testbed/aws__sagemaker-python-sdk/tests/unit/test_chainer.py

# 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
from distutils.util import strtobool

import pytest
from mock import MagicMock, Mock, ANY
from mock import patch

from sagemaker.chainer import defaults
from sagemaker.chainer import Chainer
from sagemaker.chainer import ChainerPredictor, ChainerModel

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-chainer"
JOB_NAME = "{}-{}".format(IMAGE_URI, TIMESTAMP)
IMAGE_URI_FORMAT_STRING = "520713654638.dkr.ecr.{}.amazonaws.com/{}:{}-{}-{}"
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"}]}

LIST_TAGS_RESULT = {"Tags": [{"Key": "TagtestKey", "Value": "TagtestValue"}]}


@pytest.fixture()
def 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,
    )

    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)
    return session


def _get_full_cpu_image_uri(version, py_version):
    return IMAGE_URI_FORMAT_STRING.format(REGION, IMAGE_URI, version, "cpu", py_version)


def _chainer_estimator(
    sagemaker_session,
    framework_version,
    py_version,
    instance_type=None,
    base_job_name=None,
    use_mpi=None,
    num_processes=None,
    process_slots_per_host=None,
    additional_mpi_options=None,
    **kwargs,
):
    return Chainer(
        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,
        use_mpi=use_mpi,
        num_processes=num_processes,
        process_slots_per_host=process_slots_per_host,
        additional_mpi_options=additional_mpi_options,
        **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},
        "retry_strategy": None,
        "tags": None,
        "vpc_config": None,
        "metric_definitions": None,
        "environment": None,
        "experiment_config": None,
        "debugger_hook_config": {
            "CollectionConfigurations": [],
            "S3OutputPath": "s3://{}/".format(BUCKET_NAME),
        },
        "profiler_rule_configs": [
            {
                "RuleConfigurationName": "ProfilerReport-1510006209",
                "RuleEvaluatorImage": "895741380848.dkr.ecr.us-west-2.amazonaws.com/sagemaker-debugger-rules:latest",
                "RuleParameters": {"rule_to_invoke": "ProfilerReport"},
            }
        ],
        "profiler_config": {
            "S3OutputPath": "s3://{}/".format(BUCKET_NAME),
        },
    }


def test_additional_hyperparameters(sagemaker_session, chainer_version, chainer_py_version):
    chainer = _chainer_estimator(
        sagemaker_session,
        use_mpi=True,
        num_processes=4,
        process_slots_per_host=10,
        additional_mpi_options="-x MY_ENVIRONMENT_VARIABLE",
        framework_version=chainer_version,
        py_version=chainer_py_version,
    )
    assert bool(strtobool(chainer.hyperparameters()["sagemaker_use_mpi"]))
    assert int(chainer.hyperparameters()["sagemaker_num_processes"]) == 4
    assert int(chainer.hyperparameters()["sagemaker_process_slots_per_host"]) == 10
    assert (
        str(chainer.hyperparameters()["sagemaker_additional_mpi_options"])
        == '"-x MY_ENVIRONMENT_VARIABLE"'
    )


def test_attach_with_additional_hyperparameters(
    sagemaker_session, chainer_version, chainer_py_version
):
    training_image = "1.dkr.ecr.us-west-2.amazonaws.com/sagemaker-chainer:{}-cpu-{}".format(
        chainer_version, chainer_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"',
            "sagemaker_region": '"us-west-2"',
            "sagemaker_num_processes": "4",
            "sagemaker_additional_mpi_options": '"-x MY_ENVIRONMENT_VARIABLE"',
            "sagemaker_process_slots_per_host": "10",
            "sagemaker_use_mpi": "true",
        },
        "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 = Chainer.attach(training_job_name="neo", sagemaker_session=sagemaker_session)
    assert bool(estimator.hyperparameters()["sagemaker_use_mpi"])
    assert int(estimator.hyperparameters()["sagemaker_num_processes"]) == 4
    assert int(estimator.hyperparameters()["sagemaker_process_slots_per_host"]) == 10
    assert (
        str(estimator.hyperparameters()["sagemaker_additional_mpi_options"])
        == '"-x MY_ENVIRONMENT_VARIABLE"'
    )
    assert estimator.use_mpi
    assert estimator.num_processes == 4
    assert estimator.process_slots_per_host == 10
    assert estimator.additional_mpi_options == "-x MY_ENVIRONMENT_VARIABLE"


@patch("sagemaker.estimator.name_from_base")
def test_create_model(name_from_base, sagemaker_session, chainer_version, chainer_py_version):
    container_log_level = '"logging.INFO"'
    source_dir = "s3://mybucket/source"
    base_job_name = "job"

    chainer = Chainer(
        entry_point=SCRIPT_PATH,
        role=ROLE,
        sagemaker_session=sagemaker_session,
        instance_count=INSTANCE_COUNT,
        instance_type=INSTANCE_TYPE,
        framework_version=chainer_version,
        container_log_level=container_log_level,
        py_version=chainer_py_version,
        base_job_name=base_job_name,
        source_dir=source_dir,
    )

    chainer.fit(inputs="s3://mybucket/train", job_name="new_name")

    model_name = "model_name"
    name_from_base.return_value = model_name
    model = chainer.create_model()

    assert model.sagemaker_session == sagemaker_session
    assert model.framework_version == chainer_version
    assert model.py_version == chainer.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, chainer_version, chainer_py_version):
    container_log_level = '"logging.INFO"'
    source_dir = "s3://mybucket/source"
    chainer = Chainer(
        entry_point=SCRIPT_PATH,
        role=ROLE,
        sagemaker_session=sagemaker_session,
        instance_count=INSTANCE_COUNT,
        instance_type=INSTANCE_TYPE,
        container_log_level=container_log_level,
        framework_version=chainer_version,
        py_version=chainer_py_version,
        base_job_name="job",
        source_dir=source_dir,
    )

    chainer.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 = chainer.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


def test_create_model_with_custom_image(sagemaker_session):
    container_log_level = '"logging.INFO"'
    source_dir = "s3://mybucket/source"
    custom_image = "ubuntu:latest"
    chainer = Chainer(
        entry_point=SCRIPT_PATH,
        role=ROLE,
        sagemaker_session=sagemaker_session,
        instance_count=INSTANCE_COUNT,
        instance_type=INSTANCE_TYPE,
        image_uri=custom_image,
        container_log_level=container_log_level,
        base_job_name="job",
        source_dir=source_dir,
    )

    chainer.fit(inputs="s3://mybucket/train", job_name="new_name")
    model = chainer.create_model()

    assert model.image_uri == custom_image


@patch("sagemaker.utils.create_tar_file", MagicMock())
@patch("time.time", return_value=TIME)
@patch("time.strftime", return_value=TIMESTAMP)
def test_chainer(strftime, time, sagemaker_session, chainer_version, chainer_py_version):
    chainer = Chainer(
        entry_point=SCRIPT_PATH,
        role=ROLE,
        sagemaker_session=sagemaker_session,
        instance_count=INSTANCE_COUNT,
        instance_type=INSTANCE_TYPE,
        framework_version=chainer_version,
        py_version=chainer_py_version,
    )

    inputs = "s3://mybucket/train"

    chainer.fit(inputs=inputs)

    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(chainer_version, chainer_py_version)
    expected_train_args["input_config"][0]["DataSource"]["S3DataSource"]["S3Uri"] = inputs

    actual_train_args = sagemaker_session.method_calls[0][2]
    assert actual_train_args == expected_train_args

    model = chainer.create_model()

    expected_image_base = "520713654638.dkr.ecr.us-west-2.amazonaws.com/sagemaker-chainer:{}-gpu-{}"
    assert {
        "Environment": {
            "SAGEMAKER_SUBMIT_DIRECTORY": "s3://mybucket/sagemaker-chainer-{}/source/sourcedir.tar.gz".format(
                TIMESTAMP
            ),
            "SAGEMAKER_PROGRAM": "dummy_script.py",
            "SAGEMAKER_REGION": "us-west-2",
            "SAGEMAKER_CONTAINER_LOG_LEVEL": "20",
        },
        "Image": expected_image_base.format(chainer_version, chainer_py_version),
        "ModelDataUrl": "s3://m/m.tar.gz",
    } == model.prepare_container_def(GPU)

    assert "cpu" in model.prepare_container_def(CPU)["Image"]
    predictor = chainer.deploy(1, GPU)
    assert isinstance(predictor, ChainerPredictor)


@patch("sagemaker.utils.create_tar_file", MagicMock())
def test_model(sagemaker_session, chainer_version, chainer_py_version):
    model = ChainerModel(
        "s3://some/data.tar.gz",
        role=ROLE,
        entry_point=SCRIPT_PATH,
        sagemaker_session=sagemaker_session,
        framework_version=chainer_version,
        py_version=chainer_py_version,
    )
    predictor = model.deploy(1, GPU)
    assert isinstance(predictor, ChainerPredictor)


@patch("sagemaker.utils.create_tar_file", MagicMock())
def test_model_custom_serialization(sagemaker_session, chainer_version, chainer_py_version):
    model = ChainerModel(
        "s3://some/data.tar.gz",
        role=ROLE,
        entry_point=SCRIPT_PATH,
        sagemaker_session=sagemaker_session,
        framework_version=chainer_version,
        py_version=chainer_py_version,
    )
    custom_serializer = Mock()
    custom_deserializer = Mock()
    predictor = model.deploy(
        1,
        CPU,
        serializer=custom_serializer,
        deserializer=custom_deserializer,
    )
    assert isinstance(predictor, ChainerPredictor)
    assert predictor.serializer is custom_serializer
    assert predictor.deserializer is custom_deserializer


@patch("sagemaker.fw_utils.tar_and_upload_dir", MagicMock())
def test_model_prepare_container_def_accelerator_error(
    sagemaker_session, chainer_version, chainer_py_version
):
    model = ChainerModel(
        MODEL_DATA,
        role=ROLE,
        entry_point=SCRIPT_PATH,
        sagemaker_session=sagemaker_session,
        framework_version=chainer_version,
        py_version=chainer_py_version,
    )
    with pytest.raises(ValueError):
        model.prepare_container_def(INSTANCE_TYPE, accelerator_type=ACCELERATOR_TYPE)


def test_model_prepare_container_def_no_instance_type_or_image(chainer_version, chainer_py_version):
    model = ChainerModel(
        MODEL_DATA,
        role=ROLE,
        entry_point=SCRIPT_PATH,
        framework_version=chainer_version,
        py_version=chainer_py_version,
    )

    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_training_image_default(sagemaker_session, chainer_version, chainer_py_version):
    chainer = Chainer(
        entry_point=SCRIPT_PATH,
        role=ROLE,
        sagemaker_session=sagemaker_session,
        instance_count=INSTANCE_COUNT,
        instance_type=INSTANCE_TYPE,
        framework_version=chainer_version,
        py_version=chainer_py_version,
    )

    assert (
        _get_full_cpu_image_uri(chainer_version, chainer_py_version) == chainer.training_image_uri()
    )


def test_attach(sagemaker_session, chainer_version, chainer_py_version):
    training_image = "1.dkr.ecr.us-west-2.amazonaws.com/sagemaker-chainer:{}-cpu-{}".format(
        chainer_version, chainer_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 = Chainer.attach(training_job_name="neo", sagemaker_session=sagemaker_session)
    assert estimator.latest_training_job.job_name == "neo"
    assert estimator.py_version == chainer_py_version
    assert estimator.framework_version == chainer_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:
        Chainer.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 = "1.dkr.ecr.us-west-2.amazonaws.com/my_custom_chainer_image: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 = Chainer.attach(training_job_name="neo", sagemaker_session=sagemaker_session)
    assert estimator.image_uri == training_image
    assert estimator.training_image_uri() == training_image


@patch("sagemaker.chainer.estimator.python_deprecation_warning")
def test_estimator_py2_warning(warning, sagemaker_session, chainer_version):
    estimator = Chainer(
        entry_point=SCRIPT_PATH,
        role=ROLE,
        sagemaker_session=sagemaker_session,
        instance_count=INSTANCE_COUNT,
        instance_type=INSTANCE_TYPE,
        framework_version=chainer_version,
        py_version="py2",
    )

    assert estimator.py_version == "py2"
    warning.assert_called_with(estimator._framework_name, defaults.LATEST_PY2_VERSION)


@patch("sagemaker.chainer.model.python_deprecation_warning")
def test_model_py2_warning(warning, sagemaker_session, chainer_version):
    model = ChainerModel(
        MODEL_DATA,
        role=ROLE,
        entry_point=SCRIPT_PATH,
        sagemaker_session=sagemaker_session,
        framework_version=chainer_version,
        py_version="py2",
    )
    assert model.py_version == "py2"
    warning.assert_called_with(model._framework_name, defaults.LATEST_PY2_VERSION)


@patch("sagemaker.utils.create_tar_file", MagicMock())
def test_register_chainer_model_auto_infer_framework(
    sagemaker_session, chainer_version, chainer_py_version
):

    model_package_group_name = "test-chainer-register-model"
    content_types = ["application/json"]
    response_types = ["application/json"]
    inference_instances = ["ml.m4.xlarge"]
    transform_instances = ["ml.m4.xlarge"]
    image_uri = "fakeimage"

    chainer_model = ChainerModel(
        "s3://some/data.tar.gz",
        role=ROLE,
        entry_point=SCRIPT_PATH,
        sagemaker_session=sagemaker_session,
        framework_version=chainer_version,
        py_version=chainer_py_version,
    )

    chainer_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": "CHAINER",
                "FrameworkVersion": chainer_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
    )