def create_batch_job(job_id: str, body: dict):
"""
This is the target to be run in a thread to submit a batch processing job and monitor progress
"""
job_status_table = JobStatusTable()
try:
log.info(f'server_job, create_batch_job, job_id {job_id}, {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.DEFAULT_MD_VERSION
# request_name and request_submission_timestamp are for appending to
# output file names
job_name = body.get('request_name', '') # in earlier versions we used "request" to mean a "job"
job_submission_timestamp = get_utc_time()
# 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:
log.info('server_job, create_batch_job, listing all images to process.')
# list all images to process
image_paths = sas_blob_utils.list_blobs_in_container(
container_uri=input_container_sas,
blob_prefix=image_path_prefix, # check will be case-sensitive
blob_suffix=api_config.IMAGE_SUFFIXES_ACCEPTED, # check will be case-insensitive
limit=api_config.MAX_NUMBER_IMAGES_ACCEPTED_PER_JOB + 1
# + 1 so if the number of images listed > MAX_NUMBER_IMAGES_ACCEPTED_PER_JOB
# we will know and not proceed
)
# Case 2: user supplied a list of images to process; can include metadata
else:
log.info('server_job, create_batch_job, using provided list of images.')
output_stream, blob_properties = sas_blob_utils.download_blob_to_stream(images_requested_json_sas)
image_paths = json.load(output_stream)
log.info('server_job, create_batch_job, length of image_paths provided by the user: {}'.format(
len(image_paths)))
if len(image_paths) == 0:
job_status = get_job_status(
'completed', '0 images found in provided list of images.')
job_status_table.update_job_status(job_id, job_status)
return
error, metadata_available = 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)
# filter down to those conforming to the provided prefix and accepted suffixes (image file types)
valid_image_paths = []
for p in image_paths:
locator = p[0] if metadata_available else p
# prefix is case-sensitive; suffix is not
if image_path_prefix is not None and not locator.startswith(image_path_prefix):
continue
# Although urlparse(p).path preserves the extension on local paths, it will not work for
# blob file names that contains "#", which will be treated as indication of a query.
# If the URL is generated via Azure Blob Storage, the "#" char will be properly encoded
path = urllib.parse.urlparse(locator).path if use_url else locator
if path.lower().endswith(api_config.IMAGE_SUFFIXES_ACCEPTED):
valid_image_paths.append(p)
image_paths = valid_image_paths
log.info(('server_job, create_batch_job, length of image_paths provided by user, '
f'after filtering to jpg: {len(image_paths)}'))
# apply the first_n and sample_n filters
if first_n:
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:
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
log.info('First path before shuffling:', image_paths[0])
shuffle(image_paths)
log.info('First path after shuffling:', image_paths[0])
image_paths = image_paths[:sample_n]
num_images = len(image_paths)
log.info(f'server_job, create_batch_job, num_images after applying all filters: {num_images}')
if num_images < 1:
job_status = get_job_status('completed', (
'Zero images found in container or in provided list of images '
'after filtering with the provided parameters.'))
job_status_table.update_job_status(job_id, job_status)
return
if num_images > api_config.MAX_NUMBER_IMAGES_ACCEPTED_PER_JOB:
job_status = get_job_status(
'failed',
(f'The number of images ({num_images}) requested for processing exceeds the maximum '
f'accepted {api_config.MAX_NUMBER_IMAGES_ACCEPTED_PER_JOB} in one call'))
job_status_table.update_job_status(job_id, job_status)
return
# upload the image list to the container, which is also mounted on all nodes
# all sharding and scoring use the uploaded list
images_list_str_as_bytes = bytes(json.dumps(image_paths, ensure_ascii=False), encoding='utf-8')
container_url = sas_blob_utils.build_azure_storage_uri(account=api_config.STORAGE_ACCOUNT_NAME,
container=api_config.STORAGE_CONTAINER_API)
with ContainerClient.from_container_url(container_url,
credential=api_config.STORAGE_ACCOUNT_KEY) as api_container_client:
_ = api_container_client.upload_blob(
name=f'api_{api_config.API_INSTANCE_NAME}/job_{job_id}/{job_id}_images.json',
data=images_list_str_as_bytes)
job_status = get_job_status('created', f'{num_images} images listed; submitting the job...')
job_status_table.update_job_status(job_id, job_status)
except Exception as e:
job_status = get_job_status('failed', f'Error occurred while preparing the Batch job: {e}')
job_status_table.update_job_status(job_id, job_status)
log.error(f'server_job, create_batch_job, Error occurred while preparing the Batch job: {e}')
return # do not start monitoring
try:
batch_job_manager = BatchJobManager()
model_rel_path = api_config.MD_VERSIONS_TO_REL_PATH[model_version]
batch_job_manager.create_job(job_id,
model_rel_path,
input_container_sas,
use_url)
num_tasks, task_ids_failed_to_submit = batch_job_manager.submit_tasks(job_id, num_images)
# now request_status moves from created to running
job_status = get_job_status('running',
(f'Submitted {num_images} images to cluster in {num_tasks} shards. '
f'Number of shards failed to be submitted: {len(task_ids_failed_to_submit)}'))
# an extra field to allow the monitoring thread to restart after an API restart: total number of tasks
job_status['num_tasks'] = num_tasks
# also record the number of images to process for reporting
job_status['num_images'] = num_images
job_status_table.update_job_status(job_id, job_status)
except Exception as e:
job_status = get_job_status('problem', f'Please contact us. Error occurred while submitting the Batch job: {e}')
job_status_table.update_job_status(job_id, job_status)
log.error(f'server_job, create_batch_job, Error occurred while submitting the Batch job: {e}')
return
# start the monitor thread with the same name
try:
thread = threading.Thread(
target=monitor_batch_job,
name=f'job_{job_id}',
kwargs={
'job_id': job_id,
'num_tasks': num_tasks,
'model_version': model_version,
'job_name': job_name,
'job_submission_timestamp': job_submission_timestamp
}
)
thread.start()
except Exception as e:
job_status = get_job_status('problem', f'Error occurred while starting the monitoring thread: {e}')
job_status_table.update_job_status(job_id, job_status)
log.error(f'server_job, create_batch_job, Error occurred while starting the monitoring thread: {e}')
return
def monitor_batch_job(job_id: str,
num_tasks: int,
model_version: str,
job_name: str,
job_submission_timestamp: str):
job_status_table = JobStatusTable()
batch_job_manager = BatchJobManager()
try:
num_checks = 0
while True:
time.sleep(api_config.MONITOR_PERIOD_MINUTES * 60)
num_checks += 1
# both succeeded and failed tasks are marked "completed" on Batch
num_tasks_succeeded, num_tasks_failed = batch_job_manager.get_num_completed_tasks(job_id)
job_status = get_job_status('running',
(f'Check number {num_checks}, '
f'{num_tasks_succeeded} out of {num_tasks} shards have completed '
f'successfully, {num_tasks_failed} shards have failed.'))
job_status_table.update_job_status(job_id, job_status)
log.info(f'job_id {job_id}. '
f'Check number {num_checks}, {num_tasks_succeeded} out of {num_tasks} shards completed, '
f'{num_tasks_failed} shards failed.')
if (num_tasks_succeeded + num_tasks_failed) >= num_tasks:
break
if num_checks > api_config.MAX_MONITOR_CYCLES:
job_status = get_job_status('problem',
(
f'Job unfinished after {num_checks} x {api_config.MONITOR_PERIOD_MINUTES} minutes, '
f'please contact us to retrieve the results. Number of succeeded shards: {num_tasks_succeeded}')
)
job_status_table.update_job_status(job_id, job_status)
log.warning(f'server_job, create_batch_job, MAX_MONITOR_CYCLES reached, ending thread')
break # still aggregate the Tasks' outputs
except Exception as e:
job_status = get_job_status('problem', f'Error occurred while monitoring the Batch job: {e}')
job_status_table.update_job_status(job_id, job_status)
log.error(f'server_job, create_batch_job, Error occurred while monitoring the Batch job: {e}')
return
try:
output_sas_url = aggregate_results(job_id, model_version, job_name, job_submission_timestamp)
# preserving format from before, but SAS URL to 'failed_images' and 'images' are no longer provided
# failures should be contained in the output entries, indicated by an 'error' field
msg = {
'num_failed_shards': num_tasks_failed,
'output_file_urls': {
'detections': output_sas_url
}
}
job_status = get_job_status('completed', msg)
job_status_table.update_job_status(job_id, job_status)
except Exception as e:
job_status = get_job_status('problem',
f'Please contact us to retrieve the results. Error occurred while aggregating results: {e}')
job_status_table.update_job_status(job_id, job_status)
log.error(f'server_job, create_batch_job, Error occurred while aggregating results: {e}')
return