def hostnames(output_dir, job_id, verbose):
"""Show the hostnames of active nodes participating in the batch."""
level = logging.DEBUG if verbose else logging.INFO
setup_logging(__name__, None, console_level=level)
try:
cluster, _ = Cluster.deserialize(output_dir, deserialize_jobs=True)
except InvalidConfiguration:
print(
f"{output_dir} is not a JADE output directory used in cluster mode",
file=sys.stderr)
sys.exit(1)
if cluster.is_complete():
print("All jobs are already complete.")
sys.exit()
groups = make_submission_group_lookup(
[cluster.config.submission_groups[0]])
hpc_mgr = HpcManager(groups, output_dir)
nodes = []
for _job_id in cluster.job_status.hpc_job_ids:
if job_id is not None and _job_id != job_id:
continue
nodes += hpc_mgr.list_active_nodes(_job_id)
if not nodes:
print("No nodes were detected.", file=sys.stderr)
sys.exit(1)
print(" ".join(nodes))
def manager_node(output_dir, job_id, verbose):
"""Print the name of the manager node to the console. Requires a single job in the batch."""
level = logging.DEBUG if verbose else logging.INFO
setup_logging(__name__, None, console_level=level)
try:
cluster, _ = Cluster.deserialize(output_dir, deserialize_jobs=True)
except InvalidConfiguration:
print(
f"{output_dir} is not a JADE output directory used in cluster mode",
file=sys.stderr)
sys.exit(1)
if cluster.is_complete():
print("All jobs are already complete.")
sys.exit()
groups = make_submission_group_lookup(
[cluster.config.submission_groups[0]])
hpc_mgr = HpcManager(groups, output_dir)
if job_id not in cluster.job_status.hpc_job_ids:
print(f"job_id={job_id} is not active", file=sys.stderr)
sys.exit(1)
node = hpc_mgr.list_active_nodes(job_id)[0]
print(node)
def cancel_jobs(self, cluster):
"""Cancel running and pending jobs."""
groups = make_submission_group_lookup(cluster.config.submission_groups)
hpc = HpcManager(groups, self._output)
for job_id in cluster.job_status.hpc_job_ids:
hpc.cancel_job(job_id)
cluster.mark_canceled()
def show_times(output_dirs, verbose):
"""Show the run times of all allocated jobs."""
level = logging.DEBUG if verbose else logging.INFO
setup_logging(__name__, None, console_level=level)
job_ids = []
for output in output_dirs:
path = Path(output)
try:
cluster, _ = Cluster.deserialize(path, deserialize_jobs=False)
except InvalidConfiguration:
print(
f"{output} is not a JADE output directory used in cluster mode",
file=sys.stderr)
sys.exit(1)
job_ids += [
x.name.split("_")[2].replace(".e", "") for x in path.glob("*.e")
]
job_ids.sort(key=lambda x: int(x))
groups = make_submission_group_lookup(
[cluster.config.submission_groups[0]])
hpc_mgr = HpcManager(groups, output)
total_duration = timedelta(seconds=0)
table = PrettyTable()
table.field_names = HpcJobStats._fields
total_aus = 0
if os.environ.get("NREL_CLUSTER") == "eagle":
au_parser = get_nrel_eagle_aus
else:
au_parser = None
for job_id in job_ids:
stats = hpc_mgr.get_job_stats(job_id)
if stats is None:
continue
duration = stats.end - stats.start
if stats.state == HpcJobStatus.COMPLETE and isinstance(
stats.end, datetime):
total_duration += duration
data = stats._asdict()
data["state"] = data["state"].value
if au_parser is not None:
total_aus += au_parser(duration, stats.qos)
table.add_row(data.values())
print(table)
print(f"\nTotal duration = {total_duration}")
print("Total hours = {:.2f}".format(total_duration.total_seconds() / 3600))
if au_parser is not None:
print("Total AUs = {:.2f}".format(total_aus))
def am_i_manager(output_dir, verbose):
"""Print 'true' or 'false' depending on whether the current node is the manager node."""
level = logging.DEBUG if verbose else logging.INFO
setup_logging(__name__, None, console_level=level)
try:
cluster, _ = Cluster.deserialize(output_dir, deserialize_jobs=True)
except InvalidConfiguration:
print(
f"{output_dir} is not a JADE output directory used in cluster mode",
file=sys.stderr)
sys.exit(1)
if cluster.is_complete():
print("All jobs are already complete.")
sys.exit()
groups = make_submission_group_lookup(
[cluster.config.submission_groups[0]])
hpc_mgr = HpcManager(groups, output_dir)
am_manager = hpc_mgr.am_i_manager()
print(str(am_manager).lower(), end="")
def _make_async_submitter(self, jobs, num_processes, output, verbose):
config = copy.copy(self._base_config)
config["jobs"] = jobs
suffix = f"_batch_{self._batch_index}"
self._batch_index += 1
new_config_file = self._config_file.replace(".json", f"{suffix}.json")
dump_data(config, new_config_file, cls=ExtendedJSONEncoder)
logger.info("Created split config file %s with %s jobs",
new_config_file, len(config["jobs"]))
run_script = os.path.join(output, f"run{suffix}.sh")
self._create_run_script(new_config_file, run_script, num_processes,
output, verbose)
hpc_mgr = HpcManager(self._hpc_config_file, output)
name = self._name + suffix
return AsyncHpcSubmitter(hpc_mgr, run_script, name, output)
def create_hpc_manager(cluster, config):
os.environ["NREL_CLUSTER"] = cluster
mgr = None
try:
hpc_file = "test-hpc-config.toml"
dump_data(config, hpc_file)
mgr = HpcManager(hpc_file, OUTPUT_DIR)
finally:
os.remove(hpc_file)
if cluster == "eagle":
assert mgr.hpc_type == HpcType.SLURM
elif cluster == "peregrine":
assert mgr.hpc_type == HpcType.PBS
else:
assert False, "unknown cluster={}".format(cluster)
return mgr
def __init__(self, config: JobConfiguration, config_file, cluster: Cluster,
output):
self._config = config
self._submission_groups = make_submission_group_lookup(
cluster.config.submission_groups)
self._config_file = config_file
self._base_config = config.serialize()
self._batch_index = cluster.job_status.batch_index
self._cluster = cluster
self._hpc_mgr = HpcManager(self._submission_groups, output)
self._output = output
# Limitation: these settings apply to all groups in aggregate.
# This could be made more flexible if needed.
group = next(iter(self._submission_groups.values()))
self._max_nodes = group.submitter_params.max_nodes
if self._max_nodes is None:
self._max_nodes = sys.maxsize
self._poll_interval = group.submitter_params.poll_interval
self._status_collector = HpcStatusCollector(self._hpc_mgr,
self._poll_interval)
def __init__(
self,
config_file,
output,
batch_id=0,
):
super(JobRunner, self).__init__(config_file, output)
self._handle_submission_groups()
group = self.config.get_default_submission_group()
config = group.submitter_params.hpc_config
self._intf = HpcManager.create_hpc_interface(config)
self._node_id = self._intf.get_node_id()
self._intf_type = config.hpc_type
self._batch_id = batch_id
self._event_filename = os.path.join(
output,
f"run_jobs_batch_{batch_id}_{self._node_id}_events.log",
)
self._event_logger = None
logger.debug("Constructed JobRunner output=%s batch=%s", output,
batch_id)
def submit_jobs(self, cluster, force_local=False):
"""Submit simulations. Auto-detect whether the current system is an HPC
and submit to its queue. Otherwise, run locally.
Parameters
----------
cluster : Cluster
force_local : bool
If on HPC, run jobs through subprocess as if local.
Returns
-------
Status
"""
if self._is_new:
logger.info("Submit %s jobs for execution.",
self._config.get_num_jobs())
logger.info("JADE version %s", jade.version.__version__)
registry = Registry()
loggers = registry.list_loggers()
logger.info("Registered modules for logging: %s",
", ".join(loggers))
self._save_repository_info(registry)
ResultsAggregator.create(self._output)
# If an events summary file exists, it is invalid.
events_file = os.path.join(self._output, EVENTS_FILENAME)
if os.path.exists(events_file):
os.remove(events_file)
event = StructuredLogEvent(
source="submitter",
category=EVENT_CATEGORY_RESOURCE_UTIL,
name=EVENT_NAME_SUBMIT_COMPLETED,
message="job submission started",
num_jobs=self.get_num_jobs(),
)
log_event(event)
os.environ["JADE_RUNTIME_OUTPUT"] = self._output
if self._config.setup_command is not None:
cmd = f"JADE_RUNTIME_OUTPUT={self._output} {self._config.setup_command}"
logger.info("Running setup command: %s", cmd)
check_run_command(self._config.setup_command)
else:
self._handle_submission_groups()
result = Status.IN_PROGRESS
group = self._config.get_default_submission_group()
groups = make_submission_group_lookup(cluster.config.submission_groups)
self._hpc = HpcManager(groups, self._output)
if self._hpc.hpc_type == HpcType.LOCAL or force_local:
runner = JobRunner(self._config_file, output=self._output)
num_processes = group.submitter_params.num_processes
verbose = group.submitter_params.verbose
result = runner.run_jobs(verbose=verbose,
num_processes=num_processes)
agg = ResultsAggregator.load(self._output)
agg.process_results()
is_complete = True
else:
is_complete = self._submit_to_hpc(cluster)
if is_complete:
result = self._handle_completion(cluster)
return result
def test_create_slurm_invalid_file(hpc_fixture):
os.environ["NREL_CLUSTER"] = "eagle"
with pytest.raises(FileNotFoundError):
HpcManager("invalid_filename", OUTPUT_DIR)
def submit_jobs(self,
name="job",
per_node_batch_size=DEFAULTS["per_node_batch_size"],
max_nodes=DEFAULTS["max_nodes"],
force_local=False,
verbose=False,
poll_interval=DEFAULTS["poll_interval"],
num_processes=None,
previous_results=None,
reports=True,
try_add_blocked_jobs=False):
"""Submit simulations. Auto-detect whether the current system is an HPC
and submit to its queue. Otherwise, run locally.
Parameters
----------
name : str
batch name, applies to HPC job submission only
per_node_batch_size : int
Number of jobs to run on one node in one batch.
max_nodes : int
Max number of node submission requests to make in parallel.
force_local : bool
If on HPC, run jobs through subprocess as if local.
wait : bool
Don't return until HPC jobs have finished.
verbose : bool
Enable debug logging.
poll_interval : int
Inteval in seconds on which to poll jobs.
num_processes : int
Number of processes to run in parallel; defaults to num CPUs
Returns
-------
Status
"""
logger.info("Submit %s jobs for execution.",
self._config.get_num_jobs())
logger.info("JADE version %s", jade.version.__version__)
registry = Registry()
loggers = registry.list_loggers()
logger.info("Registered modules for logging: %s", ", ".join(loggers))
self._save_repository_info(registry)
self._config.check_job_dependencies()
self._hpc = HpcManager(self._hpc_config_file, self._output)
result = Status.GOOD
# If an events summary file exists, it is invalid.
events_file = os.path.join(self._output, EVENTS_FILENAME)
if os.path.exists(events_file):
os.remove(events_file)
start_time = time.time()
if self._hpc.hpc_type == HpcType.LOCAL or force_local:
runner = JobRunner(self._config_file, output=self._output)
result = runner.run_jobs(verbose=verbose,
num_processes=num_processes)
else:
self._submit_to_hpc(name, max_nodes, per_node_batch_size, verbose,
poll_interval, num_processes,
try_add_blocked_jobs)
results_summary = ResultsAggregatorSummary(self._results_dir)
self._results = results_summary.get_results()
if len(self._results) != self._config.get_num_jobs():
logger.error(
"Number of results doesn't match number of jobs: "
"results=%s jobs=%s. Check for process crashes "
"or HPC timeouts.", len(self._results),
self._config.get_num_jobs())
result = Status.ERROR
if previous_results:
self._results += previous_results
self.write_results(RESULTS_FILE)
results_summary.delete_files()
shutil.rmtree(self._results_dir)
self._log_error_log_messages(self._output)
bytes_consumed = get_directory_size_bytes(self._output,
recursive=False)
event = StructuredLogEvent(
source="submitter",
category=EVENT_CATEGORY_RESOURCE_UTIL,
name=EVENT_NAME_BYTES_CONSUMED,
message="main output directory size",
bytes_consumed=bytes_consumed,
)
log_event(event)
event = StructuredLogEvent(
source="submitter",
category=EVENT_CATEGORY_RESOURCE_UTIL,
name=EVENT_NAME_CONFIG_EXEC_SUMMARY,
message="config execution summary",
config_execution_time=time.time() - start_time,
num_jobs=self.get_num_jobs(),
)
log_event(event)
if reports:
self.generate_reports(self._output)
return result