Comments (9)
Thanks @PhilipMay will submit a PR for this now
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Also see here:
Lines 1 to 3 in a12a658
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As a sidenote, OMLflowCallback
extends the transformers
MLflowCallback.
As far as I've been able to read, derivative works of an Apache license can be distributed under different license terms, as long as they are compliant with the Apache license terms. BSD-2 appears to be compliant with the Apache terms
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This would be a good template:
# Copyright (c) <year> by <name>
# This software is distributed under the terms of the BSD 2-Clause License.
# For details see the LICENSE file in the root directory.
If the contribution was not private but in context of your company add
, <company>
after <name>
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I am realy not sure when work is an derivative.
Where is the line between derivative and "just using"?
Is inheritance "just using" or derivative ? I dunno...
@twolffpiggott do you have an idea?
I mean when I save a file I use the underlying OS.
Which might be Linux or Windows, and so on...
When is the integration so tight that "using" changes to "derivative work"?
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Maybe @RoHech has an idea?
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I am realy not sure when work is an derivative.
I am also not sure exactly where to draw this distinction.
I would classify the OMLflowCallback
as derivative because it does not just inherit from the TrainerCallback
, but exposes an interface modelled directly on the MLflowCallback
, extended to work with our class and with some added optionality.
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@twolffpiggott this is interesting: https://en.wikipedia.org/wiki/Apache_License#Licensing_conditions
The Apache License is permissive; unlike copyleft licenses, it does not require a derivative work of the software, or modifications to the original, to be distributed using the same license.
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done
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Related Issues (20)
- Normalize Metric (and other names)
- improve string usage like some lines below HOT 1
- Provide more run status info
- Log info about enqued Optuna trials to MLflow
- This needs to be reset after a trial is done HOT 3
- Investigate race condition when 2 MLflow experiments are started at the same time under the same name
- use set_tags with dict
- Limit str. length of optuna user attributes HOT 1
- Use smarter logging
- Extend optuna wrapper for `trial.report` and `trial.should_prune` HOT 1
- Add logging to "should prune"
- optuna pruner: handle the last fold
- add correct version to scipy -
- Feature Suggestion: Transformers OMLflow Callback HOT 5
- Add copyright to all script headers
- Add copyright to keras.py HOT 1
- Remove redundant code from mltb HOT 1
- Use scikit-learn instead of sklearn.
- LogisticRegression.fit
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