def _w(args):
log.info("Registry {r}".format(r=registry))
log.info(registry.to_summary())
log.info("Emitting TCs to {i}".format(i=args.output_dir))
tcs = registry.rtc_runners.keys()
for tc in tcs:
output_file = os.path.join(args.output_dir, tc.task.task_id + "_tool_contract.json")
write_tool_contract(tc, output_file)
return 0
def _w(args):
log.info("Registry {r}".format(r=registry))
log.info(registry.to_summary())
log.info("Emitting TCs to {i}".format(i=args.output_dir))
tcs = registry.rtc_runners.keys()
for tc in tcs:
output_file = os.path.join(args.output_dir, tc.task.task_id + "_tool_contract.json")
write_tool_contract(tc, output_file)
return 0
def __exe_workflow(global_registry, ep_d, bg, task_opts, workflow_opts, output_dir,
workers, shutdown_event, service_uri_or_none):
"""
Core runner of a workflow.
:type bg: BindingsGraph
:type workflow_opts: WorkflowLevelOptions
:type output_dir: str
:type service_uri_or_none: str | None
:param workers: {taskid:Worker}
:return:
The function is doing way too much. This is really terrible.
"""
job_id = random.randint(100000, 999999)
started_at = time.time()
m_ = "Distributed" if workflow_opts.distributed_mode is not None else "Local"
slog.info("starting to execute {m} workflow with assigned job_id {i}".format(i=job_id, m=m_))
log.info("exe'ing workflow Cluster renderer {c}".format(c=global_registry.cluster_renderer))
slog.info("Service URI: {i} {t}".format(i=service_uri_or_none, t=type(service_uri_or_none)))
slog.info("Max number of Chunks {n} ".format(n=workflow_opts.max_nchunks))
slog.info("Max number of nproc {n}".format(n=workflow_opts.max_nproc))
slog.info("Max number of workers {n}".format(n=workflow_opts.max_nworkers))
# Setup logger, job directory and initialize DS
slog.info("creating job resources in {o}".format(o=output_dir))
job_resources, ds = DU.job_resource_create_and_setup_logs(output_dir, bg, task_opts, workflow_opts, ep_d)
slog.info("successfully created job resources.")
# Some Pre-flight checks
# Help initialize graph/epoints
B.resolve_entry_points(bg, ep_d)
# Update Task-esque EntryBindingPoint
B.resolve_entry_binding_points(bg)
# initialization File node attributes
for eid, path in ep_d.iteritems():
B.resolve_entry_point(bg, eid, path)
B.resolve_successor_binding_file_path(bg)
# Mark Chunkable tasks
B.label_chunkable_tasks(bg, global_registry.chunk_operators)
slog.info("validating binding graph")
# Check the degree of the nodes
B.validate_binding_graph_integrity(bg)
slog.info("successfully validated binding graph.")
# Add scattered
# This will add new nodes to the graph if necessary
B.apply_chunk_operator(bg, global_registry.chunk_operators, global_registry.tasks, workflow_opts.max_nchunks)
# log.info(BU.to_binding_graph_summary(bg))
# "global" file type id counter {str: int} that will be
# used to generate ids
file_type_id_to_count = {file_type.file_type_id: 0 for _, file_type in global_registry.file_types.iteritems()}
# Create a closure to allow file types to assign unique id
# this returns a func
to_resolve_files_func = B.to_resolve_files(file_type_id_to_count)
# Local vars
max_total_nproc = workflow_opts.total_max_nproc
max_nworkers = workflow_opts.max_nworkers
max_nproc = workflow_opts.max_nproc
max_nchunks = workflow_opts.max_nchunks
tmp_dir = workflow_opts.tmp_dir
q_out = multiprocessing.Queue()
worker_sleep_time = 1
# To store all the reports that are displayed in the analysis.html
# {id:task-id, report_path:path/to/report.json}
analysis_file_links = []
# Flag for pipeline execution failure
has_failed = False
# Time to sleep between each step the execution loop
# after the first 1 minute of exe, update the sleep time to 2 sec
sleep_time = 1
# Running total of current number of slots/cpu's used
total_nproc = 0
# Define a bunch of util funcs to try to make the main driver while loop
# more understandable. Not the greatest model.
def _to_run_time():
return time.time() - started_at
def write_analysis_report(analysis_file_links_):
analysis_report_html = os.path.join(job_resources.html, 'analysis.html')
R.write_analysis_link_report(analysis_file_links_, analysis_report_html)
def update_analysis_file_links(task_id_, report_path_):
analysis_link = AnalysisLink(task_id_, report_path_)
log.info("updating report analysis file links {a}".format(a=analysis_link))
analysis_file_links.append(analysis_link)
write_analysis_report(analysis_file_links)
# factories for getting a Worker instance
# utils for getting the running func and worker type
def _to_worker(w_is_distributed, wid, task_id, manifest_path_):
# the IO loading will forceful set this to None
# if the cluster manager not defined or cluster_mode is False
if global_registry.cluster_renderer is None or not w_is_distributed:
r_func = T.run_task_manifest
else:
r_func = T.run_task_manifest_on_cluster
return TaskManifestWorker(q_out, shutdown_event, worker_sleep_time,
r_func, task_id, manifest_path_, name=wid)
# Define a bunch of util funcs to try to make the main driver while loop
# more understandable. Not the greatest model.
def write_report_(bg_, current_state_, was_successful_):
return DU.write_main_workflow_report(job_id, job_resources, workflow_opts,
task_opts, bg_, current_state_, was_successful_, _to_run_time())
def write_task_summary_report(bg_):
task_summary_report = DU.to_task_summary_report(bg_)
p = os.path.join(job_resources.html, 'task_summary.html')
R.write_report_to_html(task_summary_report, p)
def services_log_update_progress(source_id_, level_, message_):
if service_uri_or_none is not None:
total_log_uri = "{u}/log".format(u=service_uri_or_none)
WS.log_pbsmrtpipe_progress(total_log_uri, message_, level_, source_id_, ignore_errors=True)
def services_add_datastore_file(datastore_file_):
if service_uri_or_none is not None:
total_ds_uri = "{u}/datastore".format(u=service_uri_or_none)
WS.add_datastore_file(total_ds_uri, datastore_file_, ignore_errors=True)
def _update_analysis_reports_and_datastore(tnode_, task_):
for file_type_, path_ in zip(tnode_.meta_task.output_types, task_.output_files):
source_id = "{t}-{f}".format(t=task_.task_id, f=file_type_.file_type_id)
ds_uuid = _get_dataset_uuid_or_create_uuid(path_)
ds_file_ = DataStoreFile(ds_uuid, source_id, file_type_.file_type_id, path_)
ds.add(ds_file_)
ds.write_update_json(job_resources.datastore_json)
# Update Services
services_add_datastore_file(ds_file_)
dsr = DU.datastore_to_report(ds)
R.write_report_to_html(dsr, os.path.join(job_resources.html, 'datastore.html'))
if file_type_ == FileTypes.REPORT:
T.write_task_report(job_resources, task_.task_id, path_, DU._get_images_in_dir(task_.output_dir))
update_analysis_file_links(tnode_.idx, path_)
def _log_task_failure_and_call_services(path_to_stderr, task_id_):
"""log the error messages extracted from stderr"""
lines = _get_last_lines_of_stderr(20, path_to_stderr)
for line_ in lines:
log.error(line_.strip())
# these already have newlines
sys.stderr.write(line_)
sys.stderr.write("\n")
services_log_update_progress("pbsmrtpipe::{i}".format(i=task_id_), WS.LogLevels.ERROR, "\n".join(lines))
def has_available_slots(n):
if max_total_nproc is None:
return True
return total_nproc + n <= max_total_nproc
# Misc setup
write_report_(bg, TaskStates.CREATED, False)
# write empty analysis reports
write_analysis_report(analysis_file_links)
BU.write_binding_graph_images(bg, job_resources.workflow)
# For book-keeping
# task id -> tnode
tid_to_tnode = {}
# tnode -> Task instance
tnode_to_task = {}
# local loop for adjusting sleep time, this will get reset after each new
# task is created
niterations = 0
dt_ramp = 0.25
# number of iterations before switching to steady state sleep
stead_state_n = 50
# sleep for 5 sec
dt_stead_state = 4
try:
log.debug("Starting execution loop... in process {p}".format(p=os.getpid()))
# After the initial startup, bump up the time to reduce resource usage
# (since multiple instances will be launched from the services)
niterations += 1
if niterations < stead_state_n:
sleep_time = dt_ramp
else:
sleep_time = dt_stead_state
while True:
# Convert Task -> ScatterAble task (emits a Chunk.json file)
B.apply_scatterable(bg, global_registry.chunk_operators, global_registry.tasks)
# This will add new TaskBinding nodes to the graph if necessary
B.apply_chunk_operator(bg, global_registry.chunk_operators, global_registry.tasks, max_nchunks)
# B.write_binding_graph_images(bg, job_resources.workflow)
# If a TaskScatteredBindingNode is completed successfully and
# output chunk.json is resolved, read in the file and
# generate the new chunked tasks. This mutates the graph
# significantly.
B.add_gather_to_completed_task_chunks(bg, global_registry.chunk_operators, global_registry.tasks, job_resources.tasks)
if not _are_workers_alive(workers):
for tix_, w_ in workers.iteritems():
if not w_.is_alive():
log.warn("Worker {i} (pid {p}) is not alive for task {x}. Worker exit code {e}.".format(i=w_.name, p=w_.pid, e=w_.exitcode, x=tix_))
#w_.terminate()
# Check if Any tasks are running or that there still runnable tasks
is_completed = bg.is_workflow_complete()
has_task_running = B.has_running_task(bg)
if not has_task_running:
if not is_completed:
if not B.has_task_in_states(bg, TaskStates.RUNNABLE_STATES()):
if not B.has_next_runnable_task(bg):
msg = "Unable to find runnable task or any tasks running and workflow is NOT completed."
log.error(msg)
log.error(BU.to_binding_graph_summary(bg))
services_log_update_progress("pbsmrtpipe", WS.LogLevels.ERROR, msg)
raise PipelineRuntimeError(msg)
time.sleep(sleep_time)
# log.debug("Sleeping for {s}".format(s=sleep_time))
log.debug("\n" + BU.to_binding_graph_summary(bg))
# BU.to_binding_graph_task_summary(bg)
# This should only be triggered after events. The main reason
# to keep updating it was the html report is up to date with the
# runtime
write_report_(bg, TaskStates.RUNNING, is_completed)
if is_completed:
msg_ = "Workflow is completed. breaking out."
log.info(msg_)
services_log_update_progress("pbsmrtpipe", WS.LogLevels.INFO, msg_)
break
try:
result = q_out.get_nowait()
except Queue.Empty:
result = None
# log.info("Results {r}".format(r=result))
if isinstance(result, TaskResult):
niterations = 0
log.debug("Task result {r}".format(r=result))
tid_, state_, msg_, run_time_ = result
tnode_ = tid_to_tnode[tid_]
task_ = tnode_to_task[tnode_]
# Process Successful Task Result
if state_ == TaskStates.SUCCESSFUL:
msg_ = "Task was successful {r}".format(r=result)
slog.info(msg_)
# this will raise if a task output is failed to be resolved
B.validate_outputs_and_update_task_to_success(bg, tnode_, run_time_, bg.node[tnode_]['task'].output_files)
slog.info("Successfully validated outputs of {t}".format(t=repr(tnode_)))
services_log_update_progress("pbsmrtpipe::{i}".format(i=tid_), WS.LogLevels.INFO, msg_)
B.update_task_output_file_nodes(bg, tnode_, tnode_to_task[tnode_])
B.resolve_successor_binding_file_path(bg)
total_nproc -= task_.nproc
w_ = workers.pop(tid_)
_terminate_worker(w_)
# Update Analysis Reports and Register output files to Datastore
_update_analysis_reports_and_datastore(tnode_, task_)
BU.write_binding_graph_images(bg, job_resources.workflow)
else:
# Process Non-Successful Task Result
B.update_task_state(bg, tnode_, state_)
slog.error(result.error_message)
log.error(result.error_message + "\n")
sys.stderr.write(result.error_message + "\n")
_log_task_failure_and_call_services(result.error_message, tid_)
# let the remaining running jobs continue
w_ = workers.pop(tid_)
_terminate_worker(w_)
total_nproc -= task_.nproc
has_failed = True
BU.write_binding_graph_images(bg, job_resources.workflow)
_update_msg = _status(bg)
log.info(_update_msg)
slog.info(_update_msg)
s_ = TaskStates.FAILED if has_failed else TaskStates.RUNNING
write_report_(bg, s_, False)
write_task_summary_report(bg)
elif isinstance(result, types.NoneType):
pass
else:
log.error("Unexpected queue result type {t} {r}".format(t=type(result), r=result))
if has_failed:
log.error("job has failed. breaking out.")
# Just kill everything
break
# Computational resources are tapped
if len(workers) >= max_nworkers:
# don't do anything
continue
tnode = B.get_next_runnable_task(bg)
if tnode is None:
continue
elif isinstance(tnode, TaskBindingNode):
niterations = 0
# Found a Runnable Task
# base task_id-instance_id
tid = '-'.join([tnode.meta_task.task_id, str(tnode.instance_id)])
task_dir = os.path.join(job_resources.tasks, tid)
if not os.path.exists(task_dir):
os.mkdir(task_dir)
to_resources_func = B.to_resolve_di_resources(task_dir, root_tmp_dir=workflow_opts.tmp_dir)
input_files = B.get_task_input_files(bg, tnode)
# convert metatask -> task
try:
task = GX.meta_task_to_task(tnode.meta_task, input_files, task_opts, task_dir, max_nproc, max_nchunks,
to_resources_func, to_resolve_files_func)
except Exception as e:
slog.error("Failed to convert metatask {i} to task. {m}".format(i=tnode.meta_task.task_id, m=e.message))
raise
# log.debug(task)
bg.node[tnode]['nproc'] = task.nproc
if not has_available_slots(task.nproc):
# not enough slots to run in
continue
bg.node[tnode]['task'] = task
tnode_to_task[tnode] = task
if isinstance(tnode.meta_task, (ToolContractMetaTask, ScatterToolContractMetaTask, GatherToolContractMetaTask)):
# the task.options have actually already been resolved here, but using this other
# code path for clarity
if isinstance(tnode.meta_task, ToolContractMetaTask):
rtc = IO.static_meta_task_to_rtc(tnode.meta_task, task, task_opts, task_dir, tmp_dir, max_nproc)
elif isinstance(tnode.meta_task, ScatterToolContractMetaTask):
rtc = IO.static_scatter_meta_task_to_rtc(tnode.meta_task, task, task_opts, task_dir, tmp_dir, max_nproc, max_nchunks, tnode.meta_task.chunk_keys)
elif isinstance(tnode.meta_task, GatherToolContractMetaTask):
# this should always be a TaskGatherBindingNode which will have a .chunk_key
rtc = IO.static_gather_meta_task_to_rtc(tnode.meta_task, task, task_opts, task_dir, tmp_dir, max_nproc, tnode.chunk_key)
else:
raise TypeError("Unsupported task type {t}".format(t=tnode.meta_task))
# write driver manifest, which calls the resolved-tool-contract.json
# there's too many layers of indirection here. Partly due to the pre-tool-contract era
# python defined tasks.
# Always write the RTC json for debugging purposes
tc_path = os.path.join(task_dir, GlobalConstants.TOOL_CONTRACT_JSON)
write_tool_contract(tnode.meta_task.tool_contract, tc_path)
rtc_json_path = os.path.join(task_dir, GlobalConstants.RESOLVED_TOOL_CONTRACT_JSON)
rtc_avro_path = os.path.join(task_dir, GlobalConstants.RESOLVED_TOOL_CONTRACT_AVRO)
if rtc.driver.serialization == 'avro':
# hack to fix command
task.cmds[0] = task.cmds[0].replace('.json', '.avro')
write_resolved_tool_contract_avro(rtc, rtc_avro_path)
# for debugging
write_resolved_tool_contract(rtc, rtc_json_path)
runnable_task_path = os.path.join(task_dir, GlobalConstants.RUNNABLE_TASK_JSON)
runnable_task = RunnableTask(task, global_registry.cluster_renderer)
runnable_task.write_json(runnable_task_path)
# Create an instance of Worker
w = _to_worker(tnode.meta_task.is_distributed, "worker-task-{i}".format(i=tid), tid, runnable_task_path)
workers[tid] = w
w.start()
total_nproc += task.nproc
slog.info("Starting worker {i} ({n} workers running, {m} total proc in use)".format(i=tid, n=len(workers), m=total_nproc))
# Submit job to be run.
B.update_task_state(bg, tnode, TaskStates.SUBMITTED)
msg_ = "Updating task {t} to SUBMITTED".format(t=tid)
log.debug(msg_)
tid_to_tnode[tid] = tnode
services_log_update_progress("pbsmrtpipe::{i}".format(i=tnode.idx), WS.LogLevels.INFO, msg_)
BU.write_binding_graph_images(bg, job_resources.workflow)
elif isinstance(tnode, EntryOutBindingFileNode):
# Handle EntryPoint types. This is not a particularly elegant design :(
bg.node[tnode]['nproc'] = 1
log.info("Marking task as completed {t}".format(t=tnode))
B.update_task_state_to_success(bg, tnode, 0.0)
# Update output paths
mock_file_index = 0
for fnode in bg.successors(tnode):
file_path = "/path/to/mock-file-{i}.txt".format(i=mock_file_index)
B.update_file_state_to_resolved(bg, fnode, file_path)
mock_file_index += 1
else:
raise TypeError("Unsupported node type {t} of '{x}'".format(t=type(tnode), x=tnode))
# Update state of any files
B.resolve_successor_binding_file_path(bg)
# Final call to write empty analysis reports
write_analysis_report(analysis_file_links)
# end of while loop
_terminate_all_workers(workers.values(), shutdown_event)
if has_failed:
log.debug("\n" + BU.to_binding_graph_summary(bg))
was_successful = B.was_workflow_successful(bg)
s_ = TaskStates.SUCCESSFUL if was_successful else TaskStates.FAILED
write_report_(bg, s_, was_successful)
except PipelineRuntimeKeyboardInterrupt:
write_report_(bg, TaskStates.KILLED, False)
write_task_summary_report(bg)
BU.write_binding_graph_images(bg, job_resources.workflow)
was_successful = False
except Exception as e:
slog.error("Unexpected error {e}. Writing reports and shutting down".format(e=e.message))
# update workflow reports to failed
write_report_(bg, TaskStates.FAILED, False)
write_task_summary_report(bg)
services_log_update_progress("pbsmrtpipe", WS.LogLevels.ERROR, "Error {e}".format(e=e))
BU.write_binding_graph_images(bg, job_resources.workflow)
raise
finally:
write_task_summary_report(bg)
BU.write_binding_graph_images(bg, job_resources.workflow)
return True if was_successful else False
def __exe_workflow(global_registry, ep_d, bg, task_opts, workflow_opts, output_dir,
workers, shutdown_event, service_uri_or_none):
"""
Core runner of a workflow.
:type bg: BindingsGraph
:type workflow_opts: WorkflowLevelOptions
:type output_dir: str
:type service_uri_or_none: str | None
:param workers: {taskid:Worker}
:return:
The function is doing way too much. This is really terrible.
"""
job_id = random.randint(100000, 999999)
started_at = time.time()
m_ = "Distributed" if workflow_opts.distributed_mode is not None else "Local"
slog.info("starting to execute {m} workflow with assigned job_id {i}".format(i=job_id, m=m_))
log.info("exe'ing workflow Cluster renderer {c}".format(c=global_registry.cluster_renderer))
slog.info("Service URI: {i} {t}".format(i=service_uri_or_none, t=type(service_uri_or_none)))
slog.info("Max number of Chunks {n} ".format(n=workflow_opts.max_nchunks))
slog.info("Max number of nproc {n}".format(n=workflow_opts.max_nproc))
slog.info("Max number of workers {n}".format(n=workflow_opts.max_nworkers))
# Setup logger, job directory and initialize DS
slog.info("creating job resources in {o}".format(o=output_dir))
job_resources, ds = DU.job_resource_create_and_setup_logs(output_dir, bg, task_opts, workflow_opts, ep_d)
slog.info("successfully created job resources.")
# Some Pre-flight checks
# Help initialize graph/epoints
B.resolve_entry_points(bg, ep_d)
# Update Task-esque EntryBindingPoint
B.resolve_entry_binding_points(bg)
# initialization File node attributes
for eid, path in ep_d.iteritems():
B.resolve_entry_point(bg, eid, path)
B.resolve_successor_binding_file_path(bg)
# Mark Chunkable tasks
B.label_chunkable_tasks(bg, global_registry.chunk_operators)
slog.info("validating binding graph")
# Check the degree of the nodes
B.validate_binding_graph_integrity(bg)
slog.info("successfully validated binding graph.")
# Add scattered
# This will add new nodes to the graph if necessary
B.apply_chunk_operator(bg, global_registry.chunk_operators, global_registry.tasks, workflow_opts.max_nchunks)
# log.info(BU.to_binding_graph_summary(bg))
# "global" file type id counter {str: int} that will be
# used to generate ids
file_type_id_to_count = {file_type.file_type_id: 0 for _, file_type in global_registry.file_types.iteritems()}
# Create a closure to allow file types to assign unique id
# this returns a func
to_resolve_files_func = B.to_resolve_files(file_type_id_to_count)
# Local vars
max_total_nproc = workflow_opts.total_max_nproc
max_nworkers = workflow_opts.max_nworkers
max_nproc = workflow_opts.max_nproc
max_nchunks = workflow_opts.max_nchunks
q_out = multiprocessing.Queue()
worker_sleep_time = 1
# To store all the reports that are displayed in the analysis.html
# {id:task-id, report_path:path/to/report.json}
analysis_file_links = []
# Flag for pipeline execution failure
has_failed = False
# Time to sleep between each step the execution loop
# after the first 1 minute of exe, update the sleep time to 2 sec
sleep_time = 1
# Running total of current number of slots/cpu's used
total_nproc = 0
# Define a bunch of util funcs to try to make the main driver while loop
# more understandable. Not the greatest model.
def _to_run_time():
return time.time() - started_at
def write_analysis_report(analysis_file_links_):
analysis_report_html = os.path.join(job_resources.html, 'analysis.html')
R.write_analysis_link_report(analysis_file_links_, analysis_report_html)
def update_analysis_file_links(task_id_, report_path_):
analysis_link = AnalysisLink(task_id_, report_path_)
log.info("updating report analysis file links {a}".format(a=analysis_link))
analysis_file_links.append(analysis_link)
write_analysis_report(analysis_file_links)
# factories for getting a Worker instance
# utils for getting the running func and worker type
def _to_worker(w_is_distributed, wid, task_id, manifest_path_):
# the IO loading will forceful set this to None
# if the cluster manager not defined or cluster_mode is False
if global_registry.cluster_renderer is None or not w_is_distributed:
r_func = T.run_task_manifest
else:
r_func = T.run_task_manifest_on_cluster
return TaskManifestWorker(q_out, shutdown_event, worker_sleep_time,
r_func, task_id, manifest_path_, name=wid)
# Define a bunch of util funcs to try to make the main driver while loop
# more understandable. Not the greatest model.
def write_report_(bg_, current_state_, was_successful_):
return DU.write_main_workflow_report(job_id, job_resources, workflow_opts,
task_opts, bg_, current_state_, was_successful_, _to_run_time())
def write_task_summary_report(bg_):
task_summary_report = DU.to_task_summary_report(bg_)
p = os.path.join(job_resources.html, 'task_summary.html')
R.write_report_to_html(task_summary_report, p)
def services_log_update_progress(source_id_, level_, message_):
if service_uri_or_none is not None:
total_log_uri = "{u}/log".format(u=service_uri_or_none)
WS.log_pbsmrtpipe_progress(total_log_uri, message_, level_, source_id_, ignore_errors=True)
def services_add_datastore_file(datastore_file_):
if service_uri_or_none is not None:
total_ds_uri = "{u}/datastore".format(u=service_uri_or_none)
WS.add_datastore_file(total_ds_uri, datastore_file_, ignore_errors=True)
def _update_analysis_reports_and_datastore(tnode_, task_):
for file_type_, path_ in zip(tnode_.meta_task.output_types, task_.output_files):
source_id = "{t}-{f}".format(t=task_.task_id, f=file_type_.file_type_id)
ds_uuid = _get_dataset_uuid_or_create_uuid(path_)
ds_file_ = DataStoreFile(ds_uuid, source_id, file_type_.file_type_id, path_)
ds.add(ds_file_)
ds.write_update_json(job_resources.datastore_json)
# Update Services
services_add_datastore_file(ds_file_)
dsr = DU.datastore_to_report(ds)
R.write_report_to_html(dsr, os.path.join(job_resources.html, 'datastore.html'))
if file_type_ == FileTypes.REPORT:
T.write_task_report(job_resources, task_.task_id, path_, DU._get_images_in_dir(task_.output_dir))
update_analysis_file_links(tnode_.idx, path_)
def _log_task_failure_and_call_services(path_to_stderr, task_id_):
"""log the error messages extracted from stderr"""
lines = _get_last_lines_of_stderr(20, path_to_stderr)
for line_ in lines:
log.error(line_.strip())
# these already have newlines
sys.stderr.write(line_)
sys.stderr.write("\n")
services_log_update_progress("pbsmrtpipe::{i}".format(i=task_id_), WS.LogLevels.ERROR, "\n".join(lines))
def has_available_slots(n):
if max_total_nproc is None:
return True
return total_nproc + n <= max_total_nproc
# Misc setup
write_report_(bg, TaskStates.CREATED, False)
# write empty analysis reports
write_analysis_report(analysis_file_links)
BU.write_binding_graph_images(bg, job_resources.workflow)
# For book-keeping
# task id -> tnode
tid_to_tnode = {}
# tnode -> Task instance
tnode_to_task = {}
# local loop for adjusting sleep time, this will get reset after each new
# task is created
niterations = 0
dt_ramp = 0.25
# number of iterations before switching to steady state sleep
stead_state_n = 50
# sleep for 5 sec
dt_stead_state = 4
try:
log.debug("Starting execution loop... in process {p}".format(p=os.getpid()))
# After the initial startup, bump up the time to reduce resource usage
# (since multiple instances will be launched from the services)
niterations += 1
if niterations < stead_state_n:
sleep_time = dt_ramp
else:
sleep_time = dt_stead_state
while True:
# Convert Task -> ScatterAble task (emits a Chunk.json file)
B.apply_scatterable(bg, global_registry.chunk_operators, global_registry.tasks)
# This will add new TaskBinding nodes to the graph if necessary
B.apply_chunk_operator(bg, global_registry.chunk_operators, global_registry.tasks, max_nchunks)
# B.write_binding_graph_images(bg, job_resources.workflow)
# If a TaskScatteredBindingNode is completed successfully and
# output chunk.json is resolved, read in the file and
# generate the new chunked tasks. This mutates the graph
# significantly.
B.add_gather_to_completed_task_chunks(bg, global_registry.chunk_operators, global_registry.tasks, job_resources.tasks)
if not _are_workers_alive(workers):
for tix_, w_ in workers.iteritems():
if not w_.is_alive():
log.warn("Worker {i} (pid {p}) is not alive for task {x}. Worker exit code {e}.".format(i=w_.name, p=w_.pid, e=w_.exitcode, x=tix_))
#w_.terminate()
# Check if Any tasks are running or that there still runnable tasks
is_completed = bg.is_workflow_complete()
has_task_running = B.has_running_task(bg)
if not has_task_running:
if not is_completed:
if not B.has_task_in_states(bg, TaskStates.RUNNABLE_STATES()):
if not B.has_next_runnable_task(bg):
msg = "Unable to find runnable task or any tasks running and workflow is NOT completed."
log.error(msg)
log.error(BU.to_binding_graph_summary(bg))
services_log_update_progress("pbsmrtpipe", WS.LogLevels.ERROR, msg)
raise PipelineRuntimeError(msg)
time.sleep(sleep_time)
# log.debug("Sleeping for {s}".format(s=sleep_time))
log.debug("\n" + BU.to_binding_graph_summary(bg))
# BU.to_binding_graph_task_summary(bg)
# This should only be triggered after events. The main reason
# to keep updating it was the html report is up to date with the
# runtime
write_report_(bg, TaskStates.RUNNING, is_completed)
if is_completed:
msg_ = "Workflow is completed. breaking out."
log.info(msg_)
services_log_update_progress("pbsmrtpipe", WS.LogLevels.INFO, msg_)
break
try:
result = q_out.get_nowait()
except Queue.Empty:
result = None
# log.info("Results {r}".format(r=result))
if isinstance(result, TaskResult):
niterations = 0
log.debug("Task result {r}".format(r=result))
tid_, state_, msg_, run_time_ = result
tnode_ = tid_to_tnode[tid_]
task_ = tnode_to_task[tnode_]
# Process Successful Task Result
if state_ == TaskStates.SUCCESSFUL:
msg_ = "Task was successful {r}".format(r=result)
slog.info(msg_)
# this will raise if a task output is failed to be resolved
B.validate_outputs_and_update_task_to_success(bg, tnode_, run_time_, bg.node[tnode_]['task'].output_files)
slog.info("Successfully validated outputs of {t}".format(t=repr(tnode_)))
services_log_update_progress("pbsmrtpipe::{i}".format(i=tid_), WS.LogLevels.INFO, msg_)
B.update_task_output_file_nodes(bg, tnode_, tnode_to_task[tnode_])
B.resolve_successor_binding_file_path(bg)
total_nproc -= task_.nproc
w_ = workers.pop(tid_)
_terminate_worker(w_)
# Update Analysis Reports and Register output files to Datastore
_update_analysis_reports_and_datastore(tnode_, task_)
BU.write_binding_graph_images(bg, job_resources.workflow)
else:
# Process Non-Successful Task Result
B.update_task_state(bg, tnode_, state_)
msg_ = "Task was not successful {r}".format(r=result)
slog.error(msg_)
log.error(msg_ + "\n")
sys.stderr.write(msg_ + "\n")
stderr_ = os.path.join(task_.output_dir, "stderr")
_log_task_failure_and_call_services(stderr_, tid_)
# let the remaining running jobs continue
w_ = workers.pop(tid_)
_terminate_worker(w_)
total_nproc -= task_.nproc
has_failed = True
BU.write_binding_graph_images(bg, job_resources.workflow)
_update_msg = _status(bg)
log.info(_update_msg)
slog.info(_update_msg)
s_ = TaskStates.FAILED if has_failed else TaskStates.RUNNING
write_report_(bg, s_, False)
write_task_summary_report(bg)
elif isinstance(result, types.NoneType):
pass
else:
log.error("Unexpected queue result type {t} {r}".format(t=type(result), r=result))
if has_failed:
log.error("job has failed. breaking out.")
# Just kill everything
break
# Computational resources are tapped
if len(workers) >= max_nworkers:
# don't do anything
continue
tnode = B.get_next_runnable_task(bg)
if tnode is None:
continue
elif isinstance(tnode, TaskBindingNode):
niterations = 0
# Found a Runnable Task
# base task_id-instance_id
tid = '-'.join([tnode.meta_task.task_id, str(tnode.instance_id)])
task_dir = os.path.join(job_resources.tasks, tid)
if not os.path.exists(task_dir):
os.mkdir(task_dir)
to_resources_func = B.to_resolve_di_resources(task_dir, root_tmp_dir=workflow_opts.tmp_dir)
input_files = B.get_task_input_files(bg, tnode)
# convert metatask -> task
try:
task = GX.meta_task_to_task(tnode.meta_task, input_files, task_opts, task_dir, max_nproc, max_nchunks,
to_resources_func, to_resolve_files_func)
except Exception as e:
slog.error("Failed to convert metatask {i} to task. {m}".format(i=tnode.meta_task.task_id, m=e.message))
raise
# log.debug(task)
bg.node[tnode]['nproc'] = task.nproc
if not has_available_slots(task.nproc):
# not enough slots to run in
continue
bg.node[tnode]['task'] = task
tnode_to_task[tnode] = task
if isinstance(tnode.meta_task, (ToolContractMetaTask, ScatterToolContractMetaTask, GatherToolContractMetaTask)):
# the task.options have actually already been resolved here, but using this other
# code path for clarity
if isinstance(tnode.meta_task, ToolContractMetaTask):
rtc = IO.static_meta_task_to_rtc(tnode.meta_task, task, task_opts, task_dir, '/tmp', max_nproc)
elif isinstance(tnode.meta_task, ScatterToolContractMetaTask):
rtc = IO.static_scatter_meta_task_to_rtc(tnode.meta_task, task, task_opts, task_dir, '/tmp', max_nproc, max_nchunks, tnode.meta_task.chunk_keys)
elif isinstance(tnode.meta_task, GatherToolContractMetaTask):
# this should always be a TaskGatherBindingNode which will have a .chunk_key
rtc = IO.static_gather_meta_task_to_rtc(tnode.meta_task, task, task_opts, task_dir, '/tmp', max_nproc, tnode.chunk_key)
else:
raise TypeError("Unsupported task type {t}".format(t=tnode.meta_task))
# write driver manifest, which calls the resolved-tool-contract.json
# there's too many layers of indirection here. Partly due to the pre-tool-contract era
# python defined tasks.
# Always write the RTC json for debugging purposes
tc_path = os.path.join(task_dir, GlobalConstants.TOOL_CONTRACT_JSON)
write_tool_contract(tnode.meta_task.tool_contract, tc_path)
rtc_json_path = os.path.join(task_dir, GlobalConstants.RESOLVED_TOOL_CONTRACT_JSON)
rtc_avro_path = os.path.join(task_dir, GlobalConstants.RESOLVED_TOOL_CONTRACT_AVRO)
if rtc.driver.serialization == 'avro':
# hack to fix command
task.cmds[0] = task.cmds[0].replace('.json', '.avro')
write_resolved_tool_contract_avro(rtc, rtc_avro_path)
# for debugging
write_resolved_tool_contract(rtc, rtc_json_path)
runnable_task_path = os.path.join(task_dir, GlobalConstants.RUNNABLE_TASK_JSON)
runnable_task = RunnableTask(task, global_registry.cluster_renderer)
runnable_task.write_json(runnable_task_path)
# Create an instance of Worker
w = _to_worker(tnode.meta_task.is_distributed, "worker-task-{i}".format(i=tid), tid, runnable_task_path)
workers[tid] = w
w.start()
total_nproc += task.nproc
slog.info("Starting worker {i} ({n} workers running, {m} total proc in use)".format(i=tid, n=len(workers), m=total_nproc))
# Submit job to be run.
B.update_task_state(bg, tnode, TaskStates.SUBMITTED)
msg_ = "Updating task {t} to SUBMITTED".format(t=tid)
log.debug(msg_)
tid_to_tnode[tid] = tnode
services_log_update_progress("pbsmrtpipe::{i}".format(i=tnode.idx), WS.LogLevels.INFO, msg_)
BU.write_binding_graph_images(bg, job_resources.workflow)
elif isinstance(tnode, EntryOutBindingFileNode):
# Handle EntryPoint types. This is not a particularly elegant design :(
bg.node[tnode]['nproc'] = 1
log.info("Marking task as completed {t}".format(t=tnode))
B.update_task_state_to_success(bg, tnode, 0.0)
# Update output paths
mock_file_index = 0
for fnode in bg.successors(tnode):
file_path = "/path/to/mock-file-{i}.txt".format(i=mock_file_index)
B.update_file_state_to_resolved(bg, fnode, file_path)
mock_file_index += 1
else:
raise TypeError("Unsupported node type {t} of '{x}'".format(t=type(tnode), x=tnode))
# Update state of any files
B.resolve_successor_binding_file_path(bg)
# Final call to write empty analysis reports
write_analysis_report(analysis_file_links)
# end of while loop
_terminate_all_workers(workers.values(), shutdown_event)
if has_failed:
log.debug("\n" + BU.to_binding_graph_summary(bg))
was_successful = B.was_workflow_successful(bg)
s_ = TaskStates.SUCCESSFUL if was_successful else TaskStates.FAILED
write_report_(bg, s_, was_successful)
except PipelineRuntimeKeyboardInterrupt:
write_report_(bg, TaskStates.KILLED, False)
write_task_summary_report(bg)
BU.write_binding_graph_images(bg, job_resources.workflow)
was_successful = False
except Exception as e:
slog.error("Unexpected error {e}. Writing reports and shutting down".format(e=e.message))
# update workflow reports to failed
write_report_(bg, TaskStates.FAILED, False)
write_task_summary_report(bg)
services_log_update_progress("pbsmrtpipe", WS.LogLevels.ERROR, "Error {e}".format(e=e))
BU.write_binding_graph_images(bg, job_resources.workflow)
raise
finally:
write_task_summary_report(bg)
BU.write_binding_graph_images(bg, job_resources.workflow)
return True if was_successful else False