def test_knn_regressor(sagemaker_session, cpu_instance_type, training_set):
job_name = unique_name_from_base("knn")
with timeout(minutes=TRAINING_DEFAULT_TIMEOUT_MINUTES):
knn = KNN(
role="SageMakerRole",
instance_count=1,
instance_type=cpu_instance_type,
k=10,
predictor_type="regressor",
sample_size=500,
sagemaker_session=sagemaker_session,
)
# training labels must be 'float32'
knn.fit(
knn.record_set(training_set[0][:200],
training_set[1][:200].astype("float32")),
job_name=job_name,
)
with timeout_and_delete_endpoint_by_name(job_name, sagemaker_session):
model = KNNModel(knn.model_data,
role="SageMakerRole",
sagemaker_session=sagemaker_session)
predictor = model.deploy(1, cpu_instance_type, endpoint_name=job_name)
result = predictor.predict(training_set[0][:10])
assert len(result) == 10
for record in result:
assert record.label["score"] is not None
def test_knn_airflow_config_uploads_data_source_to_s3(sagemaker_session, cpu_instance_type):
with timeout(seconds=AIRFLOW_CONFIG_TIMEOUT_IN_SECONDS):
data_path = os.path.join(DATA_DIR, "one_p_mnist", "mnist.pkl.gz")
pickle_args = {} if sys.version_info.major == 2 else {"encoding": "latin1"}
# Load the data into memory as numpy arrays
with gzip.open(data_path, "rb") as f:
train_set, _, _ = pickle.load(f, **pickle_args)
knn = KNN(
role=ROLE,
train_instance_count=SINGLE_INSTANCE_COUNT,
train_instance_type=cpu_instance_type,
k=10,
predictor_type="regressor",
sample_size=500,
sagemaker_session=sagemaker_session,
)
records = knn.record_set(train_set[0][:200], train_set[1][:200].astype("float32"))
training_config = _build_airflow_workflow(
estimator=knn, instance_type=cpu_instance_type, inputs=records
)
_assert_that_s3_url_contains_data(
sagemaker_session,
training_config["InputDataConfig"][0]["DataSource"]["S3DataSource"]["S3Uri"],
)
def test_knn_airflow_config_uploads_data_source_to_s3(sagemaker_session,
cpu_instance_type):
with timeout(seconds=AIRFLOW_CONFIG_TIMEOUT_IN_SECONDS):
knn = KNN(
role=ROLE,
instance_count=SINGLE_INSTANCE_COUNT,
instance_type=cpu_instance_type,
k=10,
predictor_type="regressor",
sample_size=500,
sagemaker_session=sagemaker_session,
)
training_set = datasets.one_p_mnist()
records = knn.record_set(training_set[0][:200],
training_set[1][:200].astype("float32"))
training_config = _build_airflow_workflow(
estimator=knn, instance_type=cpu_instance_type, inputs=records)
_assert_that_s3_url_contains_data(
sagemaker_session,
training_config["InputDataConfig"][0]["DataSource"]["S3DataSource"]
["S3Uri"],
)
def test_knn_regressor(sagemaker_session, cpu_instance_type):
job_name = unique_name_from_base("knn")
with timeout(minutes=TRAINING_DEFAULT_TIMEOUT_MINUTES):
data_path = os.path.join(DATA_DIR, "one_p_mnist", "mnist.pkl.gz")
pickle_args = {} if sys.version_info.major == 2 else {"encoding": "latin1"}
# Load the data into memory as numpy arrays
with gzip.open(data_path, "rb") as f:
train_set, _, _ = pickle.load(f, **pickle_args)
knn = KNN(
role="SageMakerRole",
train_instance_count=1,
train_instance_type=cpu_instance_type,
k=10,
predictor_type="regressor",
sample_size=500,
sagemaker_session=sagemaker_session,
)
# training labels must be 'float32'
knn.fit(
knn.record_set(train_set[0][:200], train_set[1][:200].astype("float32")),
job_name=job_name,
)
with timeout_and_delete_endpoint_by_name(job_name, sagemaker_session):
model = KNNModel(knn.model_data, role="SageMakerRole", sagemaker_session=sagemaker_session)
predictor = model.deploy(1, cpu_instance_type, endpoint_name=job_name)
result = predictor.predict(train_set[0][:10])
assert len(result) == 10
for record in result:
assert record.label["score"] is not None
def test_knn_regressor(sagemaker_session):
with timeout(minutes=TRAINING_DEFAULT_TIMEOUT_MINUTES):
data_path = os.path.join(DATA_DIR, 'one_p_mnist', 'mnist.pkl.gz')
pickle_args = {} if sys.version_info.major == 2 else {'encoding': 'latin1'}
# Load the data into memory as numpy arrays
with gzip.open(data_path, 'rb') as f:
train_set, _, _ = pickle.load(f, **pickle_args)
knn = KNN(role='SageMakerRole', train_instance_count=1,
train_instance_type='ml.c4.xlarge',
k=10, predictor_type='regressor', sample_size=500,
sagemaker_session=sagemaker_session, base_job_name='test-knn-rr')
# training labels must be 'float32'
knn.fit(knn.record_set(train_set[0][:200], train_set[1][:200].astype('float32')))
endpoint_name = name_from_base('knn')
with timeout_and_delete_endpoint_by_name(endpoint_name, sagemaker_session):
model = KNNModel(knn.model_data, role='SageMakerRole', sagemaker_session=sagemaker_session)
predictor = model.deploy(1, 'ml.c4.xlarge', endpoint_name=endpoint_name)
result = predictor.predict(train_set[0][:10])
assert len(result) == 10
for record in result:
assert record.label["score"] is not None
def test_knn_regressor(sagemaker_session):
with timeout(minutes=TRAINING_DEFAULT_TIMEOUT_MINUTES):
data_path = os.path.join(DATA_DIR, 'one_p_mnist', 'mnist.pkl.gz')
pickle_args = {} if sys.version_info.major == 2 else {
'encoding': 'latin1'
}
# Load the data into memory as numpy arrays
with gzip.open(data_path, 'rb') as f:
train_set, _, _ = pickle.load(f, **pickle_args)
knn = KNN(role='SageMakerRole',
train_instance_count=1,
train_instance_type='ml.c4.xlarge',
k=10,
predictor_type='regressor',
sample_size=500,
sagemaker_session=sagemaker_session,
base_job_name='test-knn-rr')
# training labels must be 'float32'
knn.fit(
knn.record_set(train_set[0][:200],
train_set[1][:200].astype('float32')))
endpoint_name = unique_name_from_base('knn')
with timeout_and_delete_endpoint_by_name(endpoint_name, sagemaker_session):
model = KNNModel(knn.model_data,
role='SageMakerRole',
sagemaker_session=sagemaker_session)
predictor = model.deploy(1,
'ml.c4.xlarge',
endpoint_name=endpoint_name)
result = predictor.predict(train_set[0][:10])
assert len(result) == 10
for record in result:
assert record.label["score"] is not None
def test_async_knn_classifier(sagemaker_session):
training_job_name = ""
endpoint_name = name_from_base('knn')
with timeout(minutes=5):
data_path = os.path.join(DATA_DIR, 'one_p_mnist', 'mnist.pkl.gz')
pickle_args = {} if sys.version_info.major == 2 else {'encoding': 'latin1'}
# Load the data into memory as numpy arrays
with gzip.open(data_path, 'rb') as f:
train_set, _, _ = pickle.load(f, **pickle_args)
knn = KNN(role='SageMakerRole',
train_instance_count=1, train_instance_type='ml.c4.xlarge',
k=10, predictor_type='classifier', sample_size=500,
index_type='faiss.IVFFlat', index_metric='L2',
sagemaker_session=sagemaker_session, base_job_name='test-knn-cl')
# training labels must be 'float32'
knn.fit(knn.record_set(train_set[0][:200], train_set[1][:200].astype('float32')), wait=False)
training_job_name = knn.latest_training_job.name
print("Detached from training job. Will re-attach in 20 seconds")
time.sleep(20)
print("attaching now...")
with timeout_and_delete_endpoint_by_name(endpoint_name, sagemaker_session):
estimator = KNN.attach(training_job_name=training_job_name,
sagemaker_session=sagemaker_session)
model = KNNModel(estimator.model_data, role='SageMakerRole',
sagemaker_session=sagemaker_session)
predictor = model.deploy(1, 'ml.c4.xlarge', endpoint_name=endpoint_name)
result = predictor.predict(train_set[0][:10])
assert len(result) == 10
for record in result:
assert record.label["score"] is not None
def test_async_knn_classifier(sagemaker_session):
job_name = unique_name_from_base("knn")
with timeout(minutes=5):
data_path = os.path.join(DATA_DIR, "one_p_mnist", "mnist.pkl.gz")
pickle_args = {} if sys.version_info.major == 2 else {
"encoding": "latin1"
}
# Load the data into memory as numpy arrays
with gzip.open(data_path, "rb") as f:
train_set, _, _ = pickle.load(f, **pickle_args)
knn = KNN(
role="SageMakerRole",
train_instance_count=1,
train_instance_type="ml.c4.xlarge",
k=10,
predictor_type="classifier",
sample_size=500,
index_type="faiss.IVFFlat",
index_metric="L2",
sagemaker_session=sagemaker_session,
)
# training labels must be 'float32'
knn.fit(
knn.record_set(train_set[0][:200],
train_set[1][:200].astype("float32")),
wait=False,
job_name=job_name,
)
print("Detached from training job. Will re-attach in 20 seconds")
time.sleep(20)
print("attaching now...")
with timeout_and_delete_endpoint_by_name(job_name, sagemaker_session):
estimator = KNN.attach(training_job_name=job_name,
sagemaker_session=sagemaker_session)
model = KNNModel(estimator.model_data,
role="SageMakerRole",
sagemaker_session=sagemaker_session)
predictor = model.deploy(1, "ml.c4.xlarge", endpoint_name=job_name)
result = predictor.predict(train_set[0][:10])
assert len(result) == 10
for record in result:
assert record.label["score"] is not None
def test_async_knn_classifier(sagemaker_session):
training_job_name = ""
endpoint_name = unique_name_from_base('knn')
with timeout(minutes=5):
data_path = os.path.join(DATA_DIR, 'one_p_mnist', 'mnist.pkl.gz')
pickle_args = {} if sys.version_info.major == 2 else {
'encoding': 'latin1'
}
# Load the data into memory as numpy arrays
with gzip.open(data_path, 'rb') as f:
train_set, _, _ = pickle.load(f, **pickle_args)
knn = KNN(role='SageMakerRole',
train_instance_count=1,
train_instance_type='ml.c4.xlarge',
k=10,
predictor_type='classifier',
sample_size=500,
index_type='faiss.IVFFlat',
index_metric='L2',
sagemaker_session=sagemaker_session,
base_job_name='test-knn-cl')
# training labels must be 'float32'
knn.fit(knn.record_set(train_set[0][:200],
train_set[1][:200].astype('float32')),
wait=False)
training_job_name = knn.latest_training_job.name
print("Detached from training job. Will re-attach in 20 seconds")
time.sleep(20)
print("attaching now...")
with timeout_and_delete_endpoint_by_name(endpoint_name, sagemaker_session):
estimator = KNN.attach(training_job_name=training_job_name,
sagemaker_session=sagemaker_session)
model = KNNModel(estimator.model_data,
role='SageMakerRole',
sagemaker_session=sagemaker_session)
predictor = model.deploy(1,
'ml.c4.xlarge',
endpoint_name=endpoint_name)
result = predictor.predict(train_set[0][:10])
assert len(result) == 10
for record in result:
assert record.label["score"] is not None
def test_async_knn_classifier(sagemaker_session, cpu_instance_type,
training_set):
job_name = unique_name_from_base("knn")
with timeout(minutes=5):
knn = KNN(
role="SageMakerRole",
instance_count=1,
instance_type=cpu_instance_type,
k=10,
predictor_type="classifier",
sample_size=500,
index_type="faiss.IVFFlat",
index_metric="L2",
sagemaker_session=sagemaker_session,
)
# training labels must be 'float32'
knn.fit(
knn.record_set(training_set[0][:200],
training_set[1][:200].astype("float32")),
wait=False,
job_name=job_name,
)
print("Detached from training job. Will re-attach in 20 seconds")
time.sleep(20)
print("attaching now...")
with timeout_and_delete_endpoint_by_name(job_name, sagemaker_session):
estimator = KNN.attach(training_job_name=job_name,
sagemaker_session=sagemaker_session)
model = KNNModel(estimator.model_data,
role="SageMakerRole",
sagemaker_session=sagemaker_session)
predictor = model.deploy(1, cpu_instance_type, endpoint_name=job_name)
result = predictor.predict(training_set[0][:10])
assert len(result) == 10
for record in result:
assert record.label["score"] is not None