encoder-aas

An architecture providing Universal Sentence Encoder as a service by exploiting job-level parallelism of multi-core architectures and can be used as a transformer model for downstream NLP and NLU tasks like Language modelling, Sentiment Classification, Question Answering, Semantic Search and more. Inspired from Bert-as-a-service.

Architecture:

encoder-aas uses highly asynchronous programming model to exploit Job level parallelism of multi-core processor machines, since it exploits job-level parallelism it can spin up highly concurrent and independant tensorflow session, each scheduled on a core. The model we are using is Universal Sentence Encoder lite - more information can be found here. The model is very light weight and can run on a single processor, we expolited this opportunity to schedule multiple models on the same machine. The architecture is very simple and a developer can easily understand this.

Build the service:

You can use the dockerfile provided in server/Dockerfile to build the server. Follow the instructions below:

1. Build the docker container:

   docker build . -t use_encoder
   

2. Run the docker container:

   docker run --rm -ti --net=host use_encoder --workers=4 --batch=1 --port=8000 --portpub=8001
   

   Parameters:
   1. workers : Number of worker instances to run, use workers=number of cpus for max performance.
   2. batch : Workers will consume from the queue only when entries=batch, batch=1 is the default.
   3. port : ZMQSource port
   4. portpub : Service will publish results at this port.

Running the client:

We have provided a small client library that makes concurrent requests and and waits for responses. See client for its implementation. Usage example is shown here:

from use_client import EncoderClient
import time
results = []

def callback(result) :

    #print(result)
    results.append(result)

#create an encoder client
encoder = EncoderClient(host = '127.0.0.1', port = 8000, port_pub = 8001, callback=callback)

#start the receiver channel to receive results
encoder.createChannel()

sentences = ['Hello!', 'If you think you are bad, I am your dad!!', 'Just kiddnig', 'I am good']

for idx, sentence in enumerate(sentences)  :
    encoder.generateEmbeddings(idx, sentence)

while len(results) != len(sentences) :
    time.sleep(0.01)

print(len(results))

encoder.close()

In case of any problems or feature requests, please feel free to raise an issue.

Acknowledgements:

Bert-as-a-Service