tensorflow 1 Session.run is taking too much time to embed sentence using universal sentence encoder
Using tensforflow with flask REST API
How should i reduce the time for session.run
I am using tf 1/2 in REST API, instead of serving it i am using it on my server.
i have tried tensorflow 1 and 2.
tensorflow 1 is taking too much time.
tensorflow 2 is not even returning the vectors for text.
in tensorflow 1 initialising is taking 2-4 seconds and session.run is taking 5-8 seconds. and time is getting increased as i keep hitting the requests.
tensorflow 1
import tensorflow.compat.v1 as tfo import tensorflow_hub as hub tfo.disable_eager_execution() module_url = "https://tfhub.dev/google/universal-sentence-encoder-qa/3" # Import the Universal Sentence Encoder's TF Hub module embed = hub.Module(module_url) def convert_text_to_vector(text): # Compute a representation for each message, showing various lengths supported. try: #text = "qwerty" or ["qwerty"] if isinstance(text, str): text = [text] with tfo.Session() as session: t_time = time.time() session.run([tfo.global_variables_initializer(), tfo.tables_initializer()]) m_time = time.time() message_embeddings = session.run(embed(text)) vector_array = message_embeddings.tolist()[0] return vector_array except Exception as err: raise Exception(str(err)) tensorflow 2
its getting stuck at vector_array = embedding_fn(text)
import tensorflow as tf import tensorflow_hub as hub module_url = "https://tfhub.dev/google/universal-sentence-encoder-qa/3" embedding_fn = hub.load(module_url) @tf.function def convert_text_to_vector(text): try: #text = ["qwerty"] vector_array = embedding_fn(text) return vector_array except Exception as err: raise Exception(str(err)) 
for the tensorflow 2 version I made few corrections. Basically I followed the example in universal sentence encoder that you provided.
import tensorflow as tf import tensorflow_hub as hub import numpy as np module_url = "https://tfhub.dev/google/universal-sentence-encoder-qa/3" embedding_fn = hub.load(module_url) @tf.function def convert_text_to_vector(text): try: vector_array = embedding_fn.signatures['question_encoder']( tf.constant(text)) return vector_array['outputs'] except Exception as err: raise Exception(str(err)) ### run the function vector = convert_text_to_vector(['is this helpful ?']) print(vector.shape()) 
from flask import Flask from flask_restplus import Api, Resource from werkzeug.utils import cached_property import tensorflow as tf import tensorflow_hub as hub module_url = "https://tfhub.dev/google/universal-sentence-encoder-qa/3" embedding_fn = hub.load(module_url) app = Flask(__name__) @app.route('/embedding', methods=['POST']) def entry_point(args): if args.get("text"): text_term = args.get("text") if isinstance(text_term, str): text_term = [text_term] vectors = convert_text_to_vector(text_term) return vectors @tf.function def convert_text_to_vector(text): try: vector_array = embedding_fn.signatures['question_encoder'](tf.constant(text)) return vector_array['outputs'] except Exception as err: raise Exception(str(err)) if __name__ == '__main__': app.run(host='0.0.0.0', port=5000, debug=True) """ ----- Requirements.txt ---- flask-restplus==0.13.0 Flask==1.1.1 Werkzeug==0.15.5 tensorboard==2.2.2 tensorboard-plugin-wit==1.6.0.post3 tensorflow==2.2.0 tensorflow-estimator==2.2.0 tensorflow-hub==0.8.0 tensorflow-text==2.2.1 """ 
