def test_input_fn_with_input_channels(docker_image, sagemaker_session, opt_ml, processor):
resource_path = os.path.join(SCRIPT_PATH, '../resources/iris')
copy_resource(resource_path, opt_ml, 'code')
copy_resource(resource_path, opt_ml, 'data', 'input/data')
s3_source_archive = fw_utils.tar_and_upload_dir(session=sagemaker_session.boto_session,
bucket=sagemaker_session.default_bucket(),
s3_key_prefix='test_job',
script='iris_train_input_fn_with_channels.py',
directory=os.path.join(resource_path, 'code'))
additional_hyperparameters = dict(training_steps=1, evaluation_steps=1)
create_config_files('iris_train_input_fn_with_channels.py', s3_source_archive.s3_prefix,
opt_ml, additional_hyperparameters)
os.makedirs(os.path.join(opt_ml, 'model'))
train(docker_image, opt_ml, processor)
assert file_exists(opt_ml, 'model/export/Servo'), 'model was not exported'
assert file_exists(opt_ml, 'model/checkpoint'), 'checkpoint was not created'
assert file_exists(opt_ml, 'output/success'), 'Success file was not created'
assert not file_exists(opt_ml, 'output/failure'), 'Failure happened'
with HostingContainer(image=docker_image, opt_ml=opt_ml,
script_name='iris_train_input_fn_with_channels.py', processor=processor) as c:
c.execute_pytest('test/integ/container_tests/estimator_classification.py')
def test_save_restore_assets(docker_image, sagemaker_session, opt_ml,
processor):
resource_path = os.path.join(SCRIPT_PATH, '../resources/sentiment')
default_bucket = sagemaker_session.default_bucket()
copy_resource(resource_path, opt_ml, 'data', 'input/data')
s3_source_archive = fw_utils.tar_and_upload_dir(
session=sagemaker_session.boto_session,
bucket=sagemaker_session.default_bucket(),
s3_key_prefix='test_job',
script='sentiment.py',
directory=os.path.join(resource_path, 'code'))
checkpoint_s3_path = 's3://{}/save_restore_assets/output-{}'.format(
default_bucket, uuid.uuid4())
additional_hyperparameters = dict(training_steps=1000,
evaluation_steps=100,
checkpoint_path=checkpoint_s3_path)
create_config_files('sentiment.py', s3_source_archive.s3_prefix, opt_ml,
additional_hyperparameters)
os.makedirs(os.path.join(opt_ml, 'model'))
train(docker_image, opt_ml, processor)
with HostingContainer(opt_ml=opt_ml,
image=docker_image,
script_name='sentiment.py',
processor=processor) as c:
c.execute_pytest(
'test/integ/container_tests/sentiment_classification.py')
def test_train_input_fn_with_input_channels(docker_image, sagemaker_session, opt_ml, processor):
resource_path = os.path.join(SCRIPT_PATH, '../resources/iris')
copy_resource(resource_path, opt_ml, 'code')
copy_resource(resource_path, opt_ml, 'data', 'input/data')
s3_source_archive = fw_utils.tar_and_upload_dir(session=sagemaker_session.boto_session,
bucket=sagemaker_session.default_bucket(),
s3_key_prefix='test_job',
script='iris_train_input_fn_with_channels.py',
directory=os.path.join(resource_path, 'code'))
additional_hyperparameters = dict(training_steps=1, evaluation_steps=1)
create_config_files('iris_train_input_fn_with_channels.py', s3_source_archive.s3_prefix,
opt_ml, additional_hyperparameters)
os.makedirs(os.path.join(opt_ml, 'model'))
train(docker_image, opt_ml, processor)
assert file_exists(opt_ml, 'model/export/Servo'), 'model was not exported'
assert file_exists(opt_ml, 'model/checkpoint'), 'checkpoint was not created'
assert file_exists(opt_ml, 'output/success'), 'Success file was not created'
assert not file_exists(opt_ml, 'output/failure'), 'Failure happened'
with HostingContainer(image=docker_image, opt_ml=opt_ml,
script_name='iris_train_input_fn_with_channels.py', processor=processor) as c:
c.execute_pytest('test/integ/container_tests/estimator_classification.py')
def test_s3_checkpoint_save_timeout(docker_image, opt_ml, sagemaker_session):
resource_path = os.path.join(SCRIPT_PATH, '../resources/python_sdk')
default_bucket = sagemaker_session.default_bucket()
s3_source_archive = fw_utils.tar_and_upload_dir(
session=sagemaker_session.boto_session,
bucket=default_bucket,
s3_key_prefix='test_job',
script='rand_model_emb.py',
directory=resource_path)
checkpoint_s3_path = 's3://{}/integ-s3-timeout/checkpoints-{}'.format(
default_bucket, uuid.uuid4())
hyperparameters = dict(training_steps=10,
evaluation_steps=10,
checkpoint_path=checkpoint_s3_path)
create_config_files('rand_model_emb.py', s3_source_archive.s3_prefix,
opt_ml, hyperparameters)
train(docker_image, opt_ml)
assert file_exists(opt_ml,
'output/success'), 'Success file was not created'
assert not file_exists(opt_ml, 'output/failure'), 'Failure happened'
def test_save_restore_assets(docker_image, sagemaker_session, opt_ml, processor):
resource_path = os.path.join(SCRIPT_PATH, '../resources/sentiment')
default_bucket = sagemaker_session.default_bucket()
copy_resource(resource_path, opt_ml, 'data', 'input/data')
s3_source_archive = fw_utils.tar_and_upload_dir(session=sagemaker_session.boto_session,
bucket=sagemaker_session.default_bucket(),
s3_key_prefix='test_job',
script='sentiment.py',
directory=os.path.join(resource_path, 'code'))
checkpoint_s3_path = 's3://{}/save_restore_assets/output-{}'.format(default_bucket, uuid.uuid4())
additional_hyperparameters = dict(
training_steps=1000,
evaluation_steps=100,
checkpoint_path=checkpoint_s3_path)
create_config_files('sentiment.py', s3_source_archive.s3_prefix, opt_ml, additional_hyperparameters)
os.makedirs(os.path.join(opt_ml, 'model'))
train(docker_image, opt_ml, processor)
with HostingContainer(opt_ml=opt_ml, image=docker_image, script_name='sentiment.py', processor=processor) as c:
c.execute_pytest('test/integ/container_tests/sentiment_classification.py')
def test_layers_prediction(docker_image, sagemaker_session, opt_ml):
resource_path = os.path.join(SCRIPT_PATH, '../resources/mnist')
copy_resource(resource_path, opt_ml, 'code')
copy_resource(resource_path, opt_ml, 'data', 'input/data')
s3_source_archive = fw_utils.tar_and_upload_dir(session=sagemaker_session.boto_session,
bucket=sagemaker_session.default_bucket(),
s3_key_prefix='test_job',
script='mnist.py',
directory=os.path.join(resource_path, 'code'))
create_config_files('mnist.py', s3_source_archive.s3_prefix, opt_ml,
dict(training_steps=1, evaluation_steps=1))
os.makedirs(os.path.join(opt_ml, 'model'))
train(docker_image, opt_ml)
assert file_exists(opt_ml, 'model/export/Servo'), 'model was not exported'
assert file_exists(opt_ml, 'model/checkpoint'), 'checkpoint was not created'
assert file_exists(opt_ml, 'output/success'), 'Success file was not created'
assert not file_exists(opt_ml, 'output/failure'), 'Failure happened'
with HostingContainer(image=docker_image, opt_ml=opt_ml,
script_name='mnist.py') as c:
c.execute_pytest('test/integ/container_tests/layers_prediction.py')
def test_estimator_classification(docker_image, sagemaker_session, opt_ml):
resource_path = os.path.join(SCRIPT_PATH, '../resources/iris')
copy_resource(resource_path, opt_ml, 'code')
copy_resource(resource_path, opt_ml, 'data', 'input/data')
s3_source_archive = fw_utils.tar_and_upload_dir(
session=sagemaker_session.boto_session,
bucket=sagemaker_session.default_bucket(),
s3_key_prefix='test_job',
script='iris.py',
directory=os.path.join(resource_path, 'code'))
additional_hyperparameters = dict(
training_steps=1,
evaluation_steps=1,
sagemaker_requirements='requirements.txt')
create_config_files('iris.py', s3_source_archive.s3_prefix, opt_ml,
additional_hyperparameters)
os.makedirs(os.path.join(opt_ml, 'model'))
train(docker_image, opt_ml)
assert file_exists(opt_ml, 'model/export/Servo'), 'model was not exported'
assert file_exists(opt_ml,
'model/checkpoint'), 'checkpoint was not created'
assert file_exists(opt_ml,
'output/success'), 'Success file was not created'
assert not file_exists(opt_ml, 'output/failure'), 'Failure happened'
with HostingContainer(opt_ml=opt_ml,
image=docker_image,
script_name='iris.py',
requirements_file='requirements.txt') as c:
c.execute_pytest(
'test/integ/container_tests/estimator_classification.py')
modules = c.execute_command(['pip', 'freeze'])
assert 'beautifulsoup4==4.6.0' in modules
def test_s3_checkpoint_save_timeout(docker_image, opt_ml, sagemaker_session, processor):
resource_path = os.path.join(SCRIPT_PATH, '../resources/python_sdk')
default_bucket = sagemaker_session.default_bucket()
s3_source_archive = fw_utils.tar_and_upload_dir(session=sagemaker_session.boto_session,
bucket=default_bucket,
s3_key_prefix='test_job',
script='rand_model_emb.py',
directory=resource_path)
checkpoint_s3_path = 's3://{}/integ-s3-timeout/checkpoints-{}'.format(default_bucket,
uuid.uuid4())
hyperparameters = dict(
training_steps=3,
evaluation_steps=3,
checkpoint_path=checkpoint_s3_path
)
create_config_files('rand_model_emb.py', s3_source_archive.s3_prefix, opt_ml, hyperparameters)
train(docker_image, opt_ml, processor)
assert file_exists(opt_ml, 'output/success'), 'Success file was not created'
assert not file_exists(opt_ml, 'output/failure'), 'Failure happened'
def test_no_serving_input_fn(docker_image, sagemaker_session, opt_ml, processor):
resource_path = os.path.join(SCRIPT_PATH, '../resources/iris')
copy_resource(resource_path, opt_ml, 'code')
copy_resource(resource_path, opt_ml, 'data', 'input/data')
s3_source_archive = fw_utils.tar_and_upload_dir(session=sagemaker_session.boto_session,
bucket=sagemaker_session.default_bucket(),
s3_key_prefix='test_job',
script='iris_no_serving_input.py',
directory=os.path.join(resource_path, 'code'))
additional_hyperparameters = dict(training_steps=1, evaluation_steps=1)
create_config_files('iris_no_serving_input.py', s3_source_archive.s3_prefix, opt_ml, additional_hyperparameters)
os.makedirs(os.path.join(opt_ml, 'model'))
train(docker_image, opt_ml, processor)
# model is not saved so do not expect it there
assert not file_exists(opt_ml, 'model/export/Servo'), 'model was not exported'
assert file_exists(opt_ml, 'model/checkpoint'), 'checkpoint was not created'
assert file_exists(opt_ml, 'output/success'), 'Success file was not created'
assert not file_exists(opt_ml, 'output/failure'), 'Failure happened'
