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476455e | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 | # 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 json
import os
import pytest
import sagemaker.utils
import tests.integ as integ
from tests.integ.s3_utils import extract_files_from_s3
from tests.integ.utils import gpu_list, retry_with_instance_list
from sagemaker.tensorflow import TensorFlow
from tests.integ import timeout
horovod_dir = os.path.join(os.path.dirname(__file__), "..", "data", "horovod")
@pytest.mark.release
def test_hvd_cpu(
sagemaker_session,
tensorflow_training_latest_version,
tensorflow_training_latest_py_version,
cpu_instance_type,
tmpdir,
):
_create_and_fit_estimator(
sagemaker_session,
tensorflow_training_latest_version,
tensorflow_training_latest_py_version,
cpu_instance_type,
tmpdir,
)
@pytest.mark.release
@pytest.mark.skipif(
integ.test_region() in integ.TRAINING_NO_P2_REGIONS
and integ.test_region() in integ.TRAINING_NO_P3_REGIONS,
reason="no ml.p2 or ml.p3 instances in this region",
)
@retry_with_instance_list(gpu_list(integ.test_region()))
def test_hvd_gpu(
sagemaker_session,
tensorflow_training_latest_version,
tensorflow_training_latest_py_version,
tmpdir,
**kwargs,
):
_create_and_fit_estimator(
sagemaker_session,
tensorflow_training_latest_version,
tensorflow_training_latest_py_version,
kwargs["instance_type"],
tmpdir,
)
def read_json(file, tmp):
with open(os.path.join(tmp, file)) as f:
return json.load(f)
def _create_and_fit_estimator(sagemaker_session, tf_version, py_version, instance_type, tmpdir):
job_name = sagemaker.utils.unique_name_from_base("tf-horovod")
estimator = TensorFlow(
entry_point=os.path.join(horovod_dir, "hvd_basic.py"),
role="SageMakerRole",
instance_count=2,
instance_type=instance_type,
sagemaker_session=sagemaker_session,
py_version=py_version,
framework_version=tf_version,
distribution={"mpi": {"enabled": True}},
disable_profiler=True,
)
with timeout.timeout(minutes=integ.TRAINING_DEFAULT_TIMEOUT_MINUTES):
estimator.fit(job_name=job_name)
tmp = str(tmpdir)
extract_files_from_s3(estimator.model_data, tmp, sagemaker_session)
for rank in range(2):
assert read_json("rank-%s" % rank, tmp)["rank"] == rank
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