def doCompute():
endpoint_name = "endpoint-from-saved-model"
predictor = TensorFlowPredictor(endpoint_name)
myfile = open(
r"C:\Users\Polash-Dell\coder_guy\PycharmProjects\aws_dl\1_aws_dl.txt",
'w+')
myfile.write("Norm Amp : Porosity")
while True:
xa.doInput()
x_test_list = xa.Input['Input'][0, 0, :].tolist()
x_test = pd.DataFrame(x_test_list)
stats = x_test.describe()
stats = stats.transpose()
x_test = (x_test - stats['mean']) / stats['std']
for a in x_test.values:
myfile.write(str(a) + ": ")
try:
test_predictions = predictor.predict(a)
myfile.write(str(test_predictions['predictions'][0][0]) + "\n")
except:
e = sys.exc_info()[0]
myfile.write("\nError:" + str(e))
xa.Output['Output'] = xa.Input['Input']
xa.doOutput()
myfile.close()
def doCompute():
endpoint_name = "endpoint-from-saved-model"
predictor = TensorFlowPredictor(endpoint_name)
while True:
xa.doInput()
x_test_list = xa.Input['Input'][0, 0, :].tolist()
x_test = pd.DataFrame(x_test_list)
stats = x_test.describe()
stats = stats.transpose()
x_test = (x_test - stats['mean']) / stats['std']
y_test = predictor.predict(np.array(x_test))
xa.Output['Output'] = np.array(y_test['predictions'])
xa.doOutput()
def test_predictor_csv(sagemaker_session):
predictor = TensorFlowPredictor("endpoint", sagemaker_session, serializer=CSVSerializer())
mock_response(json.dumps(PREDICT_RESPONSE).encode("utf-8"), sagemaker_session)
result = predictor.predict([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]])
assert_invoked(
sagemaker_session,
EndpointName="endpoint",
ContentType=CSV_CONTENT_TYPE,
Accept=JSON_CONTENT_TYPE,
Body="1.0,2.0,3.0\n4.0,5.0,6.0",
)
assert PREDICT_RESPONSE == result
def test_predictor(sagemaker_session):
predictor = TensorFlowPredictor("endpoint", sagemaker_session)
mock_response(json.dumps(PREDICT_RESPONSE).encode("utf-8"), sagemaker_session)
result = predictor.predict(PREDICT_INPUT)
assert_invoked(
sagemaker_session,
EndpointName="endpoint",
ContentType=JSON_CONTENT_TYPE,
Accept=JSON_CONTENT_TYPE,
Body=json.dumps(PREDICT_INPUT),
)
assert PREDICT_RESPONSE == result
def test_predictor_classify(sagemaker_session):
predictor = TensorFlowPredictor("endpoint", sagemaker_session)
mock_response(json.dumps(CLASSIFY_RESPONSE).encode("utf-8"), sagemaker_session)
result = predictor.classify(CLASSIFY_INPUT)
assert_invoked_with_body_dict(
sagemaker_session,
EndpointName="endpoint",
ContentType=JSON_CONTENT_TYPE,
Accept=JSON_CONTENT_TYPE,
CustomAttributes="tfs-method=classify",
Body=json.dumps(CLASSIFY_INPUT),
)
assert CLASSIFY_RESPONSE == result
def home():
def np2csv(arr):
csv = io.BytesIO()
numpy.savetxt(csv, arr, delimiter=',', fmt='%g')
return csv.getvalue().decode().rstrip()
mynumber = request.args.getlist('image')
predictor = TensorFlowPredictor('xgboost-001')
mynumberarray = ast.literal_eval(mynumber[0])
payload = np2csv(mynumberarray)
runtime = boto3.Session().client('runtime.sagemaker')
#response = runtime.invoke_endpoint(EndpointName='DEMO-XGBoostEndpoint-2018-04-20-03-07-34',
# ContentType='text/csv',
# Body=payload)
response = runtime.invoke_endpoint(EndpointName='xgboost-001',
ContentType='text/csv',
Body=payload)
result = response['Body'].read().decode('ascii')
floatArr = numpy.array(json.loads(result))
predictedLabel = numpy.argmax(floatArr)
answer = ("Most likely answer: {}".format(predictedLabel))
return (answer)
def tensor():
mynumber = request.args.getlist('image')
predictor = TensorFlowPredictor(
'sagemaker-tensorflow-2018-04-15-02-09-49-209')
mynumberarray = ast.literal_eval(mynumber[0])
response = predictor.predict(mynumberarray)
prediction = response['outputs']['classes']['int64Val'][0]
answer = ("Most likely answer: {}".format(prediction))
return (answer)
def test_predictor_regress(sagemaker_session):
predictor = TensorFlowPredictor(
"endpoint", sagemaker_session, model_name="model", model_version="123"
)
mock_response(json.dumps(REGRESS_RESPONSE).encode("utf-8"), sagemaker_session)
result = predictor.regress(REGRESS_INPUT)
assert_invoked_with_body_dict(
sagemaker_session,
EndpointName="endpoint",
ContentType=JSON_CONTENT_TYPE,
Accept=JSON_CONTENT_TYPE,
CustomAttributes="tfs-method=regress,tfs-model-name=model,tfs-model-version=123",
Body=json.dumps(REGRESS_INPUT),
)
assert REGRESS_RESPONSE == result
def test_predictor_jsons(sagemaker_session):
predictor = TensorFlowPredictor(
"endpoint",
sagemaker_session,
serializer=IdentitySerializer(content_type="application/jsons"),
)
mock_response(json.dumps(PREDICT_RESPONSE).encode("utf-8"), sagemaker_session)
result = predictor.predict("[1.0, 2.0, 3.0]\n[4.0, 5.0, 6.0]")
assert_invoked(
sagemaker_session,
EndpointName="endpoint",
ContentType="application/jsons",
Accept=JSON_CONTENT_TYPE,
Body="[1.0, 2.0, 3.0]\n[4.0, 5.0, 6.0]",
)
assert PREDICT_RESPONSE == result
def main():
parser = argparse.ArgumentParser()
parser.add_argument('endpoint_name')
parser.add_argument('--data_dir', default='data/')
parser.add_argument('--mnist_index', '-i', type=int, default=0)
args = parser.parse_args()
predictor = TensorFlowPredictor(args.endpoint_name)
image = get_mnist_data(args.data_dir, index=args.mnist_index)
inputs = {'instances': image}
t = time.time()
outputs = predictor.predict(inputs)
print(f'inference time: {(time.time() - t) * 1000:.2f} ms')
prediction = np.array(outputs['predictions'][0])
pred_label = np.argmax(prediction)
pred_confidence = np.max(prediction)
print(f'prediction: {pred_label} ({pred_confidence * 100:.1f}%)')
def main():
parser = argparse.ArgumentParser()
parser.add_argument('endpoint_name')
args = parser.parse_args()
predictor = TensorFlowPredictor(args.endpoint_name)
predictor.delete_model()
predictor.delete_endpoint()
def predictor_from_config_maybe(self):
endpoint = AppConfig.setting('AWS_DNN_PREDICTOR_ENDPOINT')
return TensorFlowPredictor(endpoint) if endpoint not in (None, '') else None
import pandas as pd
endpoint_name = "endpoint-from-saved-model"
from sagemaker.tensorflow import TensorFlowPredictor
predictor = TensorFlowPredictor(endpoint_name)
x_test = pd.read_csv('seismic_poro_test.csv')
stats = x_test.describe()
stats.pop('porosity')
stats = stats.transpose()
x_test = (x_test - stats['mean']) / stats['std']
x_test.pop('porosity')
for a in x_test.values:
test_predictions = predictor.predict(a)
print(test_predictions['predictions'][0][0])
from sagemaker.tensorflow import TensorFlowPredictor
print(' ## Building predictor ')
tensor_name = 'SageMakerEndpoint'
predictor = TensorFlowPredictor(tensor_name)
print(' ### Executing prediction ')
features_data = [
34.49726772511229, 12.65565114916675, 39.57766801952616, 4.0826206329529615
]
predictions = predictor.predict([features_data])
print(predictions)
def test_predictor_classify_bad_content_type(sagemaker_session):
predictor = TensorFlowPredictor("endpoint", sagemaker_session,
CSVSerializer())
with pytest.raises(ValueError):
predictor.classify(CLASSIFY_INPUT)
def test_predictor_regress_bad_content_type(sagemaker_session):
predictor = TensorFlowPredictor("endpoint", sagemaker_session,
CSVSerializer())
with pytest.raises(ValueError):
predictor.regress(REGRESS_INPUT)
import sagemaker
from sagemaker.tensorflow import TensorFlowPredictor
import numpy as np
from tensorflow.python.keras.preprocessing.image import load_img
remove_endpoint = False
endpoint_name = "[YOUR ENDPOINT]"
predictor = TensorFlowPredictor(endpoint_name)
cat_image = load_img("./tmp/PetImages/Cat/2282.jpg", target_size=(128, 128))
cat_image_array = np.array(cat_image).reshape((1, 128, 128, 3))
print(predictor.predict({ "inputs_input": cat_image_array}))
if remove_endpoint:
sagemaker.Session().delete_endpoint(predictor.endpoint)
np_im.shape
imshow(np_im)
# Image reshape for keras tf input 1x4
np_new_im = np_new_im[np.newaxis, ...]
np_new_im.shape
# predict inference
response = predictor.predict({'inputs_input': np_new_im})
response
# predict with existing endpoint
import json
from sagemaker.tensorflow import TensorFlowPredictor
predictor = TensorFlowPredictor('sagemaker-tensorflow-2019-07-17-03-09-42-707')
result = predictor.predict({'inputs_input': np_new_im})
# Cifar 10 class
# 0 : airplane
# 1 : automobile
# 2 : bird
# 3 : cat
# 4 : deer
# 5 : dog
# 6 : frog
# 7 : horse
# 8 : ship
# 9 : truck
object_categories = ['airplane', 'automobile', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']
print(object_categories[int(predict_class)])