Serverless Python Dependencies Test

This project was created using the serverless framework using AWS Lambda functions as backend adapted from this guide. To begin run:

npm install -g serverless
serverless create \
  --template aws-python3 \
  --name sls-python-test \
  --path sls-python-test
cd sls-python-test/

In this example, a python virtual-environment is created as venv/ on the root folder and is ignored from version control. it also requires the use of the libraries pyenv, pipenv and virtualenv, so that the project can run without depending on computer-specific resources.

pyenv local # sets the python version to 3.8.X. Install both pyenv and the version if necessary
virtualenv venv -p python # uses the previously set python to create the env
source venv/bin/activate # to activate the local venv. Run it every time you cd into this function
python -V # make sure you are running 3.8.X
deactivate # to deactivate later, or just close the terminal window
pipenv --venv # to check the path to the running venv

Install your dependencies using pipenv:

pipenv install # to install what's on the Pipfile.lock
pipenv install 'numpy==1.16' # if you want to add any new lib with a specific version to your Pipfile
pipenv lock -r # to check the list of requirements that will be installed in the Lambda function

Install the wrapper to allow requirements.txt to be used in the Lambda function:

sls plugin install -n serverless-python-requirements

Notice that these lines have been added to the serverless.yml file so that it uses docker to generate the requirements, making it even more standard and reducing potential config failures:

custom:
  pythonRequirements:
    dockerizePip: true

With the dependencies installed and your AWS profile activated you can deploy your function:

sls deploy # creates or updates the stack
# sample output:
Serverless: Generating requirements.txt from Pipfile...
Serverless: Parsed requirements.txt from Pipfile in ./.serverless/requirements.txt...
Serverless: Using static cache of requirements found at /Users/yourusername/Library/Caches/serverless-python-requirements/d7bbf49e6701fce9a812a827555a05c57c02c06f0145a97a7f5f54b214db67fc_slspyc ...
Serverless: Packaging service...
Serverless: Excluding development dependencies...
Serverless: Injecting required Python packages to package...
Serverless: Uploading CloudFormation file to S3...
Serverless: Uploading artifacts...
Serverless: Uploading service sls-python-test.zip file to S3 (37.41 MB)...
Serverless: Validating template...
Serverless: Updating Stack...
Serverless: Checking Stack update progress...
.........
Serverless: Stack update finished...
Service Information
service: sls-python-test
stage: dev
region: us-east-1
stack: sls-python-test-dev
resources: 6
api keys:
  None
endpoints:
  None
functions:
  hello: sls-python-test-dev-hello
layers:
  None
Serverless: Removing old service artifacts from S3...
Serverless: Run the "serverless" command to setup monitoring, troubleshooting and testing.

To test your deployed function run this command and you should get a response like this:

sls invoke -f hello --log -d '{"foo": "bar"}' # to invoke the function and passing some JSON data
# sample output:
{
    "statusCode": 200,
    "body": "{\"message\": \"Go Serverless v1.0! Your function executed successfully!\", \"input\": {\"foo\": \"bar\"}}"
}
--------------------------------------------------------------------
START RequestId: 8766db24-c5c9-44b1-8da3-751a192a23a7 Version: $LATEST
Your numpy array:
[[ 0  1  2  3  4]
 [ 5  6  7  8  9]
 [10 11 12 13 14]]
END RequestId: 8766db24-c5c9-44b1-8da3-751a192a23a7
REPORT RequestId: 8766db24-c5c9-44b1-8da3-751a192a23a7	Duration: 1.24 ms	Billed Duration: 100 ms	Memory Size: 1024 MB	Max Memory Used: 80 MB	Init Duration: 320.25 ms

To enable AWS X-Ray in your function, activate it on the Lambda function detail page on your AWS Console and if necessary add AWSXRayWriteOnlyAccess to the execution role.

The you can see your chart, metadata and annotations like this:

To remove all resources and don't leave anything lying around in your AWS account run:

sls remove # deletes all the created resources