Daemonless campaign-scale simulation orchestration
Eventually, we expect to develop a useful dashboard. Until then, the most
convenient way for users to get an overview of the task status database is by
using pandas (version 2.0 or greater):
import pandas as pd
from exorcist import TaskStatusDB
db = TaskStatusDB.from_filename("path/to/my/database.db")
tasks = pd.read_sql_table("tasks", db.engine)
deps = pd.read_sql_table("dependencies", db.engine)NOTE: pandas is not in the exorcist requirement stack, so you may need to
install it separately.
The tasks table is the main thing you'll be interested in. It has columns for the task ID (a string labeling the task), the task status (see models.py), last modified (which will be not-a-time until the first update), the number of tries so far, and the maximum number tries allowed.
The dependencies table gives details on the dependencies in the DAG. Each entry in this table has a task ID for the "from" side of the edge and the "to" side of the edge, as well as a boolean "blocking" column. The "from" task must be completed before the "to" task can begin. When the "from" task has been completed, the entry should be updated so the "blocking" is False.