def predict(request: Request):
tasks = BackgroundTasks()
api = local_cache["api"]
predictor_impl = local_cache["predictor_impl"]
kwargs = build_predict_kwargs(request)
prediction = predictor_impl.predict(**kwargs)
if isinstance(prediction, bytes):
response = Response(content=prediction, media_type="application/octet-stream")
elif isinstance(prediction, str):
response = Response(content=prediction, media_type="text/plain")
elif isinstance(prediction, Response):
response = prediction
else:
try:
json_string = json.dumps(prediction)
except Exception as e:
raise UserRuntimeException(
str(e),
"please return an object that is JSON serializable (including its nested fields), a bytes object, a string, or a starlette.response.Response object",
) from e
response = Response(content=json_string, media_type="application/json")
if local_cache["provider"] != "local" and api.monitoring is not None:
try:
predicted_value = api.monitoring.extract_predicted_value(prediction)
api.post_monitoring_metrics(predicted_value)
if (
api.monitoring.model_type == "classification"
and predicted_value not in local_cache["class_set"]
):
tasks.add_task(api.upload_class, class_name=predicted_value)
local_cache["class_set"].add(predicted_value)
except:
cx_logger().warn("unable to record prediction metric", exc_info=True)
if util.has_method(predictor_impl, "post_predict"):
kwargs = build_post_predict_kwargs(prediction, request)
request_thread_pool.submit(predictor_impl.post_predict, **kwargs)
if len(tasks.tasks) > 0:
response.background = tasks
return response
def start_fn():
cache_dir = os.environ["CORTEX_CACHE_DIR"]
provider = os.environ["CORTEX_PROVIDER"]
spec_path = os.environ["CORTEX_API_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")
if provider == "local":
storage = LocalStorage(os.getenv("CORTEX_CACHE_DIR"))
else:
storage = S3(bucket=os.environ["CORTEX_BUCKET"], region=os.environ["AWS_REGION"])
has_multiple_servers = os.getenv("CORTEX_MULTIPLE_TF_SERVERS")
if has_multiple_servers:
with FileLock("/run/used_ports.json.lock"):
with open("/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:
raw_api_spec = get_spec(provider, storage, cache_dir, spec_path)
api = API(
provider=provider,
storage=storage,
model_dir=model_dir,
cache_dir=cache_dir,
**raw_api_spec,
)
client = api.predictor.initialize_client(
tf_serving_host=tf_serving_host, tf_serving_port=tf_serving_port
)
cx_logger().info("loading the predictor from {}".format(api.predictor.path))
predictor_impl = api.predictor.initialize_impl(project_dir, client, raw_api_spec, None)
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 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:
cx_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:
cx_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