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Saving the configurations is a really cool idea that helps me check what I actually did. I noticed the save timing is delayed after task execution in the 1.0.0rc1. I have encountered some scenarios that I think it would be better if the configs are saved earlier.
For example,
Tasks may exit accidentally due to bugs. Knowing detailed configurations would be helpful.
For time-consuming tasks like ML training which may last for several days, it would be helpful to be able to check the config.yaml. This helps me a lot when I have multiple tasks running but I can't remember the real difference. I do diff config.yaml.
Some tasks don't need to be finished. The full training may be designed to run 100 epochs but it also happens it's aborted/killed in the middle.
To reproduce
Default behavior in 1.0.0rc1.
** Minimal Code/Config snippet to reproduce **
NA
** Stack trace/error message **
NA
Expected Behavior
NA
System information
Hydra Version : 1.0.0rc1
Python version : NA
Virtual environment type and version : NA
Operating system : NA
The text was updated successfully, but these errors were encountered:
🐛 Bug
Saving the configurations is a really cool idea that helps me check what I actually did. I noticed the save timing is delayed after task execution in the
1.0.0rc1
. I have encountered some scenarios that I think it would be better if the configs are saved earlier.For example,
diff config.yaml
.To reproduce
Default behavior in
1.0.0rc1
.** Minimal Code/Config snippet to reproduce **
NA
** Stack trace/error message **
NA
Expected Behavior
NA
System information
The text was updated successfully, but these errors were encountered: