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 set_dependencies(self, env: BentoServiceEnv):
if self.backend == 'onnxruntime':
env.add_pip_dependencies_if_missing(['onnxruntime'])
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(['tensorflow'])
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(['coremltools>=4.0b2'])
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(['lightgbm'])
