Metadata-Version: 2.1
Name: kuber
Version: 1.11.1
Summary: High-level Kubernetes resource configuration and management library.
Home-page: https://github.com/sernst/kuber
Author: Scott Ernst
Author-email: swernst@gmail.com
License: MIT
Description: [![PyPI version](https://img.shields.io/pypi/v/kuber.svg)](https://pypi.python.org/pypi/kuber)
        [![Documentation Status](https://readthedocs.org/projects/kuber/badge/?version=latest)](https://kuber.readthedocs.io/en/latest/?badge=latest)
        [![build status](https://gitlab.com/swernst/kuber/badges/master/build.svg)](https://gitlab.com/swernst/kuber/commits/master)
        [![coverage report](https://gitlab.com/swernst/kuber/badges/master/coverage.svg)](https://gitlab.com/swernst/kuber/commits/master)
        
        
        # Kuber
        
        kuber is Python library for the management of Kubernetes resources. It's
        ideal for for collectively managing groups of resources throughout their
        lifecycle. Resource definitions can be created and managed entirely in Python
        code (the pure-Python approach), but kuber is most effective when used in a
        hybrid fashion that combines configuration files and Python code.
        kuber also integrates and maintains compatibility with the lower-level official
        [Kubernetes Python client](https://github.com/kubernetes-client/python),
        while abstracting basic CRUD operations into higher level constructs
        more inline with the behaviors of tools like *kubectl* and *helm*.
        
        ## Key Functionality
        
        Here are some key things that kuber does well:
        
        - A flexible workflow for managing Kubernetes resource configuration in Python
          code.
        - The ability to load resources directly from YAML or JSON configuration files,
          modify them in code and then use them or save them back to YAML/JSON files.
        - Resource bundling for managing groups of resource configurations collectively.
        - CRUD operations exposed directly on the resource objects to reduce the
          overhead in managing low-level clients.
        - Convenience functions that simplify common operations, e.g. managing
          containers within pods from the root resource.
        - Very thorough type-hinting and object structure to support creating accurate
          resource configurations and catch errors before runtime.
        - All resources and sub-resources support used in `with` blocks as context
          managers to simplify making multiple changes to a sub-resource.
        - Simultaneous support for multiple Kubernetes API versions. Manage multiple
          Kubernetes API versions (e.g. while promoting new versions from development
          to production) without having to pin and switch Python environments.
        
        ## Installation
        
        kuber available for installation with [pip](https://pypi.org/project/pip/):
        
        ```bash
        $ pip install kuber
        ```
         
        ## Quickstart
        
        kuber can be used to manage individual resources or a group of resources
        collectively. kuber is also very flexible about how resources are created - 
        either directly from Python or by loading and parsing YAML/JSON configuration
        files. The first example shows the multi-resource management path:
        
        ```python
        import kuber
        from kuber.latest import apps_v1
        
        # Create a bundle and load all resource definitions from the
        # `app_configs` directory as well as the `app-secret.yaml`
        # configuration file from the local `secrets` directory.
        resource_bundle = (
            kuber.create_bundle()
            .add_directory('app_configs')
            .add_file('secrets/app-secret.yaml')
        )
        
        # Modify the metadata labels on all resources in the bundle.
        for resource in resource_bundle.resources:
            resource.metadata.labels.update(environment='production')
        
        # Update the replica count of the loaded deployment named
        # "my-app" to the desired initial count.
        d: apps_v1.Deployment = resource_bundle.get(
            name='my-app',
            kind='Deployment'
        )
        d.spec.replicas = 20
        
        # Load the current `kubeconfig` cluster configuration into
        # kuber for interaction with the cluster.
        kuber.load_access_config()
        
        # Turn this bundle script into a file that can be called from
        # the command line to carry out CRUD operations on all the
        # resources contained within it collectively. For example,
        # to create the resources in this bundle, call this script
        # file with a create argument.
        resource_bundle.cli()
        ```
        
        Or managing resources individually:
        
        ```python
        from kuber.latest import batch_v1
        
        job = batch_v1.Job()
        
        # Populate metadata using context manager syntax for brevity.
        with job.metadata as md:
            md.name = 'my-job'
            md.namespace = 'jobs'
            md.labels.update(
                component='backend-tasks',
                environment='production'
            )
        
        # Add a container to the job spec.
        job.spec.append_container(
            name='main',
            image='my-registry.com/projects/my-job:1.0.1',
            image_pull_policy='Always',
            env=[batch_v1.EnvVar('ENVIRONMENT', 'production')]
        )
        
        # Print the resulting YAML configuration for display. This
        # could also be saved somewhere to use later as the
        # configuration file to deploy to the cluster in cases
        # like a multi-stage CI pipeline.
        print(job.to_yaml())
        ```
        
        Check out the [kuber documentation](https://kuber.readthedocs.io/en/latest/)
        for more details and examples.
        
Keywords: kubernetes,containers,kubectl,k8s
Platform: Linux
Platform: Mac OS X
Platform: Windows
Classifier: Development Status :: 4 - Beta
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: System Administrators
Classifier: Topic :: Software Development :: Libraries
Classifier: Topic :: System :: Systems Administration
Description-Content-Type: text/markdown
