Model Multiplexing

This section helps you understand how to write multiplexed deployment by using the serve.multiplexed and serve.get_multiplexed_model_id APIs.

This is an experimental feature and the API may change in the future. You are welcome to try it out and give us feedback!

Why model multiplexing?

Model multiplexing is a technique used to efficiently serve multiple models with similar input types from a pool of replicas. Traffic is routed to the corresponding model based on the request header. To serve multiple models with a pool of replicas, model multiplexing optimizes cost and load balances the traffic. This is useful in cases where you might have many models with the same shape but different weights that are sparsely invoked. If any replica for the deployment has the model loaded, incoming traffic for that model (based on request header) will automatically be routed to that replica avoiding unnecessary load time.

Writing a multiplexed deployment

To write a multiplexed deployment, use the serve.multiplexed and serve.get_multiplexed_model_id APIs.

Assuming you have multiple Torch models inside an aws s3 bucket with the following structure:

s3://my_bucket/1/model.pt
s3://my_bucket/2/model.pt
s3://my_bucket/3/model.pt
s3://my_bucket/4/model.pt
...

Define a multiplexed deployment:

from ray import serve
import aioboto3
import torch
import starlette


@serve.deployment
class ModelInferencer:
    def __init__(self):
        self.bucket_name = "my_bucket"

    @serve.multiplexed(max_num_models_per_replica=3)
    async def get_model(self, model_id: str):
        session = aioboto3.Session()
        async with session.resource("s3") as s3:
            obj = await s3.Bucket(self.bucket_name)
            await obj.download_file(f"{model_id}/model.pt", f"model_{model_id}.pt")
            return torch.load(f"model_{model_id}.pt")

    async def __call__(self, request: starlette.requests.Request):
        model_id = serve.get_multiplexed_model_id()
        model = await self.get_model(model_id)
        return model.forward(torch.rand(64, 3, 512, 512))


entry = ModelInferencer.bind()

Note

The serve.multiplexed API also has a max_num_models_per_replica parameter. Use it to configure how many models to load in a single replica. If the number of models is larger than max_num_models_per_replica, Serve uses the LRU policy to evict the least recently used model.

Tip

This code example uses the Pytorch Model object. You can also define your own model class and use it here. To release resources when the model is evicted, implement the __del__ method. Ray Serve internally calls the __del__ method to release resources when the model is evicted.

serve.get_multiplexed_model_id is used to retrieve the model id from the request header, and the model_id is then passed into the get_model function. If the model id is not found in the replica, Serve will load the model from the s3 bucket and cache it in the replica. If the model id is found in the replica, Serve will return the cached model.

Note

Internally, serve router will route the traffic to the corresponding replica based on the model id in the request header. If all replicas holding the model are over-subscribed, ray serve sends the request to a new replica that doesn’t have the model loaded. The replica will load the model from the s3 bucket and cache it.

To send a request to a specific model, include the serve_multiplexed_model_id field in the request header, and set the value to the model ID to which you want to send the request.

import requests  # noqa: E402

resp = requests.get(
    "http://localhost:8000", headers={"serve_multiplexed_model_id": str("1")}
)

Note

serve_multiplexed_model_id is required in the request header, and the value should be the model ID you want to send the request to.

If the serve_multiplexed_model_id is not found in the request header, Serve will treat it as a normal request and route it to a random replica.

After you run the above code, you should see the following lines in the deployment logs:

INFO 2023-05-24 01:19:03,853 default_Model default_Model#EjYmnQ CUpzhwUUNw / default replica.py:442 - Started executing request CUpzhwUUNw
INFO 2023-05-24 01:19:03,854 default_Model default_Model#EjYmnQ CUpzhwUUNw / default multiplex.py:131 - Loading model '1'.
INFO 2023-05-24 01:19:04,859 default_Model default_Model#EjYmnQ CUpzhwUUNw / default replica.py:542 - __CALL__ OK 1005.8ms

If you continue to load more models and exceed the max_num_models_per_replica, the least recently used model will be evicted and you will see the following lines in the deployment logs::

INFO 2023-05-24 01:19:15,988 default_Model default_Model#rimNjA WzjTbJvbPN / default replica.py:442 - Started executing request WzjTbJvbPN
INFO 2023-05-24 01:19:15,988 default_Model default_Model#rimNjA WzjTbJvbPN / default multiplex.py:145 - Unloading model '3'.
INFO 2023-05-24 01:19:15,988 default_Model default_Model#rimNjA WzjTbJvbPN / default multiplex.py:131 - Loading model '4'.
INFO 2023-05-24 01:19:16,993 default_Model default_Model#rimNjA WzjTbJvbPN / default replica.py:542 - __CALL__ OK 1005.7ms

You can also send a request to a specific model by using handle options API.

obj_ref = handle.options(multiplexed_model_id="1").remote("<your param>")

When using model composition, you can send requests from an upstream deployment to a multiplexed deployment using the Serve DeploymentHandle. You need to set the multiplexed_model_id in the options. For example:

@serve.deployment
class Downstream:
    def __call__(self):
        return serve.get_multiplexed_model_id()


@serve.deployment
class Upstream:
    def __init__(self, downstream: DeploymentHandle):
        self._h = downstream

    async def __call__(self, request: starlette.requests.Request):
        return await self._h.options(multiplexed_model_id="bar").remote()


serve.run(Upstream.bind(Downstream.bind()))
resp = requests.get("http://localhost:8000")