Add image transformer

dev-requirements.in

@@ -26,6 +26,7 @@ torch<=1.12.1; python_version<'3.11'
 # pytorch 2 supports python 3.11
 torch<=2.0.0; python_version>='3.11' or platform_system!='Windows'
 
+pillow
 scikit-learn
 types-protobuf
 types-croniter

flytekit/core/type_engine.py

@@ -874,6 +874,8 @@
             register_bigquery_handlers()
         if is_imported("numpy"):
             from flytekit.types import numpy  # noqa: F401
+        if is_imported("PIL"):
+            from flytekit.types.file import image  # noqa: F401
 
     @classmethod
     def to_literal_type(cls, python_type: Type) -> LiteralType:

flytekit/experimental/eager_function.py

@@ -531,7 +531,7 @@ async def wrapper(*args, **kws):
     return task(
         wrapper,
         secret_requests=secret_requests,
-        disable_deck=False,
+        enable_deck=True,
         execution_mode=PythonFunctionTask.ExecutionBehavior.EAGER,
         **kwargs,
     )

flytekit/types/file/image.py

@@ -0,0 +1,82 @@
+import pathlib
+import typing
+from typing import Type
+
+import PIL.Image
+
+from flytekit.core.context_manager import FlyteContext
+from flytekit.core.type_engine import TypeEngine, TypeTransformer, TypeTransformerFailedError
+from flytekit.models.core import types as _core_types
+from flytekit.models.literals import Blob, BlobMetadata, Literal, Scalar
+from flytekit.models.types import LiteralType
+
+T = typing.TypeVar("T")
+
+
+class PILImageTransformer(TypeTransformer[T]):
+    """
+    TypeTransformer that supports PIL.Image as a native type.
+    """
+
+    FILE_FORMAT = "PIL.Image"
+
+    def __init__(self):
+        super().__init__(name="PIL.Image", t=PIL.Image.Image)
+
+    def get_literal_type(self, t: Type[T]) -> LiteralType:
+        return LiteralType(
+            blob=_core_types.BlobType(
+                format=self.FILE_FORMAT, dimensionality=_core_types.BlobType.BlobDimensionality.SINGLE
+            )
+        )
+
+    def to_literal(
+        self, ctx: FlyteContext, python_val: PIL.Image.Image, python_type: Type[T], expected: LiteralType
+    ) -> Literal:
+
+        meta = BlobMetadata(
+            type=_core_types.BlobType(
+                format=self.FILE_FORMAT, dimensionality=_core_types.BlobType.BlobDimensionality.SINGLE
+            )
+        )
+
+        local_path = ctx.file_access.get_random_local_path() + ".png"
+        pathlib.Path(local_path).parent.mkdir(parents=True, exist_ok=True)
+        python_val.save(local_path)
+
+        remote_path = ctx.file_access.get_random_remote_path(local_path)
+        ctx.file_access.put_data(local_path, remote_path, is_multipart=False)
+        return Literal(scalar=Scalar(blob=Blob(metadata=meta, uri=remote_path)))
+
+    def to_python_value(self, ctx: FlyteContext, lv: Literal, expected_python_type: Type[T]) -> PIL.Image.Image:
+        try:
+            uri = lv.scalar.blob.uri
+        except AttributeError:
+            raise TypeTransformerFailedError(f"Cannot convert from {lv} to {expected_python_type}")
+
+        local_path = ctx.file_access.get_random_local_path()
+        ctx.file_access.get_data(uri, local_path, is_multipart=False)
+
+        return PIL.Image.open(local_path)
+
+    def guess_python_type(self, literal_type: LiteralType) -> Type[T]:
+        if (
+            literal_type.blob is not None
+            and literal_type.blob.dimensionality == _core_types.BlobType.BlobDimensionality.SINGLE
+            and literal_type.blob.format == self.FILE_FORMAT
+        ):
+            return PIL.Image.Image
+
+        raise ValueError(f"Transformer {self} cannot reverse {literal_type}")
+
+    def to_html(self, ctx: FlyteContext, python_val: PIL.Image.Image, expected_python_type: Type[T]) -> str:
+        import base64
+        from io import BytesIO
+
+        buffered = BytesIO()
+        python_val.save(buffered, format="PNG")
+        img_base64 = base64.b64encode(buffered.getvalue()).decode()
+        return f'<img src="data:image/png;base64,{img_base64}" alt="Rendered Image" />'
+
+
+TypeEngine.register(PILImageTransformer())

plugins/flytekit-deck-standard/flytekitplugins/deck/renderer.py

@@ -6,7 +6,7 @@
 if TYPE_CHECKING:
     import markdown
     import pandas as pd
-    import PIL
+    import PIL.Image
     import plotly.express as px
 else:
     pd = lazy_module("pandas")

plugins/flytekit-kf-pytorch/tests/test_elastic_task.py

@@ -120,7 +120,7 @@ def test_deck(start_method: str) -> None:
 
     @task(
         task_config=Elastic(nnodes=1, nproc_per_node=world_size, start_method=start_method),
-        disable_deck=False,
+        enable_deck=True,
     )
     def train():
         import os

plugins/flytekit-mlflow/README.md

@@ -15,7 +15,7 @@ from flytekit import task, workflow
 from flytekitplugins.mlflow import mlflow_autolog
 import mlflow
 
-@task(disable_deck=False)
+@task(enable_deck=True)
 @mlflow_autolog(framework=mlflow.keras)
 def train_model():
     ...

plugins/flytekit-mlflow/tests/test_mlflow_tracking.py

@@ -6,7 +6,7 @@
 from flytekit import task
 
 
-@task(disable_deck=False)
+@task(enable_deck=True)
 @mlflow_autolog(framework=mlflow.keras)
 def train_model(epochs: int):
     fashion_mnist = tf.keras.datasets.fashion_mnist

tests/flytekit/unit/types/file/test_image.py

@@ -0,0 +1,22 @@
+import PIL.Image
+
+from flytekit import task, workflow
+
+
+@task(enable_deck=True)
+def t1() -> PIL.Image.Image:
+    return PIL.Image.new("L", (100, 100), "black")
+
+
+@task
+def t2(im: PIL.Image.Image) -> PIL.Image.Image:
+    return im
+
+
+@workflow
+def wf():
+    t2(im=t1())
+
+
+def test_image_transformer():
+    wf()