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Reducing test flakiness #601

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Merged
merged 6 commits into from
Jan 17, 2019
Merged

Reducing test flakiness #601

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202c583
Save model locally instead of S3
mvsusp Jan 16, 2019
fa622b8
Removed checkpoint_path in distributed test
mvsusp Jan 16, 2019
bf4e9e8
Create docker network only once
mvsusp Jan 17, 2019
ca61213
Removed unused comment
mvsusp Jan 17, 2019
ead53b1
Handle PR comment
mvsusp Jan 17, 2019
2ab7c55
Merge branch 'master' into mvs-fix-flaky-tests
mvsusp Jan 17, 2019
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16 changes: 16 additions & 0 deletions tests/integ/conftest.py
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Original file line number Diff line number Diff line change
Expand Up @@ -11,9 +11,25 @@
# 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


def create_sagemaker_local_network():
"""
Docker has a known race condition which allows two parallel processes
to create a duplicated networks with the same name. This function
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s/to create a duplicated/to create duplicated

creates the network sagemaker-local beforehand, avoiding this issue
in CI.
"""
os.system('docker network create sagemaker-local')
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how is this 'if necessary' ?
shouldnt this method be renamed to create_sagemaker_local_network() ?

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Good point. Done.



create_sagemaker_local_network()


@pytest.fixture(scope='session', params=['local', 'ml.c4.xlarge'])
def instance_type(request):
return request.param
2 changes: 2 additions & 0 deletions tests/integ/test_tf.py
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Expand Up @@ -38,6 +38,7 @@ def tf_training_job(sagemaker_session, tf_full_version):
framework_version=tf_full_version,
training_steps=1,
evaluation_steps=1,
checkpoint_path='/opt/ml/model',
hyperparameters={'input_tensor_name': 'inputs'},
train_instance_count=1,
train_instance_type='ml.c4.xlarge',
Expand Down Expand Up @@ -114,6 +115,7 @@ def test_tf_async(sagemaker_session):
role='SageMakerRole',
training_steps=1,
evaluation_steps=1,
checkpoint_path='/opt/ml/model',
hyperparameters={'input_tensor_name': 'inputs'},
train_instance_count=1,
train_instance_type='ml.c4.xlarge',
Expand Down