Deploying ML-models to AWS lambda - Workshop @ PyData Berlin 2018 (WIP)

See https://bweigel.github.io/pydata_bln_2018/#/ for slides & Dockerhub for Docker images.

PyData Attendees:

<start importantInfo>

Since I am not sure how well the internet will work on the Charité premises I urge you to:

install Docker
download the docker image we will be working with (this should contain everything you need to work along):
- docker pull bweigel/ml_at_awslambda_pydatabln2018_autobuild
do items 1 to 3 in the Quickstart

<end importantInfo>

Intro

In this Workshop you will be using the Serverless Framework to deploy a pre-trained model (Naive Bayes Classifier) based on the SMS Spam Collection dataset to the cloud. The training was carried out like it is described here. The code is located here: tutorial/training/train.py

Your cloud-service will use AWS Lambda and Api-Gateway behind the scenes. AWS Api-Gateway will provide a API endpoint where texts can be classified using the POST method with the text as payload. The payloads will be forwarded to a AWS lambda function that knows the model and does the actual classification.

Quickstart

create an AWS account at https://aws.amazon.com if you have not already done so (see Instruction)
- you will need a credit card to do this, but you will not be charged if you stay in the free tier
- BEWARE the S3 & ApiGateway capabilities, that we will be using, will only be free of charge for the first 12 months!
- create a new user within IAM (do not use root user to do stuff in your account and be sure to use MFA and a secure password)
- create an access key for programmatic access
install and setup the AWS Command Line Interface
- pip install awscli
- aws configure (see here)
clone repo git clone https://github.com/bweigel/ml_at_awslambda_pydatabln2018.git
- and change into the project cd ml_at_awslambda_pydatabln2018
start docker container: ./start_docker.sh
work along merrily ... or, if you feel adventurous check out the complete demo code

We will build this:

Test lambda function locally:

sls invoke local -f categorizer_lambda --data '{"body": "am i spam or am i ham?"}'

Things to keep in mind

All the ML frameworks in Python are quite heavy weight when it comes to size. However for individual lambda deployments (not as part of a cloudformation stack) there is a hard filesize limit of 50 MB enforced by AWS. This is not only because AWS wants to screw with you, but because it wants developers to build apps with the highest performance. There are a couple of tricks to reduce the size of your deployment zip-file. See here and the resources section for more info.

FAQ

How do I test my function? You can test your function locally using serverless (only works when you have a valid serverless.yml):

sls invoke local -f categorizer_lambda --data '{"body": "am i spam or am i ham?"}'

This tries to emulate the lambda runtime environment and runs the function categorizer_lambda with the provided --data as the event. Testing like that isn't perfect, but should work fine for almost every usecase. For more info see here.

LPT: Unittests are always a great idea and easy to write and use.

You can also test like this (no need for a valid serverless.yml):

python -c "import main; print(main.lambda_handler({'body': 'foo'}, None))"

How do I tear down my service stack? To remove the service in AWS just run serverless remove inside the from_scratch directory.

How to run start_docker.sh in Windows? Download Git for Windows (https://gitforwindows.org/) and install it. It comes with the Git BASH which you can use to run start_docker.sh. If you run into errors: You might need to set some environmental variables before running the command. Do export AWS_ACCESS_KEY_ID=<your aws access key id> AWS_SECRET_ACCESS_KEY=<your aws secret ccess key> (see Quickstart 1. on how to obtain these) and run ./start_docker.sh again.

Why use the serverless framework (and not something like the AWS Serverless Application Model, SAM)? The serverless framework is the most mature tooling around for deploying serverless services. It is actively developed, has a big community and a rich ecosystem of plugins, which help with keeping our dependencies slim (see https://tech.europace.de/slimifying-aws-lambdas/) among other things.

What else is inside the event that is provided inside the lambda_handler function? Depending on the event type that triggers the function (e.g. SNS, SQS, Kinesis, Alexa...) the event payload will be different. We are using ApiGateway Proxy integration in this workshop. A sample proxy integration event can be seen here. You can see how the different events look through the AWS Lambda Console (go to configure test event and there will be a long list of events).

Troubleshooting

make test* fails with FileNotFoundError:

$ make test-all
set -eux pipefail
if [ ! -d ".venv" ]; then \
        export PIPENV_IGNORE_VIRTUALENVS=1, PIPENV_VENV_IN_PROJECT=1 && pipenv lock && pipenv sync --dev; \
fi
pipenv run tox
Traceback (most recent call last):
  File "/var/lang/bin/pipenv", line 11, in <module>
    sys.exit(cli())
  File "/var/lang/lib/python3.6/site-packages/pipenv/vendor/click/core.py", line 722, in __call__
    return self.main(*args, **kwargs)
  File "/var/lang/lib/python3.6/site-packages/pipenv/vendor/click/core.py", line 697, in main
    rv = self.invoke(ctx)
  File "/var/lang/lib/python3.6/site-packages/pipenv/vendor/click/core.py", line 1066, in invoke
    return _process_result(sub_ctx.command.invoke(sub_ctx))
  File "/var/lang/lib/python3.6/site-packages/pipenv/vendor/click/core.py", line 895, in invoke
    return ctx.invoke(self.callback, **ctx.params)
  File "/var/lang/lib/python3.6/site-packages/pipenv/vendor/click/core.py", line 535, in invoke
    return callback(*args, **kwargs)
  File "/var/lang/lib/python3.6/site-packages/pipenv/cli.py", line 637, in run
    do_run(command=command, args=args, three=three, python=python)
  File "/var/lang/lib/python3.6/site-packages/pipenv/core.py", line 2305, in do_run
    do_run_posix(script, command=command)
  File "/var/lang/lib/python3.6/site-packages/pipenv/core.py", line 2285, in do_run_posix
    os.execl(command_path, command_path, *script.args)
  File "/var/lang/lib/python3.6/os.py", line 527, in execl
    execv(file, args)
FileNotFoundError: [Errno 2] No such file or directory

Solution: make clean && make setup then run make test-all

Error when running serverless deploy:
```
Serverless: Building custom docker image from Dockerfile...

Error --------------------------------------------------

Cannot connect to the Docker daemon at unix:///var/run/docker.sock. Is the docker daemon running?
...
```
- Solution: You are running serverless deploy inside a docker container and the serverless-requirements-plugin tries to spawn another nested container to package dependecies. Run deploy outside a docker container, or set custom.pythonRequirements.dockerizePip to false in the serverless.yml.

Resources

AWS Lambda Python magic. Tips for creating powerful Lambda functions.

bweigel/ml_at_awslambda_pydatabln2018