Add vertex runner

.github/workflows/pipeline.yaml

@@ -11,7 +11,7 @@ jobs:
     runs-on: ubuntu-latest
     strategy:
       matrix:
-        python-version: ['3.8', '3.9', '3.10', '3.11']
+        python-version: ['3.8', '3.9', '3.10']
     steps:
       - uses: actions/checkout@v2
       - name: Set up Python ${{ matrix.python-version }}

pyproject.toml

@@ -41,7 +41,7 @@ classifiers = [
 ]
 
 [tool.poetry.dependencies]
-python = ">= 3.8 < 3.12"
+python = ">= 3.8 < 3.11"
 dask = {extras = ["dataframe"], version = ">= 2023.4.1"}
 importlib-resources = { version = ">= 1.3", python = "<3.9" }
 jsonschema = ">= 4.18"
@@ -53,13 +53,14 @@ s3fs = { version = ">= 2023.4.0", optional = true }
 adlfs = { version = ">= 2023.4.0", optional = true }
 kfp = { version = "2.0.1", optional = true }
 pandas = { version = ">= 1.3.5", optional = true }
+google-cloud-aiplatform =  { version = "1.32.0", optional = true}
 
 [tool.poetry.extras]
 aws = ["fsspec", "s3fs"]
 azure = ["fsspec", "adlfs"]
 gcp = ["fsspec", "gcsfs"]
 kfp = ["kfp"]
-vertex = ["kfp"]
+vertex = ["kfp", "google-cloud-aiplatform"]
 
 [tool.poetry.group.test.dependencies]
 pre-commit = "^3.1.1"

src/fondant/runner.py

@@ -85,3 +85,36 @@ def get_name_from_spec(self, input_spec: str):
         with open(input_spec) as f:
             spec = yaml.safe_load(f)
             return spec["pipelineInfo"]["name"]
+
+
+class VertexRunner(Runner):
+    def __resolve_imports(self):
+        import google.cloud.aiplatform as aip
+
+        self.aip = aip
+
+    def __init__(self, project_id: str, project_region: str, service_account: str = ""):
+        self.__resolve_imports()
+
+        self.aip.init(
+            project=project_id,
+            location=project_region,
+        )
+        self.service_account = service_account
+
+    def run(self, input_spec: str, *args, **kwargs):
+        job = self.aip.PipelineJob(
+            display_name=self.get_name_from_spec(input_spec),
+            template_path=input_spec,
+            enable_caching=False,
+        )
+        if self.service_account:
+            job.submit(service_account=self.service_account)
+        else:
+            job.submit()
+
+    def get_name_from_spec(self, input_spec: str):
+        """Get the name of the pipeline from the spec."""
+        with open(input_spec) as f:
+            spec = yaml.safe_load(f)
+            return spec["pipelineInfo"]["name"]

tests/test_runner.py

@@ -4,7 +4,7 @@
 from unittest import mock
 
 import pytest
-from fondant.runner import DockerRunner, KubeflowRunner
+from fondant.runner import DockerRunner, KubeflowRunner, VertexRunner
 
 VALID_PIPELINE = Path("./tests/example_pipelines/compiled_pipeline/")
 
@@ -79,3 +79,20 @@ def test_kfp_import():
         sys.modules["kfp"] = None
         with pytest.raises(ImportError):
             _ = KubeflowRunner(host="some_host")
+
+
+def test_vertex_runner():
+    input_spec_path = str(VALID_PIPELINE / "kubeflow_pipeline.yml")
+    with mock.patch("google.cloud.aiplatform.init", return_value=None), mock.patch(
+        "google.cloud.aiplatform.PipelineJob",
+    ):
+        runner = VertexRunner(project_id="some_project", project_region="some_region")
+        runner.run(input_spec=input_spec_path)
+
+        # test with service account
+        runner2 = VertexRunner(
+            project_id="some_project",
+            project_region="some_region",
+            service_account="some_account",
+        )
+        runner2.run(input_spec=input_spec_path)