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[P] Tensorflow deployment

Hi, I gather different methods how to deploy Tensorflow model to cater different processing, https://github.com/huseinzol05/Gather-Deployment

1 . Object Detection. Flask SocketIO + WebRTC

  • Stream from webcam using WebRTC -> Flask SocketIO to detect objects -> WebRTC -> Website.

2 . Object Detection. Flask SocketIO + opencv

  • Stream from OpenCV -> Flask SocketIO to detect objects -> OpenCV.

3 . Speech streaming. Flask SocketIO

  • Stream speech from microphone -> Flask SocketIO to do realtime speech recognition.

4 . Text classification. Flask + Gunicorn

  • Serve Tensorflow text model using Flask multiworker + Gunicorn.

5 . Image classification. TF Serving

  • Serve image classification model using TF Serving.

6 . Image Classification using Inception. Flask SocketIO

  • Stream image using SocketIO -> Flask SocketIO to classify.

7 . Object Detection. Flask + opencv

  • Webcam -> Opencv -> Flask -> web dashboard.

8 . Face-detection using MTCNN. Flask SocketIO + opencv

  • Stream from OpenCV -> Flask SocketIO to detect faces -> OpenCV.

9 . Face-detection using MTCNN. opencv

  • Webcam -> Opencv.

10 . Image classification using Inception. Flask + Docker

  • Serve Tensorflow image model using Flask multiworker + Gunicorn on Docker container.

11 . Image classification using Inception. Flask + EC2 Docker Swarm + Nginx load balancer

  • Serve inception on multiple AWS EC2, scale using Docker Swarm, balancing using Nginx.

12 . Text classification. Hadoop streaming MapReduce

  • Batch processing to classify texts using Tensorflow text model on Hadoop MapReduce.

13 . Text classification. Kafka

  • Stream text to Kafka producer and classify using Kafka consumer.

14 . Text classification. Distributed TF using Flask + Gunicorn + Eventlet

  • Serve text model on multiple machines using Distributed TF + Flask + Gunicorn + Eventlet. Means that, Distributed TF will split a single neural network model to multiple machines to do feed-forward.

15 . Text classification. Tornado + Gunicorn

  • Serve Tensorflow text model using Tornado + Gunicorn.

16 . Text classification. Flask + Celery + Hadoop

  • Submit large texts using Flask, signal queue celery job to process using Hadoop, delay Hadoop MapReduce.

17 . Text classification. Luigi scheduler + Hadoop

  • Submit large texts on Luigi scheduler, run Hadoop inside Luigi, event based Hadoop MapReduce.

18 . Text classification. Luigi scheduler + Distributed Celery

  • Submit large texts on Luigi scheduler, run Hadoop inside Luigi, delay processing.

19 . Text classification. Airflow scheduler + elasticsearch + Flask

  • Scheduling based processing using Airflow, store inside elasticsearch, serve it using Flask.

20 . Text classification. Apache Kafka + Apache Storm

  • Stream from twitter -> Kafka Producer -> Apache Storm, to do distributed minibatch realtime processing.

21 . Text classification. Dask

  • Batch processing to classify texts using Tensorflow text model on Dask.

22 . Text classification. Pyspark

  • Batch processing to classify texts using Tensorflow text model on Pyspark.

23 . Text classification. Pyspark streaming + Kafka

  • Stream texts to Kafka Producer -> Pyspark Streaming, to do minibatch realtime processing.

24 . Text classification. PyFlink

  • Batch processing to classify texts using Tensorflow text model on Flink batch processing.

25 . Text classification. Streamz + Dask + Kafka

  • Stream texts to Kafka Producer -> Streamz -> Dask, to do minibatch realtime processing.

Discussion

  1. I am waiting for official binary released flink 1.10, support custom Python function, right now stable released 1.9 cannot.
  2. Just realized, I missed FastAPI, will going to add later.
  3. Kubeflow required at least minikube, maybe will add later.
  4. Will add imagezmq -> nginx load balancer -> process multiple video sources on multiple machines, display on single machine.

Feel free to comment!

submitted by /u/huseinzol05
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Toronto AI is a social and collaborative hub to unite AI innovators of Toronto and surrounding areas. We explore AI technologies in digital art and music, healthcare, marketing, fintech, vr, robotics and more. Toronto AI was founded by Dave MacDonald and Patrick O'Mara.