def prepare_tfs_servers_api(api_spec: dict, model_dir: str) -> TFSModelLoader:
# get TFS address-specific details
tf_serving_host = os.getenv("CORTEX_TF_SERVING_HOST", "localhost")
tf_base_serving_port = int(os.getenv("CORTEX_TF_BASE_SERVING_PORT", "9000"))
# determine if multiple TF processes are required
num_processes = 1
has_multiple_tf_servers = os.getenv("CORTEX_MULTIPLE_TF_SERVERS")
if has_multiple_tf_servers:
num_processes = int(os.environ["CORTEX_PROCESSES_PER_REPLICA"])
# initialize models for each TF process
addresses = []
for w in range(int(num_processes)):
addresses.append(f"{tf_serving_host}:{tf_base_serving_port+w}")
if len(addresses) == 1:
return TFSModelLoader(
interval=10,
api_spec=api_spec,
address=addresses[0],
tfs_model_dir=model_dir,
download_dir=model_dir,
)
return TFSModelLoader(
interval=10,
api_spec=api_spec,
addresses=addresses,
tfs_model_dir=model_dir,
download_dir=model_dir,
)
def main():
# wait until neuron-rtd sidecar is ready
uses_inferentia = os.getenv("CORTEX_ACTIVE_NEURON")
if uses_inferentia:
wait_neuron_rtd()
# strictly for Inferentia
has_multiple_tf_servers = os.getenv("CORTEX_MULTIPLE_TF_SERVERS")
num_processes = int(os.environ["CORTEX_PROCESSES_PER_REPLICA"])
if has_multiple_tf_servers:
base_serving_port = int(os.environ["CORTEX_TF_BASE_SERVING_PORT"])
used_ports = {}
for w in range(int(num_processes)):
used_ports[str(base_serving_port + w)] = False
with open("/run/used_ports.json", "w+") as f:
json.dump(used_ports, f)
# get API spec
provider = os.environ["CORTEX_PROVIDER"]
spec_path = os.environ["CORTEX_API_SPEC"]
cache_dir = os.getenv("CORTEX_CACHE_DIR")
region = os.getenv("AWS_REGION") # when it's deployed to AWS
_, api_spec = get_spec(provider, spec_path, cache_dir, region)
predictor_type = predictor_type_from_api_spec(api_spec)
multiple_processes = api_spec["predictor"]["processes_per_replica"] > 1
caching_enabled = is_model_caching_enabled(api_spec)
model_dir = os.getenv("CORTEX_MODEL_DIR")
# start live-reloading when model caching not enabled > 1
cron = None
if not caching_enabled:
# create cron dirs if they don't exist
os.makedirs("/run/cron", exist_ok=True)
os.makedirs("/tmp/cron", exist_ok=True)
# prepare crons
if predictor_type in [PythonPredictorType, ONNXPredictorType] and are_models_specified(
api_spec
):
cron = FileBasedModelsTreeUpdater(
interval=10,
api_spec=api_spec,
download_dir=model_dir,
)
cron.start()
elif predictor_type == TensorFlowPredictorType:
tf_serving_port = os.getenv("CORTEX_TF_BASE_SERVING_PORT", "9000")
tf_serving_host = os.getenv("CORTEX_TF_SERVING_HOST", "localhost")
cron = TFSModelLoader(
interval=10,
api_spec=api_spec,
address=f"{tf_serving_host}:{tf_serving_port}",
tfs_model_dir=model_dir,
download_dir=model_dir,
)
cron.start()
elif predictor_type == TensorFlowNeuronPredictorType:
cron = prepare_tfs_servers_api(api_spec, model_dir)
cron.start()
# wait until the cron finishes its first pass
if cron:
while cron.is_alive() and not cron.ran_once():
time.sleep(0.25)
# disable live reloading when the BatchAPI kind is used
# disable live reloading for the TF predictor when Inferentia is used and when multiple processes are used (num procs > 1)
if api_spec["kind"] != "RealtimeAPI" or (
predictor_type == TensorFlowNeuronPredictorType
and has_multiple_tf_servers
and num_processes > 1
):
cron.stop()
# to syncronize with the other serving processes
open("/mnt/workspace/init_script_run.txt", "a").close()
# don't exit the script if the cron is running
while cron and cron.is_alive():
time.sleep(0.25)
# exit if cron has exited with errors
if cron and isinstance(cron.exitcode, int) and cron.exitcode != 0:
# if it was killed by a signal
if cron.exitcode < 0:
sys.exit(-cron.exitcode)
sys.exit(cron.exitcode)