Dynamic Batching for Deep Learning Serving

Ventu already implement this protocol, so it can be used as the worker for deep learning inference.

Attention

This project is just a proof of concept. Check the MOSEC for production usage.

Features

• dynamic batching with batch size and latency
• invalid request won't affects others in the same batch
• communicate with workers through Unix domain socket or TCP
• load balancing

If you are interested in the design, check my blog Deep Learning Serving Framework.

Configs

go run service/app.go --help

Usage app:
  -address string
        socket file or host:port (default "batch.socket")
  -batch int
        max batch size (default 32)
  -capacity int
        max jobs in the queue (default 1024)
  -host string
        host address (default "0.0.0.0")
  -latency int
        max latency (millisecond) (default 10)
  -port int
        service port (default 8080)
  -protocol string
        unix or tcp (default "unix")
  -timeout int
        timeout for a job (millisecond) (default 5000)

Demo

go run service/app.go
python examples/app.py
python examples/client.py