Comments (11)
Alternatively/additionally, a flag that you can throw on the flambe running that deletes the output folder once the experiment ends.
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At one point running Flambe with the debug
flag did this, but I'm not sure if that's still the case. @iitzco-asapp
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@williamabrwolf have you considered using the -f
flag to keep overriding the flambe-output folder?
from flambe.
@jeremyasapp yeah, I use it (or else the experiment won't run); however, I'm running about 10 flambe experiments (looping over different trainsets, kinda thing), so 10 flambe-output
folders stick around.
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I think the best thing would be an option to delete folder once the experiment terminates?
from flambe.
Yeah makes sense. We'll add that in!
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<3 #flambe
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Option 1: require a safe path and have an extra flag delete_output
Option 2: save path is flambe-output
by default, but specifying None will not save anything
from flambe.
I like save_path: null
.
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Bump :)
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Added a --no-save
option in the 0.5 refactor! :)
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Related Issues (20)
- `Script` should support positional arguments HOT 2
- Cannot add custom attributes to a flambe module HOT 1
- Relative paths no longer work for local resources
- Reporting server not logging requests when tensorboard is installed.
- More useful stage dependency graph
- Check cluster compatibility of the yaml config before starting the cluster
- AvgPooling fails even when input dimensions match expected ones. HOT 1
- Richer debug mode HOT 1
- Separate embedder and encoder in flambe.nn.embedder HOT 3
- Warning when copying a tensor in the base sampler
- Reporting script can die on the orchestrator HOT 1
- Out-of-the-box experiments with standard datasets HOT 1
- Make `extra_validation_metrics` a key-value mapping rather than a list HOT 1
- RNN (sru) is incompatible with torch 1.3
- Make all Flambe base-models torchscript compatible HOT 1
- No in-built functionality for tracking of metrics during training HOT 3
- Allow pickle for saving checkpoints in Experiment HOT 1
- TaggedScalar instead of Schema when using make_component HOT 1
- Early checks for constructor argument incompatibilities HOT 2
- Better data handling for datasets HOT 1
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