def _request_detections(**kwargs):
try:
body = kwargs.get('post_body')
input_container_sas = body['input_container_sas']
images_requested_json_sas = body.get('images_requested_json_sas', None)
image_path_prefix = body.get('image_path_prefix', None)
first_n = body.get('first_n', None)
first_n = int(first_n) if first_n else None
sample_n = body.get('sample_n', None)
sample_n = int(sample_n) if sample_n else None
request_id = kwargs['request_id']
api_task_manager.UpdateTaskStatus(request_id, 'running.')
if images_requested_json_sas is None:
api_task_manager.UpdateTaskStatus(request_id, 'running - listing all images to process.')
print('runserver.py, running - listing all images to process.')
# list all images to process
blob_prefix = None if image_path_prefix is None else image_path_prefix
image_paths = SasBlob.list_blobs_in_container(api_config.MAX_NUMBER_IMAGES_ACCEPTED,
sas_uri=input_container_sas,
blob_prefix=blob_prefix, blob_suffix='.jpg')
else:
print('runserver.py, running - using provided list of images.')
image_paths_text = SasBlob.download_blob_to_text(images_requested_json_sas)
image_paths = json.loads(image_paths_text)
print('runserver.py, length of image_paths provided by the user: {}'.format(len(image_paths)))
image_paths = [i for i in image_paths if str(i).lower().endswith(api_config.ACCEPTED_IMAGE_FILE_ENDINGS)]
print('runserver.py, length of image_paths provided by the user, after filtering to jpg: {}'.format(
len(image_paths)))
if image_path_prefix is not None:
image_paths =[i for i in image_paths if str(i).startswith(image_path_prefix)]
print('runserver.py, length of image_paths provided by the user, after filtering for image_path_prefix: {}'.format(
len(image_paths)))
res = orchestrator.spot_check_blob_paths_exist(image_paths, input_container_sas)
if res is not None:
raise LookupError('path {} provided in list of images to process does not exist in the container pointed to by data_container_sas.'.format(res))
# apply the first_n and sample_n filters
if first_n is not None:
assert first_n > 0, 'parameter first_n is 0.'
image_paths = image_paths[:first_n] # will not error if first_n > total number of images
if sample_n is not None:
assert sample_n > 0, 'parameter sample_n is 0.'
if sample_n > len(image_paths):
raise ValueError('parameter sample_n specifies more images than available (after filtering by other provided params).')
# we sample by just shuffling the image paths and take the first sample_n images
print('First path before shuffling:', image_paths[0])
shuffle(image_paths)
print('First path after shuffling:', image_paths[0])
image_paths = image_paths[:sample_n]
image_paths = sorted(image_paths)
num_images = len(image_paths)
print('runserver.py, num_images after applying all filters: {}'.format(num_images))
if num_images < 1:
api_task_manager.UpdateTaskStatus(request_id, 'completed - zero images found in container or in provided list of images after filtering with the provided parameters.')
return
if num_images > api_config.MAX_NUMBER_IMAGES_ACCEPTED:
api_task_manager.UpdateTaskStatus(request_id,
'failed - the number of images ({}) requested for processing exceeds the maximum accepted ({}) in one call.'.format(
num_images, api_config.MAX_NUMBER_IMAGES_ACCEPTED))
return
image_paths_string = json.dumps(image_paths, indent=2)
internal_storage_service.create_blob_from_text(internal_container,
'{}/{}_images.json'.format(request_id, request_id),
image_paths_string)
api_task_manager.UpdateTaskStatus(request_id, 'running - images listed; processing {} images.'.format(num_images))
print('runserver.py, running - images listed; processing {} images.'.format(num_images))
# set up connection to AML Compute and data stores
# do this for each request since pipeline step is associated with the data stores
aml_compute = orchestrator.AMLCompute(request_id, input_container_sas, internal_datastore)
print('AMLCompute resource connected successfully.')
num_images_per_job = api_config.NUM_IMAGES_PER_JOB
num_jobs = math.ceil(num_images / num_images_per_job)
list_jobs = {}
for job_index in range(num_jobs):
begin, end = job_index * num_images_per_job, (job_index + 1) * num_images_per_job
job_id = 'request{}_jobindex{}_total{}'.format(request_id, job_index, num_jobs)
list_jobs[job_id] = { 'begin': begin, 'end': end }
list_jobs_submitted = aml_compute.submit_jobs(request_id, list_jobs, api_task_manager, num_images)
api_task_manager.UpdateTaskStatus(request_id,
'running - all {} images submitted to cluster for processing.'.format(num_images))
except Exception as e:
api_task_manager.UpdateTaskStatus(request_id,
'failed - an error occurred while processing the request: {}'.format(str(e)))
print('runserver.py, exception in _request_detections: {}'.format(str(e)))
return # do not initiate _monitor_detections_request
try:
aml_monitor = orchestrator.AMLMonitor(request_id, list_jobs_submitted)
# start another thread to monitor the jobs and consolidate the results when they finish
ai4e_wrapper.wrap_async_endpoint(_monitor_detections_request, 'post:_monitor_detections_request',
request_id=request_id,
aml_monitor=aml_monitor)
except Exception as e:
api_task_manager.UpdateTaskStatus(request_id,
'failed - an error occurred when starting the status monitoring process. ' +
'The images should be submitted for processing though - please contact us to retrieve your results. Error: {}'.format(str(e)))
print('runserver.py, exception when starting orchestrator.AMLMonitor: ', str(e))
def _request_detections(**kwargs: Any) -> None:
try:
body = kwargs.get('post_body')
assert body is not None
input_container_sas = body.get('input_container_sas', None)
use_url = body.get('use_url', False)
images_requested_json_sas = body.get('images_requested_json_sas', None)
image_path_prefix = body.get('image_path_prefix', None)
first_n = body.get('first_n', None)
first_n = int(first_n) if first_n else None
sample_n = body.get('sample_n', None)
sample_n = int(sample_n) if sample_n else None
model_version = body.get('model_version', '')
if model_version == '':
model_version = api_config.AML_CONFIG['default_model_version']
model_name = api_config.AML_CONFIG['models'][model_version]
# request_name and request_submission_timestamp are for appending to
# output file names
request_name = body.get('request_name', '')
request_submission_timestamp = orchestrator.get_utc_timestamp()
request_id = kwargs['request_id']
task_status = orchestrator.get_task_status(
'running', 'Request received.')
update_task_status(api_task_manager, request_id, task_status)
print('runserver.py, request_id {}, '.format(request_id),
'model_version {}, model_name {}, '.format(model_version, model_name),
'request_name {}, '.format(request_name),
'submission timestamp is {}'.format(request_submission_timestamp))
# image_paths can be a list of strings (Azure blob names or public URLs)
# or a list of length-2 lists where each is a [image_id, metadata] pair
# Case 1: listing all images in the container
# - not possible to have attached metadata if listing images in a blob
if images_requested_json_sas is None:
metadata_available = False
task_status = orchestrator.get_task_status(
'running', 'Listing all images to process.')
update_task_status(api_task_manager, request_id, task_status)
print('runserver.py, running - listing all images to process.')
# list all images to process
image_paths = SasBlob.list_blobs_in_container(
api_config.MAX_NUMBER_IMAGES_ACCEPTED + 1,
# so > MAX_NUMBER_IMAGES_ACCEPTED will find that there are too many images requested so should not proceed
sas_uri=input_container_sas,
blob_prefix=image_path_prefix, blob_suffix='.jpg')
# Case 2: user supplied a list of images to process; can include metadata
else:
print('runserver.py, running - using provided list of images.')
image_paths_text = SasBlob.download_blob_to_text(
images_requested_json_sas)
image_paths = json.loads(image_paths_text)
print('runserver.py, length of image_paths provided by the user: {}'.format(len(image_paths)))
if len(image_paths) == 0:
task_status = orchestrator.get_task_status(
'completed', '0 images found in provided list of images.')
update_task_status(api_task_manager, request_id, task_status)
return
error, metadata_available = orchestrator.validate_provided_image_paths(image_paths)
if error is not None:
msg = 'image paths provided in the json are not valid: {}'.format(error)
raise ValueError(msg)
valid_image_paths = []
for p in image_paths:
locator = p[0] if metadata_available else p
# urlparse(p).path also preserves the extension on local paths
path = urllib.parse.urlparse(locator).path.lower()
if path.endswith(api_config.ACCEPTED_IMAGE_FILE_ENDINGS):
valid_image_paths.append(p)
image_paths = valid_image_paths
print('runserver.py, length of image_paths provided by user, '
'after filtering to jpg: {}'.format(len(image_paths)))
valid_image_paths = []
if image_path_prefix is not None:
for p in image_paths:
locator = p[0] if metadata_available else p
if locator.startswith(image_path_prefix):
valid_image_paths.append(p)
image_paths = valid_image_paths
print('runserver.py, length of image_paths provided by user, '
'after filtering for image_path_prefix: {}'.format(len(image_paths)))
if not use_url:
res = orchestrator.spot_check_blob_paths_exist(
image_paths, input_container_sas, metadata_available)
if res is not None:
msg = ('path {} provided in list of images to process '.format(res),
'does not exist in the container pointed to by '
'data_container_sas.')
raise LookupError(msg)
# apply the first_n and sample_n filters
if first_n is not None:
assert first_n > 0, 'parameter first_n is 0.'
# OK if first_n > total number of images
image_paths = image_paths[:first_n]
if sample_n is not None:
assert sample_n > 0, 'parameter sample_n is 0.'
if sample_n > len(image_paths):
msg = ('parameter sample_n specifies more images than '
'available (after filtering by other provided params).')
raise ValueError(msg)
# sample by shuffling image paths and take the first sample_n images
print('First path before shuffling:', image_paths[0])
shuffle(image_paths)
print('First path after shuffling:', image_paths[0])
image_paths = orchestrator.sort_image_paths(
image_paths[:sample_n], metadata_available)
num_images = len(image_paths)
print('runserver.py, num_images after applying all filters: {}'.format(num_images))
if num_images < 1:
task_status = orchestrator.get_task_status(
'completed',
'Zero images found in container or in provided list of images '
'after filtering with the provided parameters.')
update_task_status(api_task_manager, request_id, task_status)
return
if num_images > api_config.MAX_NUMBER_IMAGES_ACCEPTED:
task_status = orchestrator.get_task_status(
'failed',
'The number of images ({}) requested for processing exceeds the maximum accepted {} in one call'.format(
num_images, api_config.MAX_NUMBER_IMAGES_ACCEPTED))
update_task_status(api_task_manager, request_id, task_status)
return
# finalized image_paths is uploaded to internal_container; all sharding
# and scoring use the uploaded list
image_paths_string = json.dumps(image_paths, indent=1)
internal_storage_service.create_blob_from_text(
internal_container, '{}/{}_images.json'.format(request_id, request_id),
image_paths_string)
# the list of images json does not have request_name or timestamp in the
# file name so that score.py can locate it
task_status = orchestrator.get_task_status(
'running', 'Images listed; processing {} images.'.format(num_images))
update_task_status(api_task_manager, request_id, task_status)
print('runserver.py, running - images listed; processing {} images'.format(num_images))
# set up connection to AML Compute and data stores
# do this for each request since pipeline step is associated with the
# data stores
aml_compute = orchestrator.AMLCompute(
request_id=request_id, use_url=use_url,
input_container_sas=input_container_sas,
internal_datastore=internal_datastore, model_name=model_name)
print('AMLCompute resource connected successfully.')
num_images_per_job = api_config.NUM_IMAGES_PER_JOB
num_jobs = math.ceil(num_images / num_images_per_job)
# list_jobs: Dict[str, Dict[str, int]] = {}
list_jobs = {}
for job_index in range(num_jobs):
begin = job_index * num_images_per_job
end = begin + num_images_per_job
# Experiment name must be between 1 and 36 characters long. Its
# first character has to be alphanumeric, and the rest may contain
# hyphens and underscores.
shortened_request_id = request_id.split('-')[0]
if len(shortened_request_id) > 8:
shortened_request_id = shortened_request_id[:8]
# request ID, job index, total
job_id = 'r{}_i{}_t{}'.format(shortened_request_id, job_index, num_jobs)
list_jobs[job_id] = {'begin': begin, 'end': end}
list_jobs_submitted = aml_compute.submit_jobs(
list_jobs, api_task_manager, num_images)
task_status = orchestrator.get_task_status(
'running',
'All {} images submitted to cluster for processing.'.format(num_images))
update_task_status(api_task_manager, request_id, task_status)
except Exception as e:
task_status = orchestrator.get_task_status(
'failed', 'An error occurred while processing the request: {}'.format(e))
update_task_status(api_task_manager, request_id, task_status)
print('runserver.py, exception in _request_detections: {}'.format(e))
return # do not initiate _monitor_detections_request
try:
aml_monitor = orchestrator.AMLMonitor(
request_id=request_id,
shortened_request_id=shortened_request_id,
list_jobs_submitted=list_jobs_submitted,
request_name=request_name,
request_submission_timestamp=request_submission_timestamp,
model_version=model_version)
# start another thread to monitor the jobs and consolidate the results
# when they finish
# HACK
ai4e_service._create_and_execute_thread(
func=_monitor_detections_request,
api_path='/request_detections_aml',
request_id=request_id, aml_monitor=aml_monitor)
# ai4e_service.wrap_async_endpoint(
# _monitor_detections_request,
# trace_name='post:_monitor_detections_request',
# request_id=request_id, aml_monitor=aml_monitor)
except Exception as e:
task_status = orchestrator.get_task_status(
'problem',
('An error occurred when starting the status monitoring process. '
'The images should be submitted for processing though - please '
'contact us to retrieve your results. Error: {}'.format(e)))
update_task_status(api_task_manager, request_id, task_status)
print('runserver.py, exception when starting orchestrator.AMLMonitor: {}'.format(e))
def _request_detections(**kwargs):
try:
body = kwargs.get('post_body')
input_container_sas = body.get('input_container_sas', None)
use_url = body.get('use_url', False)
images_requested_json_sas = body.get('images_requested_json_sas', None)
image_path_prefix = body.get('image_path_prefix', None)
first_n = body.get('first_n', None)
first_n = int(first_n) if first_n else None
sample_n = body.get('sample_n', None)
sample_n = int(sample_n) if sample_n else None
model_version = body.get('model_version', '')
if model_version == '':
model_version = api_config.AML_CONFIG['default_model_version']
model_name = api_config.AML_CONFIG['models'][model_version]
# request_name and request_submission_timestamp are for appending to output file names
request_name = body.get('request_name', '')
request_submission_timestamp = orchestrator.get_utc_timestamp()
request_id = kwargs['request_id']
api_task_manager.UpdateTaskStatus(
request_id, get_task_status('running', 'Request received.'))
print((
'runserver.py, request_id {}, model_version {}, model_name {}, request_name {}, submission timestamp '
'is {}').format(request_id, model_version, model_name,
request_name, request_submission_timestamp))
# image_paths can be a list of strings (paths on Azure blobs or public URLs), or a list of lists,
# each of length 2 and is the [image_id, metadata] pair
# case 1 - listing all images in the container
if images_requested_json_sas is None:
metadata_available = False # not possible to have attached metadata if listing images in a blob
api_task_manager.UpdateTaskStatus(
request_id,
get_task_status('running', 'Listing all images to process.'))
print('runserver.py, running - listing all images to process.')
# list all images to process
image_paths = SasBlob.list_blobs_in_container(
api_config.MAX_NUMBER_IMAGES_ACCEPTED +
1, # so > MAX_NUMBER_IMAGES_ACCEPTED will find that there are too many images requested so should not proceed
sas_uri=input_container_sas,
blob_prefix=image_path_prefix,
blob_suffix='.jpg')
# case 2 - user supplied a list of images to process; can include metadata
else:
print('runserver.py, running - using provided list of images.')
image_paths_text = SasBlob.download_blob_to_text(
images_requested_json_sas)
image_paths = json.loads(image_paths_text)
print(
'runserver.py, length of image_paths provided by the user: {}'.
format(len(image_paths)))
if len(image_paths) == 0:
api_task_manager.UpdateTaskStatus(
request_id,
get_task_status(
'completed',
'Zero images found in provided list of images.'))
return
error, metadata_available = orchestrator.validate_provided_image_paths(
image_paths)
if error is not None:
raise ValueError(
'image paths provided in the json are not valid: {}'.
format(error))
valid_image_paths = []
for p in image_paths:
locator = p[0] if metadata_available else p
if locator.lower().endswith(
api_config.ACCEPTED_IMAGE_FILE_ENDINGS):
valid_image_paths.append(p)
image_paths = valid_image_paths
print(
'runserver.py, length of image_paths provided by the user, after filtering to jpg: {}'
.format(len(image_paths)))
valid_image_paths = []
if image_path_prefix is not None:
for p in image_paths:
locator = p[0] if metadata_available else p
if locator.startswith(image_path_prefix):
valid_image_paths.append(p)
image_paths = valid_image_paths
print(
'runserver.py, length of image_paths provided by the user, after filtering for image_path_prefix: {}'
.format(len(image_paths)))
if not use_url:
res = orchestrator.spot_check_blob_paths_exist(
image_paths, input_container_sas, metadata_available)
if res is not None:
raise LookupError(
'path {} provided in list of images to process does not exist in the container pointed to by data_container_sas.'
.format(res))
# apply the first_n and sample_n filters
if first_n is not None:
assert first_n > 0, 'parameter first_n is 0.'
image_paths = image_paths[:
first_n] # will not error if first_n > total number of images
if sample_n is not None:
assert sample_n > 0, 'parameter sample_n is 0.'
if sample_n > len(image_paths):
raise ValueError(
'parameter sample_n specifies more images than available (after filtering by other provided params).'
)
# we sample by shuffling the image paths and take the first sample_n images
print('First path before shuffling:', image_paths[0])
shuffle(image_paths)
print('First path after shuffling:', image_paths[0])
image_paths = image_paths[:sample_n]
image_paths = orchestrator.sort_image_paths(
image_paths, metadata_available)
num_images = len(image_paths)
print('runserver.py, num_images after applying all filters: {}'.format(
num_images))
if num_images < 1:
api_task_manager.UpdateTaskStatus(
request_id,
get_task_status(
'completed',
'Zero images found in container or in provided list of images after filtering with the provided parameters.'
))
return
if num_images > api_config.MAX_NUMBER_IMAGES_ACCEPTED:
api_task_manager.UpdateTaskStatus(
request_id,
get_task_status(
'failed',
'The number of images ({}) requested for processing exceeds the maximum accepted ({}) in one call.'
.format(num_images,
api_config.MAX_NUMBER_IMAGES_ACCEPTED)))
return
# finalized image_paths is uploaded to internal_container; all sharding and scoring use the uploaded list
image_paths_string = json.dumps(image_paths, indent=1)
internal_storage_service.create_blob_from_text(
internal_container,
'{}/{}_images.json'.format(request_id,
request_id), image_paths_string)
# the list of images json does not have request_name or timestamp in the file name so that score.py can locate it
api_task_manager.UpdateTaskStatus(
request_id,
get_task_status(
'running',
'Images listed; processing {} images.'.format(num_images)))
print('runserver.py, running - images listed; processing {} images.'.
format(num_images))
# set up connection to AML Compute and data stores
# do this for each request since pipeline step is associated with the data stores
aml_compute = orchestrator.AMLCompute(
request_id=request_id,
use_url=use_url,
input_container_sas=input_container_sas,
internal_datastore=internal_datastore,
model_name=model_name)
print('AMLCompute resource connected successfully.')
num_images_per_job = api_config.NUM_IMAGES_PER_JOB
num_jobs = math.ceil(num_images / num_images_per_job)
list_jobs = {}
for job_index in range(num_jobs):
begin, end = job_index * num_images_per_job, (
job_index + 1) * num_images_per_job
job_id = 'request{}_jobindex{}_total{}'.format(
request_id, job_index, num_jobs)
list_jobs[job_id] = {'begin': begin, 'end': end}
list_jobs_submitted = aml_compute.submit_jobs(list_jobs,
api_task_manager,
num_images)
api_task_manager.UpdateTaskStatus(
request_id,
get_task_status(
'running',
'All {} images submitted to cluster for processing.'.format(
num_images)))
except Exception as e:
api_task_manager.UpdateTaskStatus(
request_id,
get_task_status(
'failed',
'An error occurred while processing the request: {}'.format(
e)))
print('runserver.py, exception in _request_detections: {}'.format(
str(e)))
return # do not initiate _monitor_detections_request
try:
aml_monitor = orchestrator.AMLMonitor(
request_id=request_id,
list_jobs_submitted=list_jobs_submitted,
request_name=request_name,
request_submission_timestamp=request_submission_timestamp,
model_version=model_version)
# start another thread to monitor the jobs and consolidate the results when they finish
ai4e_wrapper.wrap_async_endpoint(_monitor_detections_request,
'post:_monitor_detections_request',
request_id=request_id,
aml_monitor=aml_monitor)
except Exception as e:
api_task_manager.UpdateTaskStatus(
request_id,
get_task_status('problem', (
'An error occurred when starting the status monitoring process. '
'The images should be submitted for processing though - please contact us to retrieve your results. '
'Error: {}'.format(e))))
print(
'runserver.py, exception when starting orchestrator.AMLMonitor: ',
str(e))