openjd-adaptor-runtime 0.7.2

A python library for building adaptors that integrate applications with Open Job Description jobs.

Open Job Description - Adaptor Runtime

This package provides a runtime library for creating a command-line Adaptor to assist with integrating an existing application, such as a rendering application, into batch computing systems that run Jobs in a way that is compatible with Open Job Description Sessions. That is, when running a Job on a host consists of a phase to initialize a local compute environment, then running one or more Tasks that each run the same application for the Job on the host, and finally tearing down the initialized compute environment when complete.

Some of the reasons that you should consider creating an Adaptor are if you want to:

1. Optimize the runtime of your Job on a compute host by loading the application once and dynamically running many units of work with that single application instance before shutting down the application;
2. Programmatically respond to signals that the application provides, such as stopping the application early if it prints a message to stdout that indicates that the run may produce undesirable results like watermarks due to missing a floating license, or a bad image render due to missing textures;
3. Dynamically select which version of an application to run based on what is available and modify the command-line options provided to the application based on which version will be run;
4. Emit Open Job Description Stdout Messages, or equivalent, to update the batch computing system on the status or progress of the running unit of work; and/or
5. Integrate Open Job Description Path Mapping information into the application in the format that it is expecting.

Compatibility

This library requires:

1. Python 3.9 or higher; and
2. Linux, MacOS, or Windows operating system.

Versioning

This package's version follows Semantic Versioning 2.0, but is still considered to be in its initial development, thus backwards incompatible versions are denoted by minor version bumps. To help illustrate how versions will increment during this initial development stage, they are described below:

1. The MAJOR version is currently 0, indicating initial development.
2. The MINOR version is currently incremented when backwards incompatible changes are introduced to the public API.
3. The PATCH version is currently incremented when bug fixes or backwards compatible changes are introduced to the public API.

Downloading

You can download this package from:

• PyPI
• GitHub releases

Verifying GitHub Releases

See VERIFYING_PGP_SIGNATURE for more information.

Usage

To create your own Adaptor you create a Python application that uses this package and consists of:

1. A console script entrypoint that passes control flow to an instance of this runtime's EntryPoint class;
2. A JSON file for configuration options of your Adaptor; and
3. An Adaptor class derived from either the CommandAdaptor or Adaptor class.
   • CommandAdaptor is ideal for applications where you do not need to initialize the local compute environment by, say, preloading your application, and simply need to run a single commandline for each Task that is run on the compute host. Please see CommandAdaptorExample in this GitHub repository for a simple example.
   • Adaptor exposes callbacks for every stage of an Adaptor's lifecycle, and is is suited for Adaptors where you want full control. Please see AdaptorExample in this GitHub repository for a simple example.

You can also find many more examples within the AWS Deadline Cloud Organization on GitHub.

Adaptor Lifecycle

All Adaptors undergo a lifecycle consisting of the following stages:

1. start: Occurs once during construction and initialization of the Adaptor. This is the stage where your Adaptor should perform any expensive initialization actions for the local compute environment; such as starting and loading an application in the background for use in later stages of the Adaptor's lifecycle.
   • Runs the on_start() method of Adaptors derived from the Adaptor base class.
2. run: May occur one or more times for a single running Adaptor. This is the stage where your Adaptor is performing the work required of a Task that is being run.
   • Run the on_run() method of Adaptors derived from the Adaptor base class.
   • Run the on_prerun() then get_managed_process() then on_postrun() methods of Adaptors derived from the CommandAdaptor base class.
3. stop: Occurs once as part of shutting down the Adaptor. This stage is the inverse of the start stage and should undo the actions done in that phase; such as stopping any background processes that are still running.
   • Runs the on_stop() method of Adaptors derived from the Adaptor base class.
4. cleanup: A final opportunity to cleanup any remaining processes and data left behind by the Adaptor.
   • Runs the on_cleanup() method of Adaptors derived from the Adaptor base class.

A running Adaptor can also be canceled by sending the Adaptor process a signal (SIGINT/SIGTERM on posix, or CTRL-C/CTRL-BREAK on Windows). This will call the on_cancel() method of your Adaptor, if one is defined. You should ensure that the design of your Adaptor allows this cancelation to interrupt any actions that may be running, and gracefully exit any running background processes.

Running an Adaptor

The EntryPoint provided by this runtime allows for an Adaptor to be run directly through its entire lifecycle in a single command, or to be run as a background daemon that lets you drive the lifecycle of the Adaptor yourself.

The run Subcommand

The run subcommand of an Adaptor will run it through its entire lifecycle (start, then run, then stop, and finally cleanup), and then exit. This is useful for initial development and testing, and for running Adaptors created from the CommandAdaptor base class.

To see this in action install the openjd-adaptor-runtime package into your Python environment, and then within your local clone of this repository:

cd test/openjd
python3 -m integ.AdaptorExample run --init-data '{"name": "MyAdaptor"}'  --run-data '{"hello": "world"}'

The arguments to the run subcommand are:

• --init-data is a JSON-encoded dictionary either inline or in a given file (file://<path-to-file>). This data is decoded and automatically stored in the self.init_data member of the running Adaptor.
• --run-data is, similarly, a JSON-encoded dictionary either inline or in a given file (file://<path-to-file>). This data is passed as the argument to the on_run() method of an Adaptor or the get_managed_process() method of a CommandAdaptor.

The daemon Subcommand

With the daemon subcommand, you must transition the Adaptor through its lifecycle yourself by running the subcommands of the daemon subcommand in order.

1. Start the Adaptor: Initializes the Adaptor as a background daemon subprocess and leaves it running. This runs the on_start() method of your Adaptor-derived Adaptor if the method is available.

   python -m integ.AdaptorExample daemon start --connection-file ./AdaptorExampleConnection.json --init-data '{"name": "MyAdaptor"}'
   

   • --init-data is as described in the run subcommand, above.
   • --connection-file provide a path to a JSON file for the Adaptor to create. This file contains information on how to connect to the daemon subprocess remains running, and you must provide it to all subsequent runs of the Adaptor until you have stopped it.
2. Run the Adaptor: Connects to the daemon subprocess that is running the Adaptor and instructs it to perform its run lifecycle phase. The command remains connected to the daemon subprocess for the entire duration of this run phase, and forwards all data logged by the Adaptor to stdout or stderr. This step can be repeated multiple times.

   python -m integ.AdaptorExample daemon run --connection-file ./AdaptorExampleConnection.json --run-data '{"hello": "world"}'
   

   • --run-data is as described in the run subcommand, above.
   • --connection-file is as described in above.
3. Stop the Adaptor: Connects to the daemon subprocess that is running the Adaptor and instructs it to transition to the stop then cleanup lifecycle phases, and then instructs the daemon subprocess to exit when complete. The command remains connected to the daemon subprocess for the entire duration, and forwards all data logged by the Adaptor to stdout or stderr.

   python -m integ.AdaptorExample daemon stop --connection-file ./AdaptorExampleConnection.json
   

Security

See CONTRIBUTING for more information.

License

This project is licensed under the Apache-2.0 License.