reproducible-ml

LocalStack Demo: Train, save and evaluate a scikit-learn machine learning model

In this tutorial, we will train a simple machine-learning model that recognizes handwritten digits on an image. We will use the following services:

• an S3 bucket to host our training data;
• a Lambda function to train and save the model to an S3 bucket;
• a Lambda layer that contains the dependencies for our training code;
• a second Lambda function to download the saved model and perform a prediction with it.

Prerequisites

• LocalStack
• Docker
• awslocal CLI

Installing

To install the dependencies:

make install

Starting LocalStack

Make sure that LocalStack is started:

LOCALSTACK_API_KEY=... DEBUG=1 localstack start

Running

The entire workflow is executed by the run.sh script. To trigger it, execute:

make run

The model will be first trained by the ml-train Lambda function and then uploaded on the S3 bucket. A second Lambda function will download the model and run predictions on a test set of character inputs. The logs of the Lambda invocation should be visible in the LocalStack container output (with DEBUG=1 enabled):

null
>START RequestId: 65dc894d-25e0-168e-dea1-a3e8bfdb563b Version: $LATEST
> --> prediction result: [8 8 4 9 0 8 9 8 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 9 6 7 8 9
...
...
>  9 5 4 8 8 4 9 0 8 9 8]
> END RequestId: 6...