async def __call_sync(self,
task,
outputs,
idx,
limiter,
max_retries=1,
pass_fail_job=False):
if limiter is not None:
async with limiter:
for retry_idx in range(max_retries):
try:
outputs[idx] = task()
self.update_progress_bar()
return None
except Exception:
if retry_idx == max_retries - 1 and not pass_fail_job:
with except_handler():
raise AssertionError(
"ERROR in {}th task.\n {1}".format(
idx,
format_exc(
name="mlchain.workflows.parallel")
))
if retry_idx < max_retries - 1 or not self.verbose:
logger.error(
"PARALLEL ERROR in {0}th task and retry task,"
" run times = {1}".format(idx, retry_idx + 1))
else:
logger.debug(
"PASSED PARALLEL ERROR in {}th task:".format(
idx,
format_exc(
name="mlchain.workflows.parallel")))
else:
for retry_idx in range(max_retries):
try:
outputs[idx] = task()
self.update_progress_bar()
return None
except Exception:
if retry_idx == max_retries - 1 and not pass_fail_job:
with except_handler():
raise AssertionError("ERROR in {}th task\n".format(
idx,
format_exc(name="mlchain.workflows.parallel")))
if retry_idx < max_retries - 1 or not self.verbose:
logger.error(
"PARALLEL ERROR in {0}th task and retry task,"
" run times = {1}".format(idx, retry_idx + 1))
else:
logger.debug("PASSED PARALLEL ERROR: {0}".format(
format_exc(name="mlchain.workflows.parallel")))
self.update_progress_bar()
def __init__(self, api_key=None, api_address=None, serializer='msgpack',
image_encoder=None, name=None, version='lastest',
check_status=False, headers=None, **kwargs):
MLClient.__init__(self, api_key=api_key, api_address=api_address,
serializer=serializer, image_encoder=image_encoder,
name=name, version=version,
check_status=check_status, headers=headers, **kwargs)
if isinstance(self.api_address, str):
self.api_address = self.api_address.strip()
if len(self.api_address) > 0 and self.api_address[-1] == '/':
self.api_address = self.api_address[:-1]
if len(self.api_address) > 0 and self.api_address[0] != 'h':
self.api_address = 'http://{0}'.format(api_address)
self.content_type = 'application/{0}'.format(self.serializer_type)
if check_status:
try:
ping = self.get('ping', timeout=5)
logger.info("Connected to server {0}: {1}".format(api_address, ping))
except Exception as e:
logger.info("Can't connect to server {0}: {1}".format(api_address, e))
output_description = self.get('description', timeout=20)
if 'error' in output_description:
with except_handler():
raise AssertionError("ERROR: Model {0} in version {1} is not found".format(
name,
version))
else:
output_description = output_description['output']
self.__doc__ = output_description['__main__']
self.all_func_des = output_description['all_func_des']
self.all_func_params = output_description['all_func_params']
self.all_attributes = output_description['all_attributes']
def __call__(self, *args, **kwargs):
output = self.client.post(function_name=self.function_name,
args=args,
kwargs=kwargs)
if isinstance(output, RawResponse):
return output.value
output = self.serializer.decode(output)
if 'error' in output:
with except_handler():
raise Exception(
"MLCHAIN VERSION: {} \n API VERSION: {} \n ERROR_CODE: {} \n INFO_ERROR: {}, "
.format(output.get('mlchain_version', None),
output.get('api_version', None),
output.get('code', None), output['error']))
return output['output']