diff --git a/sdk/ml/azure-ai-ml/azure/ai/ml/dsl/_pipeline_decorator.py b/sdk/ml/azure-ai-ml/azure/ai/ml/dsl/_pipeline_decorator.py index d43292178706..f23b200e71d7 100644 --- a/sdk/ml/azure-ai-ml/azure/ai/ml/dsl/_pipeline_decorator.py +++ b/sdk/ml/azure-ai-ml/azure/ai/ml/dsl/_pipeline_decorator.py @@ -12,7 +12,7 @@ from pathlib import Path from typing import Any, Callable, Dict, TypeVar -from azure.ai.ml.entities import Data, PipelineJob, PipelineJobSettings +from azure.ai.ml.entities import Data, PipelineJob, PipelineJobSettings, Model from azure.ai.ml.entities._builders.pipeline import Pipeline from azure.ai.ml.entities._inputs_outputs import Input, is_parameter_group from azure.ai.ml.entities._job.pipeline._io import NodeOutput, PipelineInput, _GroupAttrDict @@ -36,6 +36,7 @@ PipelineInput, NodeOutput, Input, + Model, Data, # For the case use a Data object as an input, we will convert it to Input object Pipeline, # For the case use a pipeline node as the input, we use its only one output as the real input. str, diff --git a/sdk/ml/azure-ai-ml/azure/ai/ml/entities/_builders/base_node.py b/sdk/ml/azure-ai-ml/azure/ai/ml/entities/_builders/base_node.py index c82027f8639c..0af03c02d69e 100644 --- a/sdk/ml/azure-ai-ml/azure/ai/ml/entities/_builders/base_node.py +++ b/sdk/ml/azure-ai-ml/azure/ai/ml/entities/_builders/base_node.py @@ -12,7 +12,7 @@ from azure.ai.ml._utils._arm_id_utils import get_resource_name_from_arm_id_safe from azure.ai.ml.constants import JobType -from azure.ai.ml.entities import Data +from azure.ai.ml.entities import Data, Model from azure.ai.ml.entities._component.component import Component from azure.ai.ml.entities._inputs_outputs import Input, Output from azure.ai.ml.entities._job._input_output_helpers import build_input_output @@ -189,6 +189,7 @@ def _get_supported_inputs_types(cls): NodeOutput, Input, Data, + Model, str, bool, int, diff --git a/sdk/ml/azure-ai-ml/azure/ai/ml/entities/_job/pipeline/_io/base.py b/sdk/ml/azure-ai-ml/azure/ai/ml/entities/_job/pipeline/_io/base.py index a57c2f90fd6a..fe891206b42e 100644 --- a/sdk/ml/azure-ai-ml/azure/ai/ml/entities/_job/pipeline/_io/base.py +++ b/sdk/ml/azure-ai-ml/azure/ai/ml/entities/_job/pipeline/_io/base.py @@ -12,6 +12,7 @@ from azure.ai.ml.constants import AssetTypes from azure.ai.ml.constants._component import IOConstants from azure.ai.ml.entities._assets._artifacts.data import Data +from azure.ai.ml.entities._assets._artifacts.model import Model from azure.ai.ml.entities._inputs_outputs import Input, Output from azure.ai.ml.entities._job.pipeline._pipeline_expression import PipelineExpressionMixin from azure.ai.ml.entities._util import resolve_pipeline_parameter @@ -241,7 +242,7 @@ def _build_data(self, data, key=None): # pylint: disable=unused-argument # for data binding case, set is_singular=False for case like "${{parent.inputs.job_in_folder}}/sample1.csv" if isinstance(data, Input) or is_data_binding_expression(data, is_singular=False): return data - if isinstance(data, Data): + if isinstance(data, (Data, Model)): return _data_to_input(data) # self._meta.type could be None when sub pipeline has no annotation if isinstance(self._meta, Input) and self._meta.type and not self._meta._is_primitive_type: @@ -452,7 +453,7 @@ def _build_data(self, data, key=None): # pylint: disable=unused-argument error_category=ErrorCategory.USER_ERROR, ) return data - if isinstance(data, Data): + if isinstance(data, (Data, Model)): # If value is Data, we convert it to an corresponding Input return _data_to_input(data) return data diff --git a/sdk/ml/azure-ai-ml/tests/dsl/e2etests/test_dsl_pipeline_on_registry.py b/sdk/ml/azure-ai-ml/tests/dsl/e2etests/test_dsl_pipeline_on_registry.py index c74a5d45a5c3..f88742e9946c 100644 --- a/sdk/ml/azure-ai-ml/tests/dsl/e2etests/test_dsl_pipeline_on_registry.py +++ b/sdk/ml/azure-ai-ml/tests/dsl/e2etests/test_dsl_pipeline_on_registry.py @@ -1,8 +1,12 @@ import pytest +from pathlib import Path from test_utilities.utils import _PYTEST_TIMEOUT_METHOD -from azure.ai.ml import MLClient, load_component -from azure.core.exceptions import HttpResponseError +from azure.ai.ml import MLClient, load_component, Input, load_model +from azure.ai.ml.dsl import pipeline +from azure.ai.ml.constants import AssetTypes +from azure.core.exceptions import HttpResponseError, ResourceNotFoundError +from azure.core.polling import LROPoller from .._util import _DSL_TIMEOUT_SECOND @@ -12,15 +16,13 @@ @pytest.mark.usefixtures("enable_pipeline_private_preview_features", "recorded_test") @pytest.mark.timeout(timeout=_DSL_TIMEOUT_SECOND, method=_PYTEST_TIMEOUT_METHOD) @pytest.mark.e2etest -@pytest.mark.skip(reason="not able to re-record") @pytest.mark.pipeline_test class TestDSLPipelineOnRegistry(AzureRecordedTestCase): + @pytest.mark.skip(reason="not able to re-record") def test_pipeline_job_create_with_registered_component_on_registry( self, registry_client: MLClient, ) -> None: - from azure.ai.ml.dsl import pipeline - local_component = load_component("./tests/test_configs/components/basic_component_code_local_path.yml") try: created_component = registry_client.components.get(local_component.name, version=local_component.version) @@ -35,3 +37,105 @@ def sample_pipeline(): pipeline_job = sample_pipeline() assert registry_client.jobs.validate(pipeline_job).passed # TODO: add test for pipeline job create with registered component on registry after support is ready on canary + + @pytest.mark.skip(reason="request body still exits when re-record and will raise error " + "'Unable to find a record for the request' in playback mode") + def test_pipeline_with_local_component_and_registry_model_as_input(self, registry_client: MLClient, client: MLClient): + # get dataset + test_data = Input( + type=AssetTypes.URI_FILE, + path="./tests/test_configs/pipeline_jobs/job_with_registry_model_as_input/data/sample1.csv" + ) + + # load_component + score_func = load_component("./tests/test_configs/pipeline_jobs/job_with_registry_model_as_input/score.yml") + + pipeline_score_model = Input( + type='mlflow_model', + path='azureml://registries/testFeed/models/iris_model/versions/1' + ) + + @pipeline() + def score_pipeline_with_registry_model(model_input, test_data): + score = score_func(model_input=model_input, test_data=test_data) + score_duplicate = score_func(model_input=pipeline_score_model, test_data=test_data) + + pipeline_job = score_pipeline_with_registry_model( + model_input=pipeline_score_model, + test_data=test_data + ) + pipeline_job.settings.default_compute = "cpu-cluster" + pipeline_job = client.jobs.create_or_update(pipeline_job) + cancel_poller = client.jobs.begin_cancel(pipeline_job.name) + assert isinstance(cancel_poller, LROPoller) + + @pytest.mark.skip(reason="request body still exits when re-record and will raise error " + "'Unable to find a record for the request' in playback mode") + def test_pipeline_with_local_component_and_registry_model_as_input_with_model_input( + self, + registry_client: MLClient, + client: MLClient): + # get dataset + test_data = Input( + type=AssetTypes.URI_FILE, + path="./tests/test_configs/pipeline_jobs/job_with_registry_model_as_input/data/sample1.csv" + ) + + # load_component + score_func = load_component("./tests/test_configs/pipeline_jobs/job_with_registry_model_as_input/score.yml") + + model_path = Path("./tests/test_configs/model/model_iris.yml") + model_entity = load_model(model_path) + try: + pipeline_score_model = registry_client.models.get(name=model_entity.name, version=model_entity.version) + except ResourceNotFoundError: + model_entity = registry_client.models.create_or_update(model_entity) + pipeline_score_model = registry_client.models.get(name=model_entity.name, version=model_entity.version) + + @pipeline() + def score_pipeline_with_registry_model(model_input, test_data): + score = score_func(model_input=model_input, test_data=test_data) + score_duplicate = score_func(model_input=pipeline_score_model, test_data=test_data) + + pipeline_job = score_pipeline_with_registry_model( + model_input=pipeline_score_model, test_data=test_data + ) + pipeline_job.settings.default_compute = "cpu-cluster" + pipeline_job = client.jobs.create_or_update(pipeline_job) + cancel_poller = client.jobs.begin_cancel(pipeline_job.name) + assert isinstance(cancel_poller, LROPoller) + + @pytest.mark.skip(reason="request body still exits when re-record and will raise error " + "'Unable to find a record for the request' in playback mode") + def test_pipeline_with_registry_component_and_model_as_input(self, registry_client: MLClient, client: MLClient): + # get dataset + test_data = Input( + type=AssetTypes.URI_FILE, + path="./tests/test_configs/pipeline_jobs/job_with_registry_model_as_input/data/sample1.csv" + ) + + # load_component + score_component_name = "v2_dsl_score_component" + component_version = "0.0.8" + score_func = registry_client.components.get( + name=score_component_name, version=component_version + ) + + pipeline_score_model = Input( + type='mlflow_model', + path='azureml://registries/testFeed/models/iris_model/versions/1' + ) + + @pipeline() + def score_pipeline_with_registry_model(model_input, test_data): + score = score_func(model_input=model_input, test_data=test_data) + score_duplicate = score_func(model_input=pipeline_score_model, test_data=test_data) + + pipeline_job = score_pipeline_with_registry_model( + model_input=pipeline_score_model, + test_data=test_data + ) + pipeline_job.settings.default_compute = "cpu-cluster" + pipeline_job = client.jobs.create_or_update(pipeline_job) + cancel_poller = client.jobs.begin_cancel(pipeline_job.name) + assert isinstance(cancel_poller, LROPoller) diff --git a/sdk/ml/azure-ai-ml/tests/pipeline_job/e2etests/test_pipeline_job.py b/sdk/ml/azure-ai-ml/tests/pipeline_job/e2etests/test_pipeline_job.py index 83a806d8d015..18786dc9964e 100644 --- a/sdk/ml/azure-ai-ml/tests/pipeline_job/e2etests/test_pipeline_job.py +++ b/sdk/ml/azure-ai-ml/tests/pipeline_job/e2etests/test_pipeline_job.py @@ -1543,6 +1543,22 @@ def test_remote_pipeline_component_job(self, client: MLClient, randstr: Callable # assert pipeline_dict["outputs"] == {"output_path": {"mode": "ReadWriteMount", "job_output_type": "uri_folder"}} assert pipeline_dict["settings"] == {"default_compute": "cpu-cluster", "_source": "REMOTE.WORKSPACE.COMPONENT"} + @pytest.mark.skip(reason="request body still exits when re-record and will raise error " + "'Unable to find a record for the request' in playback mode") + def test_pipeline_job_create_with_registry_model_as_input( + self, + client: MLClient, + registry_client: MLClient, + randstr: Callable[[str], str], + ) -> None: + params_override = [{"name": randstr("name")}] + pipeline_job = load_job( + source="./tests/test_configs/pipeline_jobs/job_with_registry_model_as_input/pipeline.yml", + params_override=params_override, + ) + job = client.jobs.create_or_update(pipeline_job) + assert job.name == params_override[0]["name"] + def test_pipeline_node_with_default_component(self, client: MLClient, randstr: Callable[[str], str]): params_override = [{"name": randstr("job_name")}] pipeline_job = load_job( diff --git a/sdk/ml/azure-ai-ml/tests/recordings/dsl/e2etests/test_dsl_pipeline_on_registry.pyTestDSLPipelineOnRegistrytest_pipeline_with_local_component_and_registry_model_as_input.json b/sdk/ml/azure-ai-ml/tests/recordings/dsl/e2etests/test_dsl_pipeline_on_registry.pyTestDSLPipelineOnRegistrytest_pipeline_with_local_component_and_registry_model_as_input.json new file mode 100644 index 000000000000..f6117c0bdc0e --- /dev/null +++ b/sdk/ml/azure-ai-ml/tests/recordings/dsl/e2etests/test_dsl_pipeline_on_registry.pyTestDSLPipelineOnRegistrytest_pipeline_with_local_component_and_registry_model_as_input.json @@ -0,0 +1,818 @@ +{ + "Entries": [ + { + "RequestUri": 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b/sdk/ml/azure-ai-ml/tests/test_configs/model/model_iris.yml new file mode 100644 index 000000000000..7018640c002a --- /dev/null +++ b/sdk/ml/azure-ai-ml/tests/test_configs/model/model_iris.yml @@ -0,0 +1,15 @@ +name: iris_model +type: mlflow_model +path: ./iris_model +version: 1 +description: "this is a iris model" +tags: + foo: bar + abc: 123 +utc_time_created: '2020-10-19 17:44:02.096572' +flavors: + sklearn: + sklearn_version: 0.23.2 + python_function: + loader_module: office.plrmodel + python_version: 3.8.5 diff --git a/sdk/ml/azure-ai-ml/tests/test_configs/pipeline_jobs/job_with_registry_model_as_input/data/sample1.csv b/sdk/ml/azure-ai-ml/tests/test_configs/pipeline_jobs/job_with_registry_model_as_input/data/sample1.csv new file mode 100644 index 000000000000..a27e6468c2c0 --- /dev/null +++ b/sdk/ml/azure-ai-ml/tests/test_configs/pipeline_jobs/job_with_registry_model_as_input/data/sample1.csv @@ -0,0 +1,9 @@ +"Month", "Average", "2005", "2006", "2007", "2008", "2009", "2010", "2011", "2012", "2013", "2014", "2015" +"May", 0.1, 0, 0, 1, 1, 0, 0, 0, 2, 0, 0, 0 +"Jun", 0.5, 2, 1, 1, 0, 0, 1, 1, 2, 2, 0, 1 +"Jul", 0.7, 5, 1, 1, 2, 0, 1, 3, 0, 2, 2, 1 +"Aug", 2.3, 6, 3, 2, 4, 4, 4, 7, 8, 2, 2, 3 +"Sep", 3.5, 6, 4, 7, 4, 2, 8, 5, 2, 5, 2, 5 +"Oct", 2.0, 8, 0, 1, 3, 2, 5, 1, 5, 2, 3, 0 +"Nov", 0.5, 3, 0, 0, 1, 1, 0, 1, 0, 1, 0, 1 +"Dec", 0.0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1 diff --git a/sdk/ml/azure-ai-ml/tests/test_configs/pipeline_jobs/job_with_registry_model_as_input/pipeline.yml b/sdk/ml/azure-ai-ml/tests/test_configs/pipeline_jobs/job_with_registry_model_as_input/pipeline.yml new file mode 100644 index 000000000000..af23121ca853 --- /dev/null +++ b/sdk/ml/azure-ai-ml/tests/test_configs/pipeline_jobs/job_with_registry_model_as_input/pipeline.yml @@ -0,0 +1,50 @@ + +type: pipeline + +description: "Dummy score pipeline with local components" +display_name: "e2e_registered_components_with_registry_model_as_pipeline_input" + +inputs: + pipeline_score_model: + path: azureml://registries/testFeed/models/iris_model/versions/1 + type: mlflow_model + pipeline_job_test_input: + path: ./data + +compute: azureml:cpu-cluster + +jobs: + score_job: + type: command + component: file:./score.yml + compute: azureml:cpu-cluster + inputs: + model_input: ${{parent.inputs.pipeline_score_model}} + test_data: ${{parent.inputs.pipeline_job_test_input}} + outputs: + score_output: + mode: upload + + score_job_duplicate: + type: command + component: file:./score.yml + compute: azureml:cpu-cluster + inputs: + model_input: + path: azureml://registries/testFeed/models/iris_model/versions/1 + type: mlflow_model + test_data: ${{parent.inputs.pipeline_job_test_input}} + outputs: + score_output: + mode: upload + + score_job_with_registry_component: + type: command + component: azureml://registries/testFeed/components/v2_dsl_score_component/versions/0.0.8 + compute: azureml:cpu-cluster + inputs: + model_input: ${{parent.inputs.pipeline_score_model}} + test_data: ${{parent.inputs.pipeline_job_test_input}} + outputs: + score_output: + mode: upload \ No newline at end of file diff --git a/sdk/ml/azure-ai-ml/tests/test_configs/pipeline_jobs/job_with_registry_model_as_input/score.yml b/sdk/ml/azure-ai-ml/tests/test_configs/pipeline_jobs/job_with_registry_model_as_input/score.yml new file mode 100644 index 000000000000..7557bf2050e1 --- /dev/null +++ b/sdk/ml/azure-ai-ml/tests/test_configs/pipeline_jobs/job_with_registry_model_as_input/score.yml @@ -0,0 +1,19 @@ +name: score +version: 10 +display_name: Score +type: command +inputs: + model_input: + type: uri_folder + test_data: + type: uri_folder +outputs: + score_output: + type: uri_folder +environment: azureml:AzureML-sklearn-0.24-ubuntu18.04-py37-cpu:5 +code: ./score_src +command: >- + python score.py + --model_input ${{inputs.model_input}} + --test_data ${{inputs.test_data}} + --score_output ${{outputs.score_output}} diff --git a/sdk/ml/azure-ai-ml/tests/test_configs/pipeline_jobs/job_with_registry_model_as_input/score_src/score.py b/sdk/ml/azure-ai-ml/tests/test_configs/pipeline_jobs/job_with_registry_model_as_input/score_src/score.py new file mode 100644 index 000000000000..6a1e7682ff5a --- /dev/null +++ b/sdk/ml/azure-ai-ml/tests/test_configs/pipeline_jobs/job_with_registry_model_as_input/score_src/score.py @@ -0,0 +1,20 @@ +import argparse +from pathlib import Path + +parser = argparse.ArgumentParser("score") +parser.add_argument("--model_input", type=str, help="Path of input model") +parser.add_argument("--test_data", type=str, help="Path to test data") +parser.add_argument("--score_output", type=str, help="Path of scoring output") + +args = parser.parse_args() + +print("hello scoring world...") + +lines = [ + f"Model path: {args.model_input}", + f"Test data path: {args.test_data}", + f"Scoring output path: {args.score_output}", +] + +for line in lines: + print(line)