# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"). You # may not use this file except in compliance with the License. A copy of # the License is located at # # http://aws.amazon.com/apache2.0/ # # or in the "license" file accompanying this file. This file is # distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF # ANY KIND, either express or implied. See the License for the specific # language governing permissions and limitations under the License. from __future__ import absolute_import import logging import json import os import pytest from mock import ANY, MagicMock, Mock, patch from packaging.version import Version from sagemaker import image_uris from sagemaker.pytorch import defaults from sagemaker.pytorch import PyTorch, PyTorchPredictor, PyTorchModel from sagemaker.instance_group import InstanceGroup from sagemaker.session_settings import SessionSettings DATA_DIR = os.path.join(os.path.dirname(__file__), "..", "data") SCRIPT_PATH = os.path.join(DATA_DIR, "dummy_script.py") SERVING_SCRIPT_FILE = "another_dummy_script.py" MODEL_DATA = "s3://some/data.tar.gz" ENV = {"DUMMY_ENV_VAR": "dummy_value"} TIMESTAMP = "2017-11-06-14:14:15.672" TIME = 1510006209.073025 BUCKET_NAME = "mybucket" INSTANCE_COUNT = 1 INSTANCE_TYPE = "ml.c4.4xlarge" ACCELERATOR_TYPE = "ml.eia.medium" IMAGE_URI = "sagemaker-pytorch" JOB_NAME = "{}-{}".format(IMAGE_URI, TIMESTAMP) ROLE = "Dummy" REGION = "us-west-2" GPU = "ml.p2.xlarge" CPU = "ml.c4.xlarge" ENDPOINT_DESC = {"EndpointConfigName": "test-endpoint"} ENDPOINT_CONFIG_DESC = {"ProductionVariants": [{"ModelName": "model-1"}, {"ModelName": "model-2"}]} ENV_INPUT = {"env_key1": "env_val1", "env_key2": "env_val2", "env_key3": "env_val3"} LIST_TAGS_RESULT = {"Tags": [{"Key": "TagtestKey", "Value": "TagtestValue"}]} EXPERIMENT_CONFIG = { "ExperimentName": "exp", "TrialName": "trial", "TrialComponentDisplayName": "tc", "RunName": "rn", } DISTRIBUTION_PYTORCH_DDP_ENABLED = {"pytorchddp": {"enabled": True}} @pytest.fixture(name="sagemaker_session") def fixture_sagemaker_session(): boto_mock = Mock(name="boto_session", region_name=REGION) session = Mock( name="sagemaker_session", boto_session=boto_mock, boto_region_name=REGION, config=None, local_mode=False, s3_resource=None, s3_client=None, settings=SessionSettings(), default_bucket_prefix=None, ) describe = {"ModelArtifacts": {"S3ModelArtifacts": "s3://m/m.tar.gz"}} session.sagemaker_client.describe_training_job = Mock(return_value=describe) session.sagemaker_client.describe_endpoint = Mock(return_value=ENDPOINT_DESC) session.sagemaker_client.describe_endpoint_config = Mock(return_value=ENDPOINT_CONFIG_DESC) session.sagemaker_client.list_tags = Mock(return_value=LIST_TAGS_RESULT) session.default_bucket = Mock(name="default_bucket", return_value=BUCKET_NAME) session.expand_role = Mock(name="expand_role", return_value=ROLE) # For tests which doesn't verify config file injection, operate with empty config session.sagemaker_config = {} return session def _get_full_cpu_image_uri(version, py_version): return image_uris.retrieve( "pytorch", REGION, version=version, py_version=py_version, instance_type=CPU, image_scope="training", ) def _pytorch_estimator( sagemaker_session, framework_version, py_version, instance_type=None, base_job_name=None, **kwargs, ): return PyTorch( entry_point=SCRIPT_PATH, framework_version=framework_version, py_version=py_version, role=ROLE, sagemaker_session=sagemaker_session, instance_count=INSTANCE_COUNT, instance_type=instance_type if instance_type else INSTANCE_TYPE, base_job_name=base_job_name, **kwargs, ) def _create_train_job(version, py_version): return { "image_uri": _get_full_cpu_image_uri(version, py_version), "input_mode": "File", "input_config": [ { "ChannelName": "training", "DataSource": { "S3DataSource": { "S3DataDistributionType": "FullyReplicated", "S3DataType": "S3Prefix", } }, } ], "role": ROLE, "job_name": JOB_NAME, "output_config": {"S3OutputPath": "s3://{}/".format(BUCKET_NAME)}, "resource_config": { "InstanceType": "ml.c4.4xlarge", "InstanceCount": 1, "VolumeSizeInGB": 30, }, "hyperparameters": { "sagemaker_program": json.dumps("dummy_script.py"), "sagemaker_container_log_level": str(logging.INFO), "sagemaker_job_name": json.dumps(JOB_NAME), "sagemaker_submit_directory": json.dumps( "s3://{}/{}/source/sourcedir.tar.gz".format(BUCKET_NAME, JOB_NAME) ), "sagemaker_region": '"us-west-2"', }, "stop_condition": {"MaxRuntimeInSeconds": 24 * 60 * 60}, "tags": None, "vpc_config": None, "metric_definitions": None, "environment": None, "retry_strategy": None, "experiment_config": None, "debugger_hook_config": { "CollectionConfigurations": [], "S3OutputPath": "s3://{}/".format(BUCKET_NAME), }, "profiler_config": { "DisableProfiler": False, "S3OutputPath": "s3://{}/".format(BUCKET_NAME), }, } def _get_environment(submit_directory, model_url, image_uri): return { "Environment": { "SAGEMAKER_SUBMIT_DIRECTORY": submit_directory, "SAGEMAKER_PROGRAM": "dummy_script.py", "SAGEMAKER_REGION": "us-west-2", "SAGEMAKER_CONTAINER_LOG_LEVEL": "20", }, "Image": image_uri, "ModelDataUrl": model_url, } @patch("sagemaker.estimator.name_from_base") def test_create_model( name_from_base, sagemaker_session, pytorch_inference_version, pytorch_inference_py_version ): container_log_level = '"logging.INFO"' source_dir = "s3://mybucket/source" base_job_name = "job" pytorch = PyTorch( entry_point=SCRIPT_PATH, role=ROLE, sagemaker_session=sagemaker_session, instance_count=INSTANCE_COUNT, instance_type=INSTANCE_TYPE, framework_version=pytorch_inference_version, py_version=pytorch_inference_py_version, container_log_level=container_log_level, base_job_name=base_job_name, source_dir=source_dir, ) pytorch.fit(inputs="s3://mybucket/train", job_name="new_name") model_name = "model_name" name_from_base.return_value = model_name model = pytorch.create_model() assert model.sagemaker_session == sagemaker_session assert model.framework_version == pytorch_inference_version assert model.py_version == pytorch_inference_py_version assert model.entry_point == SCRIPT_PATH assert model.role == ROLE assert model.name == model_name assert model.container_log_level == container_log_level assert model.source_dir == source_dir assert model.vpc_config is None name_from_base.assert_called_with(base_job_name) def test_create_model_with_optional_params( sagemaker_session, pytorch_inference_version, pytorch_inference_py_version ): container_log_level = '"logging.INFO"' source_dir = "s3://mybucket/source" pytorch = PyTorch( entry_point=SCRIPT_PATH, framework_version=pytorch_inference_version, py_version=pytorch_inference_py_version, role=ROLE, sagemaker_session=sagemaker_session, instance_count=INSTANCE_COUNT, instance_type=INSTANCE_TYPE, container_log_level=container_log_level, base_job_name="job", source_dir=source_dir, ) pytorch.fit(inputs="s3://mybucket/train", job_name="new_name") new_role = "role" model_server_workers = 2 vpc_config = {"Subnets": ["foo"], "SecurityGroupIds": ["bar"]} model_name = "model-name" model = pytorch.create_model( role=new_role, model_server_workers=model_server_workers, vpc_config_override=vpc_config, entry_point=SERVING_SCRIPT_FILE, env=ENV, name=model_name, ) assert model.role == new_role assert model.model_server_workers == model_server_workers assert model.vpc_config == vpc_config assert model.entry_point == SERVING_SCRIPT_FILE assert model.env == ENV assert model.name == model_name @patch("sagemaker.estimator.name_from_base") def test_create_model_with_custom_image(name_from_base, sagemaker_session): container_log_level = '"logging.INFO"' source_dir = "s3://mybucket/source" image = "pytorch:9000" base_job_name = "job" pytorch = PyTorch( entry_point=SCRIPT_PATH, role=ROLE, sagemaker_session=sagemaker_session, instance_count=INSTANCE_COUNT, instance_type=INSTANCE_TYPE, container_log_level=container_log_level, image_uri=image, base_job_name=base_job_name, source_dir=source_dir, ) pytorch.fit(inputs="s3://mybucket/train", job_name="new_name") model_name = "model_name" name_from_base.return_value = model_name model = pytorch.create_model() assert model.sagemaker_session == sagemaker_session assert model.image_uri == image assert model.entry_point == SCRIPT_PATH assert model.role == ROLE assert model.name == model_name assert model.container_log_level == container_log_level assert model.source_dir == source_dir name_from_base.assert_called_with(base_job_name) @patch("sagemaker.utils.repack_model", MagicMock()) @patch("sagemaker.utils.create_tar_file", MagicMock()) @patch("sagemaker.estimator.name_from_base", return_value=JOB_NAME) @patch("time.time", return_value=TIME) def test_pytorch( time, name_from_base, sagemaker_session, pytorch_inference_version, pytorch_inference_py_version, gpu_pytorch_instance_type, ): pytorch = PyTorch( entry_point=SCRIPT_PATH, role=ROLE, sagemaker_session=sagemaker_session, instance_count=INSTANCE_COUNT, instance_type=INSTANCE_TYPE, framework_version=pytorch_inference_version, py_version=pytorch_inference_py_version, enable_sagemaker_metrics=False, ) inputs = "s3://mybucket/train" pytorch.fit(inputs=inputs, experiment_config=EXPERIMENT_CONFIG) sagemaker_call_names = [c[0] for c in sagemaker_session.method_calls] assert sagemaker_call_names == ["train", "logs_for_job"] boto_call_names = [c[0] for c in sagemaker_session.boto_session.method_calls] assert boto_call_names == ["resource"] expected_train_args = _create_train_job(pytorch_inference_version, pytorch_inference_py_version) expected_train_args["input_config"][0]["DataSource"]["S3DataSource"]["S3Uri"] = inputs expected_train_args["experiment_config"] = EXPERIMENT_CONFIG expected_train_args["enable_sagemaker_metrics"] = False actual_train_args = sagemaker_session.method_calls[0][2] assert actual_train_args == expected_train_args model = pytorch.create_model() expected_image_uri = image_uris.retrieve( "pytorch", REGION, version=pytorch_inference_version, py_version=pytorch_inference_py_version, instance_type=gpu_pytorch_instance_type, image_scope="inference", ) actual_environment = model.prepare_container_def(gpu_pytorch_instance_type) submit_directory = actual_environment["Environment"]["SAGEMAKER_SUBMIT_DIRECTORY"] model_url = actual_environment["ModelDataUrl"] expected_environment = _get_environment(submit_directory, model_url, expected_image_uri) assert actual_environment == expected_environment assert "cpu" in model.prepare_container_def(CPU)["Image"] predictor = pytorch.deploy(1, gpu_pytorch_instance_type) assert isinstance(predictor, PyTorchPredictor) @patch("sagemaker.utils.repack_model", MagicMock()) @patch("sagemaker.utils.create_tar_file", MagicMock()) def test_model( sagemaker_session, pytorch_inference_version, pytorch_inference_py_version, gpu_pytorch_instance_type, ): model = PyTorchModel( MODEL_DATA, role=ROLE, entry_point=SCRIPT_PATH, framework_version=pytorch_inference_version, py_version=pytorch_inference_py_version, sagemaker_session=sagemaker_session, ) predictor = model.deploy(1, gpu_pytorch_instance_type) assert isinstance(predictor, PyTorchPredictor) @patch("sagemaker.utils.create_tar_file", MagicMock()) @patch("sagemaker.utils.repack_model") @pytest.mark.parametrize("gpu_pytorch_instance_type", ["1.2"], indirect=True) def test_mms_model(repack_model, sagemaker_session, gpu_pytorch_instance_type): PyTorchModel( MODEL_DATA, role=ROLE, entry_point=SCRIPT_PATH, sagemaker_session=sagemaker_session, framework_version="1.2", py_version="py3", ).deploy(1, gpu_pytorch_instance_type) repack_model.assert_called_with( dependencies=[], inference_script=SCRIPT_PATH, kms_key=None, model_uri="s3://some/data.tar.gz", repacked_model_uri=ANY, sagemaker_session=sagemaker_session, source_directory=None, ) @patch("sagemaker.utils.create_tar_file", MagicMock()) @patch("sagemaker.utils.repack_model") def test_non_mms_model(repack_model, sagemaker_session): PyTorchModel( MODEL_DATA, role=ROLE, entry_point=SCRIPT_PATH, sagemaker_session=sagemaker_session, framework_version="1.1", py_version="py3", ).deploy(1, GPU) repack_model.assert_not_called() @patch("sagemaker.fw_utils.tar_and_upload_dir", MagicMock()) def test_model_image_accelerator(sagemaker_session): with pytest.raises(ValueError) as error: model = PyTorchModel( MODEL_DATA, role=ROLE, entry_point=SCRIPT_PATH, sagemaker_session=sagemaker_session, framework_version="1.3.1", py_version="py2", ) model.deploy(1, CPU, accelerator_type=ACCELERATOR_TYPE) assert "Unsupported Python version: py2." in str(error) @patch("sagemaker.utils.create_tar_file", MagicMock()) @patch("sagemaker.utils.repack_model", MagicMock()) def test_model_custom_serialization( sagemaker_session, pytorch_inference_version, pytorch_inference_py_version, gpu_pytorch_instance_type, ): model = PyTorchModel( MODEL_DATA, role=ROLE, entry_point=SCRIPT_PATH, framework_version=pytorch_inference_version, py_version=pytorch_inference_py_version, sagemaker_session=sagemaker_session, ) custom_serializer = Mock() custom_deserializer = Mock() predictor = model.deploy( 1, gpu_pytorch_instance_type, serializer=custom_serializer, deserializer=custom_deserializer, ) assert isinstance(predictor, PyTorchPredictor) assert predictor.serializer is custom_serializer assert predictor.deserializer is custom_deserializer def test_model_prepare_container_def_no_instance_type_or_image(): model = PyTorchModel( MODEL_DATA, role=ROLE, entry_point=SCRIPT_PATH, framework_version="1.3.1", py_version="py3", ) with pytest.raises(ValueError) as e: model.prepare_container_def() expected_msg = "Must supply either an instance type (for choosing CPU vs GPU) or an image URI." assert expected_msg in str(e) def test_attach(sagemaker_session, pytorch_training_version, pytorch_training_py_version): training_image = "1.dkr.ecr.us-west-2.amazonaws.com/sagemaker-pytorch:{}-cpu-{}".format( pytorch_training_version, pytorch_training_py_version ) returned_job_description = { "AlgorithmSpecification": {"TrainingInputMode": "File", "TrainingImage": training_image}, "HyperParameters": { "sagemaker_submit_directory": '"s3://some/sourcedir.tar.gz"', "sagemaker_program": '"iris-dnn-classifier.py"', "sagemaker_s3_uri_training": '"sagemaker-3/integ-test-data/tf_iris"', "sagemaker_container_log_level": '"logging.INFO"', "sagemaker_job_name": '"neo"', "training_steps": "100", "sagemaker_region": '"us-west-2"', }, "RoleArn": "arn:aws:iam::366:role/SageMakerRole", "ResourceConfig": { "VolumeSizeInGB": 30, "InstanceCount": 1, "InstanceType": "ml.c4.xlarge", }, "StoppingCondition": {"MaxRuntimeInSeconds": 24 * 60 * 60}, "TrainingJobName": "neo", "TrainingJobStatus": "Completed", "TrainingJobArn": "arn:aws:sagemaker:us-west-2:336:training-job/neo", "OutputDataConfig": {"KmsKeyId": "", "S3OutputPath": "s3://place/output/neo"}, "TrainingJobOutput": {"S3TrainingJobOutput": "s3://here/output.tar.gz"}, } sagemaker_session.sagemaker_client.describe_training_job = Mock( name="describe_training_job", return_value=returned_job_description ) estimator = PyTorch.attach(training_job_name="neo", sagemaker_session=sagemaker_session) assert estimator.latest_training_job.job_name == "neo" assert estimator.py_version == pytorch_training_py_version assert estimator.framework_version == pytorch_training_version assert estimator.role == "arn:aws:iam::366:role/SageMakerRole" assert estimator.instance_count == 1 assert estimator.max_run == 24 * 60 * 60 assert estimator.input_mode == "File" assert estimator.base_job_name == "neo" assert estimator.output_path == "s3://place/output/neo" assert estimator.output_kms_key == "" assert estimator.hyperparameters()["training_steps"] == "100" assert estimator.source_dir == "s3://some/sourcedir.tar.gz" assert estimator.entry_point == "iris-dnn-classifier.py" def test_attach_wrong_framework(sagemaker_session): rjd = { "AlgorithmSpecification": { "TrainingInputMode": "File", "TrainingImage": "1.dkr.ecr.us-west-2.amazonaws.com/sagemaker-mxnet-py2-cpu:1.0.4", }, "HyperParameters": { "sagemaker_submit_directory": '"s3://some/sourcedir.tar.gz"', "checkpoint_path": '"s3://other/1508872349"', "sagemaker_program": '"iris-dnn-classifier.py"', "sagemaker_container_log_level": '"logging.INFO"', "training_steps": "100", "sagemaker_region": '"us-west-2"', }, "RoleArn": "arn:aws:iam::366:role/SageMakerRole", "ResourceConfig": { "VolumeSizeInGB": 30, "InstanceCount": 1, "InstanceType": "ml.c4.xlarge", }, "StoppingCondition": {"MaxRuntimeInSeconds": 24 * 60 * 60}, "TrainingJobName": "neo", "TrainingJobStatus": "Completed", "TrainingJobArn": "arn:aws:sagemaker:us-west-2:336:training-job/neo", "OutputDataConfig": {"KmsKeyId": "", "S3OutputPath": "s3://place/output/neo"}, "TrainingJobOutput": {"S3TrainingJobOutput": "s3://here/output.tar.gz"}, } sagemaker_session.sagemaker_client.describe_training_job = Mock( name="describe_training_job", return_value=rjd ) with pytest.raises(ValueError) as error: PyTorch.attach(training_job_name="neo", sagemaker_session=sagemaker_session) assert "didn't use image for requested framework" in str(error) def test_attach_custom_image(sagemaker_session): training_image = "pytorch:latest" returned_job_description = { "AlgorithmSpecification": {"TrainingInputMode": "File", "TrainingImage": training_image}, "HyperParameters": { "sagemaker_submit_directory": '"s3://some/sourcedir.tar.gz"', "sagemaker_program": '"iris-dnn-classifier.py"', "sagemaker_s3_uri_training": '"sagemaker-3/integ-test-data/tf_iris"', "sagemaker_container_log_level": '"logging.INFO"', "sagemaker_job_name": '"neo"', "training_steps": "100", "sagemaker_region": '"us-west-2"', }, "RoleArn": "arn:aws:iam::366:role/SageMakerRole", "ResourceConfig": { "VolumeSizeInGB": 30, "InstanceCount": 1, "InstanceType": "ml.c4.xlarge", }, "StoppingCondition": {"MaxRuntimeInSeconds": 24 * 60 * 60}, "TrainingJobName": "neo", "TrainingJobStatus": "Completed", "TrainingJobArn": "arn:aws:sagemaker:us-west-2:336:training-job/neo", "OutputDataConfig": {"KmsKeyId": "", "S3OutputPath": "s3://place/output/neo"}, "TrainingJobOutput": {"S3TrainingJobOutput": "s3://here/output.tar.gz"}, } sagemaker_session.sagemaker_client.describe_training_job = Mock( name="describe_training_job", return_value=returned_job_description ) estimator = PyTorch.attach(training_job_name="neo", sagemaker_session=sagemaker_session) assert estimator.latest_training_job.job_name == "neo" assert estimator.image_uri == training_image assert estimator.training_image_uri() == training_image @patch("sagemaker.pytorch.estimator.python_deprecation_warning") def test_estimator_py2_warning(warning, sagemaker_session, pytorch_training_version): estimator = PyTorch( entry_point=SCRIPT_PATH, role=ROLE, sagemaker_session=sagemaker_session, instance_count=INSTANCE_COUNT, instance_type=INSTANCE_TYPE, framework_version=pytorch_training_version, py_version="py2", ) assert estimator.py_version == "py2" warning.assert_called_with(estimator._framework_name, defaults.LATEST_PY2_VERSION) @patch("sagemaker.pytorch.model.python_deprecation_warning") def test_model_py2_warning(warning, sagemaker_session, pytorch_inference_version): model = PyTorchModel( MODEL_DATA, role=ROLE, entry_point=SCRIPT_PATH, sagemaker_session=sagemaker_session, framework_version=pytorch_inference_version, py_version="py2", ) assert model.py_version == "py2" warning.assert_called_with(model._framework_name, defaults.LATEST_PY2_VERSION) def test_pt_enable_sm_metrics( sagemaker_session, pytorch_training_version, pytorch_training_py_version ): pytorch = _pytorch_estimator( sagemaker_session, framework_version=pytorch_training_version, py_version=pytorch_training_py_version, enable_sagemaker_metrics=True, ) assert pytorch.enable_sagemaker_metrics def test_pt_disable_sm_metrics( sagemaker_session, pytorch_training_version, pytorch_training_py_version ): pytorch = _pytorch_estimator( sagemaker_session, framework_version=pytorch_training_version, py_version=pytorch_training_py_version, enable_sagemaker_metrics=False, ) assert not pytorch.enable_sagemaker_metrics def test_pt_add_environment_variables( sagemaker_session, pytorch_training_version, pytorch_training_py_version ): pytorch = _pytorch_estimator( sagemaker_session, framework_version=pytorch_training_version, py_version=pytorch_training_py_version, environment=ENV_INPUT, ) assert pytorch.environment def test_pt_miss_environment_variables( sagemaker_session, pytorch_training_version, pytorch_training_py_version ): pytorch = _pytorch_estimator( sagemaker_session, framework_version=pytorch_training_version, py_version=pytorch_training_py_version, environment=None, ) assert not pytorch.environment def test_pt_default_sm_metrics( sagemaker_session, pytorch_training_version, pytorch_training_py_version ): pytorch = _pytorch_estimator( sagemaker_session, framework_version=pytorch_training_version, py_version=pytorch_training_py_version, ) if Version(pytorch_training_version) < Version("1.3"): assert pytorch.enable_sagemaker_metrics is None else: assert pytorch.enable_sagemaker_metrics def test_custom_image_estimator_deploy( sagemaker_session, pytorch_inference_version, pytorch_inference_py_version ): custom_image = "mycustomimage:latest" pytorch = _pytorch_estimator( sagemaker_session, framework_version=pytorch_inference_version, py_version=pytorch_inference_py_version, ) pytorch.fit(inputs="s3://mybucket/train", job_name="new_name") model = pytorch.create_model(image_uri=custom_image) assert model.image_uri == custom_image def test_pt_heterogeneous_cluster_distribution_config( sagemaker_session, pytorch_training_version, pytorch_training_py_version ): training_group = InstanceGroup("train_group", "ml.c4.xlarge", 1) expected_return = {"mpi": {"enabled": True}, "instance_groups": ["train_group"]} pytorch = _pytorch_estimator( sagemaker_session, framework_version=pytorch_training_version, py_version=pytorch_training_py_version, instance_groups=[training_group], distribution={ "mpi": {"enabled": True}, "instance_groups": [training_group], }, ) assert pytorch.distribution == expected_return @patch("sagemaker.utils.repack_model", MagicMock()) @patch("sagemaker.utils.create_tar_file", MagicMock()) def test_register_pytorch_model_auto_infer_framework( sagemaker_session, pytorch_inference_version, pytorch_inference_py_version ): model_package_group_name = "test-pytorch-register-model" content_types = ["application/json"] response_types = ["application/json"] inference_instances = ["ml.m4.xlarge"] transform_instances = ["ml.m4.xlarge"] image_uri = "fakeimage" pytorch_model = PyTorchModel( MODEL_DATA, role=ROLE, entry_point=SCRIPT_PATH, framework_version=pytorch_inference_version, py_version=pytorch_inference_py_version, sagemaker_session=sagemaker_session, ) pytorch_model.register( content_types, response_types, inference_instances, transform_instances, model_package_group_name=model_package_group_name, marketplace_cert=True, image_uri=image_uri, ) expected_create_model_package_request = { "containers": [ { "Image": image_uri, "Environment": ANY, "ModelDataUrl": ANY, "Framework": "PYTORCH", "FrameworkVersion": pytorch_inference_version, }, ], "content_types": content_types, "response_types": response_types, "inference_instances": inference_instances, "transform_instances": transform_instances, "model_package_group_name": model_package_group_name, "marketplace_cert": True, } sagemaker_session.create_model_package_from_containers.assert_called_with( **expected_create_model_package_request ) def test_pytorch_ddp_distribution_configuration( sagemaker_session, pytorch_ddp_framework_version, pytorch_ddp_py_version ): test_instance_type = "ml.p4d.24xlarge" pytorch = _pytorch_estimator( sagemaker_session, framework_version=pytorch_ddp_framework_version, py_version=pytorch_ddp_py_version, distribution=DISTRIBUTION_PYTORCH_DDP_ENABLED, instance_type=test_instance_type, ) actual_pytorch_ddp = pytorch._pytorch_distribution_configuration( distribution=pytorch.distribution ) expected_torch_ddp = { "sagemaker_pytorch_ddp_enabled": True, "sagemaker_instance_type": test_instance_type, } assert actual_pytorch_ddp == expected_torch_ddp def test_pytorch_ddp_distribution_configuration_unsupported(sagemaker_session): unsupported_framework_version = "1.9.1" unsupported_py_version = "py2" with pytest.raises(ValueError) as error: _pytorch_estimator( sagemaker_session, framework_version=unsupported_framework_version, py_version=unsupported_py_version, distribution=DISTRIBUTION_PYTORCH_DDP_ENABLED, ) assert (f"framework_version {unsupported_framework_version} is not supported") in str(error) assert (f"py_version {unsupported_py_version} is not supported") in str(error)