def start():
cache_dir = os.environ["CORTEX_CACHE_DIR"]
provider = os.environ["CORTEX_PROVIDER"]
project_dir = os.environ["CORTEX_PROJECT_DIR"]
region = os.getenv("AWS_REGION")
api_spec_path = os.environ["CORTEX_API_SPEC"]
task_spec_path = os.environ["CORTEX_TASK_SPEC"]
_, api_spec = get_spec(provider, api_spec_path, cache_dir, region)
_, task_spec = get_spec(provider,
task_spec_path,
cache_dir,
region,
spec_name="task-spec.json")
logger.info("loading the task definition from {}".format(
api_spec["definition"]["path"]))
task_api = TaskAPI(provider, api_spec)
logger.info("executing the task definition from {}".format(
api_spec["definition"]["path"]))
callable_fn = task_api.get_callable(project_dir)
config = deepcopy(api_spec["definition"]["config"])
if task_spec is not None and task_spec.get("config") is not None:
util.merge_dicts_in_place_overwrite(config, task_spec["config"])
try:
callable_fn(config)
except Exception as err:
logger.error(f"failed to run task {task_spec['job_id']}",
exc_info=True)
sys.exit(1)
def test_merge_dicts():
dict1 = {"k1": "v1", "k2": "v2", "k3": {"k1": "v1", "k2": "v2"}}
dict2 = {"k1": "V1", "k4": "V4", "k3": {"k1": "V1", "k4": "V4"}}
expected1 = {
"k1": "V1",
"k2": "v2",
"k4": "V4",
"k3": {
"k1": "V1",
"k2": "v2",
"k4": "V4"
}
}
expected2 = {
"k1": "v1",
"k2": "v2",
"k4": "V4",
"k3": {
"k1": "v1",
"k2": "v2",
"k4": "V4"
}
}
merged = util.merge_dicts_overwrite(dict1, dict2)
assert expected1 == merged
assert dict1 != expected1
assert dict2 != expected1
merged = util.merge_dicts_no_overwrite(dict1, dict2)
assert expected2 == merged
assert dict1 != expected2
assert dict2 != expected2
dict1_copy = deepcopy(dict1)
util.merge_dicts_in_place_overwrite(dict1_copy, dict2)
assert expected1 == dict1_copy
assert dict1 != dict1_copy
dict1_copy = deepcopy(dict1)
util.merge_dicts_in_place_no_overwrite(dict1_copy, dict2)
assert expected2 == dict1_copy
assert dict1 != dict1_copy
def initialize_impl(
self,
project_dir: str,
client: Union[PythonClient, TensorFlowClient, ONNXClient],
job_spec: Optional[dict] = None,
):
"""
Initialize predictor class as provided by the user.
job_spec is a dictionary when the "kind" of the API is set to "BatchAPI". Otherwise, it's None.
"""
# build args
class_impl = self.class_impl(project_dir)
constructor_args = inspect.getfullargspec(class_impl.__init__).args
config = deepcopy(self.config)
args = {}
if job_spec is not None and job_spec.get("config") is not None:
util.merge_dicts_in_place_overwrite(config, job_spec["config"])
if "config" in constructor_args:
args["config"] = config
if "job_spec" in constructor_args:
args["job_spec"] = job_spec
# initialize predictor class
try:
if self.type == PythonPredictorType:
if _are_models_specified(self.api_spec):
args["python_client"] = client
initialized_impl = class_impl(**args)
client.set_load_method(initialized_impl.load_model)
else:
initialized_impl = class_impl(**args)
if self.type in [
TensorFlowPredictorType, TensorFlowNeuronPredictorType
]:
args["tensorflow_client"] = client
initialized_impl = class_impl(**args)
if self.type == ONNXPredictorType:
args["onnx_client"] = client
initialized_impl = class_impl(**args)
except Exception as e:
raise UserRuntimeException(self.path, "__init__", str(e)) from e
finally:
refresh_logger()
# initialize the crons if models have been specified and if the API kind is RealtimeAPI
if _are_models_specified(
self.api_spec) and self.api_spec["kind"] == "RealtimeAPI":
if not self.multiple_processes and self.caching_enabled:
self.crons += [
ModelTreeUpdater(
interval=10,
api_spec=self.api_spec,
tree=self.models_tree,
ondisk_models_dir=self.model_dir,
),
ModelsGC(
interval=10,
api_spec=self.api_spec,
models=self.models,
tree=self.models_tree,
),
]
if not self.caching_enabled and self.type in [
PythonPredictorType, ONNXPredictorType
]:
self.crons += [
FileBasedModelsGC(interval=10,
models=self.models,
download_dir=self.model_dir)
]
for cron in self.crons:
cron.start()
return initialized_impl
def initialize_impl(
self,
project_dir: str,
client: Union[PythonClient, TensorFlowClient],
metrics_client: DogStatsd,
job_spec: Optional[Dict[str, Any]] = None,
proto_module_pb2: Optional[Any] = None,
):
"""
Initialize predictor class as provided by the user.
job_spec is a dictionary when the "kind" of the API is set to "BatchAPI". Otherwise, it's None.
proto_module_pb2 is a module of the compiled proto when grpc is enabled for the "RealtimeAPI" kind. Otherwise, it's None.
Can raise UserRuntimeException/UserException/CortexException.
"""
# build args
class_impl = self.class_impl(project_dir)
constructor_args = inspect.getfullargspec(class_impl.__init__).args
config = deepcopy(self.config)
args = {}
if job_spec is not None and job_spec.get("config") is not None:
util.merge_dicts_in_place_overwrite(config, job_spec["config"])
if "config" in constructor_args:
args["config"] = config
if "job_spec" in constructor_args:
args["job_spec"] = job_spec
if "metrics_client" in constructor_args:
args["metrics_client"] = metrics_client
if "proto_module_pb2" in constructor_args:
args["proto_module_pb2"] = proto_module_pb2
# initialize predictor class
try:
if self.type == PythonPredictorType:
if are_models_specified(self.api_spec):
args["python_client"] = client
# set load method to enable the use of the client in the constructor
# setting/getting from self in load_model won't work because self will be set to None
client.set_load_method(lambda model_path: class_impl.
load_model(None, model_path))
initialized_impl = class_impl(**args)
client.set_load_method(initialized_impl.load_model)
else:
initialized_impl = class_impl(**args)
if self.type in [
TensorFlowPredictorType, TensorFlowNeuronPredictorType
]:
args["tensorflow_client"] = client
initialized_impl = class_impl(**args)
except Exception as e:
raise UserRuntimeException(self.path, "__init__", str(e)) from e
# initialize the crons if models have been specified and if the API kind is RealtimeAPI
if are_models_specified(
self.api_spec) and self.api_spec["kind"] == "RealtimeAPI":
if not self.multiple_processes and self.caching_enabled:
self.crons += [
ModelTreeUpdater(
interval=10,
api_spec=self.api_spec,
tree=self.models_tree,
ondisk_models_dir=self.model_dir,
),
ModelsGC(
interval=10,
api_spec=self.api_spec,
models=self.models,
tree=self.models_tree,
),
]
if not self.caching_enabled and self.type == PythonPredictorType:
self.crons += [
FileBasedModelsGC(interval=10,
models=self.models,
download_dir=self.model_dir)
]
for cron in self.crons:
cron.start()
return initialized_impl