Serveless functions#

Important

@functions.serverless supports Python 3.9, 3.10, and 3.11

Important

The serverless functions is free while in beta. Community users get 10 calls per day, PRO users get 100 calls per day.

In many applications, keeping hardware running 24/7 is wasteful (and expensive!) since it’s likely that some of your CPUs and memory are idle most of the time. In such cases, you can save money by using serverless functions: you deploy an application with small resources (say 0.5 CPU and 1GB of RAM) and delegate compute-intensive operations to a serverless function.

Use case: ML model predictions#

Assume you’re developing a Flask application to make predictions from a ML model (assume the model requires 2CPUs and 6GB of RAM), your code might look like this:

from flask import Flask, request, jsonify
from my_project import load_model

app = Flask(__name__)
model = load_model()

@app.route('/predict', methods=['POST'])
def predict():
    data = request.get_json()
    input_data = data['input_data']
    return jsonify({'prediction': model.predict(input_data)})

For this app to work, you’d need to deploy an app with 2 CPUs and 6 GB, which costs $85.4 monthly! Moving the heavy workload into a serverless function can reduce your bill significantly. Let’s revisit the example:

from flask import Flask, request, jsonify
from ploomber_cloud import functions


app = Flask(__name__)


@functions.serverless(requirements=["scikit-learn==1.4.0"])
def predict(input_data):
    from my_project import load_model
    model = load_model()
    return model.predict(input_data)

@app.route('/predict', methods=['POST'])
def predict():
    data = request.get_json()
    input_data = data['input_data']
    # predict runs in an ephemeral container and shuts down after it's done
    prediction = predict(input_data)
    return jsonify({'prediction': prediction})

The code changes are minimal, but this new architecture can save you a lot of money. Since Flask doesn’t consume lots of resources, you can deploy this app with 0.5 CPU and 1GB, then, whenever the /predict endpoint gets a request, the predict function will run in an ephemeral container. Assuming the model takes a few seconds to perform a prediction, you can do thousands of predictions for $1!

Since the resources are ephemeral, this will impact prediction time, as there is some start overhead, but in most cases, this is an acceptable tradeoff.

User guide#

This section will cover the basics of using serverless functions. Before continuing, install the client package:

pip install ploomber-cloud --upgrade

Important

Ensure you’re running the latest version of ploomber-cloud since the API will change over the beta period.

Decorating functions#

To convert your function into a serverless function, add the @functions.serverless decorator, and pass any requirements, they’ll be installed when your function is executed:

from ploomber_cloud import functions

@functions.serverless(requirements=["numpy==1.26.4"])
def random_array(size):
    import numpy as np
    return np.random.rand(size)

Important

The first execution is likely to take more time since dependencies must be installed, subsequent executions will use the cache.

Data types#

If your function returns a data type that requires a third-party package (for example, a numpy array), then, the environment that receives the resuslts must also have the same package and version of such package:

from ploomber_cloud import functions

@functions.serverless(requirements=["numpy==1.26.4"])
def random_array(size):
    import numpy as np
    return np.random.rand(size)

# this will fail if numpy is not installed locally
arr = random_array(100)

To fix it, install numpy locally or return an object that doesn’t require it:

from ploomber_cloud import functions

@functions.serverless(requirements=["numpy==1.26.4"])
def random_array(size):
    import numpy as np
    # no need to install numpy anymore!
    return [float(x) for x in np.random.rand(size)]

arr = random_array(100)

Imports#

All packages that your serverless function uses must be imported inside the function:

from ploomber_cloud import functions
import numpy as np

@functions.serverless(requirements=["numpy==1.26.4"])
def random_array(size):
    # need to add all imports here!
    import numpy as np
    return np.random.rand(size)

arr = random_array(100) + np.random.rand(100)

Task queues#

If your application performs long-running tasks, it’s a good idea to run them in the background. You can run background tasks by calling .background() in decorated functions:

from ploomber_cloud import functions

@functions.serverless(requirements=["numpy==1.26.4"])
def random_array(size):
    # need to add all imports here!
    import numpy as np
    return np.random.rand(size)


# run function in the background, returns immediately with a job_id
job_id = random_array.background(10)

# returns a dictionary with 'status' and 'traceback'
status = functions.get_job_status(job_id)

# if status['status'] == 'SUCCEEDED', you can retrieve the output
result = functions.get_result_from_remote_function(job_id)

# if status['status'] == 'SUBMITTED', the function is running
# if status['status'] == 'FAILED', you can see the error message
print(status["traceback"])

Resources#

Serverless functions are currently limited to a maximum of 6GB and 10 minutes of runtime. The output of each function is also limited to 100MB. If you need to increase this quota, contact us at contact@ploomber.io.