Serving with AWS Lambda#

The Serverless Application Model Command Line Interface (SAM CLI) is an extension of the AWS CLI that adds functionality for building and testing Lambda applications.

It uses Docker to run our functions in an Amazon Linux environment that matches Lambda. It can also emulate our application’s build environment and API locally.


To follow this guide, we’ll need the following tools:

We need to use Amazon ECR-based images so be sure to authenticate to the AWS ECR registry.

Initialize a UnionML App for AWS Lambda#

Initialize a UnionML app that supports serving for AWS Lambda:

unionml init aws_lambda_app --template basic-aws-lambda
cd aws_lambda_app

This will create a UnionML project directory called aws_lambda_app which contains all of the scripts and configuration needed to build and deploy the app.

As we can see in the script, the main additions to the UnionML app definition are that we need to define a FastAPI app and wrap it in a Mangum object.

from fastapi import FastAPI
from mangum import Mangum

# dataset and model definition
dataset = ...
model = ...

# serve with FastAPI
app = FastAPI()

# run ASGI applications in AWS Lambda to handle API Gateway using Mangum
lambda_handler = Mangum(app)

Mangum is an adapter for running ASGI applications in AWS Lambda, so what we’re doing here is using it to convert a FastAPI app into an AWS Lambda serverless function that you can invoke over a web endpoint.

Build and Test Locally#

First we need to create the model object that we want to deploy. we can do this by simply invoking the script:


This will create a joblib-serialized sklearn model called model_object.joblib in our current directory.

Then, build our application with the sam build command.

sam build

The SAM CLI builds a Docker image from the Dockerfile and then installs dependencies defined in requirements.txt inside the docker image. The processed template file is saved in the .aws-sam/build folder.

Test a single function by invoking it directly with a test event. An event is a JSON document that represents the input that the function receives from the event source. Test events are included in the events folder in this project.

Run functions locally and invoke them with the sam local invoke command.

sam local invoke UnionmlFunction --event events/event.json

Emulating the Lambda API#

We can also emulate our application’s API:

sam local start-api

Then in another terminal session run the following:

curl -X POST http://localhost:3000/predict \
    -H "Content-Type: application/json"  \
    -d "{\"features\": $(cat data/sample_features.json)}"


SAM CLI reads the application template in template.yaml to determine the API’s routes and the functions that they invoke. The Events property on each function’s definition includes the route and method for each path.

        Type: Api
            Path: /{proxy+}
            Method: post

Unit Tests#

Tests are defined in the tests folder in the app directory.

Use pip to install pytest and run unit tests locally.

pip install pytest
python -m pytest tests

Deploying to AWS Lambda#

Once we’re satisfied with our application’s state, we can then build and deploy it to AWS:


If you don’t have an account on AWS, create one here.

sam deploy --guided


The first command will build a docker image from a Dockerfile and then copy the source of our application inside the Docker image. The second command will package and deploy our application to AWS, with a series of prompts:

  • Stack Name: The name of the stack to deploy to CloudFormation. This should be unique to our account and region, and a good starting point would be something matching our project name.

  • AWS Region: The AWS region we want to deploy our app to.

  • Confirm changes before deploy: If set to yes, any change sets will be shown to we before execution for manual review. If set to no, the AWS SAM CLI will automatically deploy application changes.

  • Allow SAM CLI IAM Role creation: Many AWS SAM templates, including this example, create AWS IAM roles required for the AWS Lambda function(s) included to access AWS services. By default, these are scoped down to minimum required permissions. To deploy an AWS CloudFormation stack which creates or modifies IAM roles, the CAPABILITY_IAM value for capabilities must be provided. If permission isn’t provided through this prompt, to deploy this example we must explicitly pass --capabilities CAPABILITY_IAM to the sam deploy command.

  • Save arguments to samconfig.toml: If set to yes, our choices will be saved to a configuration file inside the project, so that in the future we can just re-run sam deploy without parameters to deploy changes to our application.

The output should look something like this:

CloudFormation outputs from deployed stack
Key                 UnionmlFunction
Description         unionml Lambda Function ARN
Value               arn:aws:lambda:...

Key                 UnionmlApi
Description         API Gateway endpoint URL for Prod stage for unionml function

Key                 UnionmlFunctionIamRole
Description         Implicit IAM Role created for unionml function
Value               arn:aws:iam::...

Successfully created/updated stack - unionml-example in us-east-2

We can find our API Gateway Endpoint URL in the output values displayed after deployment. In this case, the URL is

We can hit that endpoint to generate predictions our unionml app, for example, using the requests library:

import requests
from sklearn.datasets import load_digits

digits = load_digits(as_frame=True)
features = digits.frame[digits.feature_names]

prediction_response =
    json={"features": features.sample(5, random_state=42).to_dict(orient="records")},


Lambda Function Logs#

To simplify troubleshooting, SAM CLI has a command called sam logs.

sam logs lets we fetch logs generated by our deployed Lambda function from the command line. In addition to printing the logs on the terminal, this command has several nifty features to help we quickly find the bug.

sam logs -n unionmlFunction --stack-name unionml-aws-lambda-example --tail


This command works for all AWS Lambda functions, not just the ones we deploy using SAM.

App Resource Cleanup#

To delete the sample application that we created, use the AWS CLI. Assuming we used our project name for the stack name, we can run the following:

sam delete --stack-name unionml-aws-lambda-example

Add a Resource to our Application#

The application template uses AWS Serverless Application Model (AWS SAM) to define application resources. AWS SAM is an extension of AWS CloudFormation with a simpler syntax for configuring common serverless application resources such as functions, triggers, and APIs.

For resources not included in the SAM specification, we can use standard AWS CloudFormation resource types.

We can find more information and examples about filtering Lambda function logs in the SAM CLI Documentation.

Additional Resources#

See the AWS SAM developer guide for an introduction to SAM specification, the SAM CLI, and serverless application concepts.

Using a Model Trained on Flyte#

So far in this guide we’ve used a model that we trained locally. But what if we want to us a model that we trained on a Flyte cluster backend?

The last section of this guide will show you how to do that. Recall that we ran our script like so to generate a model object:


We can actually run the same steps that we went through in Deploying to Flyte to train a model on our Flyte backend:

flytectl demo start --source .
unionml deploy app:model
unionml train app:model -i '{"hyperparameters": {"C": 1.0, "max_iter": 10000}}'

In order to fetch the trained model, you can use unionml fetch-model to download and save the model object to your local directory:

unionml fetch-model app:model --model-version latest --output-file model_object.joblib

This will create a model_object.joblib file in your app project directory, which is equivalent to the model object we created with python


Recall that you can list all the current model versions of your app with

unionml list-model-versions app:model

From here, you can follow the steps from the Build and Test Locally or Deploy to AWS Lambda sections to deploy this model to a serverless endpoint!