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Would like to see an option for the data load function to automatically run if task not marked complete. I find myself writing if else statements like the below to get the desired effect:
if TaskExample().complete: df = TaskExample().output()['df'].load() else: d6tflow.run(TaskExample()) df = TaskExample().output()['df'].load()
The text was updated successfully, but these errors were encountered:
How about asking the user if you want to run the task if it's not complete on load. Not sure if it should auto run.
For now suggest to always run d6tflow.run(TaskExample()) before loading any data. That will guarantee tasks are complete and no need for if statements.
There a multiple issues that make this non-trivial to implement. 1) a target doesn't know which tasks it belongs to and 2) this could work better in Task().outputLoad(auto_run=True) but would lead to circular imports. TBD. For now suggest to always just run d6tflow.run(TaskExample()) if all tasks are complete this is fast.
Would like to see an option for the data load function to automatically run if task not marked complete. I find myself writing if else statements like the below to get the desired effect:
if TaskExample().complete: df = TaskExample().output()['df'].load() else: d6tflow.run(TaskExample()) df = TaskExample().output()['df'].load()
The text was updated successfully, but these errors were encountered: