Metadata-Version: 2.1
Name: cloudless
Version: 0.0.1
Summary: The clouldless infrastructure project.
Home-page: https://github.com/sverch/cloudless
Author: Shaun Verch
Author-email: shaun@getcloudless.com
License: Apache 2.0
Description: 
        # Cloudless
        
        Cloudless makes it easier to interact with cloud resources by doing most of the
        work that a human doesn't need to care about for you, and by being transparent
        about what it's doing.
        
        ## Installation
        
        This project depends on [Python
        3.6.0](https://www.python.org/downloads/release/python-360/) or greater.  It can
        be installed as a normal python package using pip, but an environment manager
        such as [pipenv](https://pipenv.readthedocs.io/en/latest/) is recommended.
        
        To install locally, make a dedicated directory where you want to test this out
        and run:
        
        ```shell
        cd cloudless_experimentation
        pipenv install cloudless
        ```
        
        Having a dedicated directory will allow pipenv to scope the dependencies to that
        project directory and prevent this project from installing stuff on your main
        system.
        
        ## Client Setup
        
        First, you must create a client object to connect to the cloud platform that
        you'll be working with.  The client handles authentication with the cloud
        provider, so you must pass it the name of the provider and the authentication
        credentials.
        
        If you are trying this project for the first time, it's recommended that you use
        the "mock-aws" client.
        
        ### Google Compute Engine Client
        
        To use the Google Compute Engine client, you must create a service account and
        download the credentials locally.  Because this provider is implemented using
        [Apache Libcloud](https://libcloud.apache.org/), you can refer to the [Google
        Compute Engine Driver
        Setup](https://libcloud.readthedocs.io/en/latest/compute/drivers/gce.html#getting-driver-with-service-account-authentication)
        documentation in that project for more details.
        
        When you have the credentials, you can do something like this, preferably in a
        dotfile you don't commit to version control.  Note the credentials file is in
        JSON format:
        
        ```shell
        export BUTTER_GCE_USER_ID="sverch-cloudless@cloudless-000000.iam.gserviceaccount.com"
        export BUTTER_GCE_CREDENTIALS_PATH="/home/sverch/.gce/credentials.json"
        export BUTTER_GCE_PROJECT_NAME="cloudless-000000"
        ```
        
        Then, you can run these commands in a python shell to create a GCE client:
        
        ```python
        import cloudless
        import os
        client = cloudless.Client("gce", credentials={
            "user_id": os.environ['BUTTER_GCE_USER_ID'],
            "key": os.environ['BUTTER_GCE_CREDENTIALS_PATH'],
            "project": os.environ['BUTTER_GCE_PROJECT_NAME']})
        ```
        
        ### Amazon Web Services Client
        
        Currently no credentials can be passed in as arguments for the AWS provider
        (they are ignored).  However this provider is implemented with
        [Boto](http://docs.pythonboto.org/en/latest/), which looks in many other places
        for the credentials, so you can configure them in other ways.  See the [boto3
        credential setup
        documentation](https://boto3.readthedocs.io/en/latest/guide/configuration.html)
        for more details.
        
        Once you have set up your credentials, you can run the following to create an
        AWS client:
        
        ```python
        import cloudless
        client = cloudless.Client("aws", credentials={})
        ```
        
        ### Mock Amazon Web Services Client
        
        The Mock AWS client is for demonstration and testing.  Since it is all running
        locally, you don't need any credentials.  Simply run:
        
        ```python
        import cloudless
        client = cloudless.Client("mock-aws", credentials={})
        ```
        
        ## Architecture
        
        There are only three objects in Cloudless: A Network, a Service, and a Path.  This
        is an example that shows a Network `dev`, a `public_load_balancer` Service, an
        `internal_service` Service, a Path from the internet to `public_load_balancer`
        on port 443, and a Path from `public_load_balancer` to `internal_service` on
        port 80.  See the [visualization](#visualization) section for how to generate
        this graph.
        
        ![Cloudless Simple Service Example](docs/images/example.svg)
        
        ### Network
        
        A Network is the top level container for everything else.  To create a new
        network, run:
        
        ```python
        dev_network = client.network.create("dev")
        ```
        
        This will return the "Network" object that describes the network that was
        created.  You can retrieve an existing network or list all existing networks by
        running:
        
        ```python
        dev_network = client.network.get("dev")
        all_networks = client.network.list()
        ```
        
        Finally, to destroy a network:
        
        ```python
        client.network.destroy(dev_network)
        ```
        
        Create should use sane defaults, but if you need to do something special see
        [docs/network-configuration.md](docs/network-configuration.md).
        
        In [ipython](https://ipython.org/), you can run `<object>?` to [get help on any
        object](https://ipython.readthedocs.io/en/stable/interactive/python-ipython-diff.html#accessing-help),
        for example `client.network.create?`.
        
        ### Service
        
        A Service a logical group of instances and whatever resources are needed to
        support them (subnetworks, firewalls, etc.).
        
        To create a Service, you must first define a configuration file called a
        "blueprint" that specifies how the service should be configured.  This is an
        example of what a Service blueprint might look like:
        
        ```yaml
        ---
        network:
          subnetwork_max_instance_count: 768
        
        placement:
          availability_zones: 3
        
        instance:
          public_ip: True
          memory: 4GB
          cpus: 1
          gpu: false
          disks:
            - size: 8GB
              type: standard
              device_name: /dev/sda1
        
        image:
          name: "ubuntu/images/hvm-ssd/ubuntu-xenial-16.04-amd64-server-*"
        
        initialization:
          - path: "haproxy-cloud-config.yml"
            vars:
              PrivateIps:
                required: true
        ```
        
        The "network" section tells Cloudless to create subnetworks for this service big
        enough for 768 instances.
        
        The "placement" section tells Cloudless to ensure instances in this service are
        provisioned across three availaibility zones (which most cloud providers
        guarantee are meaningfully isolated from each other for resilience).
        
        The "instance" section describes the resource reqirements of each instance.
        Cloudless will automatically choose a instance type that meets these requirements.
        
        The "image" section represents the name of the image you want your instances to
        have.  In this case, we are using an image name only found in AWS by default, so
        this example will only work there.  See `example-blueprints/gce-apache` for a
        GCE example blueprint.
        
        The "initialization" section describes startup scripts that you want to run when
        the instance boots.  You may also pass in variables, which will get passed to
        the given file as [jinja2](http://jinja.pocoo.org/) template arguments.  This is
        a good place to specify environment specific configuration, so your base image
        can stay the same across environments.
        
        Once you have the blueprint, the example below shows how you could use it.
        These examples create a group of private instances and then create some HAProxy
        instances in front of those instances to balance load.  Note that many commands
        take `dev_network` as the first argument.  That's the same network object
        returned by the network commands shown above.
        
        ```python
        internal_service = client.service.create(dev_network, "private",
                                                 blueprint="example-blueprints/aws-nginx/blueprint.yml")
        private_ips = [instance.private_ip for instance in client.service.get_instances(internal_service)]
        load_balancer_service = client.service.create(dev_network, "public",
                                                      blueprint="example-blueprints/aws-haproxy/blueprint.yml",
                                                      template_vars={"PrivateIps": private_ips})
        internal_service = client.service.get(dev_network, "public")
        load_balancer_service client.service.get(dev_network, "private")
        client.service.list()
        client.service.destroy(internal_service)
        client.service.destroy(load_balancer_service)
        ```
        
        There is another example blueprint that works with GCE if you created the GCE
        client above:
        
        ```python
        client.instances.create(dev_nework, "public", blueprint="example-blueprints/gce-apache/blueprint.yml")
        ```
        
        ### Path
        
        The Path is how you tell Cloudless that two services should be able to communicate.
        No blueprint is needed for this, but you need to have the service objects you
        created earlier.  This example adds a path from the load balancer to the
        internal service on port 80 and makes the load balancer internet accessible on
        port 443:
        
        ```python
        from cloudless.types.networking import CidrBlock
        internet = CidrBlock("0.0.0.0/0")
        client.paths.add(load_balancer_service, internal_service, 80)
        client.paths.add(internet, load_balancer_service, 443)
        ```
        
        You can check whether things have access to other things or print out all paths
        with the following functions:
        
        ```python
        client.paths.has_access(load_balancer_service, internal_service, 80)
        client.paths.internet_accessible(load_balancer_service, 443)
        client.paths.internet_accessible(internal_service, 443)
        client.paths.list()
        print(client.graph())
        ```
        
        ## Visualization
        
        Get a summary in the form of a graphviz compatible dot file by running:
        
        ```python
        client.graph()
        ```
        
        To generate the vizualizations, run:
        
        ```shell
        cd ui && env PROVIDER=<provider> bash graph.sh
        ```
        
        And open `ui/graph.html` in a browser.  Note this won't work for the `mock-aws`
        provider since it will be running in a different process.
        
        ## Blueprint Tester
        
        This project also provides a framework to help test that blueprint files work as
        expected.
        
        Example (cloudless must be installed):
        
        ```shell
        cloudless-test --provider aws --blueprint_dir example-blueprints/haproxy run
        ```
        
        Run `cloudless-test` with no arguments for usage.
        
        This runner tries to import `blueprint_fixture.BlueprintTest` from the root of
        your blueprint directory.  This must be a class that inherits from
        `cloudless.testutils.fixture.BlueprintTestInterface` and implements all the
        required methods.  See the documentation on that class for usage details.
        
        The runner expects the blueprint file that you are testing to be name
        `blueprint.yml` in the blueprint directory.
        
        See [example-blueprints](example-blueprints) for all examples.
        
        ## Testing
        
        To run the local tests run:
        
        ```shell
        pipenv install --dev
        tox
        ```
        
        To run tests against GCE and AWS, run:
        
        ```shell
        tox -e gce
        tox -e aws
        ```
        
        For GCE, you must set `BUTTER_GCE_USER_ID`, `BUTTER_GCE_CREDENTIALS_PATH`, and
        `BUTTER_GCE_PROJECT_NAME` as described above.
        
Platform: UNKNOWN
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: Implementation :: CPython
Classifier: Programming Language :: Python :: Implementation :: PyPy
Requires-Python: >=3.6.0
Description-Content-Type: text/markdown
Provides-Extra: testing
