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https://github.com/model-zoo/scikit-learn-lambda

Toolkit for deploying scikit-learn models for realtime inference on AWS Lambda
https://github.com/model-zoo/scikit-learn-lambda

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Toolkit for deploying scikit-learn models for realtime inference on AWS Lambda

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# scikit-learn-lambda



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scikit-learn-lambda is a toolkit for deploying scikit-learn models for realtime inference on AWS Lambda.



## Why use scikit-learn-lambda?



* **Get started quickly** - `scikit-learn-lambda` handles the boilerplate code for you,

  simply drop in a `joblib` or `pickle` model file and deploy.

* **Cost efficient** - The equivalent architecture on [AWS

  SageMaker](https://aws.amazon.com/sagemaker/) will cost you ~$50 per endpoint per month.

  Deploying on AWS Lambda allows you to pay-per-request and not worry about

  the number of models you're deploying to production.

* **Built-in autoscaling** - Deploying on [Elastic Container

  Service](https://aws.amazon.com/ecs/) or [Kubernetes](https://kubernetes.io/)

  requires configuring and maintaining autoscaling groups. AWS Lambda abstracts

  this complexity away for you.



Read more in our blogpost: [Saving 95% on infrastructure costs using AWS Lambda for scikit-learn predictions](https://modelzoo.dev/blog/lambda.html).



## Overview



scikit-learn-lambda provides three components:



1) `scikit-learn-lambda`: A Python package that includes a handler for serving

   `scikit-learn` predictions via AWS Lambda, designed for use with API Gateway.

2) A repository of Lambda layers for various versions of Python (3.6 - 3.8)

   and `scikit-learn` (0.22+).

3) Example Serverless template configuration for deploying a model to an HTTP

   endpoint.



## Quickstart (Serverless)



You have two options for deploying a model with Serverless framework.



* Package your model as part of the deployment package upload to AWS Lambda.

This option will require your model file to be under ~50 MB, but achieve the best

cold-start latency.



* Store your model into Amazon S3 and load it from there on AWS Lambda

initialization. This option has no model size constraints.



#### Prerequisites



* [Install Serverless](https://www.serverless.com/framework/docs/providers/aws/guide/installation/)

* [Configure your AWS credentials](https://www.serverless.com/framework/docs/providers/aws/guide/credentials/)



#### Package your model with the code (~50 MB limit)



1) Copy your model joblib file to `scikit-learn-lambda/model.joblib`, the same

directory that `serverless.yaml` is in.



```

$ cp testdata/svm.joblib scikit-learn-lambda/model.joblib

```



2) Deploy your model with Serverless framework.



```

$ serverless deploy

```



3) Test the your endpoint with some example data:



```

$ curl --header "Content-Type: application/json" \

  --request POST \

  --data '{"input":[[0, 0, 0, 0]]}' \

  https://.execute-api.us-west-2.amazonaws.com/dev/predict

$ {"prediction": [0]}

```



#### Package your model via Amazon S3



1) Edit the environment variable `SKLEARN_MODEL_PATH` in

`scikit-learn-lambda/serverless.yaml` to specify an s3 URL.



```

function:

  scikitLearnLambda:

    ...

    environment:

      SKLEARN_MODEL_PATH: "s3://my-bucket/my-model-file.joblib"

```



2) Add an IAM Role that gives the Lambda function permission to access the

model file at the specified S3 path.



```

function:

  scikitLearnLambda:

    ...

    role: ScikitLearnLambdaRole



resources:

  ScikitLearnLambdaRole:

    Type: AWS::IAM::Role

    Properties:

      RoleName: ScikitLearnLambdaRole

      AssumeRolePolicyDocument:

        Version: '2012-10-17'

        Statement:

          - Effect: Allow

            Principal:

              Service:

                - lambda.amazonaws.com

            Action: sts:AssumeRole

      Policies:

        - PolicyName: ModelInfoGetObjectPolicy-${self:custom.stage}

          PolicyDocument:

            Version: '2012-10-17'

            Statement:

              - Effect: Allow

                Action: ["s3:GetObject"]

                Resource: "arn:aws:s3:::my-bucket/my-model.file.joblib"

```



3) Deploy your model with Serverless framework.



```

$ serverless deploy

```



#### Customize the Python runtime version or scikit-learn version.



It is a good idea to match your Python version and scikit-learn version with

the environment that was used to train and serialize the model.



The default template is configured to use *Python 3.7* and *scikit-learn

0.23.1*. To use a different version, change the layer ARN in the serverless

template. We have prebuilt and published a set of AWS Lambda you can get

started with quickly. For production usage, we recommend that you use our

provided scripts to build and host your own AWS Lambda layer -- see

[Layers](#layers).



```

function:

  scikitLearnLambda:

    ...

    layers:

      - ""

```



When changing the Python runtime version, make sure to also edit the `runtime`

in `serverless`:



```

provider:

  ...

  runtime: ""

```



## Layers



To get around a the [50 MB (zipped) deployment package

limit](https://docs.aws.amazon.com/lambda/latest/dg/gettingstarted-limits.html),

it is useful to create a distinct layer for the `scikit-learn` dependency. This

frees up more room in your deployment package for your model or other

dependencies.



`scikit-learn-lambda` comes with a pre-built set of AWS Lambda layers that include

`scikit-learn` and `joblib` that you can use out of the box on `us-west-2`.

These layers are hosted on the Model Zoo AWS account  with public permissions

for any AWS account to use. We also provide a script `tools/build-layers.sh`

that allows you to build and upload layers owned by your own AWS account.



#### Prebuilt Layers



We have published a set of layers for combinations of Python 3.6 - 3.8 and

scikit-learn 0.22 - 0.24 to different US regions. These are published to

`layers.csv`:



[`layers.csv`](https://github.com/model-zoo/scikit-learn-lambda/blob/master/layers.csv)



#### Build your own layers



`tools/build-layers.sh` is a bash script that can be used to build one or more

AWS Lambda Layers with the `scikit-learn` and `joblib` dependencies.



_This script assumes you have jq, Docker, and the AWS cli installed on your machine._



**Example 1**: Build layer for Python 3.7 and scikit-learn 0.23.0.



    ./build-layers.sh --scikit-learn=0.23.0 --python=3.7



**Example 2**: Build layer for Python 3.7 and scikit-learn 0.23.0 and

publish to us-west-2.



     ./build-layers.sh --python=3.7 --scikit-learn==0.23.0 --region=us-west-2



    ./build-layers.sh



## Expected HTTP Schema



`scikit-learn-lambda` is designed to be used with [Lambda Proxy Integrations in

API

Gateway](https://docs.aws.amazon.com/apigateway/latest/developerguide/set-up-lambda-proxy-integrations.html).

When using the provided serverless template, the HTTP endpoint will expect a

POST request with a `Content-Type` of `application/json`.



The expected input fields are:



* `input` (`string`): The list of array-like values to predict. The shape

  should match the value that is typically fed into a `model.predict` call.

* `return_prediction` (`boolean`): Will return `prediction` in output if true.

  This field is optional and defaults to true.

* `return_probabilities` (`boolean`): Will return `probabilities` in output if

  true. This field is optional and defaults to false.



One of `return_prediction` or `return_probabilities` or both must be true.



The return response will be JSON-encoded with the following fields:



* `prediction`: A list of array-like prediction from `model.predict()`, one for

  every sample in the batch.  Present if `return_prediction` is true.

* `probabilities`: A list of dictionaries mapping string class names to

  probabilities from `model.predict_proba()`, one for every sample in the

batch. Present if `return_probabilities` is true.



#### Examples



```

$ curl --header "Content-Type: application/json" \

      --request POST \

      --data '{"input":[[0, 0, 0, 0]]}' \

      https://.execute-api.us-west-2.amazonaws.com/mlp-250-250-250/predict

{"prediction": [0]}

```



```

$ curl --header "Content-Type: application/json" \

      --request POST \

      --data '{"input":[[0, 0, 0, 0]], "return_probabilities": true}' \

      https://.execute-api.us-west-2.amazonaws.com/mlp-250-250-250/predict

{"prediction": [0], "probabilities": [{"0": 0.3722170997279803, "1": 0.29998954257031885, "2": 0.32779335770170076}]}

```



## Performance Benchmarks



To run these benchmarks, we generate [MLPClassifier](https://scikit-learn.org/stable/modules/generated/sklearn.neural_network.MLPClassifier.html) models with three hidden layers each using layer sizes of 100, 250, 500, 750, and 900. Each model is trained on the iris dataset and serialized using [joblib](https://joblib.readthedocs.io/en/latest/index.html). This results in model files sized 0.68 MB, 4.08 MB, 16.17 MB, 36.25 MB, and 52.13 MB respectively. We deploy each to a lambda function with 1024 MB memory and measure an average cold-start latency over three samples:



![](cold-start-benchmarks.png)



We also plot the “warm” latency below averaged over fifteen samples for each model:



![](warm-start-benchmarks.png)



## Cost Benchmarks



[https://modelzoo.dev/lambda-vs-sagemaker-cost/](https://modelzoo.dev/lambda-vs-sagemaker-cost/)



We've created this interactive visualization to help us understand the cost per month under various usage scenarios. It also includes a comparison to SageMaker inference endpoint costs.