Multi-model endpoints - Amazon SageMaker AI
How Amazon SageMaker AI multi-model endpoints workSample notebooks

Multi-model endpoints

Multi-model endpoints provide a scalable and cost-effective solution to deploying large numbers of models. They use the same fleet of resources and a shared serving container to host all of your models. This reduces hosting costs by improving endpoint utilization compared with using single-model endpoints. It also reduces deployment overhead because Amazon SageMaker AI manages loading models in memory and scaling them based on the traffic patterns to your endpoint.

The following diagram shows how multi-model endpoints work compared to single-model endpoints.

Diagram that shows how multi-model versus how single-model endpoints host models.

Multi-model endpoints are ideal for hosting a large number of models that use the same ML framework on a shared serving container. If you have a mix of frequently and infrequently accessed models, a multi-model endpoint can efficiently serve this traffic with fewer resources and higher cost savings. Your application should be tolerant of occasional cold start-related latency penalties that occur when invoking infrequently used models.

Multi-model endpoints support hosting both CPU and GPU backed models. By using GPU backed models, you can lower your model deployment costs through increased usage of the endpoint and its underlying accelerated compute instances.

Multi-model endpoints also enable time-sharing of memory resources across your models. This works best when the models are fairly similar in size and invocation latency. When this is the case, multi-model endpoints can effectively use instances across all models. If you have models that have significantly higher transactions per second (TPS) or latency requirements, we recommend hosting them on dedicated endpoints.

You can use multi-model endpoints with the following features:

You can use the AWS SDK for Python (Boto) or the SageMaker AI console to create a multi-model endpoint. For CPU backed multi-model endpoints, you can create your endpoint with custom-built containers by integrating the Multi Model Server library.

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How multi-model endpoints work

SageMaker AI manages the lifecycle of models hosted on multi-model endpoints in the container's memory. Instead of downloading all of the models from an Amazon S3 bucket to the container when you create the endpoint, SageMaker AI dynamically loads and caches them when you invoke them. When SageMaker AI receives an invocation request for a particular model, it does the following:

  1. Routes the request to an instance behind the endpoint.

  2. Downloads the model from the S3 bucket to that instance's storage volume.

  3. Loads the model to the container's memory (CPU or GPU, depending on whether you have CPU or GPU backed instances) on that accelerated compute instance. If the model is already loaded in the container's memory, invocation is faster because SageMaker AI doesn't need to download and load it.

SageMaker AI continues to route requests for a model to the instance where the model is already loaded. However, if the model receives many invocation requests, and there are additional instances for the multi-model endpoint, SageMaker AI routes some requests to another instance to accommodate the traffic. If the model isn't already loaded on the second instance, the model is downloaded to that instance's storage volume and loaded into the container's memory.

When an instance's memory utilization is high and SageMaker AI needs to load another model into memory, it unloads unused models from that instance's container to ensure that there is enough memory to load the model. Models that are unloaded remain on the instance's storage volume and can be loaded into the container's memory later without being downloaded again from the S3 bucket. If the instance's storage volume reaches its capacity, SageMaker AI deletes any unused models from the storage volume.

To delete a model, stop sending requests and delete it from the S3 bucket. SageMaker AI provides multi-model endpoint capability in a serving container. Adding models to, and deleting them from, a multi-model endpoint doesn't require updating the endpoint itself. To add a model, you upload it to the S3 bucket and invoke it. You don’t need code changes to use it.

Note

When you update a multi-model endpoint, initial invocation requests on the endpoint might experience higher latencies as Smart Routing in multi-model endpoints adapt to your traffic pattern. However, once it learns your traffic pattern, you can experience low latencies for most frequently used models. Less frequently used models may incur some cold start latencies since the models are dynamically loaded to an instance.

Sample notebooks for multi-model endpoints

To learn more about how to use multi-model endpoints, you can try the following sample notebooks:

For instructions on how to create and access Jupyter notebook instances that you can use to run the previous examples in SageMaker AI, see Amazon SageMaker Notebook Instances. After you've created a notebook instance and opened it, choose the SageMaker AI Examples tab to see a list of all the SageMaker AI samples. The multi-model endpoint notebooks are located in the ADVANCED FUNCTIONALITY section. To open a notebook, choose its Use tab and choose Create copy.

For more information about use cases for multi-model endpoints, see the following blogs and resources: