Comments (6)
The idea would be that we allow for list-valued hyperparameters in a config space. Each component can be a Domain
or a fixed value.
Internally, this would be flattened when it comes to searchers and encodings.
This can certainly be done, but we should warn folks that high-D HPO is often failing if no special structure is present and assumed by the surrogate model.
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One technical blocker is that command line arguments for SageMaker training jobs are limited in length. What is possible is to pass parameters to a script as JSON file config.json
. We could maybe provide some tooling to load this file inside a training script.
This is not a simple change.
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https://github.com/awslabs/syne-tune/blob/main/benchmarking/nursery/tuning_with_hyperparameter_file
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Example of how to read config from JSON file. This works for the local backend only at the moment. I can try and support this for the SageMaker backend as well.
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#577 is adressing this issue to some extent. It allows to pass fixed lists to the training script, but all hyperparameters to be optimized still have to be elementary types.
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I am closing this, since #577 is addressing the request partly. Since HPO methods are not ready to jointly tune many hyperparameters, we hesitate to offer list-valued domains, as this may send the wrong signal.
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Related Issues (20)
- Change default output folder for model checkpointing. HOT 3
- RTD Search broken + Question Regarding Status HOT 11
- Wrong Usage of np.random.randint HOT 1
- follow-up: make sure customers can easily run example scripts HOT 1
- Reduce the number of manual steps required for each release
- Flexibility in the number of GPUs when using LocalBackend HOT 2
- Questions on benchmarking HOT 5
- Run launch_lcbench_simulated.py, get ValueError exception HOT 7
- Unreproducible Results given seed HOT 9
- MORE fails with uniform configuration space HOT 1
- Slow Execution of MOBSTER Algorithm for Tabulated Benchmarks HOT 4
- examples/launch_asha_yahpo.py not working HOT 2
- examples/launch_height_sagemaker_remotely.py does not work if distribute_trials_on_sagemaker = True HOT 1
- Unit tests in test_remote_launcher_path are commented out HOT 3
- RemoteLauncher corrupts requirements.txt when not ending with newline HOT 5
- Conditional/Inactive hyperparameters HOT 6
- Troubles with maximising using MORandomScalarizationBayesOpt HOT 4
- Run BOHB/SyncBOHB using lcbench HOT 2
- Open `MultiObjectiveMultiSurrogateSearcher` to additional arguments HOT 2
- Simple example for learning curve plotting HOT 7
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