Contact Details
No response
What is needed?
Large runs can often run for unexpectedly long times and users might be more interested in getting results on time rather than hitting a specific number of iterations. One approach is to set a run timeout value and ask the fire growth model transformers to respect it.
This could be a simple check that the time out hasn't been reached at the end of each batch and a new Output Fire Statistics table resample status to the effect of "Timed out". This would benefit from much smaller default batch sizes and would really only be helpful if the model is working through batches pretty regularly.
A more robust option would be to asynchronously track run time and kill the model calls as needed within a batch, or to leverage the fire models built-in timeout implementations if appropriate.
How will this improve the project or tool?
No response
Approvals Process
Contact Details
No response
What is needed?
Large runs can often run for unexpectedly long times and users might be more interested in getting results on time rather than hitting a specific number of iterations. One approach is to set a run timeout value and ask the fire growth model transformers to respect it.
This could be a simple check that the time out hasn't been reached at the end of each batch and a new Output Fire Statistics table resample status to the effect of "Timed out". This would benefit from much smaller default batch sizes and would really only be helpful if the model is working through batches pretty regularly.
A more robust option would be to asynchronously track run time and kill the model calls as needed within a batch, or to leverage the fire models built-in timeout implementations if appropriate.
How will this improve the project or tool?
No response
Approvals Process