Merge pull request #2 from AlejandroBaron/develop

release v0.1.1

.github/workflows/tests.yml

@@ -0,0 +1,26 @@
+name: Build and Deploy
+on:
+  workflow_dispatch:
+  pull_request:
+permissions:
+  contents: write
+jobs:
+  Testing:
+    runs-on: ${{ matrix.os }}
+    strategy:
+        matrix:
+          python-version: [3.9, '3.10', '3.11']
+          os: [ubuntu-latest, macOS-latest, windows-latest]
+
+    steps:
+        - uses: actions/checkout@v3
+        - name: Set up PDM
+          uses: pdm-project/setup-pdm@v4.1
+          with:
+            python-version: ${{ matrix.python-version }}
+        - name: Install dependencies
+          run: |
+            pdm sync -d -G testing
+        - name: Run Tests
+          run: |
+            pdm run -v pytest tests

configs/environments/conda.yaml

@@ -8,3 +8,4 @@ dependencies:
     - mldesigner==0.1.0b17
     - azure-ai-ml==1.18.0
     - azureml-mlflow==1.56.0
+    - ez-azml==0.1.0

pyproject.toml

@@ -1,5 +1,5 @@
 [project]
-name = "ez_azml"
+name = "ez-azml"
 dynamic = ["version"]
 description = "Simplified yaml/jsonargparse driven framework for AzureML interactions"
 authors = [
@@ -11,6 +11,7 @@ dependencies = [
     "azure-identity>=1.17.1",
     "loguru>=0.7.2",
     "mldesigner>=0.1.0b18",
+    "setuptools>=72.1.0",
 ]
 requires-python = ">=3.9"
 readme = "README.md"
@@ -87,6 +88,8 @@ ignore = [
     "UP007",
     "UP038"
 ]
+[tool.ruff.lint.per-file-ignores]
+"tests/**" = ["S101"]
 [tool.ruff.lint.mccabe]
 max-complexity = 10
 [tool.ruff.lint.pydocstyle]

requirements.txt

@@ -60,6 +60,7 @@ requests==2.32.3
 requests-oauthlib==2.0.0
 rpds-py==0.19.1
 rsa==4.9
+setuptools==72.1.0
 six==1.16.0
 strictyaml==1.7.3
 tomli==2.0.1

src/ez_azml/cloud_runs/pipelines/__init__.py

@@ -1,3 +1,3 @@
-from .pipeline import Pipeline
+from .pipeline import Pipeline, PipelineCommand
 
-__all__ = ["Pipeline"]
+__all__ = ["Pipeline", "PipelineCommand"]

src/ez_azml/cloud_runs/pipelines/pipeline.py

@@ -11,7 +11,7 @@
 
 
 @dataclass
-class Command:
+class PipelineCommand:
     """Class representing a mldesigner Azureml step (mldesigner.command_component).
 
     Args:
@@ -27,6 +27,11 @@ class Command:
     def __post_init__(self):
         self.component_kwargs["environment"] = self.environment
 
+    @property
+    def name(self):
+        """Command's name."""
+        return self.function.__name__
+
 
 class Pipeline(CloudRun):
     """Cloud run that uses mldesigner pipelines.
@@ -41,7 +46,7 @@ class Pipeline(CloudRun):
     def __init__(
         self,
         experiment_name: str,
-        commands: list[Command],
+        commands: list[PipelineCommand],
         pipeline: Callable,
         dec_kwargs: Optional[dict[str, Any]] = None,
         **kwargs,
@@ -52,8 +57,9 @@ def __init__(
         self.dec_kwargs = dec_kwargs or {}
         self.pipeline = pipeline
 
-    def _register_components(self, commands: list[Command]) -> list[Callable]:
-        decorated = []
+    def _register_components(
+        self, commands: list[PipelineCommand]
+    ) -> list[PipelineCommand]:
         kwargs_pattern = r"(\w+)\s*=\s*(['\"]?)(\w+)\2"
         for command in commands:
             fn = command.function
@@ -68,9 +74,8 @@ def _register_components(self, commands: list[Command]) -> list[Callable]:
                     elif "Output(" in hint:
                         hint = mld.Output(**potential_kwargs)
                     fn.__annotations__[parameter] = hint
-            decorated_command = mld.command_component(**command.component_kwargs)(fn)
-            decorated.append(decorated_command)
-        return decorated
+            command.function = mld.command_component(**command.component_kwargs)(fn)
+        return commands
 
     def _build_pipeline(self, pipeline: Callable, dec_kwargs: Optional[dict[str, Any]]):
         # Inject the decorated functions
@@ -80,9 +85,14 @@ def _build_pipeline(self, pipeline: Callable, dec_kwargs: Optional[dict[str, Any
         return pipeline_dec(**dec_kwargs)(pipeline)
 
     def _setup_dec_kwargs(self, dec_kwargs: dict[str, Any]):
-        dec_kwargs["default_compute"] = dec_kwargs.get(
-            "default_compute", self.compute.name
-        )
+        if self.compute:
+            dec_kwargs["default_compute"] = dec_kwargs.get(
+                "default_compute", self.compute.name
+            )
+        if self.environment:
+            dec_kwargs["default_compute"] = dec_kwargs.get(
+                "default_compute", self.compute.name
+            )
         return dec_kwargs
 
     @override

tests/cloud_runs/pipelines/test_pipeline.py

@@ -0,0 +1,49 @@
+import inspect
+from typing import Callable
+from unittest import mock
+
+from ez_azml.cloud_runs.pipelines import Pipeline
+
+
+def assert_function_wrapped(function: Callable, wrapped_function: Callable) -> None:
+    """Asserts that a function is properly wrapped.
+
+    Args:
+        wrapped_function: The function that is potentially wrapped.
+        function: The original function to compare against after unwrapping.
+
+    Raises:
+        AssertionError: If the function is already wrapped or if unwrapping the wrapped
+                        function does not yield the original function.
+    """
+    assert not hasattr(function, "__wrapped__"), "The function should not be wrapped."
+    assert (
+        inspect.unwrap(wrapped_function) is function
+    ), "Unwrapping the wrapped function did not yield the original function."
+
+
+def test_pipeline_register_components(pipeline: Pipeline):
+    """Tests that the components are properly decorated."""
+    raw_functions = [c.function for c in pipeline.commands]
+    registered_commands = pipeline._register_components(pipeline.commands)
+    for raw_function, registered in zip(raw_functions, registered_commands):
+        assert_function_wrapped(raw_function, registered.function)
+
+
+def test_pipeline_setup_dec_kwargs(pipeline: Pipeline):
+    """Tests that the pipeline decorator kwargs are properly postprocessed."""
+    pipeline.dec_kwargs = pipeline._setup_dec_kwargs(pipeline.dec_kwargs)
+
+
+def test_build_pipeline(pipeline: Pipeline):
+    """Tests that the pipeline is properly decorated."""
+
+    def f():
+        pass
+
+    with mock.patch.object(pipeline, "commands", return_value=f) as mocked:
+        mocked.__name__ = "command_fn"
+
+        raw_pipeline = pipeline.pipeline
+        built_pipeline = pipeline._build_pipeline(raw_pipeline, pipeline.dec_kwargs)
+        assert_function_wrapped(raw_pipeline, built_pipeline)

tests/conftest.py

@@ -0,0 +1,48 @@
+from typing import Callable
+
+import mldesigner as mld
+import pytest
+from azure.ai.ml import Input, Output
+from ez_azml.cloud_runs.pipelines import Pipeline, PipelineCommand
+
+
+@pytest.fixture()
+def command_fn() -> Callable:
+    """Function used in mldesigner command component."""
+
+    def command__fn(
+        command_input: mld.Input(type="uri_folder"),  # type: ignore
+        command_output: mld.Output(type="uri_folder"),  # type: ignore
+    ):
+        print("this is a test_fn")
+
+    return command__fn
+
+
+@pytest.fixture()
+def pipeline_fn(command_fn) -> Callable:
+    """Function used in azure.ai.ml pipelines."""
+
+    def pipeline__fn(
+        pipeline_input: Input,
+        pipeline_output: Output,
+    ):
+        command_fn(pipeline_input)
+
+    return pipeline__fn
+
+
+@pytest.fixture()
+def pipeline_command(command_fn: Callable):
+    """PipelineCommand fixture."""
+    return PipelineCommand(function=command_fn)
+
+
+@pytest.fixture()
+def pipeline(pipeline_fn: Callable, pipeline_command: PipelineCommand):
+    """Pipeline cloud run fixture."""
+    return Pipeline(
+        experiment_name="fixture",
+        commands=[pipeline_command],
+        pipeline=pipeline_fn,
+    )