def set_dependencies(self, env: BentoServiceEnv):
# Note that keras module is not required, user can use tf.keras as an
# replacement for the keras module. Although tensorflow module is required to
# be used as the default Keras backend
pip_deps = ['tensorflow']
if self._keras_module_name == 'keras':
pip_deps.append('keras')
env.add_pip_dependencies_if_missing(pip_deps)
def set_dependencies(self, env: BentoServiceEnv):
logger.warning(
"BentoML by default does not include spacy and torchvision package when "
"using FastaiModelArtifact. To make sure BentoML bundle those packages if "
"they are required for your model, either import those packages in "
"BentoService definition file or manually add them via "
"`@env(pip_dependencies=['torchvision'])` when defining a BentoService"
)
env.add_pip_dependencies_if_missing(['torch', "fastai"])
def _init_env(self):
self._env = self.__class__._env or BentoServiceEnv(self.name)
for api in self._service_apis:
self._env._add_pip_dependencies_if_missing(api.handler.pip_dependencies)
for artifact in self._artifacts:
self._env._add_pip_dependencies_if_missing(artifact.pip_dependencies)
def _init_env(self, env=None):
if env is None:
# By default use BentoServiceEnv defined on class via @env decorator
env = self.__class__._env
if isinstance(env, dict):
self._env = BentoServiceEnv.fromDict(env)
else:
self._env = env
def decorator(bento_service_cls):
bento_service_cls._env = BentoServiceEnv(
bento_service_name=bento_service_cls.name(),
setup_sh=setup_sh,
pip_dependencies=pip_dependencies,
conda_channels=conda_channels,
conda_dependencies=conda_dependencies,
)
return bento_service_cls
def decorator(bento_service_cls):
bento_service_cls._env = BentoServiceEnv(
bento_service_name=bento_service_cls.name(),
pip_dependencies=pip_dependencies,
auto_pip_dependencies=auto_pip_dependencies,
requirements_txt_file=requirements_txt_file,
conda_channels=conda_channels,
conda_dependencies=conda_dependencies,
setup_sh=setup_sh,
)
return bento_service_cls
def _config_environments(self):
self._env = self.__class__._env or BentoServiceEnv(self.name)
for api in self._inference_apis:
self._env.add_pip_dependencies_if_missing(
api.handler.pip_dependencies)
self._env.add_pip_dependencies_if_missing(
api.output_adapter.pip_dependencies)
for artifact in self.artifacts.get_artifact_list():
artifact.set_dependencies(self.env)
def _init_env(self, env=None):
if env is None:
# By default use BentoServiceEnv defined on class via @env decorator
env = self.__class__._env
if isinstance(env, dict):
self._env = BentoServiceEnv.from_dict(env)
else:
self._env = env
for api in self._service_apis:
self._env.add_handler_dependencies(api.handler.pip_dependencies)
def _init_env(self):
self._env = self.__class__._env or BentoServiceEnv(self.name)
for api in self._service_apis:
self._env._add_pip_dependencies_if_missing(
api.handler.pip_dependencies)
self._env._add_pip_dependencies_if_missing(
api.output_adapter.pip_dependencies)
# TODO(bojiang): adapter dependencies
for artifact in self._artifacts:
self._env._add_pip_dependencies_if_missing(
artifact.pip_dependencies)
def __init__(self, artifacts, env=None):
# TODO: validate artifacts arg matches self.__class__._artifacts_spec definition
if isinstance(artifacts, ArtifactCollection):
self._artifacts = artifacts
else:
self._artifacts = ArtifactCollection()
for artifact in artifacts:
self._artifacts[artifact.name] = artifact
if env is None:
# By default use BentoServiceEnv defined on class via @env decorator
env = self.__class__._env
if isinstance(env, dict):
self._env = BentoServiceEnv.fromDict(env)
else:
self._env = env
self._config_service_apis()
self.name = self.__class__.name()
def _init_env(self):
self._env = self.__class__._env or BentoServiceEnv(self.name)
for api in self._service_apis:
self._env.add_handler_dependencies(api.handler.pip_dependencies)
def set_dependencies(self, env: BentoServiceEnv):
if self.backend == 'onnxruntime':
env.add_pip_dependencies_if_missing(['onnxruntime'])
def decorator(bento_service_cls):
bento_service_cls._env = BentoServiceEnv.from_dict(kwargs)
return bento_service_cls
def set_dependencies(self, env: BentoServiceEnv):
env.add_pip_dependencies_if_missing(["fasttext"])
def set_dependencies(self, env: BentoServiceEnv):
env.add_pip_dependencies_if_missing(['h2o'])
env.add_conda_dependencies(['openjdk'])
def set_dependencies(self, env: BentoServiceEnv):
env.add_pip_dependencies_if_missing(['coremltools>=4.0b2'])
def set_dependencies(self, env: BentoServiceEnv):
env.add_pip_dependencies_if_missing(['xgboost'])
def set_dependencies(self, env: BentoServiceEnv):
env.add_pip_dependencies_if_missing(['tensorflow'])
def set_dependencies(self, env: BentoServiceEnv):
env.add_pip_dependencies_if_missing(['lightgbm'])
