def job_execution_wrapper(data):
redis_conn = redis.Redis(connection_pool=POOL)
job = get_current_job()
_context = {}
_context['redis_conn'] = redis_conn
_context['response_channel'] = data['broker_response_channel']
_context['job_id'] = job.id
_context['data_sequence_no'] = data['data_sequence_no']
_context['api_key'] = data['extra_params']['api_key']
# Register Job Running event
_update_job_event(
_context,
job_running_template(_context['data_sequence_no'], job.id)
)
result = {}
try:
if data["function_name"] == "grade_submission":
# Run the job
answer_file_path = config.answer_file_path
result = _submit(data["data"], answer_file_path, _context)
# Register Job Complete event
if data["data"]["round"] == 2:
# Hide scores in case of round 2
del result["score"]
del result["score_secondary"]
_update_job_event(
_context,
job_info_template(
_context, "Scores Submitted Successfully ! Please remember to upload your code and a description of your approach (as a short paper) to https://gitlab.crowdai.org , as a private repository, tto be eligible for the final round-2 rankings.")
)
_update_job_event(
_context,
job_complete_template(_context, result)
)
else:
_error_object = job_error_template(
job.id,
"Function not implemented error"
)
_update_job_event(
_context,
job_error_template(job.id, result)
)
result = _error_object
except Exception as e:
_error_object = job_error_template(
_context['data_sequence_no'],
job.id,
str(e)
)
_update_job_event(_context, _error_object)
return result
def _submit(client_payload, answer_file_path, context):
"""
takes a list of predicted heights and actual heights
and computes the score
and prepares the plots for submission to the leaderboard
"""
file_key = client_payload["file_key"]
_update_job_event(context,
job_info_template(context, "Grading Submission...."))
_payload = {}
_meta = {}
_meta['file_key'] = file_key
_payload["meta"] = _meta
submission_id = report_to_crowdai(context,
_payload,
submission_id=False,
status='submitted')
print("Submission id : ", submission_id)
try:
localfilepath = download_file(context, file_key)
_client_payload = {}
_client_payload["submission_file_path"] = localfilepath
_result_object = config.evaluator._evaluate(_client_payload, context)
print _result_object
_payload = _result_object
report_to_crowdai(context,
_payload,
submission_id=submission_id,
status='graded')
# Clean up file if possible
os.remove(localfilepath)
return _result_object
except Exception as e:
# Report to crowdAI
if "meta" in _payload.keys():
del _payload["meta"]
report_to_crowdai(context,
_payload,
submission_id=submission_id,
status='failed',
message=str(e))
# raise the exception again
# so that it can be handled further down the chain
raise e
def job_execution_wrapper(data):
redis_conn = redis.Redis(connection_pool=POOL)
job = get_current_job()
_context = {}
_context['redis_conn'] = redis_conn
_context['response_channel'] = data['broker_response_channel']
_context['job_id'] = job.id
_context['data_sequence_no'] = data['data_sequence_no']
_context['api_key'] = data['extra_params']['api_key']
# Register Job Running event
_update_job_event(
_context, job_running_template(_context['data_sequence_no'], job.id))
result = {}
try:
if data["function_name"] == "grade_submission":
# Run the job
answer_file_path = config.answer_file_path
result = _submit(data["data"], answer_file_path, _context)
# Register Job Complete event
_update_job_event(
_context,
job_info_template(
_context,
"Scores Submitted Successfully ! Mean Absolute Percentage Error (MAPE) : %s"
% (str(result['score']))))
_update_job_event(_context,
job_complete_template(_context, result))
else:
_error_object = job_error_template(
job.id, "Function not implemented error")
_update_job_event(_context, job_error_template(job.id, result))
result = _error_object
except Exception as e:
_error_object = job_error_template(_context['data_sequence_no'],
job.id, str(e))
_update_job_event(_context, _error_object)
return result
def _submit(client_payload, answer_file_path, context):
"""
takes a list of predicted heights and actual heights
and computes the score
and prepares the plots for submission to the leaderboard
"""
file_key = client_payload["file_key"]
_update_job_event(
context,
job_info_template(
context, "Grading Submission....")
)
if "round" not in client_payload.keys():
raise Exception("""
The round parameter has not been specified. Please upgrade your
crowdai client to atleast version 1.0.21 by :
pip install -U crowdai
and then update your submission code by following the latest instructions
from :
https://github.com/crowdAI/ieee_investment_ranking_challenge-starter-kit#submission-of-predicted-file-to-crowdai
""")
round_id = client_payload["round"]
assert round_id in config.crowdai_round_id_map.keys(), \
"Unknown Round ID Passed. Allowed values : {}".format(
str(config.crowdai_round_id_map.keys())
)
crowdai_round_id = config.crowdai_round_id_map[round_id]
_payload = {}
_meta = {}
_meta['file_key'] = file_key
_payload["meta"] = json.dumps(_meta)
_payload["challenge_round_id"] = crowdai_round_id
submission_id = report_to_crowdai(
context,
_payload,
submission_id=False,
status='submitted')
print("Submission id : ", submission_id)
try:
localfilepath = download_file(context, file_key)
_client_payload = {}
_client_payload["submission_file_path"] = localfilepath
_result_object = config.evaluator._evaluate(
client_payload=_client_payload,
round_indicator=round_id,
_context=context)
print _result_object
_payload = _result_object
report_to_crowdai(
context,
_payload,
submission_id=submission_id,
message = "graded successfully",
status='graded')
# Clean up file if possible
os.remove(localfilepath)
return _result_object
except Exception as e:
# Report to crowdAI
if "meta" in _payload.keys():
del _payload["meta"]
report_to_crowdai(
context,
_payload,
submission_id=submission_id,
status='failed',
message=str(e)
)
# raise the exception again
# so that it can be handled further down the chain
raise e