The Distributed Annotation ‘n’ Enrichment (DANE) system handles compute task assignment and file storage for the automatic annotation of content.
The use-case for which DANE was designed centres around the issue that the compute resources, and the collection of source media are not on the same device. Due to limited resources or policy choices it might not be possible or desirable to bulk transfer all source media to the compute resources, alternatively the source collection might be continuously growing or require on-demand processing.
In all cases, the computation depends on several stages which can all bottleneck (i.e., introduce delays) to the overall process. By subdividing the work into individual tasks, which can be scheduled separately DANE is capable of more optimally using the available resources. For example, in the case of intensive video analysis of a large archive, it is not feasible to move the entire video archive in a single pass to the compute server. By designing specific tasks for data transfer between servers, analysis, and post-hoc clean-up, DANE can be used to schedule these tasks such that they can be performed in parallel.
In essence the DANE ecosystem consists of three parts, (1) The back-end (DANE-server), (2) The compute workers, (3) A client that submits the tasks.
The format of the communication between these components follows the document specification format which details the source material to process, the task specification format that details the tasks to be performed, and the result specification with information on task results.
Once a document is submitted to the DANE-server tasks can be assigned to, which in turn will be assigned to a worker. As such, a worker relies on a DANE-server instance for its task assignment. To use DANE, one thus needs all three parts, namely an instance of DANE-server, some compute workers, and some client or process to submit tasks. Examples of workers and clients can be found here, whereas DANE-server is documented in its repository.
The configuration of DANE components is done through the DANE.config module, which builds on top of YACS. The DANE.config specifies some default options, with default values, but it is mainly meant to be extended with component specific options. YACS makes it possible to specify configurations in a yaml format, and in code, here is a yaml example with some of the default config options:
DANE: HOST: '0.0.0.0' PORT: 5500 API_URL: 'http://localhost:5500/DANE/' RABBITMQ: HOST: 'localhost' PORT: 5672 EXCHANGE: 'DANE-exchange'
Here, we have specified that the host that the DANE server listens on is 0.0.0.0 with port 5500, additionally, the url at which the API is reachable is given by the API_URL field. Similarly, we specify a number of options for the RabbitMQ queueing system.
To deviate from the default options there are two options, 1) the system-wide DANE config file, and 2) the component specific config file. To best illustrate how these are used we will first demonstrate how to get access to the config. The DANE.config module has an cfg object, which is a YACS config node, which we can get access to by importing it as follows:
from DANE.config import cfg
We now have access to the config, and then we can pass it for example to a worker (as shown in the Examples):
fsw = filesize_worker(cfg)
or we can retrieve specific values from the config.
print('The DANE API is available at', cfg.DANE.API_URL)
During the loading of the config module, the default configuration will be constructed. Once the default config is setup
it will, in order, search for a system-wide config, a component specific ‘base_config’, and a runtime specific config.
By loading these in this order, the most specific options will be used (i.e., system-wide overrides defaults, and component specific
overrides both the system-wide and defaults settings).
DANE.config will look for the system-wide config at
$DANE_HOME/config.yml if available).
For the component specific config DANE.config looks in the directory of file that is importing it for a base_config.yml, thus if the module which uses DANE.config is at $PYTHONLIB/site-packages/mymodule then it will look in that same directory for the base_config.yml.
Lastly, the config module will look for the component specific config (config.yml) in the current working directory. For a worker, simply consist of a directory of code, and which is not installed, the directory structure might thus look like this:
filesize_worker/ filesize_worker.py base_config.yml config.yml
A nice feature of YACS is that it is not necessary to overwrite all default configuration options, we only need to specific the ones we would like to change or add. For the filesize_worker, the base_config.yml might thus look like this:
FILESIZE_WORKER: UNIT: 'KB' PRECISION: 2
Defining new (non-functional) options for the worker, namely the units in which the filesize should be expressed, and the number of decimals we want shown in the output. It also gives a default value for this option. Subsequently, we can define an instance specific config.yml (which shouldn’t be committed to GIT), which contains the following options.
DANE: API_URL: 'http://somehost.ext:5500/DANE/' FILESIZE_WORKER: UNIT: 'MB'
This indicates that the API can be found at a different URL than the default one, and that we want the file size expressed in MB, for all other config options we rely on the previously defined defaults.
However, in some cases it might be necessary that the user always overwrites the base config, for instance when it contains paths that might be environment specific. In this case we can require that a config.yml is found by including the following in the base config:
CONFIG: REQUIRED: True
If no config.yml is found but the base config has indicated its required the config module will raise a DANE.errors.ConfigRequiredError.
Once a DANE worker has completed a task, or task progression has been interrupted due to an error, it should return a JSON object consisting of a state and a message. The message is expected to be an informative, and brief, indication of what went wrong, this message is not intended for automatic processing.
The state returned by a worker is used for automatic processing in DANE, based on this state it is determined whether a task is completed, in progress, requires retrying, or requires manual intervention. The state is one of the numerical HTTP Status codes with the aim of trying to adhere to the semantics of what the status code represents. For example, the state 200 indicates that the task has been successfully handled, whereas 102 indicates it is still in progress. Below we provide an overview of all used state codes and how they are handled by DANE.
- 102: Task has been sent to a queue, it might be being worked on or held in queue.
- 200: Task completed successfully.
- 201: Task is registered, but has not been acted upon.
- 205: Task reset state, typically after manual intervention
- 400: Malformed request, typically the document or task description.
- 403: Access denied to underlying source material.
- 404: Underlying source material not found.
- 412: Task has a dependency which has not completed yet.
- 422: If a task cannot be routed to a queue, this state is returned.
- 500: Error occurred during processing, details should be given in message.
- 502: Worker received invalid or partial input.
- 503: Worker received an error response from a remote service it depends on.
Tasks with state 205, 412, 502, or 503, can be retried automatically. Whereas states 400, 403, 404, 422, and 500 require manual intervention. Once a manual intervention has taken place the task can be resumed.