def build_job(self, session):
"""Produce the Job for this operation.
Return the Job object that has to be sent to Workers to have
them perform the operation this object describes.
session (Session): the database session to use to fetch objects
if necessary.
return (Job): the job encoding of the operation, as understood
by Workers and TaskTypes.
"""
result = None
dataset = Dataset.get_from_id(self.dataset_id, session)
if self.type_ == ESOperation.COMPILATION:
submission = Submission.get_from_id(self.object_id, session)
result = CompilationJob.from_submission(submission, dataset)
elif self.type_ == ESOperation.EVALUATION:
submission = Submission.get_from_id(self.object_id, session)
result = EvaluationJob.from_submission(submission, dataset,
self.testcase_codename)
elif self.type_ == ESOperation.USER_TEST_COMPILATION:
user_test = UserTest.get_from_id(self.object_id, session)
result = CompilationJob.from_user_test(user_test, dataset)
elif self.type_ == ESOperation.USER_TEST_EVALUATION:
user_test = UserTest.get_from_id(self.object_id, session)
result = EvaluationJob.from_user_test(user_test, dataset)
return result
def build_job(self, session):
"""Produce the Job for this operation.
Return the Job object that has to be sent to Workers to have
them perform the operation this object describes.
session (Session): the database session to use to fetch objects
if necessary.
return (Job): the job encoding of the operation, as understood
by Workers and TaskTypes.
"""
result = None
dataset = Dataset.get_from_id(self.dataset_id, session)
if self.type_ == ESOperation.COMPILATION:
submission = Submission.get_from_id(self.object_id, session)
result = CompilationJob.from_submission(submission, dataset)
elif self.type_ == ESOperation.EVALUATION:
submission = Submission.get_from_id(self.object_id, session)
result = EvaluationJob.from_submission(
submission, dataset, self.testcase_codename)
elif self.type_ == ESOperation.USER_TEST_COMPILATION:
user_test = UserTest.get_from_id(self.object_id, session)
result = CompilationJob.from_user_test(user_test, dataset)
elif self.type_ == ESOperation.USER_TEST_EVALUATION:
user_test = UserTest.get_from_id(self.object_id, session)
result = EvaluationJob.from_user_test(user_test, dataset)
return result
def acquire_worker(self, job, side_data=None):
"""Tries to assign a job to an available worker. If no workers
are available then this returns None, otherwise this returns
the chosen worker.
job (job): the job to assign to a worker
side_data (object): object to attach to the worker for later
use
returns (int): None if no workers are available, the worker
assigned to the job otherwise
"""
# We look for an available worker
try:
shard = self.find_worker(WorkerPool.WORKER_INACTIVE, require_connection=True, random_worker=True)
except LookupError:
return None
# Then we fill the info for future memory
self._job[shard] = job
self._start_time[shard] = make_datetime()
self._side_data[shard] = side_data
logger.debug("Worker %s acquired." % shard)
# And finally we ask the worker to do the job
action, object_id = job
timestamp = side_data[1]
queue_time = self._start_time[shard] - timestamp
logger.info(
"Asking worker %s to %s submission/user test %d "
" (%s after submission)." % (shard, action, object_id, queue_time)
)
with SessionGen(commit=False) as session:
if action == EvaluationService.JOB_TYPE_COMPILATION:
submission = Submission.get_from_id(object_id, session)
job_ = CompilationJob.from_submission(submission)
elif action == EvaluationService.JOB_TYPE_EVALUATION:
submission = Submission.get_from_id(object_id, session)
job_ = EvaluationJob.from_submission(submission)
elif action == EvaluationService.JOB_TYPE_TEST_COMPILATION:
user_test = UserTest.get_from_id(object_id, session)
job_ = CompilationJob.from_user_test(user_test)
elif action == EvaluationService.JOB_TYPE_TEST_EVALUATION:
user_test = UserTest.get_from_id(object_id, session)
job_ = EvaluationJob.from_user_test(user_test)
job_.get_output = True
job_.only_execution = True
self._worker[shard].execute_job(
job_dict=job_.export_to_dict(),
callback=self._service.action_finished.im_func,
plus=(action, object_id, side_data, shard),
)
return shard
def job(files):
operation = ESOperation(ESOperation.EVALUATION, 1, 1, "023")
return EvaluationJob(input="digest of input",
output="digest of correct output",
files=files,
operation=operation,
multithreaded_sandbox=True)
def job(executables):
return EvaluationJob(language="L1",
input="digest of input",
output="digest of correct output",
time_limit=2.5,
memory_limit=123,
executables=executables,
multithreaded_sandbox=True)
def new_jobgroup(number_of_jobs, prefix=None):
prefix = prefix if prefix is not None else ""
jobgroup_dict = {}
calls = []
for i in xrange(number_of_jobs):
job_params = ("fake_task_type", "fake_parameters_%s" % i)
job = EvaluationJob(*job_params, info="%s%d" % (prefix, i))
jobgroup_dict["%s" % i] = job
calls.append(call(*job_params))
return JobGroup(jobgroup_dict), calls
def debugSubmission(submission_id, dataset_id, testcase_codename):
config.keep_sandbox = True
file_cacher = FileCacher()
with SessionGen() as session:
submission = session.query(Submission)\
.filter(Submission.id == submission_id)\
.first()
if submission is None:
logger.error("There's no submission with id %d" % submission_id)
return False
if dataset_id is None:
dataset = submission.task.active_dataset
dataset_id = submission.task.active_dataset_id
else:
dataset = session.query(Dataset)\
.filter(Dataset.id == dataset_id)\
.first()
# Compilation
operation = ESOperation(ESOperation.COMPILATION, submission_id,
dataset_id)
comp_job = CompilationJob.from_submission(operation, submission,
dataset)
task_type = get_task_type(comp_job.task_type,
comp_job.task_type_parameters)
task_type.execute_job(comp_job, file_cacher)
for sandbox_path in comp_job.sandboxes:
logger.info("Compilation sandbox created in %s" % sandbox_path)
# Check if the compilation is successful
result = submission.get_result(dataset)
if result is None or result.compilation_failed():
logger.error("Compilatoin Failed")
return True
# Evaluation
operation = ESOperation(ESOperation.EVALUATION, submission_id,
dataset_id, testcase_codename)
eval_job = EvaluationJob.from_submission(operation, submission,
dataset)
task_type = get_task_type(eval_job.task_type,
eval_job.task_type_parameters)
task_type.execute_job(eval_job, file_cacher)
for sandbox_path in eval_job.sandboxes:
logger.info("Evaluation sandbox created in %s" % sandbox_path)
return True
def new_jobs(number_of_jobs, prefix=None):
prefix = prefix if prefix is not None else ""
jobs = []
calls = []
for i in xrange(number_of_jobs):
job_params = ("fake_task_type",
"fake_parameters_%s%d" % (prefix, i))
job = EvaluationJob(*job_params, info="%s%d" % (prefix, i))
jobs.append(job)
calls.append(call(*job_params))
return jobs, calls
def new_jobs(number_of_jobs, prefix=None):
prefix = prefix if prefix is not None else ""
jobs = []
calls = []
for i in range(number_of_jobs):
job_params = [
ESOperation(ESOperation.EVALUATION, unique_long_id(),
unique_long_id(), unique_unicode_id()).to_dict(),
"fake_task_type",
"fake_parameters_%s%d" % (prefix, i)
]
job = EvaluationJob(*job_params, info="%s%d" % (prefix, i))
jobs.append(job)
# Arguments to get_task_type are the same as for the job,
# but omitting the operation.
calls.append(call(*job_params[1:]))
return jobs, calls
def test_testcases(base_dir, soluzione, language, assume=None):
global task, file_cacher
# Use a disabled FileCacher with a FSBackend in order to avoid to fill
# the database with junk and to save up space.
if file_cacher is None:
file_cacher = FileCacher(path=os.path.join(config.cache_dir,
'cmsMake'),
enabled=False)
# Load the task
if task is None:
loader = YamlLoader(os.path.realpath(os.path.join(base_dir, "..")),
file_cacher)
# Normally we should import the contest before, but YamlLoader
# accepts get_task() even without previous get_contest() calls
task = loader.get_task(os.path.split(os.path.realpath(base_dir))[1])
# Prepare the EvaluationJob
dataset = task.active_dataset
if dataset.task_type != "OutputOnly":
digest = file_cacher.put_file_from_path(
os.path.join(base_dir, soluzione),
"Solution %s for task %s" % (soluzione, task.name))
executables = {
task.name: Executable(filename=task.name, digest=digest)
}
jobs = [(t,
EvaluationJob(language=language,
task_type=dataset.task_type,
task_type_parameters=json.loads(
dataset.task_type_parameters),
managers=dict(dataset.managers),
executables=executables,
input=dataset.testcases[t].input,
output=dataset.testcases[t].output,
time_limit=dataset.time_limit,
memory_limit=dataset.memory_limit))
for t in dataset.testcases]
tasktype = get_task_type(dataset=dataset)
else:
print("Generating outputs...", end='')
files = {}
for t in sorted(dataset.testcases.keys()):
with file_cacher.get_file(dataset.testcases[t].input) as fin:
with TemporaryFile() as fout:
print(str(t), end='')
call(soluzione, stdin=fin, stdout=fout, cwd=base_dir)
fout.seek(0)
digest = file_cacher.put_file_from_fobj(fout)
outname = "output_%s.txt" % t
files[outname] = File(filename=outname, digest=digest)
jobs = [(t,
EvaluationJob(task_type=dataset.task_type,
task_type_parameters=json.loads(
dataset.task_type_parameters),
managers=dict(dataset.managers),
files=files,
input=dataset.testcases[t].input,
output=dataset.testcases[t].output,
time_limit=dataset.time_limit,
memory_limit=dataset.memory_limit))
for t in dataset.testcases]
for k, job in jobs:
job._key = k
tasktype = get_task_type(dataset=dataset)
print()
ask_again = True
last_status = "ok"
status = "ok"
stop = False
info = []
points = []
comments = []
tcnames = []
for jobinfo in sorted(jobs):
print(jobinfo[0], end='')
sys.stdout.flush()
job = jobinfo[1]
# Skip the testcase if we decide to consider everything to
# timeout
if stop:
info.append("Time limit exceeded")
points.append(0.0)
comments.append("Timeout.")
continue
# Evaluate testcase
last_status = status
tasktype.evaluate(job, file_cacher)
if dataset.task_type != "OutputOnly":
status = job.plus["exit_status"]
info.append("Time: %5.3f Wall: %5.3f Memory: %s" %
(job.plus["execution_time"],
job.plus["execution_wall_clock_time"],
mem_human(job.plus["execution_memory"])))
else:
status = "ok"
info.append("N/A")
points.append(float(job.outcome))
comments.append(format_status_text(job.text))
tcnames.append(jobinfo[0])
# If we saw two consecutive timeouts, ask wether we want to
# consider everything to timeout
if ask_again and status == "timeout" and last_status == "timeout":
print()
print("Want to stop and consider everything to timeout? [y/N]",
end='')
if assume is not None:
print(assume)
tmp = assume
else:
tmp = raw_input().lower()
if tmp in ['y', 'yes']:
stop = True
else:
ask_again = False
# Result pretty printing
print()
clen = max(len(c) for c in comments)
ilen = max(len(i) for i in info)
for (i, p, c, b) in zip(tcnames, points, comments, info):
print("%s) %5.2lf --- %s [%s]" % (i, p, c.ljust(clen), b.center(ilen)))
return zip(points, comments, info)
def test_testcases(base_dir, solution, language, assume=None):
global task, file_cacher
# Use a FileCacher with a NullBackend in order to avoid to fill
# the database with junk
if file_cacher is None:
file_cacher = FileCacher(null=True)
cmscontrib.loaders.italy_yaml.logger = NullLogger()
# Load the task
# TODO - This implies copying a lot of data to the FileCacher,
# which is annoying if you have to do it continuously; it would be
# better to use a persistent cache (although local, possibly
# filesystem-based instead of database-based) and somehow detect
# when the task has already been loaded
if task is None:
loader = cmscontrib.loaders.italy_yaml.YamlLoader(
base_dir, file_cacher)
task = loader.get_task(get_statement=False)
# Prepare the EvaluationJob
dataset = task.active_dataset
digest = file_cacher.put_file_from_path(
os.path.join(base_dir, solution),
"Solution %s for task %s" % (solution, task.name))
executables = {task.name: Executable(filename=task.name, digest=digest)}
jobs = [
(t,
EvaluationJob(
operation=ESOperation(ESOperation.EVALUATION, None, dataset.id,
dataset.testcases[t].codename).to_dict(),
language=language,
task_type=dataset.task_type,
task_type_parameters=json.loads(dataset.task_type_parameters),
managers=dict(dataset.managers),
executables=executables,
input=dataset.testcases[t].input,
output=dataset.testcases[t].output,
time_limit=dataset.time_limit,
memory_limit=dataset.memory_limit)) for t in dataset.testcases
]
tasktype = get_task_type(dataset=dataset)
ask_again = True
last_status = "ok"
status = "ok"
stop = False
info = []
points = []
comments = []
tcnames = []
for jobinfo in sorted(jobs):
print(jobinfo[0])
sys.stdout.flush()
job = jobinfo[1]
# Skip the testcase if we decide to consider everything to
# timeout
if stop:
info.append("Time limit exceeded")
points.append(0.0)
comments.append("Timeout.")
move_cursor(directions.UP, erase=True)
continue
# Evaluate testcase
last_status = status
tasktype.evaluate(job, file_cacher)
status = job.plus.get("exit_status")
info.append(
(job.plus.get("execution_time"), job.plus.get("execution_memory")))
points.append(float(job.outcome))
# Avoid printing unneeded newline
job.text = [t.rstrip() for t in job.text]
comments.append(format_status_text(job.text))
tcnames.append(jobinfo[0])
# If we saw two consecutive timeouts, ask wether we want to
# consider everything to timeout
if ask_again and status == "timeout" and last_status == "timeout":
print("Want to stop and consider everything to timeout? [y/N] ",
end='')
sys.stdout.flush()
if assume is not None:
tmp = assume
print(tmp)
else:
# User input with a timeout of 5 seconds, at the end of which
# we automatically say "n". ready will be a list of input ready
# for reading, or an empty list if the timeout expired.
# See: http://stackoverflow.com/a/2904057
ready, _, _ = select.select([sys.stdin], [], [], 5)
if ready:
tmp = sys.stdin.readline().strip().lower()
else:
tmp = 'n'
print(tmp)
if tmp in ['y', 'yes']:
stop = True
else:
ask_again = False
print()
move_cursor(directions.UP, erase=True)
# Subtasks scoring
subtasks = json.loads(dataset.score_type_parameters)
if not isinstance(subtasks, list) or len(subtasks) == 0:
subtasks = [[100, len(info)]]
if dataset.score_type == 'GroupMin':
scoreFun = min
else:
if dataset.score_type != 'Sum':
logger.warning("Score type %s not yet supported! Using Sum" %
dataset.score_type)
def scoreFun(x):
return sum(x) / len(x)
pos = 0
sts = []
# For each subtask generate a list of testcase it owns, the score gained
# and the highest time and memory usage.
for i in subtasks:
stscores = []
stsdata = []
worst = [0, 0]
try:
for _ in xrange(i[1]):
stscores.append(points[pos])
stsdata.append(
(tcnames[pos], points[pos], comments[pos], info[pos]))
if info[pos][0] > worst[0]:
worst[0] = info[pos][0]
if info[pos][1] > worst[1]:
worst[1] = info[pos][1]
pos += 1
sts.append((scoreFun(stscores) * i[0], i[0], stsdata, worst))
except:
sts.append((0, i[0], stsdata, [0, 0]))
# Result pretty printing
# Strips sol/ and _EVAL from the solution's name
solution = solution[4:-5]
print()
clen = max(len(c) for c in comments)
for st, d in enumerate(sts):
print(
"Subtask %d:" % st,
add_color_to_string(
"%5.2f/%d" % (d[0], d[1]),
colors.RED if abs(d[0] - d[1]) > 0.01 else colors.GREEN,
bold=True))
for (i, p, c, w) in d[2]:
print("%s)" % i,
add_color_to_string(
"%5.2lf" % p,
colors.RED if abs(p - 1) > 0.01 else colors.BLACK),
"--- %s [Time:" % c.ljust(clen),
add_color_to_string(
("%5.3f" % w[0]) if w[0] is not None else "N/A",
colors.BLUE if w[0] is not None
and w[0] >= 0.95 * d[3][0] else colors.BLACK),
"Memory:",
add_color_to_string(
"%5s" % mem_human(w[1]) if w[1] is not None else "N/A",
colors.BLUE if w[1] is not None
and w[1] >= 0.95 * d[3][1] else colors.BLACK,
),
end="]")
move_cursor(directions.RIGHT, 1000)
move_cursor(directions.LEFT, len(solution) - 1)
print(add_color_to_string(solution, colors.BLACK, bold=True))
print()
sols.append((solution, sum([st[0] for st in sts])))
global tested_something
if not tested_something:
tested_something = True
atexit.register(print_at_exit)
return zip(points, comments, info)
def test_testcases(base_dir, soluzione, language, assume=None):
global task, file_cacher
# Use a FileCacher with a NullBackend in order to avoid to fill
# the database with junk
if file_cacher is None:
file_cacher = FileCacher(null=True)
# Load the task
# TODO - This implies copying a lot of data to the FileCacher,
# which is annoying if you have to do it continuously; it would be
# better to use a persistent cache (although local, possibly
# filesystem-based instead of database-based) and somehow detect
# when the task has already been loaded
if task is None:
loader = YamlLoader(
os.path.realpath(os.path.join(base_dir, "..")),
file_cacher)
# Normally we should import the contest before, but YamlLoader
# accepts get_task() even without previous get_contest() calls
task = loader.get_task(os.path.split(os.path.realpath(base_dir))[1])
# Prepare the EvaluationJob
dataset = task.active_dataset
digest = file_cacher.put_file_from_path(
os.path.join(base_dir, soluzione),
"Solution %s for task %s" % (soluzione, task.name))
executables = {task.name: Executable(filename=task.name, digest=digest)}
jobs = [(t, EvaluationJob(
language=language,
task_type=dataset.task_type,
task_type_parameters=json.loads(dataset.task_type_parameters),
managers=dict(dataset.managers),
executables=executables,
input=dataset.testcases[t].input, output=dataset.testcases[t].output,
time_limit=dataset.time_limit,
memory_limit=dataset.memory_limit)) for t in dataset.testcases]
tasktype = get_task_type(dataset=dataset)
ask_again = True
last_status = "ok"
status = "ok"
stop = False
info = []
points = []
comments = []
tcnames = []
for jobinfo in sorted(jobs):
print(jobinfo[0], end='')
sys.stdout.flush()
job = jobinfo[1]
# Skip the testcase if we decide to consider everything to
# timeout
if stop:
info.append("Time limit exceeded")
points.append(0.0)
comments.append("Timeout.")
continue
# Evaluate testcase
last_status = status
tasktype.evaluate(job, file_cacher)
status = job.plus["exit_status"]
info.append("Time: %5.3f Wall: %5.3f Memory: %s" %
(job.plus["execution_time"],
job.plus["execution_wall_clock_time"],
mem_human(job.plus["execution_memory"])))
points.append(float(job.outcome))
comments.append(format_status_text(job.text))
tcnames.append(jobinfo[0])
# If we saw two consecutive timeouts, ask wether we want to
# consider everything to timeout
if ask_again and status == "timeout" and last_status == "timeout":
print()
print("Want to stop and consider everything to timeout? [y/N]",
end='')
if assume is not None:
print(assume)
tmp = assume
else:
tmp = raw_input().lower()
if tmp in ['y', 'yes']:
stop = True
else:
ask_again = False
# Result pretty printing
print()
clen = max(len(c) for c in comments)
ilen = max(len(i) for i in info)
for (i, p, c, b) in zip(tcnames, points, comments, info):
print("%s) %5.2lf --- %s [%s]" % (i, p, c.ljust(clen), b.center(ilen)))
return zip(points, comments, info)
