def test_MMF_activity_recognition_model_register_and_inference_on_valid_model(
):
test_utils.start_torchserve(snapshot_file=snapshot_file_tf)
test_utils.register_model(
'MMF_activity_recognition_v2',
'https://torchserve.pytorch.org/mar_files/MMF_activity_recognition_v2.mar'
)
os.system(
'wget https://mmfartifacts.s3-us-west-2.amazonaws.com/372CC.mp4 -P ../../examples/MMF-activity-recognition'
)
input_json = "../../examples/MMF-activity-recognition/372CC.info.json"
with open(input_json) as jsonfile:
info = json.load(jsonfile)
files = {
'data': open('../../examples/MMF-activity-recognition/372CC.mp4',
'rb'),
'script': info['script'],
'labels': info['action_labels']
}
response = run_inference_using_url_with_data(
TF_INFERENCE_API + '/v1/models/MMF_activity_recognition_v2:predict',
pfiles=files)
response = response.content.decode("utf-8")
response = ast.literal_eval(response)
response = [n.strip() for n in response]
assert response == [
'Sitting at a table', 'Someone is sneezing',
'Watching a laptop or something on a laptop'
]
test_utils.unregister_model("MMF_activity_recognition_v2")
def test_mnist_model_register_and_inference_on_valid_model_explain():
"""
Validates that snapshot.cfg is created when management apis are invoked.
"""
test_utils.start_torchserve(no_config_snapshots=True)
test_utils.register_model('mnist', 'mnist.mar')
files = {
'data': (data_file_mnist, open(data_file_mnist, 'rb')),
}
response = run_inference_using_url_with_data(
TF_INFERENCE_API + '/explanations/mnist', files)
assert np.array(json.loads(response.content)).shape == (1, 28, 28)
test_utils.unregister_model("mnist")
def test_kfserving_mnist_model_register_and_inference_on_valid_model_explain():
"""
Validates the kfserving model explanations.
"""
test_utils.start_torchserve(snapshot_file=snapshot_file_kf)
test_utils.register_model('mnist', 'mnist.mar')
with open(input_json_mnist, 'r') as f:
s = f.read()
s = s.replace('\'', '\"')
data = json.loads(s)
response = run_inference_using_url_with_data_json(
KF_INFERENCE_API + '/v1/models/mnist:explain', data)
assert np.array(json.loads(
response.content)['explanations']).shape == (1, 1, 28, 28)
test_utils.unregister_model("mnist")
def test_kfserving_mnist_model_register_and_inference_on_valid_model():
"""
Validates that snapshot.cfg is created when management apis are invoked for kfserving.
"""
test_utils.start_torchserve(snapshot_file=snapshot_file_kf)
test_utils.register_model('mnist', 'mnist.mar')
with open(input_json_mnist, 'r') as f:
s = f.read()
s = s.replace('\'', '\"')
data = json.loads(s)
response = run_inference_using_url_with_data_json(
KF_INFERENCE_API + '/v1/models/mnist:predict', data)
assert (json.loads(response.content)['predictions'][0]) == 2
test_utils.unregister_model("mnist")
def test_duplicate_model_registration_using_http_url_followed_by_local_url():
# Register using http url
clean_mar_file("resnet-18.mar")
response = test_utils.register_model(
"resnet18", "https://torchserve.pytorch.org/mar_files/resnet-18.mar")
create_resnet_archive()
response = test_utils.register_model("resnet18", "resnet-18.mar")
if json.loads(response.content)['code'] == 409 and \
json.loads(response.content)['type'] == "ConflictStatusException":
assert True, "Conflict Status Exception, " \
"Duplicate model registration request"
response = test_utils.unregister_model("resnet18")
time.sleep(10)
else:
assert False, "Something is not right!! Successfully re-registered existing model "
def test_mnist_model_register_and_inference_on_valid_model():
"""
Validates that snapshot.cfg is created when management apis are invoked.
"""
test_utils.start_torchserve(no_config_snapshots=True)
test_utils.register_model('mnist', 'mnist.mar')
files = {
'data': ('../../examples/image_classifier/mnist/test_data/1.png',
open('../../examples/image_classifier/mnist/test_data/1.png',
'rb')),
}
response = run_inference_using_url_with_data(
TF_INFERENCE_API + '/predictions/mnist', files)
assert (json.loads(response.content)) == 1
test_utils.unregister_model("mnist")
def test_multiple_model_versions_registration():
# Download resnet-18 model
create_resnet_archive("resnet-18", "1.0")
create_resnet_archive("resnet-18_v2", "2.0")
test_utils.start_torchserve(no_config_snapshots=True)
response = requests.get('http://localhost:8081/models/resnet18/all')
print(response.content)
test_utils.register_model("resnet18", "resnet-18.mar")
test_utils.register_model("resnet18", "resnet-18_v2.mar")
response = requests.get('http://localhost:8081/models/resnet18/all')
time.sleep(5)
# Verify that we can use the list models api to get all versions of resnet-18
assert len(json.loads(response.content)) == 2
def test_duplicate_model_registration_using_local_url_followed_by_http_url():
# Registration through local mar url is already complete in previous test case.
# Now try to register same model using http url in this next step
response = test_utils.register_model(
"resnet18", "https://torchserve.pytorch.org/mar_files/resnet-18.mar")
time.sleep(15)
if json.loads(response.content)['code'] == 500 and \
json.loads(response.content)['type'] == "InternalServerException":
assert True, "Internal Server Exception, " \
"Model file already exists!! Duplicate model registration request"
test_utils.unregister_model("resnet18")
time.sleep(10)
else:
assert False, "Something is not right!! Successfully re-registered existing model "
