This project contains source code and supporting files for a serverless application that you can deploy with the SAM CLI.

The source code is an example computer vision classification model that returns the class and probability in json format.

It includes the following files and folders.

• vision - Code for the application's Lambda function and Project Dockerfile. The exported fastai model vision classification model should be copied here and named export.pkl.
• events - Invocation events that you can use to invoke the function.
• tests - Unit tests for the application code.
• template.yaml - A template that defines the application's AWS resources.

The application uses several AWS resources, including Lambda functions and an API Gateway API. These resources are defined in the template.yaml file in this project. You can update the template to add AWS resources through the same deployment process that updates your application code.

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 your functions in an Amazon Linux environment that matches Lambda. It can also emulate your application's build environment and API.

To use the SAM CLI, you need the following tools.

• SAM CLI - Install the SAM CLI
• Docker - Install Docker community edition

You may need the following for local testing.

• Python 3 installed

Clone this github repository to your machine where you have installed the SAM CLI and Docker.

git clone https://github.com/mattmcclean/fastai-container-sam-app.git

It is expected that you have already trained a fastai model. To export the model run the following command on your Jupyter notebook:

learn.export()

This will create a file called export.pkl. Copy this file to the vision directory in your project.

To build and deploy your application for the first time, run the following in your shell:

sam build
sam deploy --guided

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

• Stack Name: The name of the stack to deploy to CloudFormation. This should be unique to your account and region, and a good starting point would be something matching your project name.
• AWS Region: The AWS region you want to deploy your app to.
• Confirm changes before deploy: If set to yes, any change sets will be shown to you 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 modified IAM roles, the CAPABILITY_IAM value for capabilities must be provided. If permission isn't provided through this prompt, to deploy this example you must explicitly pass --capabilities CAPABILITY_IAM to the sam deploy command.
• Save arguments to samconfig.toml: If set to yes, your choices will be saved to a configuration file inside the project, so that in the future you can just re-run sam deploy without parameters to deploy changes to your application.

You can find your API Gateway Endpoint URL in the output values displayed after deployment.

Build your application with the sam build command.

fastai-container-sam-app$ sam build

The SAM CLI builds a docker image from a Dockerfile and then installs dependencies 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.

fastai-container-sam-app$ sam local invoke FastaiVisionFunction --event events/event.json

The SAM CLI can also emulate your application's API. Use the sam local start-api to run the API locally on port 3000.

fastai-container-sam-app$ sam local start-api
fastai-container-sam-app$ curl http://localhost:3000/

The SAM CLI reads the application template 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.

      Events:
        FastaiVision:
          Type: Api
          Properties:
            Path: /invocations
            Method: post

To simplify troubleshooting, SAM CLI has a command called sam logs. sam logs lets you fetch logs generated by your deployed Lambda function from the command line. In addition to printing the logs on the terminal, this command has several nifty features to help you quickly find the bug.

NOTE: This command works for all AWS Lambda functions; not just the ones you deploy using SAM.

fastai-container-sam-app$ sam logs -n FastaiVisionFunction --stack-name fastai-container-sam-app --tail

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

First create a virtual python environment and install the necessary packages.

fastai-container-sam-app$ python3 -m venv fastai-env
fastai-container-sam-app$ source fastai-env/bin/activate
fastai-container-sam-app$ pip install pytest pytest-mock
fastai-container-sam-app$ pip install -r vision/requirements.txt
fastai-container-sam-app$ ln -s vision/export.pkl export.pkl

Tests are defined in the tests folder in this project. Use PIP to install the pytest and run unit tests from your local machine.

fastai-container-sam-app$ python -m pytest tests/ -v

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

aws cloudformation delete-stack --stack-name fastai-container-sam-app

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

Next, you can use AWS Serverless Application Repository to deploy ready to use Apps that go beyond hello world samples and learn how authors developed their applications: AWS Serverless Application Repository main page