Flamenco Worker

This is the Flamenco Worker implemented in Python 3.

Author: Sybren A. Stüvel sybren@blender.studio

Installation

• Make sure you have Flamenco Manager up and running.
• Install Blender and make sure the blender binary is on $PATH.
• Install FFmpeg and make sure the ffmpeg and ffprobe binaries are on $PATH. If you only intend to run Flamenco Worker and not run any unit tests, you can skip ffprobe.
• Install Flamenco Worker in one of two ways:
  • If you have a distributable zip file (see Packaging for distribution) unzip it, cd into it, then run ./flamenco-worker (or flamenco-worker.exe on Windows).
  • If you have a copy of the source files, run pipenv install then run flamenco-worker. This requires Python 3.7 or newer.

Upgrading

Upgrading from a previous version of Flamenco Worker is simple:

• Unpack the package you downloaded from flamenco.io.
• Copy the flamenco-worker.cfg and flamenco-worker.db files from your previous installation.
• Start Flamenco Worker.

Configuration

Configuration is read from three locations:

• A hard-coded default in the Python source code.
• flamenco-worker.cfg in the current working directory, i.e. the directory that your shell is in when you invoke the flamenco-worker command.
• $HOME/.flamenco-worker.cfg; this file is optional.

The configuration files should be in INI format, as specified by the configparser documentation

Configuration contents:

All configuration keys should be placed in the [flamenco-worker] section of the config files. At least take a look at:

• manager_url: Flamenco Manager URL. Leave blank to auto-discover Flamenco Manager on your network using UPnP/SSDP.
• task_types: Space-separated list of task types this worker may execute.
• task_update_queue_db: filename of the SQLite3 database holding the queue of task updates to be sent to the Master.

These configuration keys are also required, but are created automatically in $HOME/.flamenco-worker.cfg when they don't exist yet:

• worker_id: ID of the worker, handed out by the Manager upon registration (see Registration below) and used for authentication with the Manager.
• worker_secret: Secret key of the worker, generated by the Worker and given to the Manager upon registration and authentication.

TODO

• Certain settings are currently only settable by editing constants in the Python source code. It might be nice to read them from the config file too, at some point.

Invocation

Install using pip3 install -e . for development, or python3 setup.py install for production. This creates a command flamenco-worker, which can be run with --help to obtain a list of possible CLI arguments.

Registration

If the configuration file does not contain both a worker_id and worker_secret, at startup the worker will attempt to register itself at the Master. Once registered via a POST to the manager's /register-worker endpoint, the worker_id and worker_secret will be written to $HOME/.flamenco-worker.cfg

Task fetch & execution

1. A task is obtained by the FlamencoWorker from the manager via a POST to its /task endpoint. If this fails (for example due to a connection error), the worker will retry every few seconds until a task fetch is succesful.
2. The task is given to a TaskRunner object.
3. The TaskRunner object iterates over the commands and executes them.
4. At any time, the FlamencoWorker can be called upon to register activities and log lines, and forward them to the Manager. These updates are queued in a SQLite3 database, such that task execution isn't interrupted when the Manager cannot be reached.
5. A separate coroutine of TaskUpdateQueue fetches updates from the queue, and forwards them to the Master, using a POST to its /tasks/{task-id}/update endpoint. TODO: the response to this endpoint may indicate a request to abort the currently running task. This should be implemented.

Shutdown

Pressing [CTRL]+[C] will cause a clean shutdown of the worker. If there is a task currently running, it will be aborted without changing its status. Any pending task updates are sent to the Manager, and then the Manager's /sign-off URL is POSTed to, to indicate a clean shutdown of the worker. Any active task that is still assigned to the worker is given status "claimed-by-manager" so that it can be re-activated by another worker.

Systemd integration

To run Flamenco Worker as a systemd-managed service, copy flamenco-worker.service to /etc/systemd/system/flamenco-worker.service, then run systemctl daemon-reload.

After installation of this service, systemctl {start,stop,status,restart} flamenco-worker commands can be used to manage it. To ensure that the Flamenco Worker starts at system boot, use systemctl enable flamenco-worker.

Signals

Flamenco Worker responds to the following POSIX signals:

• SIGINT, SIGTERM: performs a clean shutdown, as described in the Shutdown section above.
• SIGUSR1: immediately go to sleep state.
• SIGUSR2: immediately go to awake state.

Packaging for distribution

First run pipenv install --dev to fetch developer dependencies. On Windows, download the Microsoft Visual C++ 2010 Redistributable Package. On Ubuntu/Debian, make sure you have the 'binutils' package installed.

Run mkdistfile.py to create a distributable archive (.zip on Windows, .tar.gz on Linux and macOS) containing a runnable Flamenco Worker. This build doesn't require installing Python or any dependencies, and can be directly run on a target machine of the same OS.

NOTE: pyinstaller must be run on each supported platform, to create files for that platform. It cannot cross-build.