2. Introduction

This tutorial will help you get started with FaaS.

  1. Prerequisites

  2. Basic use case: Single function

  • Deploy a function as a service

  • Execute the service manually and view the output

  1. Advance use case: Multiple functions

  • Deploy several functions as a package

  • Deploy a service of the package

  • Set trigger events to the functions

  • Execute the functions and view the output and logs

First, log in to the platform by running the following Python code in the terminal or your IDE:

import dtlpy as dl
if dl.token_expired():
    dl.login()

Your browser will open a login screen, allowing you to enter your credentials or log in with Google. Once the “Login Successful” tab appears, you are allowed to close it.

This tutorial requires a project. You can create a new project, or alternatively use an existing one:

# Create a new project
project = dl.projects.create(project_name='project-sdk-tutorial')
# Use an existing project
project = dl.projects.get(project_name='project-sdk-tutorial')

Let’s create a dataset to work with and upload a sample item to it:

dataset = project.datasets.create(dataset_name='dataset-sdk-tutorial')
item = dataset.items.upload(
    local_path=[
        'https://raw.githubusercontent.com/dataloop-ai/dtlpy-documentation/main/assets/images/hamster.jpg'],
    remote_path='/folder_name')
# Remote path is optional, images will go to the main directory by default