Real time transcription using Serverless
creation of handler file for real time transcription app
Solution
heres the high level function handler
- edit template and add ports to it, range can be anything, e.g. 5000-5010, depends if your workload can do parallel inference or its 1 per gpu (you will need our help to do this, its not available to public yet)
- start with first port 5000, open websocket server
- look at env for external port # for 5000, and get ip from env
- use inprogress hook in our sdk to send ip, port and any other info you want
- keep port and job in progress, until a message comes back in websocket server that says "x" and shut down websocket server and return the job marking it completed
client side:
- /run a job
- wait until job is in progress and has the ip and port
- connect the local client directly to ip and port for live communication
- keep in mind any security you need to setup for ip and port layer
@Justin Merrell can help with anything python specific
- edit template and add ports to it, range can be anything, e.g. 5000-5010, depends if your workload can do parallel inference or its 1 per gpu (you will need our help to do this, its not available to public yet)
- start with first port 5000, open websocket server
- look at env for external port # for 5000, and get ip from env
- use inprogress hook in our sdk to send ip, port and any other info you want
- keep port and job in progress, until a message comes back in websocket server that says "x" and shut down websocket server and return the job marking it completed
client side:
- /run a job
- wait until job is in progress and has the ip and port
- connect the local client directly to ip and port for live communication
- keep in mind any security you need to setup for ip and port layer
@Justin Merrell can help with anything python specific
