Getting Started

Welcome to the initial setup guide for Piceli, designed to facilitate a smooth introduction to managing your Kubernetes and cloud infrastructure. This guide will walk you through the installation process, different ways to define Kubernetes objects, and the basics of deploying with Piceli.

Installation

Start by installing Piceli using pip with the following command:

pip install piceli

This command installs Piceli along with all necessary dependencies, preparing you to orchestrate your infrastructure seamlessly.

Refer to the Installation section within the Piceli Documentation for advanced installation options and detailed guidance.

Quick Start

Piceli supports defining Kubernetes objects using Piceli templates, the official Kubernetes Python client, YAML, JSON files, or a combination of these methods. Here’s how to get started with each approach.

1. Using Piceli Templates:

   Define a Kubernetes job in a Python file (e.g., define_job.py) using Piceli’s templates:

   from piceli.k8s import templates
   
   job = templates.Job(
       name="job0",
       containers=[
           templates.Container(
               name="c0", command=["python", "--version"], image="python:latest",
           )
       ],
   )
   

2. With the Kubernetes Python Library:

   Alternatively, utilize the Kubernetes client in a Python file (e.g., k8s_job.py):

   from kubernetes import client
   
   job = client.V1Job(
       api_version="batch/v1",
       kind="Job",
       metadata=client.V1ObjectMeta(name="job0"),
       spec=client.V1JobSpec(
           template=client.V1PodTemplateSpec(
               metadata=client.V1ObjectMeta(name="job0"),
               spec=client.V1PodSpec(
                   containers=[client.V1Container(
                       name="c0",
                       image="python:latest",
                       command=["python", "--version"],
                   )],
               ),
           ),
       ),
   )
   

3. Defining Jobs with YAML or JSON Files:

   Kubernetes objects can be defined in YAML or JSON files (e.g., job.yaml or job.json):

   apiVersion: batch/v1
   kind: Job
   metadata:
     name: job0
   spec:
     template:
       spec:
         containers:
           - name: c0
             image: python:latest
             command: ["python", "--version"]
         restartPolicy: Never
   

Configuration and Using Piceli CLI

Piceli accommodates multiple sources for Kubernetes object definitions simultaneously, including YAML, JSON, Piceli templates, and Kubernetes Python client objects. Set PICELI__FOLDER_PATH for YAML/JSON directories, and use PICELI__MODULE_NAME or PICELI__MODULE_PATH for Python modules:

PICELI__FOLDER_PATH=/path/to/your/definitions PICELI__MODULE_NAME=your_module_name PICELI__MODULE_PATH=/path/to/your/module python -m piceli model list

Configuration via pyproject.toml

If you’re using Piceli as part of another Python project, you can define your configuration in the pyproject.toml file under the [tool.piceli] table. This is particularly useful for maintaining a clean and centralized project setup.

   [tool.piceli]
   module_name = "path.to.model"
   module_path = "path/to/model.py"
   folder_path = "path/to/yamls/"
   sub_elements = true # include sub folders/modules

Here are some CLI commands for common Piceli operations:

• Deploying a Namespace:

  PICELI__FOLDER_PATH=/path/to/your/definitions PICELI__NAMESPACE=test-run python -m piceli deploy run
  

• Showing Detailed Deployment Plan:

  PICELI__FOLDER_PATH=/path/to/your/definitions PICELI__NAMESPACE=test-run python -m piceli deploy detail
  

• Hide No Action Required Details in Deployment Plan:

  PICELI__FOLDER_PATH=/path/to/your/definitions PICELI__NAMESPACE=test-run python -m piceli deploy detail -hna
  

This setup guide introduces the foundational steps to get started with Piceli. For comprehensive tutorials and advanced configurations, refer to the detailed documentation.