def start():
while not pathlib.Path("/mnt/workspace/init_script_run.txt").is_file():
time.sleep(0.2)
cache_dir = os.environ["CORTEX_CACHE_DIR"]
provider = os.environ["CORTEX_PROVIDER"]
api_spec_path = os.environ["CORTEX_API_SPEC"]
job_spec_path = os.environ["CORTEX_JOB_SPEC"]
project_dir = os.environ["CORTEX_PROJECT_DIR"]
model_dir = os.getenv("CORTEX_MODEL_DIR")
tf_serving_port = os.getenv("CORTEX_TF_BASE_SERVING_PORT", "9000")
tf_serving_host = os.getenv("CORTEX_TF_SERVING_HOST", "localhost")
region = os.getenv("AWS_REGION")
has_multiple_servers = os.getenv("CORTEX_MULTIPLE_TF_SERVERS")
if has_multiple_servers:
with LockedFile("/run/used_ports.json", "r+") as f:
used_ports = json.load(f)
for port in used_ports.keys():
if not used_ports[port]:
tf_serving_port = port
used_ports[port] = True
break
f.seek(0)
json.dump(used_ports, f)
f.truncate()
api = get_api(provider, api_spec_path, model_dir, cache_dir, region)
storage, api_spec = get_spec(provider, api_spec_path, cache_dir, region)
job_spec = get_job_spec(storage, cache_dir, job_spec_path)
client = api.predictor.initialize_client(tf_serving_host=tf_serving_host,
tf_serving_port=tf_serving_port)
logger.info("loading the predictor from {}".format(api.predictor.path))
predictor_impl = api.predictor.initialize_impl(project_dir, client,
job_spec)
local_cache["api_spec"] = api
local_cache["provider"] = provider
local_cache["job_spec"] = job_spec
local_cache["predictor_impl"] = predictor_impl
local_cache["predict_fn_args"] = inspect.getfullargspec(
predictor_impl.predict).args
local_cache["sqs_client"] = boto3.client("sqs", region_name=region)
open("/mnt/workspace/api_readiness.txt", "a").close()
logger.info("polling for batches...")
sqs_loop()
def _get_model(self, model_name: str, model_version: str) -> Any:
"""
Checks if versioned model is on disk, then checks if model is in memory,
and if not, it loads it into memory, and returns the model.
Args:
model_name: Name of the model, as it's specified in predictor:models:paths or in the other case as they are named on disk.
model_version: Version of the model, as it's found on disk. Can also infer the version number from the "latest" tag.
Exceptions:
RuntimeError: if another thread tried to load the model at the very same time.
Returns:
The model as returned by self._load_model method.
None if the model wasn't found or if it didn't pass the validation.
"""
model = None
tag = ""
if model_version == "latest":
tag = model_version
if not self._caching_enabled:
# determine model version
if tag == "latest":
model_version = self._get_latest_model_version_from_disk(
model_name)
model_id = model_name + "-" + model_version
# grab shared access to versioned model
resource = os.path.join(self._lock_dir, model_id + ".txt")
with LockedFile(resource, "r", reader_lock=True) as f:
# check model status
file_status = f.read()
if file_status == "" or file_status == "not-available":
raise WithBreak
current_upstream_ts = int(file_status.split(" ")[1])
update_model = False
# grab shared access to models holder and retrieve model
with LockedModel(self._models, "r", model_name, model_version):
status, local_ts = self._models.has_model(
model_name, model_version)
if status == "not-available" or (
status == "in-memory"
and local_ts != current_upstream_ts):
update_model = True
raise WithBreak
model, _ = self._models.get_model(model_name,
model_version, tag)
# load model into memory and retrieve it
if update_model:
with LockedModel(self._models, "w", model_name,
model_version):
status, _ = self._models.has_model(
model_name, model_version)
if status == "not-available" or (
status == "in-memory"
and local_ts != current_upstream_ts):
if status == "not-available":
logger.info(
f"loading model {model_name} of version {model_version} (thread {td.get_ident()})"
)
else:
logger.info(
f"reloading model {model_name} of version {model_version} (thread {td.get_ident()})"
)
try:
self._models.load_model(
model_name,
model_version,
current_upstream_ts,
[tag],
)
except Exception as e:
raise UserRuntimeException(
f"failed (re-)loading model {model_name} of version {model_version} (thread {td.get_ident()})",
str(e),
)
model, _ = self._models.get_model(
model_name, model_version, tag)
if not self._multiple_processes and self._caching_enabled:
# determine model version
try:
if tag == "latest":
model_version = self._get_latest_model_version_from_tree(
model_name, self._models_tree.model_info(model_name))
except ValueError:
# if model_name hasn't been found
raise UserRuntimeException(
f"'{model_name}' model of tag latest wasn't found in the list of available models"
)
# grab shared access to model tree
available_model = True
with LockedModelsTree(self._models_tree, "r", model_name,
model_version):
# check if the versioned model exists
model_id = model_name + "-" + model_version
if model_id not in self._models_tree:
available_model = False
raise WithBreak
# retrieve model tree's metadata
upstream_model = self._models_tree[model_id]
current_upstream_ts = int(
upstream_model["timestamp"].timestamp())
if not available_model:
return None
# grab shared access to models holder and retrieve model
update_model = False
with LockedModel(self._models, "r", model_name, model_version):
status, local_ts = self._models.has_model(
model_name, model_version)
if status in ["not-available", "on-disk"
] or (status != "not-available"
and local_ts != current_upstream_ts):
update_model = True
raise WithBreak
model, _ = self._models.get_model(model_name, model_version,
tag)
# download, load into memory the model and retrieve it
if update_model:
# grab exclusive access to models holder
with LockedModel(self._models, "w", model_name, model_version):
# check model status
status, local_ts = self._models.has_model(
model_name, model_version)
# refresh disk model
if status == "not-available" or (
status in ["on-disk", "in-memory"]
and local_ts != current_upstream_ts):
if status == "not-available":
logger.info(
f"model {model_name} of version {model_version} not found locally; continuing with the download..."
)
elif status == "on-disk":
logger.info(
f"found newer model {model_name} of vesion {model_version} on the {upstream_model['provider']} upstream than the one on the disk"
)
else:
logger.info(
f"found newer model {model_name} of vesion {model_version} on the {upstream_model['provider']} upstream than the one loaded into memory"
)
# remove model from disk and memory
if status == "on-disk":
logger.info(
f"removing model from disk for model {model_name} of version {model_version}"
)
self._models.remove_model(model_name,
model_version)
if status == "in-memory":
logger.info(
f"removing model from disk and memory for model {model_name} of version {model_version}"
)
self._models.remove_model(model_name,
model_version)
# download model
logger.info(
f"downloading model {model_name} of version {model_version} from the {upstream_model['provider']} upstream"
)
date = self._models.download_model(
upstream_model["provider"],
upstream_model["bucket"],
model_name,
model_version,
upstream_model["path"],
)
if not date:
raise WithBreak
current_upstream_ts = int(date.timestamp())
# load model
try:
logger.info(
f"loading model {model_name} of version {model_version} into memory"
)
self._models.load_model(
model_name,
model_version,
current_upstream_ts,
[tag],
)
except Exception as e:
raise UserRuntimeException(
f"failed (re-)loading model {model_name} of version {model_version} (thread {td.get_ident()})",
str(e),
)
# retrieve model
model, _ = self._models.get_model(model_name,
model_version, tag)
return model
def start_fn():
provider = os.environ["CORTEX_PROVIDER"]
project_dir = os.environ["CORTEX_PROJECT_DIR"]
spec_path = os.environ["CORTEX_API_SPEC"]
model_dir = os.getenv("CORTEX_MODEL_DIR")
cache_dir = os.getenv("CORTEX_CACHE_DIR")
region = os.getenv("AWS_REGION")
tf_serving_port = os.getenv("CORTEX_TF_BASE_SERVING_PORT", "9000")
tf_serving_host = os.getenv("CORTEX_TF_SERVING_HOST", "localhost")
has_multiple_servers = os.getenv("CORTEX_MULTIPLE_TF_SERVERS")
if has_multiple_servers:
with LockedFile("/run/used_ports.json", "r+") as f:
used_ports = json.load(f)
for port in used_ports.keys():
if not used_ports[port]:
tf_serving_port = port
used_ports[port] = True
break
f.seek(0)
json.dump(used_ports, f)
f.truncate()
try:
api = get_api(provider, spec_path, model_dir, cache_dir, region)
client = api.predictor.initialize_client(
tf_serving_host=tf_serving_host, tf_serving_port=tf_serving_port)
with FileLock("/run/init_stagger.lock"):
logger().info("loading the predictor from {}".format(
api.predictor.path))
predictor_impl = api.predictor.initialize_impl(project_dir, client)
local_cache["api"] = api
local_cache["provider"] = provider
local_cache["client"] = client
local_cache["predictor_impl"] = predictor_impl
local_cache["predict_fn_args"] = inspect.getfullargspec(
predictor_impl.predict).args
if api.server_side_batching_enabled:
dynamic_batching_config = api.api_spec["predictor"][
"server_side_batching"]
local_cache["dynamic_batcher"] = DynamicBatcher(
predictor_impl,
max_batch_size=dynamic_batching_config["max_batch_size"],
batch_interval=dynamic_batching_config["batch_interval"] /
NANOSECONDS_IN_SECOND, # convert nanoseconds to seconds
)
if util.has_method(predictor_impl, "post_predict"):
local_cache["post_predict_fn_args"] = inspect.getfullargspec(
predictor_impl.post_predict).args
predict_route = "/"
if provider != "local":
predict_route = "/predict"
local_cache["predict_route"] = predict_route
except:
logger().exception("failed to start api")
sys.exit(1)
if (provider != "local" and api.monitoring is not None
and api.monitoring.model_type == "classification"):
try:
local_cache["class_set"] = api.get_cached_classes()
except:
logger().warn("an error occurred while attempting to load classes",
exc_info=True)
app.add_api_route(local_cache["predict_route"], predict, methods=["POST"])
app.add_api_route(local_cache["predict_route"],
get_summary,
methods=["GET"])
return app
def start():
while not pathlib.Path("/mnt/workspace/init_script_run.txt").is_file():
time.sleep(0.2)
cache_dir = os.environ["CORTEX_CACHE_DIR"]
api_spec_path = os.environ["CORTEX_API_SPEC"]
job_spec_path = os.environ["CORTEX_JOB_SPEC"]
project_dir = os.environ["CORTEX_PROJECT_DIR"]
model_dir = os.getenv("CORTEX_MODEL_DIR")
tf_serving_port = os.getenv("CORTEX_TF_BASE_SERVING_PORT", "9000")
tf_serving_host = os.getenv("CORTEX_TF_SERVING_HOST", "localhost")
region = os.getenv("AWS_REGION")
has_multiple_servers = os.getenv("CORTEX_MULTIPLE_TF_SERVERS")
if has_multiple_servers:
with LockedFile("/run/used_ports.json", "r+") as f:
used_ports = json.load(f)
for port in used_ports.keys():
if not used_ports[port]:
tf_serving_port = port
used_ports[port] = True
break
f.seek(0)
json.dump(used_ports, f)
f.truncate()
api = get_api(api_spec_path, model_dir, cache_dir)
with open(job_spec_path) as json_file:
job_spec = json.load(json_file)
sqs_client = boto3.client("sqs", region_name=region)
client = api.predictor.initialize_client(
tf_serving_host=tf_serving_host, tf_serving_port=tf_serving_port
)
try:
log.info("loading the predictor from {}".format(api.predictor.path))
metrics_client = MetricsClient(api.statsd)
predictor_impl = api.predictor.initialize_impl(
project_dir=project_dir,
client=client,
metrics_client=metrics_client,
job_spec=job_spec,
)
except UserRuntimeException as err:
err.wrap(f"failed to start job {job_spec['job_id']}")
log.error(str(err), exc_info=True)
sys.exit(1)
except Exception as err:
capture_exception(err)
log.error(f"failed to start job {job_spec['job_id']}", exc_info=True)
sys.exit(1)
# crons only stop if an unhandled exception occurs
def check_if_crons_have_failed():
while True:
for cron in api.predictor.crons:
if not cron.is_alive():
os.kill(os.getpid(), signal.SIGQUIT)
time.sleep(1)
threading.Thread(target=check_if_crons_have_failed, daemon=True).start()
local_cache["api"] = api
local_cache["job_spec"] = job_spec
local_cache["predictor_impl"] = predictor_impl
local_cache["predict_fn_args"] = inspect.getfullargspec(predictor_impl.predict).args
local_cache["sqs_client"] = sqs_client
open("/mnt/workspace/api_readiness.txt", "a").close()
log.info("polling for batches...")
try:
sqs_handler = SQSHandler(
sqs_client=sqs_client,
queue_url=job_spec["sqs_url"],
renewal_period=MESSAGE_RENEWAL_PERIOD,
visibility_timeout=INITIAL_MESSAGE_VISIBILITY,
not_found_sleep_time=MESSAGE_NOT_FOUND_SLEEP,
message_wait_time=SQS_POLL_WAIT_TIME,
dead_letter_queue_url=job_spec.get("sqs_dead_letter_queue"),
stop_if_no_messages=True,
)
sqs_handler.start(
message_fn=handle_batch_message,
message_failure_fn=handle_batch_failure,
on_job_complete_fn=handle_on_job_complete,
)
except UserRuntimeException as err:
err.wrap(f"failed to run job {job_spec['job_id']}")
log.error(str(err), exc_info=True)
sys.exit(1)
except Exception as err:
capture_exception(err)
log.error(f"failed to run job {job_spec['job_id']}", exc_info=True)
sys.exit(1)
def start():
while not pathlib.Path("/mnt/workspace/init_script_run.txt").is_file():
time.sleep(0.2)
cache_dir = os.environ["CORTEX_CACHE_DIR"]
provider = os.environ["CORTEX_PROVIDER"]
api_spec_path = os.environ["CORTEX_API_SPEC"]
job_spec_path = os.environ["CORTEX_JOB_SPEC"]
project_dir = os.environ["CORTEX_PROJECT_DIR"]
model_dir = os.getenv("CORTEX_MODEL_DIR")
tf_serving_port = os.getenv("CORTEX_TF_BASE_SERVING_PORT", "9000")
tf_serving_host = os.getenv("CORTEX_TF_SERVING_HOST", "localhost")
region = os.getenv("AWS_REGION")
has_multiple_servers = os.getenv("CORTEX_MULTIPLE_TF_SERVERS")
if has_multiple_servers:
with LockedFile("/run/used_ports.json", "r+") as f:
used_ports = json.load(f)
for port in used_ports.keys():
if not used_ports[port]:
tf_serving_port = port
used_ports[port] = True
break
f.seek(0)
json.dump(used_ports, f)
f.truncate()
api = get_api(provider, api_spec_path, model_dir, cache_dir, region)
storage, _ = get_spec(provider, api_spec_path, cache_dir, region)
job_spec = get_job_spec(storage, cache_dir, job_spec_path)
client = api.predictor.initialize_client(tf_serving_host=tf_serving_host,
tf_serving_port=tf_serving_port)
try:
logger.info("loading the predictor from {}".format(api.predictor.path))
metrics_client = MetricsClient(api.statsd)
predictor_impl = api.predictor.initialize_impl(
project_dir=project_dir,
client=client,
metrics_client=metrics_client,
job_spec=job_spec,
)
except UserRuntimeException as err:
err.wrap(f"failed to start job {job_spec['job_id']}")
logger.error(str(err), exc_info=True)
sys.exit(1)
except Exception as err:
capture_exception(err)
logger.error(f"failed to start job {job_spec['job_id']}",
exc_info=True)
sys.exit(1)
# crons only stop if an unhandled exception occurs
def check_if_crons_have_failed():
while True:
for cron in api.predictor.crons:
if not cron.is_alive():
os.kill(os.getpid(), signal.SIGQUIT)
time.sleep(1)
threading.Thread(target=check_if_crons_have_failed, daemon=True).start()
local_cache["api"] = api
local_cache["provider"] = provider
local_cache["job_spec"] = job_spec
local_cache["predictor_impl"] = predictor_impl
local_cache["predict_fn_args"] = inspect.getfullargspec(
predictor_impl.predict).args
local_cache["sqs_client"] = boto3.client("sqs", region_name=region)
open("/mnt/workspace/api_readiness.txt", "a").close()
logger.info("polling for batches...")
try:
sqs_loop()
except UserRuntimeException as err:
err.wrap(f"failed to run job {job_spec['job_id']}")
logger.error(str(err), exc_info=True)
sys.exit(1)
except Exception as err:
capture_exception(err)
logger.error(f"failed to run job {job_spec['job_id']}", exc_info=True)
sys.exit(1)
def start_fn():
provider = os.environ["CORTEX_PROVIDER"]
project_dir = os.environ["CORTEX_PROJECT_DIR"]
spec_path = os.environ["CORTEX_API_SPEC"]
model_dir = os.getenv("CORTEX_MODEL_DIR")
cache_dir = os.getenv("CORTEX_CACHE_DIR")
region = os.getenv("AWS_REGION")
tf_serving_port = os.getenv("CORTEX_TF_BASE_SERVING_PORT", "9000")
tf_serving_host = os.getenv("CORTEX_TF_SERVING_HOST", "localhost")
try:
has_multiple_servers = os.getenv("CORTEX_MULTIPLE_TF_SERVERS")
if has_multiple_servers:
with LockedFile("/run/used_ports.json", "r+") as f:
used_ports = json.load(f)
for port in used_ports.keys():
if not used_ports[port]:
tf_serving_port = port
used_ports[port] = True
break
f.seek(0)
json.dump(used_ports, f)
f.truncate()
api = get_api(provider, spec_path, model_dir, cache_dir, region)
client = api.predictor.initialize_client(
tf_serving_host=tf_serving_host, tf_serving_port=tf_serving_port)
with FileLock("/run/init_stagger.lock"):
logger.info("loading the predictor from {}".format(
api.predictor.path))
predictor_impl = api.predictor.initialize_impl(project_dir, client)
# crons only stop if an unhandled exception occurs
def check_if_crons_have_failed():
while True:
for cron in api.predictor.crons:
if not cron.is_alive():
os.kill(os.getpid(), signal.SIGQUIT)
time.sleep(1)
threading.Thread(target=check_if_crons_have_failed,
daemon=True).start()
local_cache["api"] = api
local_cache["provider"] = provider
local_cache["client"] = client
local_cache["predictor_impl"] = predictor_impl
local_cache["predict_fn_args"] = inspect.getfullargspec(
predictor_impl.predict).args
if api.python_server_side_batching_enabled:
dynamic_batching_config = api.api_spec["predictor"][
"server_side_batching"]
local_cache["dynamic_batcher"] = DynamicBatcher(
predictor_impl,
max_batch_size=dynamic_batching_config["max_batch_size"],
batch_interval=dynamic_batching_config["batch_interval"] /
NANOSECONDS_IN_SECOND, # convert nanoseconds to seconds
)
if util.has_method(predictor_impl, "post_predict"):
local_cache["post_predict_fn_args"] = inspect.getfullargspec(
predictor_impl.post_predict).args
predict_route = "/predict"
local_cache["predict_route"] = predict_route
except (UserRuntimeException, Exception) as err:
if not isinstance(err, UserRuntimeException):
capture_exception(err)
logger.exception("failed to start api")
sys.exit(1)
app.add_api_route(local_cache["predict_route"], predict, methods=["POST"])
app.add_api_route(local_cache["predict_route"],
get_summary,
methods=["GET"])
return app
def init():
project_dir = os.environ["CORTEX_PROJECT_DIR"]
spec_path = os.environ["CORTEX_API_SPEC"]
model_dir = os.getenv("CORTEX_MODEL_DIR")
cache_dir = os.getenv("CORTEX_CACHE_DIR")
region = os.getenv("AWS_REGION")
tf_serving_port = os.getenv("CORTEX_TF_BASE_SERVING_PORT", "9000")
tf_serving_host = os.getenv("CORTEX_TF_SERVING_HOST", "localhost")
has_multiple_servers = os.getenv("CORTEX_MULTIPLE_TF_SERVERS")
if has_multiple_servers:
with LockedFile("/run/used_ports.json", "r+") as f:
used_ports = json.load(f)
for port in used_ports.keys():
if not used_ports[port]:
tf_serving_port = port
used_ports[port] = True
break
f.seek(0)
json.dump(used_ports, f)
f.truncate()
api = get_api(spec_path, model_dir, cache_dir, region)
config: Dict[str, Any] = {
"api": None,
"client": None,
"predictor_impl": None,
"module_proto_pb2_grpc": None,
}
proto_without_ext = pathlib.Path(api.predictor.protobuf_path).stem
module_proto_pb2 = importlib.import_module(proto_without_ext + "_pb2")
module_proto_pb2_grpc = importlib.import_module(proto_without_ext +
"_pb2_grpc")
client = api.predictor.initialize_client(tf_serving_host=tf_serving_host,
tf_serving_port=tf_serving_port)
with FileLock("/run/init_stagger.lock"):
logger.info("loading the predictor from {}".format(api.predictor.path))
metrics_client = MetricsClient(api.statsd)
predictor_impl = api.predictor.initialize_impl(
project_dir=project_dir,
client=client,
metrics_client=metrics_client,
proto_module_pb2=module_proto_pb2,
)
# crons only stop if an unhandled exception occurs
def check_if_crons_have_failed():
while True:
for cron in api.predictor.crons:
if not cron.is_alive():
os.kill(os.getpid(), signal.SIGQUIT)
time.sleep(1)
threading.Thread(target=check_if_crons_have_failed, daemon=True).start()
ServicerClass = get_servicer_from_module(module_proto_pb2_grpc)
class PredictorServicer(ServicerClass):
def __init__(self, predict_fn_args, predictor_impl, api):
self.predict_fn_args = predict_fn_args
self.predictor_impl = predictor_impl
self.api = api
def Predict(self, payload, context):
try:
kwargs = build_predict_kwargs(self.predict_fn_args, payload,
context)
response = self.predictor_impl.predict(**kwargs)
self.api.post_status_code_request_metrics(200)
except Exception:
logger.error(traceback.format_exc())
self.api.post_status_code_request_metrics(500)
context.abort(grpc.StatusCode.INTERNAL,
"internal server error")
return response
config["api"] = api
config["client"] = client
config["predictor_impl"] = predictor_impl
config["predict_fn_args"] = inspect.getfullargspec(
predictor_impl.predict).args
config["module_proto_pb2"] = module_proto_pb2
config["module_proto_pb2_grpc"] = module_proto_pb2_grpc
config["predictor_servicer"] = PredictorServicer
return config
