def test_post_processor(get_bout_run_setup: Callable[[str], BoutRunSetup]) -> None:
"""
Test the post-processor.
Parameters
----------
get_bout_run_setup : function
Function which returns the BoutRunSetup object based on the conduction directory
"""
bout_run_setup = get_bout_run_setup("test_run_group_post")
run_graph = RunGraph()
run_graph.add_function_node("1")
run_group_post = RunGroup(
run_graph, bout_run_setup, name="test_post", waiting_for="1"
)
run_group_post.add_post_processor(
{"function": lambda: None, "args": None, "kwargs": None}
)
run_group_post.add_post_processor(
{"function": lambda: None, "args": None, "kwargs": None}
)
expected = (
"1",
"bout_run_test_post",
"post_processor_test_post_0",
"post_processor_test_post_1",
)
assert set(expected) == set(run_graph.get_waiting_for_tuple("1"))
def test_constructor(get_bout_run_setup: Callable[[str], BoutRunSetup]) -> None:
"""
Test the constructor.
Parameters
----------
get_bout_run_setup : function
Function which returns the BoutRunSetup object based on the conduction directory
"""
bout_run_setup = get_bout_run_setup("test_run_group_constructor")
run_graph = RunGraph()
run_graph.add_function_node("1")
run_group_0 = RunGroup(run_graph, bout_run_setup)
run_number_first_run = int(run_group_0.bout_run_node_name.split("_")[-1])
assert run_group_0.bout_run_node_name == f"bout_run_{run_number_first_run}"
run_group_test = RunGroup(run_graph, bout_run_setup, name="test")
assert run_group_test.bout_run_node_name == "bout_run_test"
run_group_1 = RunGroup(run_graph, bout_run_setup, waiting_for="1")
run_number_second_run = int(run_group_1.bout_run_node_name.split("_")[-1])
assert run_number_second_run > run_number_first_run
assert run_group_1.bout_run_node_name == f"bout_run_{run_number_second_run}"
expected = (
"1",
f"bout_run_{run_number_second_run}",
)
assert expected == run_graph.get_waiting_for_tuple("1")
def test_pre_processor(get_bout_run_setup: Callable[[str], BoutRunSetup]) -> None:
"""
Test the pre-processor.
Parameters
----------
get_bout_run_setup : function
Function which returns the BoutRunSetup object based on the conduction directory
"""
bout_run_setup = get_bout_run_setup("test_run_group_pre")
run_graph = RunGraph()
run_graph.add_function_node("1")
run_group_pre = RunGroup(
run_graph, bout_run_setup, name="test_pre", waiting_for="1"
)
run_group_pre.add_pre_processor(
{"function": lambda: None, "args": None, "kwargs": None}
)
run_group_pre.add_pre_processor(
{"function": lambda: None, "args": None, "kwargs": None}
)
root_nodes = next(run_graph)
assert len(root_nodes) == 3
def test_get_dot_string() -> None:
"""Test the ability to get the dot string."""
run_graph = RunGraph()
run_graph.add_function_node("42")
hex_id_submitter = hex(id(run_graph["42"]["submitter"]))
expected = (
"strict digraph "
"{\n42 [args=None, function=None, kwargs=None, path=None, status=ready, "
"submitter=<bout_runners.submitter.local_submitter.LocalSubmitter object at "
f"{hex_id_submitter}"
">];\n}\n")
assert expected == run_graph.get_dot_string()
def test_add_waiting_for() -> None:
"""Test the ability to let a node wait for other nodes."""
run_graph = RunGraph()
run_graph.add_function_node("1")
run_graph.add_function_node("2")
run_graph.add_function_node("3")
run_graph.add_waiting_for("2", "1")
run_graph.add_waiting_for("3", ("2", "1"))
expected = ("1", "2", "3")
assert expected == run_graph.get_waiting_for_tuple("1")
def test_add_edge() -> None:
"""Test ability to add edges, and the ability to detect if a graph is cyclic."""
run_graph = RunGraph()
run_graph.add_function_node("1")
run_graph.add_function_node("2")
run_graph.add_edge("1", "2")
with pytest.raises(ValueError):
run_graph.add_edge("2", "1")
expected = {"1", "2"}
assert expected == set(run_graph.nodes)
def test_add_bout_run_node(get_bout_run_setup) -> None:
"""
Test ability to write and rewrite a BoutRunSetup node.
Parameters
----------
get_bout_run_setup : function
Function which returns the BoutRunSetup object based on the conduction directory
"""
run_graph = RunGraph()
bout_run_setup = get_bout_run_setup("test_run_graph")
run_graph.add_bout_run_node("test", bout_run_setup)
assert len(run_graph.nodes) == 1
assert isinstance(run_graph["test"]["bout_run_setup"], BoutRunSetup)
with pytest.raises(ValueError):
run_graph.add_function_node("test")
assert len(run_graph.nodes) == 1
def test_add_function_node() -> None:
"""Test ability to write and rewrite a function node."""
run_graph = RunGraph()
run_graph.add_function_node("test",
function_dict={
"function": None,
"args": ("pass", 42),
"kwargs": None
})
assert len(run_graph.nodes) == 1
assert run_graph["test"]["function"] is None
assert run_graph["test"]["args"] == ("pass", 42)
assert run_graph["test"]["kwargs"] is None
assert isinstance(run_graph["test"]["submitter"], LocalSubmitter)
with pytest.raises(ValueError):
run_graph.add_function_node("test")
assert len(run_graph.nodes) == 1
def make_graph() -> RunGraph:
"""
Yield a simple graph.
Returns
-------
run_graph : RunGraph
A simple graph
"""
run_graph = RunGraph()
for i in range(6):
run_graph.add_function_node(str(i))
run_graph.add_waiting_for("4", "3")
run_graph.add_waiting_for("5", "3")
run_graph.add_waiting_for("3", "2")
run_graph.add_waiting_for("2", "0")
run_graph.add_waiting_for("1", "0")
return run_graph
class BoutRunner:
r"""
Class for executing a run and store its metadata.
Attributes
----------
__run_graph : RunGraph
Getter variable for executor the run graph
run_graph : Graph
The run graph to be executed
wait_time : int
Time to wait before checking if a job has completed
Methods
-------
__add_waiting_for(node_name)
Add the job_ids to wait for in the submission script
__prepare_run(force, restart_all)
Prepare the run sequence
__updates_when_restart_all_is_true()
Update paths and nodes when restart_all is True
__inject_copy_restart_files_node(node_with_restart)
Inject a node which copy restart files
__make_restart_files_node(to_node_name, copy_restart_from, copy_restart_to)
Make nodes which copies restart files
__next_order_has_local(submitter_dict)
Check if the current order of nodes has any local submitters
__monitor_runs(submitter_dict, raise_errors)
Monitor the runs belonging to the same order
__run_status_checker(node_name)
Run the StatusChecker
__this_order_has_local(submitter_dict)
Check if the current order of nodes has any local submitters
__update_submitter_dict_after_run_bout_run(node_name, submitted, submitter_dict)
Update the submitter dict after calling run_bout_run
find_matching_order_number(node_names, node_orders)
Return the order matching the node names
run_bout_run(bout_run_setup, restart_from_bout_inp_dst, force)
Perform the BOUT++ run and capture the related metadata
run_function(path, function, args, kwargs, submitter)
Submit a function for execution
reset()
Reset the run_graph
release_nodes(nodes_to_release)
Release nodes to a submission queue if applicable
cluster_node_exist(node_names)
Check if any of the nodes have a submitter of type AbstractClusterSubmitter
wait_until_completed(self)
Wait until all submitted nodes are completed
run(restart_all, force, raise_errors)
Execute the run
Examples
--------
The easiest way to use BoutRunner is to run a script from the root directory of
the project (i.e. where the `Makefile` and `data` directory are normally
situated. The script can simply call
>>> BoutRunner().run()
and `BoutRunner` takes care of the rest.
A more elaborate example where all the dependency objects are built manually:
Import dependencies
>>> from pathlib import Path
>>> from bout_runners.executor.bout_paths import BoutPaths
>>> from bout_runners.executor.executor import BoutRunExecutor
>>> from bout_runners.database.database_connector import DatabaseConnector
>>> from bout_runners.parameters.default_parameters import DefaultParameters
>>> from bout_runners.parameters.run_parameters import RunParameters
>>> from bout_runners.parameters.final_parameters import FinalParameters
>>> from bout_runners.submitter.local_submitter import LocalSubmitter
>>> from bout_runners.runner.bout_run_setup import BoutRunSetup
>>> from bout_runners.runner.run_graph import RunGraph
>>> from bout_runners.runner.run_group import RunGroup
Create the `bout_paths` object
>>> project_path = Path().joinpath('path', 'to', 'project')
>>> bout_inp_src_dir = Path().joinpath('path', 'to', 'source', 'BOUT.inp')
>>> bout_inp_dst_dir = Path().joinpath('path', 'to', 'destination', 'BOUT.inp')
>>> bout_paths = BoutPaths(project_path=project_path,
... bout_inp_src_dir=bout_inp_src_dir,
... bout_inp_dst_dir=bout_inp_dst_dir)
Create the input objects
>>> default_parameters = DefaultParameters(bout_paths)
>>> run_parameters = RunParameters({'global': {'nout': 0}})
>>> final_parameters = FinalParameters(default_parameters,
... run_parameters)
>>> executor = BoutRunExecutor(
... bout_paths=bout_paths,
... submitter=LocalSubmitter(bout_paths.project_path),
... run_parameters=run_parameters)
>>> db_connector = DatabaseConnector('name_of_database', db_root_path=Path())
>>> bout_run_setup = BoutRunSetup(executor, db_connector, final_parameters)
>>> run_graph = RunGraph()
>>> # The RunGroup can attach pre and post-processors to the run
>>> # See the user manual for more info
>>> _ = RunGroup(run_graph, bout_run_setup, name='my_test_run')
Run the project
>>> runner = BoutRunner(run_graph)
>>> runner.run()
"""
def __init__(self,
run_graph: Optional[RunGraph] = None,
wait_time: int = 5) -> None:
"""
Set the member data.
Parameters
----------
run_graph : None or RunGraph
The run graph to be executed
If None the run graph will be constructed and added parameters from the
default BoutRunSetup
wait_time : int
Time to wait before checking if a job has completed
"""
self.wait_time = wait_time
if run_graph is None:
self.__run_graph = RunGraph()
_ = RunGroup(self.__run_graph, BoutRunSetup())
else:
self.__run_graph = run_graph
if (len([
node for node in self.__run_graph.nodes
if node.startswith("bout_run")
]) == 0):
logging.warning(
"The provided run_graph does not contain any bout_runs")
def __add_waiting_for(self, node_name: str) -> None:
"""
Add the job_ids to wait for in the submission script.
Parameters
----------
node_name : str
Name of current node
"""
predecessors = self.__run_graph.predecessors(node_name)
waiting_for = (
self.__run_graph[p_name]["submitter"].job_id
for p_name in predecessors if isinstance(
self.__run_graph[p_name]["submitter"],
AbstractClusterSubmitter,
) and not self.__run_graph[p_name]["submitter"].completed())
self.__run_graph[node_name]["submitter"].add_waiting_for(waiting_for)
def __prepare_run(self, force: bool, restart_all: bool) -> None:
"""
Prepare the run sequence.
If any bout_run nodes contain restart_from this function will create a
node which copies the restart files
Parameters
----------
restart_all : bool
All the BOUT++ runs in the run graph will be restarted
force : bool
Execute the run even if has been performed with the same parameters
Raises
------
RuntimeError
If none of the nodes in the `run_graph` has status "ready"
"""
logging.info("Start: Preparing all runs")
if force or restart_all:
if restart_all:
self.__updates_when_restart_all_is_true()
logging.debug("Resetting the graph as %s == True",
"force" if force else "restart_all")
self.reset()
if len(self.__run_graph) == 0:
if len(self.__run_graph.nodes) == 0:
msg = "The 'run_graph' does not contain any nodes."
else:
msg = (
"None of the nodes in 'run_graph' has the status 'ready'. "
"Reset the 'run_graph' if you'd like to run the original graph"
)
logging.critical(msg)
raise RuntimeError(msg)
for node in tuple(self.__run_graph.nodes):
if (node.startswith("bout_run") and self.__run_graph[node]
["bout_run_setup"].executor.restart_from is not None):
logging.info(
"Found restart_from in node %s, "
"will inject node which copies restart files",
node,
)
self.__inject_copy_restart_files_node(node)
logging.info("Done: Preparing all runs")
def __updates_when_restart_all_is_true(self) -> None:
"""Update paths and nodes when restart_all is True."""
logging.info("Updating executor.restart_from as restart_all=True")
for node in tuple(self.__run_graph.nodes):
if node.startswith("bout_run"):
# Input must now point at previous destination
self.__run_graph[node][
"bout_run_setup"].bout_paths.bout_inp_src_dir = self.__run_graph[
node]["bout_run_setup"].bout_paths.bout_inp_dst_dir
self.__run_graph[node][
"bout_run_setup"].executor.restart_from = self.__run_graph[
node]["bout_run_setup"].bout_paths.bout_inp_src_dir
# Any copy restart nodes must be copied
for predecessor in self.__run_graph.predecessors(node):
if predecessor.startswith("copy_restart_files"):
if (self.run_graph[predecessor]["function"].__module__
== "bout_runners.utils.file_operations"
and self.run_graph[predecessor]
["function"].__name__ == "copy_restart_files"):
logging.info(
"Updating the arguments to %s as restart_all=True",
predecessor,
)
old_args = self.run_graph[predecessor]["args"]
new_args = (
self.__run_graph[node]
["bout_run_setup"].executor.restart_from,
self.__run_graph[node]
["bout_run_setup"].bout_paths.bout_inp_dst_dir,
)
logging.debug(
"Changing argument 'copy_restart_from' from %s to %s",
old_args[0],
new_args[0],
)
logging.debug(
"Changing argument 'copy_restart_to' from %s to %s",
old_args[1],
new_args[1],
)
self.run_graph[predecessor]["args"] = new_args
else:
logging.warning(
"restart_all=True, but node %s waits for %s which is "
"a function from %s. No updates to this node will be "
"made",
node,
predecessor,
self.run_graph[predecessor]
["function"].__module__ + "." +
self.run_graph[predecessor]
["function"].__name__,
)
break
def __inject_copy_restart_files_node(self, node_with_restart: str) -> None:
"""
Inject a node which copy restart files.
Parameters
----------
node_with_restart : str
Name of the node which will wait for a restart node
"""
logging.info("Start: Injecting node which copies restart files")
restart_from = self.__run_graph[node_with_restart][
"bout_run_setup"].executor.restart_from
copy_to = self.__run_graph[node_with_restart][
"bout_run_setup"].bout_paths.bout_inp_dst_dir
copy_node = self.__make_restart_files_node(node_with_restart,
restart_from, copy_to)
for predecessor in self.__run_graph.predecessors(node_with_restart):
self.__run_graph.remove_edge(predecessor, node_with_restart)
self.__run_graph.add_edge(predecessor, copy_node)
self.__run_graph.add_edge(copy_node, node_with_restart)
logging.info("Done: Injecting node which copies restart files")
def __make_restart_files_node(self, to_node_name: str,
copy_restart_from: Path,
copy_restart_to: Path) -> str:
"""
Make nodes which copies restart files.
Parameters
----------
to_node_name : str
Name of the node which will wait for a restart node
copy_restart_from : Path
Path to copy restart files from
copy_restart_to : Path
Path to copy restart files to
Returns
-------
current_node_name : str
Name of the node which copies files
"""
current_node_name = (
f"copy_restart_files_from_{copy_restart_from.name}_to_"
f"{copy_restart_to.name}_for_{to_node_name}")
function_dict: Dict[str, Optional[Union[Callable, Tuple[Any, ...],
Dict[str, Any]]]] = {
"function":
copy_restart_files,
"args": (copy_restart_from,
copy_restart_to),
"kwargs": None,
}
path = copy_restart_to.joinpath(f"{current_node_name}.py")
submitter = get_submitter()
if isinstance(submitter, AbstractClusterSubmitter):
submitter.store_dir = copy_restart_to
submitter.job_name = current_node_name
self.__run_graph.add_function_node(
name=current_node_name,
function_dict=function_dict,
path=path,
submitter=submitter,
)
return current_node_name
def __next_order_has_local(
self,
submitter_dict: Dict[str, Dict[str, Union[Optional[AbstractSubmitter],
Union[DatabaseConnector,
Path]]], ],
) -> bool:
"""
Check if the current order of nodes has any local submitters.
Parameters
----------
submitter_dict : dict
Dict containing the the node names as keys and a new dict as values
The new dict contains the keywords 'submitter' with value AbstractSubmitter
Returns
-------
bool
True if the current order has local submitters
"""
for node_name in submitter_dict.keys():
for successor_name in self.__run_graph.successors(node_name):
if isinstance(self.__run_graph[successor_name]["submitter"],
LocalSubmitter):
logging.info(
"%s in the next node order is of local submitter type, "
"will monitor this node order",
successor_name,
)
return True
return False
def __monitor_runs(
self,
submitter_dict: Dict[str, Dict[str, Union[Optional[AbstractSubmitter],
Union[DatabaseConnector,
Path]]], ],
raise_errors: bool,
) -> None:
"""
Monitor the runs belonging to the same order.
Parameters
----------
submitter_dict : dict
Dict containing the the node names as keys and a new dict as values
The new dict contains the keywords 'submitter' with value AbstractSubmitter
If the submitter contains a bout run, the new dict will also contain the
keyword 'db_connector' with the value DatabaseConnector and the keyword
'project_path' with the value Path which will be used in the StatusChecker
raise_errors : bool
If True the program will raise any error caught when during the running
of the nodes
If False the program will continue execution, but all nodes depending on
the errored node will be marked as errored and not submitted
Raises
------
RuntimeError
If the types in the dict are unexpected
"""
logging.info("Start: Monitoring jobs at current order")
node_names = list(node_name for node_name in submitter_dict.keys())
while len(node_names) != 0:
for node_name in node_names:
submitter = submitter_dict[node_name]["submitter"]
if not isinstance(submitter, AbstractSubmitter):
msg = (
f"The submitter of the '{node_name}' node was expected to be "
f"of type 'AbstractSubmitter', but got '{type(submitter)}' "
f"instead")
logging.critical(msg)
raise RuntimeError(msg)
if submitter.completed():
if submitter.errored():
self.__run_graph.change_status_node_and_dependencies(
node_name)
if raise_errors:
submitter.raise_error()
node_names.remove(node_name)
else:
logging.debug(
"job_id=%s found, %s seems to be running",
submitter.job_id,
node_name,
)
if node_name.startswith("bout_run"):
self.__run_status_checker(node_name)
sleep(self.wait_time)
logging.info("Done: Monitoring jobs at current order")
def __run_status_checker(self, node_name: str) -> None:
"""
Run the StatusChecker.
Parameters
----------
node_name : str
Name of node to run the status checker for
Raises
------
RuntimeError
If the types of self.__run_graph[node_name]["db_connector"] or
self.__run_graph[node_name]["project_path"] are unexpected
"""
db_connector = self.__run_graph[node_name]["db_connector"]
if not isinstance(db_connector, DatabaseConnector):
raise RuntimeError(
f"The db_connector of the '{node_name}' node was expected "
f"to be of type 'DatabaseConnector', but got "
f"'{type(db_connector)}' instead")
project_path = self.__run_graph[node_name]["project_path"]
if not isinstance(project_path, Path):
raise RuntimeError(
f"The project_path of the '{node_name}' node was expected "
f"to be of type 'Path', but got '{type(project_path)}' "
f"instead")
StatusChecker(db_connector, project_path).check_and_update_status()
@staticmethod
def __this_order_has_local(
submitter_dict: Dict[str, Dict[str, Union[Optional[AbstractSubmitter],
Union[DatabaseConnector,
Path]]], ]
) -> bool:
"""
Check if the current order of nodes has any local submitters.
Parameters
----------
submitter_dict : dict
Dict containing the the node names as keys and a new dict as values
The new dict contains the keywords 'submitter' with value AbstractSubmitter
Returns
-------
bool
True if the current order has local submitters
"""
for node_name in submitter_dict.keys():
if isinstance(submitter_dict[node_name]["submitter"],
LocalSubmitter):
logging.debug(
"%s is of local submitter type, will monitor this node order",
node_name,
)
return True
return False
def __update_submitter_dict_after_run_bout_run(
self,
node_name: str,
submitted: bool,
submitter_dict: Dict[str, Dict[str, Union[Optional[AbstractSubmitter],
Union[DatabaseConnector,
Path]], ], ],
) -> None:
"""
Update the submitter dict after calling run_bout_run.
If the run has been submitted we add information about the database in the dict.
Else we pop the node name from the dict in order not to monitor it.
Parameters
----------
node_name : str
Name of current node
submitted : bool
Whether or not the run was submitted
submitter_dict : dict
Dict containing the the node names as keys and a new dict as values
The new dict contains the keywords 'submitter' with value AbstractSubmitter
"""
if submitted:
self.__run_graph[node_name]["db_connector"] = self.__run_graph[
node_name]["bout_run_setup"].db_connector
self.__run_graph[node_name]["project_path"] = self.__run_graph[
node_name]["bout_run_setup"].bout_paths.project_path
else:
submitter_dict.pop(node_name)
@property
def run_graph(self) -> RunGraph:
"""
Get the properties of self.run_graph.
Returns
-------
self.__run_graph : RunGraph
The RunGraph object
"""
return self.__run_graph
@staticmethod
def find_matching_order_number(
node_names: Tuple[str, ...],
node_orders: Tuple[Tuple[str, ...], ...]) -> Optional[int]:
"""
Return the order matching the node names.
Parameters
----------
node_names : tuple of str
Node names
node_orders : tuple of tuple of str
Ordered tuple of orders
Returns
-------
order_number : int or None
The first order where a match was found
If no match was found 0 is returned
"""
order_number = -1
found = False
for order_nodes in node_orders:
for node_name in node_names:
if node_name in order_nodes:
found = True
break
order_number += 1
if found:
return order_number
return None
@staticmethod
def run_bout_run(
bout_run_setup: BoutRunSetup,
force: bool = False,
) -> bool:
"""
Perform the BOUT++ run and capture the related metadata.
Parameters
----------
bout_run_setup : BoutRunSetup
The setup for the BOUT++ run
force : bool
Execute the run even if has been performed with the same parameters
Returns
-------
bool
Whether or not the run was submitted
"""
restart = bool(bout_run_setup.executor.restart_from)
if restart and force:
logging.warning(
"force has been set to True for a run which is to use restart files. "
"Will therefore ignore force")
run_id = bout_run_setup.metadata_recorder.capture_new_data_from_run(
bout_run_setup.executor.submitter.processor_split, restart, force)
if run_id is None:
if not restart:
logging.info("Executing the run")
else:
logging.info("Executing the run from restart files")
bout_run_setup.executor.execute(restart)
else:
logging.warning(
"Run with the same configuration has been executed before, "
"see run with run_id %d",
run_id,
)
if force:
logging.info("Executing the run as force==True")
bout_run_setup.executor.execute()
else:
return False
return True
@staticmethod
def run_function(
path: Path,
submitter: AbstractSubmitter,
function: Callable,
args: Optional[Tuple[Any, ...]] = None,
kwargs: Optional[Dict[str, Any]] = None,
) -> AbstractSubmitter:
"""
Submit a function for execution.
Parameters
----------
path : Path
Absolute path to store the python file which holds the function and
its arguments
submitter : AbstractSubmitter
The submitter to submit the function with
Uses the default LocalSubmitter if None
function : function
The function to call
args : None or tuple
The positional arguments
kwargs : None or dict
The keyword arguments
Returns
-------
submitter : AbstractSubmitter
The submitter used
"""
logging.info(
"Submitting %s, with positional parameters %s, and keyword parameters %s",
function.__name__,
args,
kwargs,
)
submitter.write_python_script(path, function, args, kwargs)
command = f"python3 {path}"
submitter.submit_command(command)
return submitter
def reset(self) -> None:
"""Reset the run_graph."""
logging.debug("Resetting the graph")
self.__run_graph.reset()
def release_nodes(self, nodes_to_release: Tuple[Tuple[str, ...],
...]) -> None:
"""
Release nodes to a submission queue if applicable.
Parameters
----------
nodes_to_release : iterable
Name of nodes to release
"""
if len(nodes_to_release) != 0:
logging.info("Start: Releasing held cluster nodes")
logging.debug("Release order: %s", nodes_to_release)
for order in nodes_to_release:
for node in order:
if isinstance(self.__run_graph[node]["submitter"],
AbstractClusterSubmitter):
self.__run_graph[node]["submitter"].release()
logging.info("Done: Releasing held cluster nodes")
def cluster_node_exist(self, node_names: Iterable[str]) -> bool:
"""
Check if any of the nodes have a submitter of type AbstractClusterSubmitter.
Parameters
----------
node_names : iterable of str
Iterable containing node names
Returns
-------
bool
Whether the iterable contains any cluster nodes
"""
for node in node_names:
if isinstance(self.__run_graph[node]["submitter"],
AbstractClusterSubmitter):
return True
return False
def wait_until_completed(self) -> None:
"""Wait until all submitted nodes are completed."""
logging.info("Start: Waiting for all submitted jobs to complete")
for node_name in self.__run_graph.nodes:
if self.__run_graph[node_name]["status"] == "submitted":
self.__run_graph[node_name]["submitter"].wait_until_completed()
self.__run_graph[node_name]["status"] = "completed"
if node_name.startswith("bout_run"):
self.__run_status_checker(node_name)
logging.info("Done: Waiting for all submitted jobs to complete")
def run(self,
restart_all: bool = False,
force: bool = False,
raise_errors: bool = True) -> None:
"""
Execute all the nodes in the run_graph.
Parameters
----------
restart_all : bool
All the BOUT++ runs in the run graph will be restarted
force : bool
Execute the run even if has been performed with the same parameters
raise_errors : bool
If True the program will raise any error caught when during the running
of the nodes
If False the program will continue execution, but all nodes depending on
the errored node will be marked as errored and not submitted
"""
logging.info("Start: Calling .run() in BoutRunners")
self.__prepare_run(force, restart_all)
logging.debug("Dot-graph of the run\n%s",
self.__run_graph.get_dot_string())
for nodes_at_current_order in self.__run_graph:
logging.info("Start: Processing nodes at current order")
submitter_dict: Dict[str, Dict[str,
Union[Optional[AbstractSubmitter],
Union[DatabaseConnector,
Path]], ], ] = dict()
for node_name in nodes_at_current_order:
if self.__run_graph[node_name]["status"] != "ready":
logging.info(
"Skipping node '%s' as it has status=%s",
node_name,
self.__run_graph[node_name]["status"],
)
continue
logging.info("Start: Processing %s", node_name)
if isinstance(
self.__run_graph[node_name]["submitter"],
AbstractClusterSubmitter,
):
self.__add_waiting_for(node_name)
submitter_dict[node_name] = dict()
submitter_dict[node_name]["submitter"] = self.__run_graph[
node_name]["submitter"]
if node_name.startswith("bout_run"):
submitted = self.run_bout_run(
self.__run_graph[node_name]["bout_run_setup"],
force,
)
self.__update_submitter_dict_after_run_bout_run(
node_name, submitted, submitter_dict)
else:
self.run_function(
self.__run_graph[node_name]["path"],
self.__run_graph[node_name]["submitter"],
self.__run_graph[node_name]["function"],
self.__run_graph[node_name]["args"],
self.__run_graph[node_name]["kwargs"],
)
self.__run_graph[node_name]["status"] = "submitted"
logging.info("Done: Processing %s", node_name)
# We only monitor the runs if any local_submitters are present in
# the current or the next order
# Else the clusters will handle the monitoring
monitor_run = False
if self.__this_order_has_local(
submitter_dict) or self.__next_order_has_local(
submitter_dict):
monitor_run = True
if monitor_run:
if self.cluster_node_exist(self.__run_graph.nodes):
logging.warning(
"Mixed local and cluster nodes found in graph. "
"Releasing the cluster nodes up until the order of the "
"LocalSubmitter. This can cause a node waiting for one of "
"these nodes to be submitted after those nodes have finished "
"so that the cluster will reject those jobs.")
reverse_sorted_node_orders = self.__run_graph.get_node_orders(
reverse=True)
order_number = self.find_matching_order_number(
tuple(submitter_dict.keys()),
reverse_sorted_node_orders)
orders_to_release = reverse_sorted_node_orders[
order_number:]
self.release_nodes(orders_to_release)
# We also need to release the current order in case
# the graph is not connected
self.release_nodes((tuple(submitter_dict.keys()), ))
self.__monitor_runs(submitter_dict, raise_errors)
logging.info("Done: Processing nodes at current order")
if self.cluster_node_exist(self.__run_graph.nodes):
reverse_sorted_node_orders = self.__run_graph.get_node_orders(
reverse=True)
self.release_nodes(reverse_sorted_node_orders)
logging.info("Done: Calling .run() in BoutRunners")
def assert_waiting_for_graph(
node_zero_submitter: AbstractSubmitter,
node_one_submitter: AbstractSubmitter,
node_two_submitter: AbstractSubmitter,
node_three_submitter: AbstractSubmitter,
save_dir,
) -> None:
"""
Assert that the graph is running in correct order.
Parameters
----------
node_zero_submitter : AbstractSubmitter
Submitter object for node one
node_one_submitter : AbstractSubmitter
Submitter object for node one
node_two_submitter : AbstractSubmitter
Submitter object for node one
node_three_submitter : AbstractSubmitter
Submitter object for node one
save_dir : Path
Path to where the job artifacts are stored
"""
graph = RunGraph()
graph.add_function_node(
"node_zero",
{
"function": node_zero,
"args": (save_dir, ),
"kwargs": None
},
save_dir.joinpath("node_zero.py"),
node_zero_submitter,
)
graph.add_function_node(
"node_one",
{
"function": node_one,
"args": (save_dir, ),
"kwargs": None
},
save_dir.joinpath("node_one.py"),
node_one_submitter,
)
graph.add_function_node(
"node_two",
{
"function": node_two,
"args": (save_dir, ),
"kwargs": None
},
save_dir.joinpath("node_two.py"),
node_two_submitter,
)
graph.add_function_node(
"node_three",
{
"function": node_three,
"args": (save_dir, ),
"kwargs": None
},
save_dir.joinpath("node_three.py"),
node_three_submitter,
)
graph.add_waiting_for("node_two", "node_one")
graph.add_waiting_for("node_three", ("node_one", "node_two"))
runner = BoutRunner(graph)
runner.run()
node_three_submitter.wait_until_completed()
assert save_dir.joinpath("node_three.log").is_file()