EARs_of_tasks	Get EARs belonging to multiple tasks.
get_all_EARs	Get all runs in the workflow.
get_all_element_iterations	Get all iterations in the workflow.
get_all_elements	Get all elements in the workflow.
get_all_parameter_data	Retrieve all workflow parameter data.
get_all_parameter_sources	Retrieve all persistent parameters sources.
get_all_parameters	Retrieve all persistent parameters.
get_all_submission_run_IDs	Get the run IDs of all submissions.
get_element_IDs_from_EAR_IDs	Get the element IDs of EARs.
get_element_iteration_IDs_from_EAR_IDs	Get the element iteration IDs of EARs.
get_element_iterations_from_IDs	Return element iteration objects from a list of IDs.
get_element_iterations_of_tasks	Get element iterations belonging to multiple tasks.
get_elements_from_IDs	Return element objects from a list of IDs.
get_iteration_final_run_IDs	Retrieve the run IDs of those runs that correspond to the final action within a named loop iteration.
get_iteration_task_pathway	Get the iteration task pathway.
get_loop_map	Get a description of what is going on with looping.
get_parameter	Get a single parameter.
get_parameter_data	Get the data relating to a parameter.
get_parameter_set_statuses	Get whether some parameters are set.
get_parameter_source	Get the source of a particular parameter.
get_parameter_sources	Get parameter sources known to the workflow.
get_parameters	Get parameters known to the workflow.
get_running_elements	Retrieve elements that are running according to the scheduler.
get_running_runs	Retrieve runs that are running according to the scheduler.
get_store_EARs	Get the persistent element action runs.
get_store_element_iterations	Get the persistent element iterations.
get_store_elements	Get the persistent elements.
get_store_tasks	Get the persistent tasks.
get_task_IDs_from_element_IDs	Get the task IDs of elements.
get_task_elements	Get the elements of a task.
get_task_unique_names	Return the unique names of all workflow tasks.
is_parameter_set	Test if a particular parameter is set.
process_shell_parameter_output	Process the shell stdout/stderr stream according to the associated Command object.
rechunk	Rechunk metadata/runs and parameters/base arrays, making them more efficient.
rechunk_parameter_base	Reorganise the stored data chunks for parameterss to be more efficient.
rechunk_runs	Reorganise the stored data chunks for EARs to be more efficient.
reload	Reload the workflow from disk.
resolve_jobscripts	Resolve this workflow to a set of job scripts to run.
save_parameter	Save a parameter where an EAR can find it.
set_EAR_end	Set the end time and exit code on an EAR.
set_EAR_skip	Record that an EAR is to be skipped due to an upstream failure or loop termination condition being met.
set_EAR_start	Set the start time on an EAR.
set_EAR_submission_index	Set the submission index of an EAR.
set_EARs_initialised	Set EARs_initialised to True for the specified iteration.
set_parameter_value	Set the value of a parameter.
show_all_EAR_statuses	Print a description of the status of every element action run in the workflow.
submit	Submit the workflow for execution.
temporary_rename	Rename an existing same-path workflow (directory) so we can restore it if workflow creation fails.
unzip	Convert the workflow to an unzipped form.
wait	Wait for the completion of specified/all submitted jobscripts.
write_commands	Write run-time commands for a given EAR.
zip	Convert the workflow to a zipped form.

Attributes

artifacts_path	Path to artifacts of the workflow (temporary files, etc).
creation_info	The creation descriptor for the workflow.
execution_path	Path to working directory path for executing.
id_	The ID of this workflow.
input_files_path	Path to input files for the workflow.
loops	The loops in this workflow.
name	The name of the workflow.
num_EARs	The total number of element action runs.
num_added_tasks	The total number of added tasks.
num_element_iterations	The total number of element iterations.
num_elements	The total number of elements.
num_loops	The total number of loops.
num_submissions	The total number of job submissions.
num_tasks	The total number of tasks.
store_format	The format of the workflow's persistent store.
submissions	The job submissions done by this workflow.
submissions_path	Path to submission data for ths workflow.
task_artifacts_path	Path to artifacts of tasks.
tasks	The tasks in this workflow.
template	The template that this workflow was made from.
template_components	The template components used for this workflow.
ts_fmt	The timestamp format.
ts_name_fmt	The timestamp format for names.
url	An fsspec URL for this workflow.

abort_run(submission_idx=-1, task_idx=None, task_insert_ID=None, element_idx=None)

Abort the currently running action-run of the specified task/element.

Parameters:

• task_idx (int | None) – The parent task of the run to abort.

• element_idx (int | None) – For multi-element tasks, the parent element of the run to abort.

• submission_idx (int) – Defaults to the most-recent submission.

• task_insert_ID (int | None) –

add_loop(loop)

Add a loop to a subset of workflow tasks.

Parameters:

loop (Loop) –

Return type:

None

add_submission(tasks=None, JS_parallelism=None)

Add a job submission to this workflow.

Parameters:

• tasks (list[int] | None) –

• JS_parallelism (bool | None) –

Return type:

Submission | None

add_task(task, new_index=None)

Add a task to this workflow.

Parameters:

• task (Task) –

• new_index (int | None) –

Return type:

None

add_task_after(new_task, task_ref=None)

Add a new task after the specified task.

Parameters:

• task_ref (Task | None) – If not given, the new task will be added at the end of the workflow.

• new_task (Task) –

Return type:

None

add_task_before(new_task, task_ref=None)

Add a new task before the specified task.

Parameters:

• task_ref (Task | None) – If not given, the new task will be added at the beginning of the workflow.

• new_task (Task) –

Return type:

None

property artifacts_path: Path

Path to artifacts of the workflow (temporary files, etc).

batch_update(is_workflow_creation=False)

A context manager that batches up structural changes to the workflow and commits them to disk all together when the context manager exits.

Parameters:

is_workflow_creation (bool) –

Return type:

Iterator[None]

cancel(hard=False)

Cancel any running jobscripts.

Parameters:

hard (bool) –

check_loop_termination(loop_name, run_ID)

Check if a loop should terminate, given the specified completed run, and if so, set downstream iteration runs to be skipped.

Parameters:

• loop_name (str) –

• run_ID (int) –

Return type:

None

check_parameters_exist(id_lst)

Check if all the parameters exist.

Parameters:

id_lst (int | list[int]) –

Return type:

bool

copy(path='.')

Copy the workflow to a new path and return the copied workflow path.

Parameters:

path (str | Path) –

Return type:

Path

property creation_info: CreationInfo

The creation descriptor for the workflow.

delete()

Delete the persistent data.

Return type:

None

elements()

Get the elements of the workflow’s tasks.

Return type:

Iterator[Element]

property execution_path: Path

Path to working directory path for executing.

classmethod from_JSON_file(JSON_path, path=None, name=None, overwrite=False, store='zarr', ts_fmt=None, ts_name_fmt=None, store_kwargs=None, variables=None, status=None)

Generate from a JSON file.

Parameters:

• JSON_path (PathLike) – The path to a workflow template in the JSON file format.

• path (PathLike) – The directory in which the workflow will be generated. The current directory if not specified.

• name (str | None) – The name of the workflow. If specified, the workflow directory will be path joined with name. If not specified the WorkflowTemplate name will be used, in combination with a date-timestamp.

• overwrite (bool) – If True and the workflow directory (path + name) already exists, the existing directory will be overwritten.

• store (str) – The persistent store to use for this workflow.

• ts_fmt (str | None) – The datetime format to use for storing datetimes. Datetimes are always stored in UTC (because Numpy does not store time zone info), so this should not include a time zone name.

• ts_name_fmt (str | None) – The datetime format to use when generating the workflow name, where it includes a timestamp.

• store_kwargs (dict[str, Any] | None) – Keyword arguments to pass to the store’s write_empty_workflow method.

• variables (dict[str, str] | None) – String variables to substitute in the file given by JSON_path.

• status (Status | None) –

Return type:

Workflow

classmethod from_JSON_string(JSON_str, path=None, name=None, overwrite=False, store='zarr', ts_fmt=None, ts_name_fmt=None, store_kwargs=None, variables=None, status=None)

Generate from a JSON string.

Parameters:

• JSON_str (str) – The JSON string containing a workflow template parametrisation.

• path (PathLike) – The directory in which the workflow will be generated. The current directory if not specified.

• name (str | None) – The name of the workflow. If specified, the workflow directory will be path joined with name. If not specified the WorkflowTemplate name will be used, in combination with a date-timestamp.

• overwrite (bool) – If True and the workflow directory (path + name) already exists, the existing directory will be overwritten.

• store (str) – The persistent store to use for this workflow.

• ts_fmt (str | None) – The datetime format to use for storing datetimes. Datetimes are always stored in UTC (because Numpy does not store time zone info), so this should not include a time zone name.

• ts_name_fmt (str | None) – The datetime format to use when generating the workflow name, where it includes a timestamp.

• store_kwargs (dict[str, Any] | None) – Keyword arguments to pass to the store’s write_empty_workflow method.

• variables (dict[str, str] | None) – String variables to substitute in the string JSON_str.

• status (Status | None) –

Return type:

Workflow

classmethod from_YAML_file(YAML_path, path=None, name=None, overwrite=False, store='zarr', ts_fmt=None, ts_name_fmt=None, store_kwargs=None, variables=None)

Generate from a YAML file.

Parameters:

• YAML_path (PathLike) – The path to a workflow template in the YAML file format.

• path (PathLike) – The directory in which the workflow will be generated. The current directory if not specified.

• name (str | None) – The name of the workflow. If specified, the workflow directory will be path joined with name. If not specified the WorkflowTemplate name will be used, in combination with a date-timestamp.

• overwrite (bool) – If True and the workflow directory (path + name) already exists, the existing directory will be overwritten.

• store (str) – The persistent store to use for this workflow.

• ts_fmt (str | None) – The datetime format to use for storing datetimes. Datetimes are always stored in UTC (because Numpy does not store time zone info), so this should not include a time zone name.

• ts_name_fmt (str | None) – The datetime format to use when generating the workflow name, where it includes a timestamp.

• store_kwargs (dict[str, Any] | None) – Keyword arguments to pass to the store’s write_empty_workflow method.

• variables (dict[str, str] | None) – String variables to substitute in the file given by YAML_path.

Return type:

Workflow

classmethod from_YAML_string(YAML_str, path=None, name=None, overwrite=False, store='zarr', ts_fmt=None, ts_name_fmt=None, store_kwargs=None, variables=None)

Generate from a YAML string.

Parameters:

• YAML_str (str) – The YAML string containing a workflow template parametrisation.

• path (PathLike) – The directory in which the workflow will be generated. The current directory if not specified.

• name (str | None) – The name of the workflow. If specified, the workflow directory will be path joined with name. If not specified the WorkflowTemplate name will be used, in combination with a date-timestamp.

• overwrite (bool) – If True and the workflow directory (path + name) already exists, the existing directory will be overwritten.

• store (str) – The persistent store to use for this workflow.

• ts_fmt (str | None) – The datetime format to use for storing datetimes. Datetimes are always stored in UTC (because Numpy does not store time zone info), so this should not include a time zone name.

• ts_name_fmt (str | None) – The datetime format to use when generating the workflow name, where it includes a timestamp.

• store_kwargs (dict[str, Any] | None) – Keyword arguments to pass to the store’s write_empty_workflow method.

• variables (dict[str, str] | None) – String variables to substitute in the string YAML_str.

Return type:

Workflow

classmethod from_file(template_path, template_format=None, path=None, name=None, overwrite=False, store='zarr', ts_fmt=None, ts_name_fmt=None, store_kwargs=None, variables=None, status=None)

Generate from either a YAML or JSON file, depending on the file extension.

Parameters:

• template_path (PathLike) – The path to a template file in YAML or JSON format, and with a “.yml”, “.yaml”, or “.json” extension.

• template_format (Literal['json', 'yaml'] | None) – If specified, one of “json” or “yaml”. This forces parsing from a particular format regardless of the file extension.

• path (str | None) – The directory in which the workflow will be generated. The current directory if not specified.

• name (str | None) – The name of the workflow. If specified, the workflow directory will be path joined with name. If not specified the WorkflowTemplate name will be used, in combination with a date-timestamp.

• overwrite (bool) – If True and the workflow directory (path + name) already exists, the existing directory will be overwritten.

• store (str) – The persistent store to use for this workflow.

• ts_fmt (str | None) – The datetime format to use for storing datetimes. Datetimes are always stored in UTC (because Numpy does not store time zone info), so this should not include a time zone name.

• ts_name_fmt (str | None) – The datetime format to use when generating the workflow name, where it includes a timestamp.

• store_kwargs (dict[str, Any] | None) – Keyword arguments to pass to the store’s write_empty_workflow method.

• variables (dict[str, str] | None) – String variables to substitute in the file given by template_path.

• status (Status | None) –

Return type:

Workflow

classmethod from_template(template, path=None, name=None, overwrite=False, store='zarr', ts_fmt=None, ts_name_fmt=None, store_kwargs=None, status=None)

Generate from a WorkflowTemplate object.

Parameters:

• template (WorkflowTemplate) – The WorkflowTemplate object to make persistent.

• path (PathLike) – The directory in which the workflow will be generated. The current directory if not specified.

• name (str | None) – The name of the workflow. If specified, the workflow directory will be path joined with name. If not specified the WorkflowTemplate name will be used, in combination with a date-timestamp.

• overwrite (bool) – If True and the workflow directory (path + name) already exists, the existing directory will be overwritten.

• store (str) – The persistent store to use for this workflow.

• ts_fmt (str | None) – The datetime format to use for storing datetimes. Datetimes are always stored in UTC (because Numpy does not store time zone info), so this should not include a time zone name.

• ts_name_fmt (str | None) – The datetime format to use when generating the workflow name, where it includes a timestamp.

• store_kwargs (dict[str, Any] | None) – Keyword arguments to pass to the store’s write_empty_workflow method.

• status (Status | None) –

Return type:

Workflow

classmethod from_template_data(template_name, tasks=None, loops=None, resources=None, path=None, workflow_name=None, overwrite=False, store='zarr', ts_fmt=None, ts_name_fmt=None, store_kwargs=None)

Generate from the data associated with a WorkflowTemplate object.

Parameters:

• template_name (str) – Name of the new workflow template, from which the new workflow will be generated.

• tasks (list[Task] | None) – List of Task objects to add to the new workflow.

• loops (list[Loop] | None) – List of Loop objects to add to the new workflow.

• resources (Resources) – Mapping of action scopes to resource requirements, to be applied to all element sets in the workflow. resources specified in an element set take precedence of those defined here for the whole workflow.

• path (PathLike | None) – The directory in which the workflow will be generated. The current directory if not specified.

• workflow_name (str | None) – The name of the workflow. If specified, the workflow directory will be path joined with name. If not specified template_name will be used, in combination with a date-timestamp.

• overwrite (bool) – If True and the workflow directory (path + name) already exists, the existing directory will be overwritten.

• store (str) – The persistent store to use for this workflow.

• ts_fmt (str | None) – The datetime format to use for storing datetimes. Datetimes are always stored in UTC (because Numpy does not store time zone info), so this should not include a time zone name.

• ts_name_fmt (str | None) – The datetime format to use when generating the workflow name, where it includes a timestamp.

• store_kwargs (dict[str, Any] | None) – Keyword arguments to pass to the store’s write_empty_workflow method.

Return type:

Workflow

get_EAR_IDs_of_tasks(id_lst)

Get EAR IDs belonging to multiple tasks.

Parameters:

id_lst (Iterable[int]) –

Return type:

list[int]

get_EAR_skipped(EAR_ID)

Check if an EAR is to be skipped.

Parameters:

EAR_ID (int) –

Return type:

bool

get_EARs_from_IDs(ids)

Get element action run objects from a list of IDs.

Parameters:

ids (Iterable[int] | int) –

Return type:

list[ElementActionRun] | ElementActionRun

get_EARs_of_tasks(id_lst)

Get EARs belonging to multiple tasks.

Parameters:

id_lst (Iterable[int]) –

Return type:

Iterator[ElementActionRun]

get_all_EARs()

Get all runs in the workflow.

Return type:

list[ElementActionRun]

get_all_element_iterations()

Get all iterations in the workflow.

Return type:

list[ElementIteration]

get_all_elements()

Get all elements in the workflow.

Return type:

list[Element]

get_all_parameter_data(**kwargs)

Retrieve all workflow parameter data.

Keyword Arguments:

dataset_copy (bool) – For Zarr stores only. If True, copy arrays as NumPy arrays.

Return type:

dict[int, Any]

get_all_parameter_sources(**kwargs)

Retrieve all persistent parameters sources.

Return type:

list[ParamSource]

get_all_parameters(**kwargs)

Retrieve all persistent parameters.

Keyword Arguments:

dataset_copy (bool) – For Zarr stores only. If True, copy arrays as NumPy arrays.

Return type:

list[StoreParameter]

get_all_submission_run_IDs()

Get the run IDs of all submissions.

Return type:

Iterable[int]

get_element_IDs_from_EAR_IDs(id_lst)

Get the element IDs of EARs.

Parameters:

id_lst (Iterable[int]) –

Return type:

list[int]

get_element_iteration_IDs_from_EAR_IDs(id_lst)

Get the element iteration IDs of EARs.

Parameters:

id_lst (Iterable[int]) –

Return type:

list[int]

get_element_iterations_from_IDs(id_lst)

Return element iteration objects from a list of IDs.

Parameters:

id_lst (Iterable[int]) –

Return type:

list[ElementIteration]

get_element_iterations_of_tasks(id_lst)

Get element iterations belonging to multiple tasks.

Parameters:

id_lst (Iterable[int]) –

Return type:

Iterator[ElementIteration]

get_elements_from_IDs(id_lst)

Return element objects from a list of IDs.

Parameters:

id_lst (Iterable[int]) –

Return type:

list[Element]

get_iteration_final_run_IDs(id_lst=None)

Retrieve the run IDs of those runs that correspond to the final action within a named loop iteration.

These runs represent the final action of a given element-iteration; this is used to identify which commands file to append a loop-termination check to.

Parameters:

id_lst (Iterable[int] | None) –

Return type:

Mapping[str, Sequence[int]]

get_iteration_task_pathway(ret_iter_IDs=False, ret_data_idx=False)

Get the iteration task pathway.

Parameters:

• ret_iter_IDs (bool) –

• ret_data_idx (bool) –

Return type:

Sequence[tuple]

get_loop_map(id_lst=None)

Get a description of what is going on with looping.

Parameters:

id_lst (Iterable[int] | None) –

Return type:

Mapping[str, Mapping[int, Mapping[int, Sequence[_IterationData]]]]

get_parameter(index, **kwargs)

Get a single parameter.

Parameter

index:

The index of the parameter to retrieve.

keyword dataset_copy:

For Zarr stores only. If True, copy arrays as NumPy arrays.

kwtype dataset_copy:

bool

Parameters:

index (int) –

Return type:

StoreParameter

get_parameter_data(index, **kwargs)

Get the data relating to a parameter.

Parameters:

index (int) –

Return type:

Any

get_parameter_set_statuses(id_lst)

Get whether some parameters are set.

Parameters:

id_lst (Iterable[int]) –

Return type:

list[bool]

get_parameter_source(index)

Get the source of a particular parameter.

Parameters:

index (int) –

Return type:

ParamSource

get_parameter_sources(id_lst)

Get parameter sources known to the workflow.

Parameters:

id_lst (Iterable[int]) –

Return type:

list[ParamSource]

get_parameters(id_lst, **kwargs)

Get parameters known to the workflow.

Parameter

id_lst:

The indices of the parameters to retrieve.

keyword dataset_copy:

For Zarr stores only. If True, copy arrays as NumPy arrays.

kwtype dataset_copy:

bool

Parameters:

id_lst (Iterable[int]) –

Return type:

Sequence[StoreParameter]

get_running_elements(submission_idx=-1, task_idx=None, task_insert_ID=None)

Retrieve elements that are running according to the scheduler.

Parameters:

• submission_idx (int) –

• task_idx (int | None) –

• task_insert_ID (int | None) –

Return type:

list[Element]

get_running_runs(submission_idx=-1, task_idx=None, task_insert_ID=None, element_idx=None)

Retrieve runs that are running according to the scheduler.

Parameters:

• submission_idx (int) –

• task_idx (int | None) –

• task_insert_ID (int | None) –

• element_idx (int | None) –

Return type:

list[ElementActionRun]

get_store_EARs(id_lst)

Get the persistent element action runs.

Parameters:

id_lst (Iterable[int]) –

Return type:

Sequence[StoreEAR]

get_store_element_iterations(id_lst)

Get the persistent element iterations.

Parameters:

id_lst (Iterable[int]) –

Return type:

Sequence[StoreElementIter]

get_store_elements(id_lst)

Get the persistent elements.

Parameters:

id_lst (Iterable[int]) –

Return type:

Sequence[StoreElement]

get_store_tasks(id_lst)

Get the persistent tasks.

Parameters:

id_lst (Iterable[int]) –

Return type:

Sequence[StoreTask]

get_task_IDs_from_element_IDs(id_lst)

Get the task IDs of elements.

Parameters:

id_lst (Iterable[int]) –

Return type:

list[int]

get_task_elements(task, idx_lst=None)

Get the elements of a task.

Parameters:

• task (WorkflowTask) –

• idx_lst (list[int] | None) –

Return type:

list[Element]

get_task_unique_names(map_to_insert_ID=False)

Return the unique names of all workflow tasks.

Parameters:

map_to_insert_ID (bool) – If True, return a dict whose values are task insert IDs, otherwise return a list.

Return type:

Sequence[str] | Mapping[str, int]

property id_: str

The ID of this workflow.

property input_files_path: Path

Path to input files for the workflow.

is_parameter_set(index)

Test if a particular parameter is set.

Parameters:

index (int) –

Return type:

bool

property loops: WorkflowLoopList

The loops in this workflow.

property name: str

The name of the workflow.

The workflow name may be different from the template name, as it includes the creation date-timestamp if generated.

property num_EARs: int

The total number of element action runs.

property num_added_tasks: int

The total number of added tasks.

property num_element_iterations: int

The total number of element iterations.

property num_elements: int

The total number of elements.

property num_loops: int

The total number of loops.

property num_submissions: int

The total number of job submissions.

property num_tasks: int

The total number of tasks.

process_shell_parameter_output(name, value, EAR_ID, cmd_idx, stderr=False)

Process the shell stdout/stderr stream according to the associated Command object.

Parameters:

• name (str) –

• value (str) –

• EAR_ID (int) –

• cmd_idx (int) –

• stderr (bool) –

Return type:

Any

rechunk(chunk_size=None, backup=True, status=True)

Rechunk metadata/runs and parameters/base arrays, making them more efficient.

Parameters:

• chunk_size (int | None) –

• backup (bool) –

• status (bool) –

rechunk_parameter_base(chunk_size=None, backup=True, status=True)

Reorganise the stored data chunks for parameterss to be more efficient.

Parameters:

• chunk_size (int | None) –

• backup (bool) –

• status (bool) –

rechunk_runs(chunk_size=None, backup=True, status=True)

Reorganise the stored data chunks for EARs to be more efficient.

Parameters:

• chunk_size (int | None) –

• backup (bool) –

• status (bool) –

reload()

Reload the workflow from disk.

Return type:

Self

resolve_jobscripts(tasks=None)

Resolve this workflow to a set of job scripts to run.

Parameters:

tasks (Sequence[int] | None) –

Return type:

list[Jobscript]

save_parameter(name, value, EAR_ID)

Save a parameter where an EAR can find it.

Parameters:

• name (str) –

• value (Any) –

• EAR_ID (int) –

set_EAR_end(js_idx, js_act_idx, EAR_ID, exit_code)

Set the end time and exit code on an EAR.

If the exit code is non-zero, also set all downstream dependent EARs to be skipped. Also save any generated input/output files.

Parameters:

• js_idx (int) –

• js_act_idx (int) –

• EAR_ID (int) –

• exit_code (int) –

Return type:

None

set_EAR_skip(EAR_ID)

Record that an EAR is to be skipped due to an upstream failure or loop termination condition being met.

Parameters:

EAR_ID (int) –

Return type:

None

set_EAR_start(EAR_ID)

Set the start time on an EAR.

Parameters:

EAR_ID (int) –

Return type:

None

set_EAR_submission_index(EAR_ID, sub_idx)

Set the submission index of an EAR.

Parameters:

• EAR_ID (int) –

• sub_idx (int) –

Return type:

None

set_EARs_initialised(iter_ID)

Set EARs_initialised to True for the specified iteration.

Parameters:

iter_ID (int) –

Return type:

None

set_parameter_value(param_id, value, commit=False)

Set the value of a parameter.

Parameters:

• param_id (int | list[int]) –

• value (Any) –

• commit (bool) –

Return type:

None

show_all_EAR_statuses()

Print a description of the status of every element action run in the workflow.

Return type:

None

property store_format: str

The format of the workflow’s persistent store.

property submissions: list[Submission]

The job submissions done by this workflow.

property submissions_path: Path

Path to submission data for ths workflow.

submit(*, ignore_errors=False, JS_parallelism=None, print_stdout=False, wait=False, add_to_known=True, return_idx=False, tasks=None, cancel=False, status=True)

Submit the workflow for execution.

Parameters:

• ignore_errors (bool) – If True, ignore jobscript submission errors. If False (the default) jobscript submission will halt when a jobscript fails to submit.

• JS_parallelism (bool | None) – If True, allow multiple jobscripts to execute simultaneously. Raises if set to True but the store type does not support the jobscript_parallelism feature. If not set, jobscript parallelism will be used if the store type supports it.

• print_stdout (bool) – If True, print any jobscript submission standard output, otherwise hide it.

• wait (bool) – If True, this command will block until the workflow execution is complete.

• add_to_known (bool) – If True, add the submitted submissions to the known-submissions file, which is used by the show command to monitor current and recent submissions.

• return_idx (bool) – If True, return a dict representing the jobscript indices submitted for each submission.

• tasks (list[int] | None) – List of task indices to include in the new submission if no submissions already exist. By default all tasks are included if a new submission is created.

• cancel (bool) – Immediately cancel the submission. Useful for testing and benchmarking.

• status (bool) – If True, display a live status to track submission progress.

Return type:

Mapping[int, Sequence[int]] | None

property task_artifacts_path: Path

Path to artifacts of tasks.

property tasks: WorkflowTaskList

The tasks in this workflow.

property template: WorkflowTemplate

The template that this workflow was made from.

property template_components: TemplateComponents

The template components used for this workflow.

classmethod temporary_rename(path, fs)

Rename an existing same-path workflow (directory) so we can restore it if workflow creation fails.

Renaming will occur until the successfully completed. This means multiple new paths may be created, where only the final path should be considered the successfully renamed workflow. Other paths will be deleted.

Parameters:

• path (str) –

• fs (AbstractFileSystem) –

Return type:

str

property ts_fmt: str

The timestamp format.

property ts_name_fmt: str

The timestamp format for names.

unzip(path='.', *, log=None)

Convert the workflow to an unzipped form.

Parameters:

• path (str) – Path at which to create the new unzipped workflow. If this is an existing directory, the new workflow directory will be created within this directory. Otherwise, this path will represent the new workflow directory path.

• log (str | None) –

Return type:

str

property url: str

An fsspec URL for this workflow.

wait(sub_js=None)

Wait for the completion of specified/all submitted jobscripts.

Parameters:

sub_js (Mapping[int, Sequence[int]] | None) –

write_commands(submission_idx, jobscript_idx, JS_action_idx, EAR_ID)

Write run-time commands for a given EAR.

Parameters:

• submission_idx (int) –

• jobscript_idx (int) –

• JS_action_idx (int) –

• EAR_ID (int) –

Return type:

None

zip(path='.', *, log=None, overwrite=False, include_execute=False, include_rechunk_backups=False)

Convert the workflow to a zipped form.

Parameters:

• path (str) – Path at which to create the new zipped workflow. If this is an existing directory, the zip file will be created within this directory. Otherwise, this path is assumed to be the full file path to the new zip file.

• log (str | None) –

• overwrite (bool) –

• include_execute (bool) –

• include_rechunk_backups (bool) –

Return type:

str