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#
# 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 os
import pytest
from sagemaker.mxnet.estimator import MXNet
from tests.integ import (
DATA_DIR,
TRAINING_DEFAULT_TIMEOUT_MINUTES,
EDGE_PACKAGING_SUPPORTED_REGIONS,
test_region,
)
from tests.integ.timeout import timeout
@pytest.fixture(scope="module")
def mxnet_training_job(
sagemaker_session,
cpu_instance_type,
mxnet_training_latest_version,
mxnet_training_latest_py_version,
):
with timeout(minutes=TRAINING_DEFAULT_TIMEOUT_MINUTES):
script_path = os.path.join(DATA_DIR, "mxnet_mnist", "mnist_neo.py")
data_path = os.path.join(DATA_DIR, "mxnet_mnist")
mx = MXNet(
entry_point=script_path,
role="SageMakerRole",
framework_version=mxnet_training_latest_version,
py_version=mxnet_training_latest_py_version,
instance_count=1,
instance_type=cpu_instance_type,
sagemaker_session=sagemaker_session,
)
train_input = mx.sagemaker_session.upload_data(
path=os.path.join(data_path, "train"), key_prefix="integ-test-data/mxnet_mnist/train"
)
test_input = mx.sagemaker_session.upload_data(
path=os.path.join(data_path, "test"), key_prefix="integ-test-data/mxnet_mnist/test"
)
mx.fit({"train": train_input, "test": test_input})
return mx.latest_training_job.name
@pytest.mark.skipif(
test_region() not in EDGE_PACKAGING_SUPPORTED_REGIONS,
reason="Edge packaging isn't supported in that specific region.",
)
def test_edge_packaging_job(mxnet_training_job, sagemaker_session):
estimator = MXNet.attach(mxnet_training_job, sagemaker_session=sagemaker_session)
model = estimator.compile_model(
target_instance_family="rasp3b",
input_shape={"data": [1, 1, 28, 28], "softmax_label": [1]},
output_path=estimator.output_path,
)
model.package_for_edge(
output_path=estimator.output_path,
role=estimator.role,
model_name="sdk-test-model",
model_version="1.0",
)
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