There are 4 commands we can use:

• deploy
• describe
• update
• delete

AWS Lambda deployment tool

AWS Lambda is a great service for quickly deploy service to the cloud for immediate access. It's ability to auto scale resources base on usage make it attractive to user who want to save cost and want to scale base on usage without administrative overhead.

Prerequisites

• An active AWS account configured on the machine with AWS CLI installed and configured
  • Install instruction: https://docs.aws.amazon.com/cli/latest/userguide/cli-chap-install.html
  • Configure AWS account instruction: https://docs.aws.amazon.com/cli/latest/userguide/cli-chap-configure.html
• AWS SAM CLI (>=1.27). Installation instructions https://docs.aws.amazon.com/serverless-application-model/latest/developerguide/serverless-sam-cli-install.html
• Docker is installed and running on the machine.
  • Install instruction: https://docs.docker.com/install
• Install required python packages
  • $ pip install -r requirements.txt

Deploy IrisClassifier from BentoML quick start guide to AWS Lambda

1. Build and save Bento Bundle from BentoML quick start guide

2. Copy and change the sample config file given and change it according to your deployment specifications. Check out the config section to find the differenet options.

3. Create Lambda deployment with the deployment tool.

   Run deploy script in the command line:

   $ BENTO_BUNDLE_PATH=$(bentoml get IrisClassifier:latest --print-location -q)
   $ python deploy.py $BENTO_BUNDLE_PATH my-lambda-deployment lambda_config.json

   Get deployment information and status

   $ python describe.py my-lambda-deployment
   
   # Sample output
   {
     "StackId": "arn:aws:cloudformation:us-west-1:192023623294:stack/my-lambda-deployment-stack/29c15040-db7a-11eb-a721-028d528946df",
     "StackName": "my-lambda-deployment-stack",
     "StackStatus": "CREATE_COMPLETE",
     "CreationTime": "07/02/2021, 21:12:09",
     "LastUpdatedTime": "07/02/2021, 21:12:20",
     "EndpointUrl": "https://j2gm5zn7z9.execute-api.us-west-1.amazonaws.com/Prod"
   }

4. Make sample request against deployed service. The url for the endpoint given in the output of the describe command or you can also check the API Gateway through the AWS console.

   curl -i \
     --header "Content-Type: application/json" \
     --request POST \
     --data '[[5.1, 3.5, 1.4, 0.2]]' \
     https://j2gm5zn7z9.execute-api.us-west-1.amazonaws.com/Prod/predict
   
   # Sample output
   HTTP/2 200
   content-type: application/json
   content-length: 3
   date: Sat, 03 Jul 2021 19:14:38 GMT
   x-amzn-requestid: d3b5f156-0859-4f69-8b53-c60e800bc0aa
   x-amz-apigw-id: B6GLLECTSK4FY2w=
   x-amzn-trace-id: Root=1-60e0b714-18a97eb5696cec991c460213;Sampled=0
   x-cache: Miss from cloudfront
   via: 1.1 6af3b573d8970d5db2a4d03354335b85.cloudfront.net (CloudFront)
   x-amz-cf-pop: SEA19-C3
   x-amz-cf-id: ArwZ03gbs6GooNN1fy4mPOgaEpM4h4n9gz2lpLYrHmeXZJuGUJgz0Q==
   
   [0]%

5. Delete Lambda deployment

   $ python delete.py my-lambda-deployment

Deployment operations

Configuration options

• region: AWS region for Lambda deployment
• timeout: Timeout per request
• memory_size: The memory for your function, set a value between 128 MB and 10,240 MB in 1-MB increments

Create a deployment

Use CLI

python deploy.py <Bento_bundle_path> <Deployment_name> <Config_JSON default is ./lambda_config.json>

Example:

MY_BUNDLE_PATH=${bentoml get IrisClassifier:latest --print-location -q)
python deploy.py $MY_BUNDLE_PATH my_first_deployment lambda_config.json

Use Python API

from deploy import deploy_aws_lambda

deploy_aws_lambda(BENTO_BUNDLE_PATH, DEPLOYMENT_NAME, CONFIG_JSON)

Update a deployment

Use CLI

python update.py <Bento_bundle_path> <Deployment_name> <Config_JSON>

Use Python API

from update import update_aws_lambda
update_aws_lambda(BENTO_BUNDLE_PATH, DEPLOYMENT_NAME, CONFIG_JSON)

Get deployment's status and information

Use CLI

python describe.py <Deployment_name> <Config_JSON>

Use Python API

from describe import describe_deployment
describe_deployment(DEPLOYMENT_NAME, CONFIG_JSON)

Delete deployment

Use CLI

python delete.py <Deployment_name> <Config_JSON>

Use Python API

from  delete import delete_deployment
delete_deployment(DEPLOYMENT_NAME, CONFIG_JSON)