wehub-resource-sync
51 lines
1.8 KiB
Python
51 lines
1.8 KiB
Python
import json
|
|
from typing import Dict
|
|
|
|
import numpy as np
|
|
import torch
|
|
from starlette.requests import Request
|
|
|
|
from ray import serve
|
|
from ray.rllib.core import Columns
|
|
from ray.rllib.core.rl_module.rl_module import RLModule
|
|
from ray.serve.schema import LoggingConfig
|
|
|
|
|
|
@serve.deployment(logging_config=LoggingConfig(log_level="WARN"))
|
|
class ServeRLlibRLModule:
|
|
"""Callable class used by Ray Serve to handle async requests.
|
|
|
|
All the necessary serving logic is implemented in here:
|
|
- Creation and restoring of the (already trained) RLlib Algorithm.
|
|
- Calls to algo.compute_action upon receiving an action request
|
|
(with a current observation).
|
|
"""
|
|
|
|
def __init__(self, rl_module_checkpoint):
|
|
self.rl_module = RLModule.from_checkpoint(rl_module_checkpoint)
|
|
|
|
async def __call__(self, starlette_request: Request) -> Dict:
|
|
request = await starlette_request.body()
|
|
request = request.decode("utf-8")
|
|
request = json.loads(request)
|
|
obs = request["observation"]
|
|
|
|
# Compute and return the action for the given observation (create a batch
|
|
# with B=1 and convert to torch).
|
|
output = self.rl_module.forward_inference(
|
|
batch={"obs": torch.from_numpy(np.array([obs], np.float32))}
|
|
)
|
|
# Extract action logits and unbatch.
|
|
logits = output[Columns.ACTION_DIST_INPUTS][0]
|
|
# Act greedily (argmax).
|
|
action = int(np.argmax(logits))
|
|
|
|
return {"action": action}
|
|
|
|
|
|
# Defining the builder function. This is so we can start our deployment via:
|
|
# `serve run [this py module]:rl_module checkpoint=[some algo checkpoint path]`
|
|
def rl_module(args: Dict[str, str]):
|
|
serve.start(http_options={"host": "0.0.0.0", "port": args.get("port", 12345)})
|
|
return ServeRLlibRLModule.bind(args["rl_module_checkpoint"])
|