def main():
name_arg = os.getenv('MODEL_SERVE_NAME', 'resnet_graphdef')
addr_arg = os.getenv('TRTSERVER_HOST', '10.110.20.210')
port_arg = os.getenv('TRTSERVER_PORT', '8001')
model_version = os.getenv('MODEL_VERSION', '-1')
name_arg = request.args.get("name", name_arg)
addr_arg = request.args.get("addr", addr_arg)
port_arg = request.args.get("port", port_arg)
model_version = request.args.get("version", model_version)
args = {
"name": str(name_arg),
"addr": str(addr_arg),
"port": str(port_arg),
"version": str(model_version)
}
logging.info("Request args: %s", args)
output = None
connection = {"text": "", "success": False}
try:
# get a random test MNIST image
file_name, truth, serving_path = random_image(
'/workspace/web_server/static/images')
# get prediction from TensorFlow server
pred, scores = get_prediction(file_name,
server_host=addr_arg,
server_port=int(port_arg),
model_name=name_arg,
model_version=int(model_version))
# if no exceptions thrown, server connection was a success
connection["text"] = "Connected (model version: {0}".format(
str(model_version)) + ")"
connection["success"] = True
# parse class confidence scores from server prediction
output = {
"truth": truth,
"prediction": pred,
"img_path": serving_path,
"scores": scores
}
except Exception as e: # pylint: disable=broad-except
logging.info("Exception occured: %s", e)
# server connection failed
connection["text"] = "Error: {}".format(str(e))
# after 10 seconds, delete cached image file from server
# t = Timer(10.0, remove_resource, [img_path])
# t.start()
# render results using HTML template
return render_template('index.html',
output=output,
connection=connection,
args=args)
def main():
args = {
"name": name_arg,
"addr": addr_arg,
"port": port_arg,
"version": str(model_version)
}
logging.info("Request args: %s", args)
output = None
connection = {"text": "", "success": False}
try:
# get a random test MNIST image
file_name, truth, serving_path = random_image(
'/workspace/web_server/static/images')
# get prediction from TensorFlow server
pred, scores = get_prediction(file_name,
server_host=addr_arg,
server_port=int(port_arg),
model_name=name_arg,
model_version=int(model_version))
# if no exceptions thrown, server connection was a success
connection["text"] = "Connected (model version: {0}".format(
str(model_version)) + ")"
connection["success"] = True
# parse class confidence scores from server prediction
output = {
"truth": truth,
"prediction": pred,
"img_path": serving_path,
"scores": scores
}
except Exception as e: # pylint: disable=broad-except
logging.info("Exception occured: %s", e)
# server connection failed
connection["text"] = "Exception making request: {0}".format(e)
# after 10 seconds, delete cached image file from server
# t = Timer(10.0, remove_resource, [img_path])
# t.start()
# render results using HTML template
return render_template('index.html',
output=output,
connection=connection,
args=args)
def connect():
data = request.form.to_dict(flat=False)
print(data)
server_host = data['addr'][0]
server_port = data['port'][0]
model_name = data['name'][0]
model_version = str(data['version'][0])
args = {
"name": model_name,
"addr": server_host,
"port": server_port,
"version": model_version
}
connection = connect_to_server(server_host, server_port, model_name,
model_version)
output = None
if connection['success']:
current_dir = str(pathlib.Path(__file__).parent.absolute())
print(os.path.join(current_dir, 'static/images/'))
file_name, truth, serving_path = random_image(
os.path.join(current_dir, 'static/images/'))
# get prediction from TensorFlow server
pred, scores = get_prediction(file_name,
server_host=addr_arg,
server_port=int(port_arg),
model_name=name_arg,
model_version=int(model_version))
output = {
"truth": truth,
"prediction": pred,
"img_path": serving_path,
"scores": scores
}
return render_template('index.html',
connection=connection,
args=args,
output=output)
name_arg = os.getenv('MODEL_SERVE_NAME', 'pytorch')
addr_arg = os.getenv('TRTSERVER_HOST', '10.110.20.210')
port_arg = os.getenv('TRTSERVER_PORT', '8001')
model_version = os.getenv('MODEL_VERSION', '-1')
name_arg = 'resnet'
name_arg = 'pytorch'
name_arg = 'resnet_graphdef'
args = {"name": str(name_arg), "addr": str(addr_arg), "port": str(port_arg), "version": str(model_version)}
print('args:', args)
output = None
connection = {"text": "", "success": False}
# get a random test MNIST image
file_name, truth, serving_path = random_image('/workspace/web_server/static/images')
# get prediction from TensorFlow server
pred, scores = get_prediction(file_name,
server_host=addr_arg,
server_port=int(port_arg),
model_name=name_arg,
model_version=int(model_version))
# if no exceptions thrown, server connection was a success
connection["text"] = "Connected (model version: {0}".format(str(model_version))+ ")"
connection["success"] = True
# parse class confidence scores from server prediction
output = {"truth": truth, "prediction": pred,
"img_path": serving_path, "scores": scores}
print('output', output)
print('connection', connection)