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fix: use unique training job names in TensorFlow script mode integ tests #742

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merged 1 commit into from
Apr 9, 2019
Merged

fix: use unique training job names in TensorFlow script mode integ tests #742

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18 changes: 7 additions & 11 deletions tests/integ/test_tf_script_mode.py
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Original file line number Diff line number Diff line change
Expand Up @@ -49,14 +49,13 @@ def test_mnist(sagemaker_session, instance_type):
sagemaker_session=sagemaker_session,
py_version='py3',
framework_version=TensorFlow.LATEST_VERSION,
metric_definitions=[{'Name': 'train:global_steps', 'Regex': r'global_step\/sec:\s(.*)'}],
base_job_name=unique_name_from_base('test-tf-sm-mnist'))
metric_definitions=[{'Name': 'train:global_steps', 'Regex': r'global_step\/sec:\s(.*)'}])
inputs = estimator.sagemaker_session.upload_data(
path=os.path.join(RESOURCE_PATH, 'data'),
key_prefix='scriptmode/mnist')

with timeout.timeout(minutes=integ.TRAINING_DEFAULT_TIMEOUT_MINUTES):
estimator.fit(inputs)
estimator.fit(inputs=inputs, job_name=unique_name_from_base('test-tf-sm-mnist'))
_assert_s3_files_exist(estimator.model_dir,
['graph.pbtxt', 'model.ckpt-0.index', 'model.ckpt-0.meta'])
df = estimator.training_job_analytics.dataframe()
Expand All @@ -77,8 +76,7 @@ def test_server_side_encryption(sagemaker_session):
train_instance_type='ml.c5.xlarge',
sagemaker_session=sagemaker_session,
py_version='py3',
framework_version='1.11',
base_job_name=unique_name_from_base('test-server-side-encryption'),
framework_version=TensorFlow.LATEST_VERSION,
code_location=output_path,
output_path=output_path,
model_dir='/opt/ml/model',
Expand All @@ -89,7 +87,7 @@ def test_server_side_encryption(sagemaker_session):
key_prefix='scriptmode/mnist')

with timeout.timeout(minutes=integ.TRAINING_DEFAULT_TIMEOUT_MINUTES):
estimator.fit(inputs)
estimator.fit(inputs=inputs, job_name=unique_name_from_base('test-server-side-encryption'))


@pytest.mark.canary_quick
Expand All @@ -104,14 +102,13 @@ def test_mnist_distributed(sagemaker_session, instance_type):
py_version=integ.PYTHON_VERSION,
script_mode=True,
framework_version=TensorFlow.LATEST_VERSION,
distributions=PARAMETER_SERVER_DISTRIBUTION,
base_job_name=unique_name_from_base('test-tf-sm-mnist'))
distributions=PARAMETER_SERVER_DISTRIBUTION)
inputs = estimator.sagemaker_session.upload_data(
path=os.path.join(RESOURCE_PATH, 'data'),
key_prefix='scriptmode/distributed_mnist')

with timeout.timeout(minutes=integ.TRAINING_DEFAULT_TIMEOUT_MINUTES):
estimator.fit(inputs)
estimator.fit(inputs=inputs, job_name=unique_name_from_base('test-tf-sm-distributed'))
_assert_s3_files_exist(estimator.model_dir,
['graph.pbtxt', 'model.ckpt-0.index', 'model.ckpt-0.meta'])

Expand All @@ -124,12 +121,11 @@ def test_mnist_async(sagemaker_session):
sagemaker_session=sagemaker_session,
py_version='py3',
framework_version=TensorFlow.LATEST_VERSION,
base_job_name=unique_name_from_base('test-tf-sm-mnist'),
tags=TAGS)
inputs = estimator.sagemaker_session.upload_data(
path=os.path.join(RESOURCE_PATH, 'data'),
key_prefix='scriptmode/mnist')
estimator.fit(inputs, wait=False)
estimator.fit(inputs=inputs, wait=False, job_name=unique_name_from_base('test-tf-sm-async'))
training_job_name = estimator.latest_training_job.name
time.sleep(20)
endpoint_name = training_job_name
Expand Down