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random->lhs when lacking data for GPR; fix editing session

This commit is contained in:
yangdsh 2020-02-03 20:59:23 +00:00 committed by Dana Van Aken
parent ae98fdcb32
commit c33625ca17
3 changed files with 15 additions and 11 deletions
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8
client/driver/fabfile.py vendored
View File

@ -663,6 +663,10 @@ def _http_content_to_json(content):
return json_content, decoded return json_content, decoded
def _parse_bool(val):
return str(val).lower() == 'true'
def _modify_website_object(obj_name, action, verbose=False, **kwargs): def _modify_website_object(obj_name, action, verbose=False, **kwargs):
verbose = _parse_bool(verbose) verbose = _parse_bool(verbose)
if obj_name == 'project': if obj_name == 'project':
@ -685,11 +689,11 @@ def _modify_website_object(obj_name, action, verbose=False, **kwargs):
data[k] = v data[k] = v
url_path = '/{}/{}/'.format(action, obj_name) url_path = '/{}/{}/'.format(action, obj_name)
response = requests.post(CONF['upload_url'] + url_path, data=data) response = requests.post(dconf.WEBSITE_URL + url_path, data=data)
content = response.content.decode('utf-8') content = response.content.decode('utf-8')
if response.status_code != 200: if response.status_code != 200:
raise Exception("Failed to {} new {}.\nStatus: {}\nMessage: {}\n".format( raise Exception("Failed to {} {}.\nStatus: {}\nMessage: {}\n".format(
action, obj_name, response.status_code, content)) action, obj_name, response.status_code, content))
json_content, decoded = _http_content_to_json(content) json_content, decoded = _http_content_to_json(content)

14
server/website/website/tasks/async_tasks.py
View File

@ -168,11 +168,14 @@ def aggregate_target_results(result_id, algorithm):
# implement a sampling technique to generate new training data). # implement a sampling technique to generate new training data).
newest_result = Result.objects.get(pk=result_id) newest_result = Result.objects.get(pk=result_id)
has_pipeline_data = PipelineData.objects.filter(workload=newest_result.workload).exists() has_pipeline_data = PipelineData.objects.filter(workload=newest_result.workload).exists()
if newest_result.session.tuning_session == 'lhs': if not has_pipeline_data or newest_result.session.tuning_session == 'lhs':
if not has_pipeline_data and newest_result.session.tuning_session == 'tuning_session':
LOG.debug("Background tasks haven't ran for this workload yet, picking random data.")
all_samples = JSONUtil.loads(newest_result.session.lhs_samples) all_samples = JSONUtil.loads(newest_result.session.lhs_samples)
if len(all_samples) == 0: if len(all_samples) == 0:
knobs = SessionKnob.objects.get_knobs_for_session(newest_result.session) knobs = SessionKnob.objects.get_knobs_for_session(newest_result.session)
all_samples = gen_lhs_samples(knobs, 100) all_samples = gen_lhs_samples(knobs, 10)
LOG.debug('%s: Generated LHS.\n\ndata=%s\n', LOG.debug('%s: Generated LHS.\n\ndata=%s\n',
AlgorithmType.name(algorithm), JSONUtil.dumps(all_samples[:5], pprint=True)) AlgorithmType.name(algorithm), JSONUtil.dumps(all_samples[:5], pprint=True))
samples = all_samples.pop() samples = all_samples.pop()
@ -186,10 +189,7 @@ def aggregate_target_results(result_id, algorithm):
LOG.debug('%s: Got LHS config.\n\ndata=%s\n', LOG.debug('%s: Got LHS config.\n\ndata=%s\n',
AlgorithmType.name(algorithm), JSONUtil.dumps(agg_data, pprint=True)) AlgorithmType.name(algorithm), JSONUtil.dumps(agg_data, pprint=True))
elif not has_pipeline_data or newest_result.session.tuning_session == 'randomly_generate': elif newest_result.session.tuning_session == 'randomly_generate':
if not has_pipeline_data and newest_result.session.tuning_session == 'tuning_session':
LOG.debug("Background tasks haven't ran for this workload yet, picking random data.")
result = Result.objects.filter(pk=result_id) result = Result.objects.filter(pk=result_id)
knobs = SessionKnob.objects.get_knobs_for_session(newest_result.session) knobs = SessionKnob.objects.get_knobs_for_session(newest_result.session)
@ -648,7 +648,7 @@ def configuration_recommendation(recommendation_input):
if target_data['bad'] is True: if target_data['bad'] is True:
target_data_res = create_and_save_recommendation( target_data_res = create_and_save_recommendation(
recommended_knobs=target_data['config_recommend'], result=newest_result, recommended_knobs=target_data['config_recommend'], result=newest_result,
status='bad', info='WARNING: no training data, the config is generated randomly', status='bad', info='WARNING: no training data, the config is generated by LHS',
pipeline_run=target_data['pipeline_run']) pipeline_run=target_data['pipeline_run'])
LOG.debug('%s: Skipping configuration recommendation.\nData:\n%s\n\n', LOG.debug('%s: Skipping configuration recommendation.\nData:\n%s\n\n',
AlgorithmType.name(algorithm), target_data) AlgorithmType.name(algorithm), target_data)

4
server/website/website/utils.py
View File

@ -499,7 +499,7 @@ def model_to_dict2(m, exclude=None):
def check_and_run_celery(): def check_and_run_celery():
celery_status = os.popen('python3 manage.py celery inspect ping').read() celery_status = os.popen('python3 manage.py celery inspect ping').read()
if 'OK' in celery_status: if 'pong' in celery_status:
return 'celery is running' return 'celery is running'
retries = 0 retries = 0
@ -510,7 +510,7 @@ def check_and_run_celery():
os.popen('python3 manage.py startcelery &') os.popen('python3 manage.py startcelery &')
time.sleep(30 * retries) time.sleep(30 * retries)
celery_status = os.popen('python3 manage.py celery inspect ping').read() celery_status = os.popen('python3 manage.py celery inspect ping').read()
if 'OK' in celery_status: if 'pong' in celery_status:
LOG.info('Successfully start celery.') LOG.info('Successfully start celery.')
return 'celery stopped but is restarted successfully' return 'celery stopped but is restarted successfully'
LOG.warning('Cannot restart celery.') LOG.warning('Cannot restart celery.')