ci: enable bentoml-1.0 (#1783)

* refactor: wip

* refactor: add bentoml-1.0 branch to ci

Author: aarnphm

.github/workflows/ci.yml

@@ -2,9 +2,9 @@ name: BentoML-CI
 
 on:
   push:
-    branches: [ master ]
+    branches: [ master, bentoml-1.0 ]
   pull_request:
-    branches: [ master ]
+    branches: [ master, bentoml-1.0 ]
   schedule:
     - cron: '0 2 * * *'
 
@@ -636,4 +636,4 @@ jobs:
       - name: Run tests
         run: ./ci/integration/frameworks/run_xgboost_tests.sh
       - name: Upload test coverage to Codecov
-        uses: codecov/[email protected]
+        uses: codecov/[email protected]

bentoml/__init__.py

@@ -1,7 +1,7 @@
 # From versioneer
 from ._version import get_versions
 
-__version__ = get_versions()["version"]  # type: ignore
+__version__ = get_versions()["version"]
 del get_versions
 
 from bentoml._internal.configuration import inject_dependencies

bentoml/_internal/models/base.py

@@ -4,7 +4,7 @@
 from ..types import MetadataType, PathType
 from ..utils.ruamel_yaml import YAML
 
-MT = t.TypeVar("MT")
+MT = t.TypeVar("MT", bound=t.Any)
 
 H5_EXTENSION: str = ".h5"
 HDF5_EXTENSION: str = ".hdf5"
@@ -58,7 +58,7 @@ def metadata(self: "Model") -> t.Optional[MetadataType]:
         return self._metadata
 
     @classmethod
-    def load(cls, path: PathType) -> MT:
+    def load(cls, path: PathType) -> t.Any:
         """
         Load saved model into memory.
 

bentoml/_internal/types.py

@@ -33,7 +33,7 @@
 
 JSON_CHARSET = "utf-8"
 
-PathType = Union[str, bytes, os.PathLike]
+PathType = Union[str, os.PathLike]
 
 MetadataType = Dict[str, Any]  # TODO:
 

bentoml/detectron.py

@@ -47,8 +47,6 @@ class DetectronModel(Model):
         TODO:
     """
 
-    _model: torch.nn.Module
-
     def __init__(
         self,
         model: torch.nn.Module,

bentoml/onnx.py

@@ -74,11 +74,11 @@ def load(
         if isinstance(path, onnx.ModelProto):
             return onnxruntime.InferenceSession(path.SerializeToString())
         else:
-            _get_path: str = cls.__get_model_fpath(path)
+            _get_path = str(cls.__get_model_fpath(path))
             return onnxruntime.InferenceSession(_get_path)
 
     def save(self, path: t.Union[PathType, "onnx.ModelProto"]) -> None:
         if isinstance(self._model, onnx.ModelProto):
             onnx.save_model(self._model, self.__get_model_fpath(path))
         else:
-            shutil.copyfile(self._model, self.__get_model_fpath(path))
+            shutil.copyfile(self._model, str(self.__get_model_fpath(path)))

bentoml/py.typed

@@ -61,8 +61,6 @@ class PySparkMLlibModel(Model):
         TODO:
     """
 
-    _model: pyspark.ml.Model
-
     def __init__(
         self,
         model: pyspark.ml.Model,

bentoml/pyspark.py

@@ -19,7 +19,7 @@
 try:
     import pytorch_lightning
 except ImportError:
-    pytorch_lightning = None
+    pytorch_lightning = None  # type: ignore
 
 logger = logging.getLogger(__name__)
 
@@ -67,9 +67,11 @@ def load(
     ) -> t.Union["torch.nn.Module", "torch.jit.ScriptModule"]:
         # TorchScript Models are saved as zip files
         if zipfile.is_zipfile(cls.__get_weight_fpath(path)):
-            return torch.jit.load(cls.__get_weight_fpath(path))
+            return torch.jit.load(cls.__get_weight_fpath(path))  # type: ignore
         else:
-            return cloudpickle.load(open(cls.__get_weight_fpath(path), "rb"))
+            return cloudpickle.load(  # type: ignore
+                open(cls.__get_weight_fpath(path), "rb")
+            )
 
     def save(self, path: PathType) -> None:
         # If model is a TorchScriptModule, we cannot apply standard pickling
@@ -125,7 +127,7 @@ def __get_weight_fpath(path: PathType) -> str:
 
     @classmethod
     def load(cls, path: PathType) -> "torch.jit.ScriptModule":
-        return torch.jit.load(cls.__get_weight_fpath(path))
+        return torch.jit.load(cls.__get_weight_fpath(path))  # type: ignore
 
     def save(self, path: PathType) -> None:
         torch.jit.save(self._model.to_torchscript(), self.__get_weight_fpath(path))

bentoml/pytorch.py

@@ -152,9 +152,9 @@ def __init__(
         super(TensorflowModel, self).__init__(model, metadata=metadata)
 
     @staticmethod
-    def __load_tf_saved_model(
+    def __load_tf_saved_model(  # pylint: disable=unused-private-member
         path: str,
-    ) -> t.Union[AutoTrackable, t.Any]:  # pylint: disable=unused-private-member
+    ) -> t.Union[AutoTrackable, t.Any]:
         if TF2:
             return tf.saved_model.load(path)
         else:

bentoml/tensorflow.py

@@ -71,9 +71,9 @@ def __load_from_directory(  # pylint: disable=unused-private-member
         return {"model": transformers_model, "tokenizer": tokenizer}
 
     @staticmethod
-    def __load_from_dict(
+    def __load_from_dict(  # pylint: disable=unused-private-member
         transformers_dict: t.Dict[str, t.Any]
-    ) -> dict:  # pylint: disable=unused-private-member
+    ) -> dict:
         if not transformers_dict.get("model"):
             raise InvalidArgument(
                 " 'model' key is not found in the dictionary."
@@ -103,9 +103,9 @@ def __load_from_dict(
         return transformers_dict
 
     @classmethod
-    def __load_from_string(
+    def __load_from_string(  # pylint: disable=unused-private-member
         cls, model_name: str
-    ) -> dict:  # pylint: disable=unused-private-member
+    ) -> dict:
         try:
             transformers_model = getattr(
                 import_module("transformers"), cls._model_type

bentoml/transformers.py

@@ -42,7 +42,6 @@ class XgBoostModel(Model):
     def __init__(
         self, model: xgboost.core.Booster, metadata: t.Optional[MetadataType] = None
     ):
-        self._model: xgboost.core.Booster
         super(XgBoostModel, self).__init__(model, metadata=metadata)
 
     @classmethod

bentoml/xgboost.py

@@ -37,7 +37,7 @@
 
 # TODO: Update some of the complex type hint cases for both utils.py and this one
 if t.TYPE_CHECKING:
-    from docker.models.images import Image
+    from docker.models.images import Image  # pylint: disable=unused-import
 
 README_TEMPLATE: Path = Path("./templates/docs/README.md.j2")
 
@@ -498,7 +498,7 @@ class BuildMixin(object):
 
     def build_images(self) -> None:
         """Build images."""
-        for image_tag, dockerfile_path in self.build_tags():  # type: ignore
+        for image_tag, dockerfile_path in self.build_tags():
             log.info(f"Building {image_tag} from {dockerfile_path}")
             try:
                 # this is should be when there are changed in base image.
@@ -582,7 +582,7 @@ def push_images(self) -> None:
                 return
 
             # Then push image to registry.
-            for image_tag in self.push_tags():  # type: ignore
+            for image_tag in self.push_tags():
                 image = self._push_context[image_tag]
                 reg, tag = image_tag.split(":")
                 registry = "".join(
@@ -648,7 +648,7 @@ def push_readmes(
                 cookies=logins.cookies,
             )
 
-    def background_upload(self, image: "Image", tag: str, registry: str) -> None:
+    def background_upload(self, image: Image, tag: str, registry: str) -> None:
         """Upload a docker image."""
         image.tag(registry, tag=tag)
         log.debug(f"Pushing {repr(image)} to {registry}...")

docker/manager.py

@@ -490,7 +490,7 @@ def walk(path: pathlib.Path) -> t.Generator:
         yield p.resolve()
 
 
-def sprint(*args: str, **kwargs: str) -> None:
+def sprint(*args: t.Any, **kwargs: str) -> None:
     # stream logs inside docker container to sys.stderr
     print(*args, file=sys.stderr, flush=True, **kwargs)
 

docker/utils.py

@@ -4,8 +4,7 @@ disable_error_code = attr-defined
 exclude = "|(bentoml/_internal/yatai_client/proto)|(yatai/yatai/proto)|(yatai/versioneer.py)|"
 ignore_missing_imports = True
 
-# mypy --strict --allow-any-generics --allow-subclassing-any --no-check-untyped-defs
-disallow_untyped_calls = True
+# mypy --strict --allow-any-generics --allow-subclassing-any --no-check-untyped-defs --allow-untyped-call
 disallow_untyped_defs = True
 disallow_incomplete_defs = True
 disallow_untyped_decorators = True