Run ML inference on unplanned and spiky traffic using Amazon SageMaker multi-model endpoints

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Amazon SageMaker multi-model endpoints (MMEs) are a completely managed functionality of SageMaker inference that means that you can deploy 1000’s of fashions on a single endpoint. Beforehand, MMEs pre-determinedly allotted CPU computing energy to fashions statically regardless the mannequin site visitors load, utilizing Multi Mannequin Server (MMS) as its mannequin server. On this publish, we talk about an answer by which an MME can dynamically modify the compute energy assigned to every mannequin based mostly on the mannequin’s site visitors sample. This resolution allows you to use the underlying compute of MMEs extra effectively and save prices.

MMEs dynamically load and unload fashions based mostly on incoming site visitors to the endpoint. When using MMS because the mannequin server, MMEs allocate a set variety of mannequin employees for every mannequin. For extra data, check with Mannequin internet hosting patterns in Amazon SageMaker, Half 3: Run and optimize multi-model inference with Amazon SageMaker multi-model endpoints.

Nonetheless, this will lead to some points when your site visitors sample is variable. Let’s say you might have a singular or few fashions receiving a considerable amount of site visitors. You may configure MMS to allocate a excessive variety of employees for these fashions, however this will get assigned to all of the fashions behind the MME as a result of it’s a static configuration. This results in a lot of employees utilizing hardware compute—even the idle fashions. The alternative drawback can occur if you happen to set a small worth for the variety of employees. The favored fashions received’t have sufficient employees on the mannequin server degree to correctly allocate sufficient hardware behind the endpoint for these fashions. The principle concern is that it’s troublesome to stay site visitors sample agnostic if you happen to can’t dynamically scale your employees on the mannequin server degree to allocate the required quantity of compute.

The answer we talk about on this publish makes use of DJLServing because the mannequin server, which might help mitigate a few of the points that we mentioned and allow per-model scaling and allow MMEs to be site visitors sample agnostic.

MME structure

SageMaker MMEs allow you to deploy a number of fashions behind a single inference endpoint which will include a number of situations. Every occasion is designed to load and serve a number of fashions as much as its reminiscence and CPU/GPU capability. With this structure, a software program as a service (SaaS) enterprise can break the linearly growing price of internet hosting a number of fashions and obtain reuse of infrastructure per the multi-tenancy mannequin utilized elsewhere within the software stack. The next diagram illustrates this structure.

A SageMaker MME dynamically masses fashions from Amazon Easy Storage Service (Amazon S3) when invoked, as an alternative of downloading all of the fashions when the endpoint is first created. Because of this, an preliminary invocation to a mannequin may see increased inference latency than the following inferences, that are accomplished with low latency. If the mannequin is already loaded on the container when invoked, then the obtain step is skipped and the mannequin returns the inferences with low latency. For instance, assume you might have a mannequin that’s solely used a couple of instances a day. It’s mechanically loaded on demand, whereas continuously accessed fashions are retained in reminiscence and invoked with constantly low latency.

Behind every MME are mannequin internet hosting situations, as depicted within the following diagram. These situations load and evict a number of fashions to and from reminiscence based mostly on the site visitors patterns to the fashions.

SageMaker continues to route inference requests for a mannequin to the occasion the place the mannequin is already loaded such that the requests are served from a cached mannequin copy (see the next diagram, which reveals the request path for the primary prediction request vs. the cached prediction request path). Nonetheless, if the mannequin receives many invocation requests, and there are further situations for the MME, SageMaker routes some requests to a different occasion to accommodate the rise. To benefit from automated mannequin scaling in SageMaker, be sure to have occasion auto scaling set as much as provision further occasion capability. Arrange your endpoint-level scaling coverage with both customized parameters or invocations per minute (beneficial) so as to add extra situations to the endpoint fleet.

Mannequin server overview

A mannequin server is a software program part that gives a runtime surroundings for deploying and serving machine studying (ML) fashions. It acts as an interface between the educated fashions and consumer purposes that need to make predictions utilizing these fashions.

The first function of a mannequin server is to permit easy integration and environment friendly deployment of ML fashions into manufacturing techniques. As a substitute of embedding the mannequin instantly into an software or a particular framework, the mannequin server supplies a centralized platform the place a number of fashions might be deployed, managed, and served.

Mannequin servers usually provide the next functionalities:

Mannequin loading – The server masses the educated ML fashions into reminiscence, making them prepared for serving predictions.
Inference API – The server exposes an API that permits consumer purposes to ship enter information and obtain predictions from the deployed fashions.
Scaling – Mannequin servers are designed to deal with concurrent requests from a number of purchasers. They supply mechanisms for parallel processing and managing assets effectively to make sure excessive throughput and low latency.
Integration with backend engines – Mannequin servers have integrations with backend frameworks like DeepSpeed and FasterTransformer to partition massive fashions and run extremely optimized inference.

DJL structure

DJL Serving is an open supply, excessive efficiency, common mannequin server. DJL Serving is constructed on prime of DJL, a deep studying library written within the Java programming language. It may possibly take a deep studying mannequin, a number of fashions, or workflows and make them out there by an HTTP endpoint. DJL Serving helps deploying fashions from a number of frameworks like PyTorch, TensorFlow, Apache MXNet, ONNX, TensorRT, Hugging Face Transformers, DeepSpeed, FasterTransformer, and extra.

DJL Serving gives many options that will let you deploy your fashions with excessive efficiency:

Ease of use – DJL Serving can serve most fashions out of the field. Simply deliver the mannequin artifacts, and DJL Serving can host them.
A number of gadget and accelerator help – DJL Serving helps deploying fashions on CPU, GPU, and AWS Inferentia.
Efficiency – DJL Serving runs multithreaded inference in a single JVM to spice up throughput.
Dynamic batching – DJL Serving helps dynamic batching to extend throughput.
Auto scaling – DJL Serving will mechanically scale employees up and down based mostly on the site visitors load.
Multi-engine help – DJL Serving can concurrently host fashions utilizing totally different frameworks (reminiscent of PyTorch and TensorFlow).
Ensemble and workflow fashions – DJL Serving helps deploying advanced workflows comprised of a number of fashions, and runs components of the workflow on CPU and components on GPU. Fashions inside a workflow can use totally different frameworks.

Specifically, the auto scaling function of DJL Serving makes it easy to make sure the fashions are scaled appropriately for the incoming site visitors. By default, DJL Serving determines the utmost variety of employees for a mannequin that may be supported based mostly on the hardware out there (CPU cores, GPU gadgets). You may set decrease and higher bounds for every mannequin to make it possible for a minimal site visitors degree can all the time be served, and that a single mannequin doesn’t eat all out there assets.

DJL Serving makes use of a Netty frontend on prime of backend employee thread swimming pools. The frontend makes use of a single Netty setup with a number of HttpRequestHandlers. Completely different request handlers will present help for the Inference API, Administration API, or different APIs out there from numerous plugins.

The backend is predicated across the WorkLoadManager (WLM) module. The WLM takes care of a number of employee threads for every mannequin together with the batching and request routing to them. When a number of fashions are served, WLM checks the inference request queue dimension of every mannequin first. If the queue dimension is larger than two instances a mannequin’s batch dimension, WLM scales up the variety of employees assigned to that mannequin.

Answer overview

The implementation of DJL with an MME differs from the default MMS setup. For DJL Serving with an MME, we compress the next recordsdata within the mannequin.tar.gz format that SageMaker Inference is anticipating:

mannequin.joblib – For this implementation, we instantly push the mannequin metadata into the tarball. On this case, we’re working with a .joblib file, so we offer that file in our tarball for our inference script to learn. If the artifact is just too massive, it’s also possible to push it to Amazon S3 and level in the direction of that within the serving configuration you outline for DJL. – Right here you may configure any mannequin server-related surroundings variables. The facility of DJL right here is that you may configure minWorkers and maxWorkers for every mannequin tarball. This permits for every mannequin to scale up and down on the mannequin server degree. As an illustration, if a singular mannequin is receiving nearly all of the site visitors for an MME, the mannequin server will scale the employees up dynamically. On this instance, we don’t configure these variables and let DJL decide the required variety of employees relying on our site visitors sample. – That is the inference script for any customized preprocessing or postprocessing you wish to implement. The expects your logic to be encapsulated in a deal with technique by default.
necessities.txt (optionally available) – By default, DJL comes put in with PyTorch, however any further dependencies you want might be pushed right here.

For this instance, we showcase the facility of DJL with an MME by taking a pattern SKLearn mannequin. We run a coaching job with this mannequin after which create 1,000 copies of this mannequin artifact to again our MME. We then showcase how DJL can dynamically scale to deal with any kind of site visitors sample that your MME could obtain. This could embrace a fair distribution of site visitors throughout all fashions or perhaps a few widespread fashions receiving nearly all of the site visitors. You’ll find all of the code within the following GitHub repo.


For this instance, we use a SageMaker pocket book occasion with a conda_python3 kernel and ml.c5.xlarge occasion. To carry out the load checks, you should utilize an Amazon Elastic Compute Cloud (Amazon EC2) occasion or a bigger SageMaker pocket book occasion. On this instance, we scale to over a thousand transactions per second (TPS), so we advise testing on a heavier EC2 occasion reminiscent of an ml.c5.18xlarge so that you’ve extra compute to work with.

Create a mannequin artifact

We first have to create our mannequin artifact and information that we use on this instance. For this case, we generate some synthetic information with NumPy and prepare utilizing an SKLearn linear regression mannequin with the next code snippet:

import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
import joblib

# Generate dummy information
X = np.random.rand(100, 1)
y = 2 * X + 1 + 0.1 * np.random.randn(100, 1)
# Cut up the information into coaching and testing units
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
# Create a Linear Regression mannequin
mannequin = LinearRegression()
# Practice the mannequin on the coaching information
mannequin.match(X_train, y_train)

# Create serialized mannequin artifact
model_filename = “mannequin.joblib”
joblib.dump(mannequin, model_filename)

After you run the previous code, you need to have a mannequin.joblib file created in your native surroundings.

Pull the DJL Docker picture

The Docker picture djl-inference:0.23.0-cpu-full-v1.0 is our DJL serving container used on this instance. You may modify the next URL relying in your Area:

inference_image_uri = “”

Optionally, it’s also possible to use this picture as a base picture and prolong it to construct your individual Docker picture on Amazon Elastic Container Registry (Amazon ECR) with every other dependencies you want.

Create the mannequin file

First, we create a file referred to as This instructs DJLServing to make use of the Python engine. We additionally outline the max_idle_time of a employee to be 600 seconds. This makes positive that we take longer to scale down the variety of employees we now have per mannequin. We don’t modify minWorkers and maxWorkers that we are able to outline and we let DJL dynamically compute the variety of employees wanted relying on the site visitors every mannequin is receiving. The is proven as follows. To see the whole listing of configuration choices, check with Engine Configuration.


Subsequent, we create our file, which defines the mannequin loading and inference logic. For MMEs, every file is restricted to a mannequin. Fashions are saved in their very own paths underneath the mannequin retailer (normally /choose/ml/mannequin/). When loading fashions, they are going to be loaded underneath the mannequin retailer path in their very own listing. The complete instance on this demo might be seen within the GitHub repo.

We create a mannequin.tar.gz file that features our mannequin (mannequin.joblib),, and

#Construct tar file with mannequin information + inference code, substitute this cell together with your mannequin.joblib
bashCommand = “tar -cvpzf mannequin.tar.gz mannequin.joblib necessities.txt”
course of = subprocess.Popen(bashCommand.cut up(), stdout=subprocess.PIPE)
output, error = course

For demonstration functions, we make 1,000 copies of the identical mannequin.tar.gz file to symbolize the big variety of fashions to be hosted. In manufacturing, you should create a mannequin.tar.gz file for every of your fashions.

Lastly, we add these fashions to Amazon S3.

Create a SageMaker mannequin

We now create a SageMaker mannequin. We use the ECR picture outlined earlier and the mannequin artifact from the earlier step to create the SageMaker mannequin. Within the mannequin setup, we configure Mode as MultiModel. This tells DJLServing that we’re creating an MME.

mme_model_name = “sklearn-djl-mme” + strftime(“%Y-%m-%d-%H-%M-%S”, gmtime())
print(“Mannequin title: ” + mme_model_name)

create_model_response = sm_client.create_model(
PrimaryContainer=“Picture”: inference_image_uri, “Mode”: “MultiModel”, “ModelDataUrl”: mme_artifacts,

Create a SageMaker endpoint

On this demo, we use 20 ml.c5d.18xlarge situations to scale to a TPS within the 1000’s vary. Ensure to get a restrict improve in your occasion kind, if essential, to attain the TPS you might be concentrating on.

mme_epc_name = “sklearn-djl-mme-epc” + strftime(“%Y-%m-%d-%H-%M-%S”, gmtime())
endpoint_config_response = sm_client.create_endpoint_config(

“VariantName”: “sklearnvariant”,
“ModelName”: mme_model_name,
“InstanceType”: “ml.c5d.18xlarge”,
“InitialInstanceCount”: 20

Load testing

On the time of writing, the SageMaker in-house load testing software Amazon SageMaker Inference Recommender doesn’t natively help testing for MMEs. Due to this fact, we use the open supply Python software Locust. Locust is simple to arrange and might monitor metrics reminiscent of TPS and end-to-end latency. For a full understanding of easy methods to set it up with SageMaker, see Greatest practices for load testing Amazon SageMaker real-time inference endpoints.

On this use case, we now have three totally different site visitors patterns we need to simulate with MMEs, so we now have the next three Python scripts that align with every sample. Our aim right here is to show that, no matter what our site visitors sample is, we are able to obtain the identical goal TPS and scale appropriately.

We will specify a weight in our Locust script to assign site visitors throughout totally different parts of our fashions. As an illustration, with our single scorching mannequin, we implement two strategies as follows:

# widespread mannequin
def sendPopular(self):

request_meta =
“request_type”: “InvokeEndpoint”,
“title”: “SageMaker”,
“start_time”: time.time(),
“response_length”: 0,
“response”: None,
“context”: ,
“exception”: None,

start_perf_counter = time.perf_counter()
response = self.sagemaker_client.invoke_endpoint(
TargetModel = “sklearn-0.tar.gz”

# remainder of mannequin
def sendRest(self):

request_meta =
“request_type”: “InvokeEndpoint”,
“title”: “SageMaker”,
“start_time”: time.time(),
“response_length”: 0,
“response”: None,
“context”: ,
“exception”: None,

start_perf_counter = time.perf_counter()

response = self.sagemaker_client.invoke_endpoint(
TargetModel = f’sklearn-random.randint(1,989).tar.gz’
response_body = response[“Body”].learn()

We will then assign a sure weight to every technique, which is when a sure technique receives a particular share of the site visitors:

# assign weights to fashions
class MyUser(BotoUser):

# 90% of site visitors to singular mannequin
def send_request(self):

def send_request_major(self):

For 20 ml.c5d.18xlarge situations, we see the next invocation metrics on the Amazon CloudWatch console. These values stay pretty constant throughout all three site visitors patterns. To grasp CloudWatch metrics for SageMaker real-time inference and MMEs higher, check with SageMaker Endpoint Invocation Metrics.

You’ll find the remainder of the Locust scripts within the locust-utils listing within the GitHub repository.


On this publish, we mentioned how an MME can dynamically modify the compute energy assigned to every mannequin based mostly on the mannequin’s site visitors sample. This newly launched function is obtainable in all AWS Areas the place SageMaker is obtainable. Word that on the time of announcement, solely CPU situations are supported. To be taught extra, check with Supported algorithms, frameworks, and situations.

Concerning the Authors

Ram Vegiraju is a ML Architect with the SageMaker Service crew. He focuses on serving to prospects construct and optimize their AI/ML options on Amazon SageMaker. In his spare time, he loves touring and writing.

Qingwei Li is a Machine Studying Specialist at Amazon Internet Providers. He acquired his Ph.D. in Operations Analysis after he broke his advisor’s analysis grant account and didn’t ship the Nobel Prize he promised. At the moment he helps prospects within the monetary service and insurance coverage business construct machine studying options on AWS. In his spare time, he likes studying and instructing.

James Wu is a Senior AI/ML Specialist Answer Architect at AWS. serving to prospects design and construct AI/ML options. James’s work covers a variety of ML use instances, with a main curiosity in pc imaginative and prescient, deep studying, and scaling ML throughout the enterprise. Previous to becoming a member of AWS, James was an architect, developer, and expertise chief for over 10 years, together with 6 years in engineering and 4 years in advertising and marketing & promoting industries.

Saurabh Trikande is a Senior Product Supervisor for Amazon SageMaker Inference. He’s obsessed with working with prospects and is motivated by the aim of democratizing machine studying. He focuses on core challenges associated to deploying advanced ML purposes, multi-tenant ML fashions, price optimizations, and making deployment of deep studying fashions extra accessible. In his spare time, Saurabh enjoys mountain climbing, studying about revolutionary applied sciences, following TechCrunch and spending time together with his household.

Xu Deng is a Software program Engineer Supervisor with the SageMaker crew. He focuses on serving to prospects construct and optimize their AI/ML inference expertise on Amazon SageMaker. In his spare time, he loves touring and snowboarding.

Siddharth Venkatesan is a Software program Engineer in AWS Deep Studying. He at present focusses on constructing options for giant mannequin inference. Previous to AWS he labored within the Amazon Grocery org constructing new fee options for purchasers world-wide. Exterior of labor, he enjoys snowboarding, the outside, and watching sports activities.

Rohith Nallamaddi is a Software program Growth Engineer at AWS. He works on optimizing deep studying workloads on GPUs, constructing excessive efficiency ML inference and serving options. Previous to this, he labored on constructing microservices based mostly on AWS for Amazon F3 enterprise. Exterior of labor he enjoys taking part in and watching sports activities.

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