def test_async_fit(sagemaker_session):
endpoint_name = 'test-chainer-attach-deploy-{}'.format(
sagemaker_timestamp())
with timeout(minutes=5):
training_job_name = _run_mnist_training_job(
sagemaker_session,
"ml.c4.xlarge",
1,
chainer_full_version=CHAINER_VERSION,
wait=False)
print("Waiting to re-attach to the training job: %s" %
training_job_name)
time.sleep(20)
with timeout_and_delete_endpoint_by_name(endpoint_name,
sagemaker_session,
minutes=35):
print("Re-attaching now to: %s" % training_job_name)
estimator = Chainer.attach(training_job_name=training_job_name,
sagemaker_session=sagemaker_session)
predictor = estimator.deploy(1,
"ml.c4.xlarge",
endpoint_name=endpoint_name)
_predict_and_assert(predictor)
def test_attach_deploy(chainer_training_job, sagemaker_session):
endpoint_name = 'test-chainer-attach-deploy-{}'.format(sagemaker_timestamp())
with timeout_and_delete_endpoint_by_name(endpoint_name, sagemaker_session):
estimator = Chainer.attach(chainer_training_job, sagemaker_session=sagemaker_session)
predictor = estimator.deploy(1, 'ml.m4.xlarge', endpoint_name=endpoint_name)
_predict_and_assert(predictor)
def test_attach_deploy(chainer_training_job, sagemaker_session):
endpoint_name = 'test-chainer-attach-deploy-{}'.format(sagemaker_timestamp())
with timeout_and_delete_endpoint_by_name(endpoint_name, sagemaker_session):
estimator = Chainer.attach(chainer_training_job, sagemaker_session=sagemaker_session)
predictor = estimator.deploy(1, 'ml.m4.xlarge', endpoint_name=endpoint_name)
_predict_and_assert(predictor)
def test_attach_deploy(chainer_training_job, sagemaker_session):
endpoint_name = unique_name_from_base('test-chainer-attach-deploy')
with timeout_and_delete_endpoint_by_name(endpoint_name, sagemaker_session):
estimator = Chainer.attach(chainer_training_job,
sagemaker_session=sagemaker_session)
predictor = estimator.deploy(1,
'ml.m4.xlarge',
endpoint_name=endpoint_name)
_predict_and_assert(predictor)
def test_async_fit(sagemaker_session):
endpoint_name = 'test-chainer-attach-deploy-{}'.format(sagemaker_timestamp())
with timeout(minutes=5):
training_job_name = _run_mnist_training_job(sagemaker_session, "ml.c4.xlarge", 1,
chainer_full_version=CHAINER_VERSION, wait=False)
print("Waiting to re-attach to the training job: %s" % training_job_name)
time.sleep(20)
with timeout_and_delete_endpoint_by_name(endpoint_name, sagemaker_session):
print("Re-attaching now to: %s" % training_job_name)
estimator = Chainer.attach(training_job_name=training_job_name, sagemaker_session=sagemaker_session)
predictor = estimator.deploy(1, "ml.c4.xlarge", endpoint_name=endpoint_name)
_predict_and_assert(predictor)
def test_attach_deploy(sagemaker_session, chainer_latest_version,
chainer_latest_py_version, cpu_instance_type):
with timeout(minutes=TRAINING_DEFAULT_TIMEOUT_MINUTES):
script_path = os.path.join(DATA_DIR, "chainer_mnist", "mnist.py")
data_path = os.path.join(DATA_DIR, "chainer_mnist")
chainer = Chainer(
entry_point=script_path,
role="SageMakerRole",
framework_version=chainer_latest_version,
py_version=chainer_latest_py_version,
instance_count=1,
instance_type=cpu_instance_type,
sagemaker_session=sagemaker_session,
hyperparameters={"epochs": 1},
)
train_input = sagemaker_session.upload_data(
path=os.path.join(data_path, "train"),
key_prefix="integ-test-data/chainer_mnist/train")
test_input = sagemaker_session.upload_data(
path=os.path.join(data_path, "test"),
key_prefix="integ-test-data/chainer_mnist/test")
job_name = unique_name_from_base("test-chainer-training")
chainer.fit({
"train": train_input,
"test": test_input
},
wait=False,
job_name=job_name)
endpoint_name = unique_name_from_base("test-chainer-attach-deploy")
with timeout_and_delete_endpoint_by_name(endpoint_name, sagemaker_session):
estimator = Chainer.attach(chainer.latest_training_job.name,
sagemaker_session=sagemaker_session)
predictor = estimator.deploy(1,
cpu_instance_type,
endpoint_name=endpoint_name)
_predict_and_assert(predictor)
