- add workflow CLI group:
  - add alias `ocrd workflow process` to `ocrd process`
  - add new `ocrd workflow server`, running a web server
    for the given workflow that tries to instantiate
    all Pythonic processors once (to re-use their API
    instead of starting CLI each time)
- add `run_api` analogue to existing `run_cli` and let
  `run_processor` delegate to it in `ocrd.processor.helpers`:
  - `run_processor` only has workspace de/serialization and
    processor instantiation
  - `run_api` has core `process()`, but now also enters and
    leaves the workspace directory, and passes any exceptions
- ocrd.task_sequence: differentiate between `parse_tasks`
  (independent of workspace or fileGrps) and `run_tasks`,
  generalize `run_tasks` to use either `run_cli` or new
  `run_api` (where instances are available, avoiding
  unnecessary METS de/serialisation)
- amend `TaskSequence` by `instance` attribute
  and `instantiate` method:
  - peek into a CLI to check for Pythonic processors
  - try to compile and exec, using monkey-patching
    to disable normal argument passing, execution, and
    exiting; merely importing and fetching the class
    of the processor
  - instantiate processor without workspace or fileGrps
  - avoid unnecessary CLI call to get ocrd-tool.json

…lementations currently expect them in the constructor)

- replace Flask dev server with external uwsgi call
- factor out Flask app code into separate Python module
  which uWSGI can pick up
- make uWSGI run given number of workers via multi-processing
  but not multi-threading, and prefork before loading app
  (to protect GPU and non-thread-safe processors, and because of GIL)
- pass tasks and other settings via CLI options (wrapped in JSON)
- set worker Harakiri (reload after timeout) based on number of
  pages multiplied by given page timeout
- add option for number of processes and page timeout

- add `--server` option to CLI decorator
- implement via new `ocrd.server.ProcessingServer`:
  - based on gunicorn (for preforking directly from
    configured CLI in Python, but instantiating the
    processor after forking to avoid any shared GPU
    context)
  - using multiprocessing.Lock and Manager to lock
    (synchronize) workspaces among workers
  - using signal.alarm for worker timeout mechanics
  - using pre- and post-fork hooks for GPU- vs CPU-
    worker mechanics
  - doing Workspace validation within the request

…cular import

…rver

…rver