ml_at_awslambda_pydatabln2018

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ml_at_awslambda_pydatabln2018

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Material for working alongside my workshop session at PyData Berlin 2018

ml_at_awslambda_pydatabln2018 최신버젼 다운로드

최종 버전 다운로드 (.zip)

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

See https://bweigel.github.io/pydata_bln_2018/#/ for slides & [Dockerhub](https://hub.docker.com/r/bweigel/ml_at_awslambda_pydatabln2018_autobuild/) 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](https://www.docker.com/community-edition)
- 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](https://github.com/bweigel/ml_at_awslambda_pydatabln2018#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](https://www.kaggle.com/uciml/sms-spam-collection-dataset/version/1) to the cloud.
The training was carried out like it is described [here](https://www.kaggle.com/mzsrtgzr2/naive-bayes-classifier-spam-ham).
The code is located here: [`tutorial/training/train.py`](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

1. create an AWS account at [https://aws.amazon.com](https://aws.amazon.com) if you have not already done so (see [Instruction](https://aws.amazon.com/premiumsupport/knowledge-center/create-and-activate-aws-account/))
    - you will need a credit card to do this, but you __will not be charged__ if you stay in the [free tier](https://aws.amazon.com/free/)
    - **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](https://docs.aws.amazon.com/IAM/latest/UserGuide/id_users_create.html) (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](https://docs.aws.amazon.com/IAM/latest/UserGuide/id_credentials_access-keys.html#Using_CreateAccessKey) for programmatic access
2. install and setup the [AWS Command Line Interface](https://aws.amazon.com/cli/)
    - `pip install awscli`
    - `aws configure` (see [here](https://docs.aws.amazon.com/cli/latest/userguide/cli-chap-getting-started.html))
3. clone repo `git clone https://github.com/bweigel/ml_at_awslambda_pydatabln2018.git` 
    - and change into the project `cd ml_at_awslambda_pydatabln2018`
4. start docker container: `./start_docker.sh`
5. work along merrily ... or, if you feel adventurous check out the [complete demo code](DEMO.md)

**We will build this:**

![architecture](https://bweigel.github.io/pydata_bln_2018/images/architecture.svg)

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](https://tech.europace.de/slimifying-aws-lambdas/) and the [resources section](https://github.com/bweigel/ml_at_awslambda_pydatabln2018#resources) 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](https://serverless.com/framework/docs/providers/aws/cli-reference/invoke-local/).

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`):
```bash
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.](https://github.com/bweigel/ml_at_awslambda_pydatabln2018#quickstart) 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](resources/apigateway_proxy_event_sample.json).
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

1. `make test*` fails with `FileNotFoundError`:
    ```bash
    $ 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`
2. Error when running `serverless deploy`: 
    ```bash
    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.][1]

[1]: https://blog.mapbox.com/aws-lambda-python-magic-e0f6a407ffc6
[Serverless]: https://serverless.com/framework/

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