Sequence is designed to model an algorithm. It represents a direct acyclic graph of input, intermediate, and output
data nodes linked together by tasks. A sequence is a set of tasks designed to perform functions.
For instance, in a typical machine learning application, we may have several sequences: a sequence dedicated to preprocessing and preparing data, a sequence for computing a training model, and a sequence dedicated to scoring.
In the example
We have chosen to model only two sequences corresponding to a manufacturer having first to predict the sales forecast, then, based on the sales forecast, plan its production in its plant.
First, the sales sequence (boxed in green in the picture) contains training and predict tasks.
Second, a production sequence (boxed in dark gray in the picture) contains the planning task.
This problem has been modeled in two sequences - one sequence for the forecasting algorithm and one for the production planning algorithm. As a consequence, the two algorithms can have two different workflows. They can run independently, under different schedules. For example, one on a fixed schedule (e.g. every week) and one on demand, interactively triggered by end-users.
Note that the sequences are not necessarily disjoint.
The attributes of a sequence (the set of tasks) are populated based on the sequence configuration provided in the
ScenarioConfig provided when instantiating a new sequence. (Please refer to the
configuration details documentation for more details on configuration).