Copybara import of the project:

-- Enforce Linux `x86_64` Docker image builds. Under the assumption that only machine types with x86 processors are supported for prediction and custom training. Extract Docker build platform arg to method parameter. COPYBARA_INTEGRATE_REVIEW=#2115 PiperOrigin-RevId: 735416705

Author: abcdefgs0324

google/cloud/aiplatform/docker_utils/build.py

@@ -418,6 +418,7 @@ def build_image(
  pip_command: str = "pip",
  python_command: str = "python",
  no_cache: bool = True,
+ platform: Optional[str] = None,
  **kwargs,
 ) -> Image:
  """Builds a Docker image.
@@ -459,6 +460,10 @@ def build_image(
  reduces the image building time. See
  https://docs.docker.com/develop/develop-images/dockerfile_best-practices/#leverage-build-cache
  for more details.
+ platform (str):
+ Optional. The target platform for the Docker image build. See
+ https://docs.docker.com/build/building/multi-platform/#building-multi-platform-images
+ for more details.
  **kwargs:
  Other arguments to pass to underlying method that generates the Dockerfile.
 
@@ -472,9 +477,14 @@ def build_image(
 
  tag_options = ["-t", output_image_name]
  cache_args = ["--no-cache"] if no_cache else []
+ platform_args = ["--platform", platform] if platform is not None else []
 
  command = (
- ["docker", "build"] + cache_args + tag_options + ["--rm", "-f-", host_workdir]
+ ["docker", "build"]
+ + cache_args
+ + platform_args
+ + tag_options
+ + ["--rm", "-f-", host_workdir]
  )
 
  requirements_relative_path = _get_relative_path_to_workdir(

google/cloud/aiplatform/prediction/local_model.py

@@ -246,6 +246,7 @@ def build_cpr_model(
  requirements_path: Optional[str] = None,
  extra_packages: Optional[List[str]] = None,
  no_cache: bool = False,
+ platform: Optional[str] = None,
  ) -> "LocalModel":
  """Builds a local model from a custom predictor.
 
@@ -274,6 +275,7 @@ def build_cpr_model(
  predictor=$CUSTOM_PREDICTOR_CLASS,
  requirements_path="./user_src_dir/requirements.txt",
  extra_packages=["./user_src_dir/user_code/custom_package.tar.gz"],
+ platform="linux/amd64", # i.e., if you're building on a non-x86 machine
  )
 
  In the built image, user provided files will be copied as follows:
@@ -340,6 +342,10 @@ def build_cpr_model(
  reduces the image building time. See
  https://docs.docker.com/develop/develop-images/dockerfile_best-practices/#leverage-build-cache
  for more details.
+ platform (str):
+ Optional. The target platform for the Docker image build. See
+ https://docs.docker.com/build/building/multi-platform/#building-multi-platform-images
+ for more details.
 
  Returns:
  local model: Instantiated representation of the local model.
@@ -391,6 +397,7 @@ def build_cpr_model(
  pip_command="pip3" if is_prebuilt_prediction_image else "pip",
  python_command="python3" if is_prebuilt_prediction_image else "python",
  no_cache=no_cache,
+ platform=platform,
  )
 
  container_spec = gca_model_compat.ModelContainerSpec(

tests/system/aiplatform/test_prediction_cpr.py

@@ -49,7 +49,8 @@ class TestPredictionCpr(e2e_base.TestEndToEnd):
 
  _temp_prefix = "temp-vertex-sdk-e2e-prediction-cpr"
 
- def test_build_cpr_model_upload_and_deploy(self, shared_state, caplog):
+ @pytest.mark.parametrize("platform", [None, "linux/amd64"])
+ def test_build_cpr_model_upload_and_deploy(self, shared_state, caplog, platform):
  """Creates a CPR model from custom predictor, uploads it and deploys."""
 
  caplog.set_level(logging.INFO)
@@ -61,6 +62,7 @@ def test_build_cpr_model_upload_and_deploy(self, shared_state, caplog):
  _IMAGE_URI,
  predictor=SklearnPredictor,
  requirements_path=os.path.join(_USER_CODE_DIR, _REQUIREMENTS_FILE),
+ platform=platform,
  )
 
  with local_model.deploy_to_local_endpoint(

tests/unit/aiplatform/test_prediction.py

@@ -1304,6 +1304,7 @@ class {predictor_class}:
  pip_command="pip",
  python_command="python",
  no_cache=False,
+ platform=None,
  )
 
  def test_build_cpr_model_fails_handler_is_none(
@@ -1418,6 +1419,7 @@ class {handler_class}:
  pip_command="pip",
  python_command="python",
  no_cache=False,
+ platform=None,
  )
 
  def test_build_cpr_model_with_custom_handler_and_predictor_is_none(
@@ -1472,6 +1474,7 @@ class {handler_class}:
  pip_command="pip",
  python_command="python",
  no_cache=False,
+ platform=None,
  )
 
  def test_build_cpr_model_creates_and_get_localmodel_base_is_prebuilt(
@@ -1527,6 +1530,7 @@ class {predictor_class}:
  pip_command="pip3",
  python_command="python3",
  no_cache=False,
+ platform=None,
  )
 
  def test_build_cpr_model_creates_and_get_localmodel_with_requirements_path(
@@ -1584,6 +1588,7 @@ class {predictor_class}:
  pip_command="pip",
  python_command="python",
  no_cache=False,
+ platform=None,
  )
 
  def test_build_cpr_model_creates_and_get_localmodel_with_extra_packages(
@@ -1641,6 +1646,7 @@ class {predictor_class}:
  pip_command="pip",
  python_command="python",
  no_cache=False,
+ platform=None,
  )
 
  def test_build_cpr_model_creates_and_get_localmodel_no_cache(
@@ -1695,6 +1701,70 @@ class {predictor_class}:
  pip_command="pip",
  python_command="python",
  no_cache=no_cache,
+ platform=None,
+ )
+
+ @pytest.mark.parametrize(
+ "platform",
+ [
+ None,
+ "linux/amd64",
+ "some_arbitrary_platform_value_that_will_by_validated_by_docker_build_command",
+ ],
+ )
+ def test_build_cpr_model_creates_and_get_localmodel_platform(
+ self,
+ tmp_path,
+ inspect_source_from_class_mock_predictor_only,
+ is_prebuilt_prediction_container_uri_is_false_mock,
+ build_image_mock,
+ platform,
+ ):
+ src_dir = tmp_path / _TEST_SRC_DIR
+ src_dir.mkdir()
+ predictor = src_dir / _TEST_PREDICTOR_FILE
+ predictor.write_text(
+ textwrap.dedent(
+ """
+ class {predictor_class}:
+ pass
+ """
+ ).format(predictor_class=_TEST_PREDICTOR_CLASS)
+ )
+ my_predictor = self._load_module(_TEST_PREDICTOR_CLASS, str(predictor))
+
+ local_model = LocalModel.build_cpr_model(
+ str(src_dir), _TEST_OUTPUT_IMAGE, predictor=my_predictor, platform=platform
+ )
+
+ assert local_model.serving_container_spec.image_uri == _TEST_OUTPUT_IMAGE
+ assert local_model.serving_container_spec.predict_route == DEFAULT_PREDICT_ROUTE
+ assert local_model.serving_container_spec.health_route == DEFAULT_HEALTH_ROUTE
+ inspect_source_from_class_mock_predictor_only.assert_called_once_with(
+ my_predictor, str(src_dir)
+ )
+ is_prebuilt_prediction_container_uri_is_false_mock.assert_called_once_with(
+ _DEFAULT_BASE_IMAGE
+ )
+ build_image_mock.assert_called_once_with(
+ _DEFAULT_BASE_IMAGE,
+ str(src_dir),
+ _TEST_OUTPUT_IMAGE,
+ python_module=_DEFAULT_PYTHON_MODULE,
+ requirements_path=None,
+ extra_requirements=_DEFAULT_SDK_REQUIREMENTS,
+ extra_packages=None,
+ exposed_ports=[DEFAULT_HTTP_PORT],
+ environment_variables={
+ "HANDLER_MODULE": _DEFAULT_HANDLER_MODULE,
+ "HANDLER_CLASS": _DEFAULT_HANDLER_CLASS,
+ "PREDICTOR_MODULE": f"{_TEST_SRC_DIR}.{_TEST_PREDICTOR_FILE_STEM}",
+ "PREDICTOR_CLASS": _TEST_PREDICTOR_CLASS,
+ },
+ pip_command="pip",
+ python_command="python",
+ no_cache=False,
+ platform=platform,
  )
 
  def test_deploy_to_local_endpoint(