Flag to remove intermediate tasks' states #289
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wistuba
requested changes
Jun 8, 2023
| @@ -179,6 +180,10 @@ def execute_experiment_job( | |||
| deterministic_trainer: When true the Trainer adopts a deterministic behaviour also on GPU. | |||
| In this function this parameter is set to True by default. | |||
| job_name: Name of the experiment job. | |||
| strategy: String denoting lightning distributed strategy. | |||
| precision: String for which precision to use. | |||
| retain_intermediate_state: Flag to retain intermediate models and buffer states. | |||
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maybe rephrase to "retain states after each update"? otherwise very redundant description and not helpful for anyone who doesn't understand what "intermediate" refers to.
| @@ -57,6 +57,7 @@ | |||
| JOB_NAME = "renate" | |||
| SUPPORTED_TUNING_MODE = ["min", "max"] | |||
| SUPPORTED_TUNING_MODE_TYPE = Literal["min", "max"] | |||
| RETAIN_INTERMEDIATE_STATE = True | |||
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The current mode of operation is True. We shouldn't alter that, right?
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given that we never used the intermediate states, I'm fine changing the default behavior
Coverage reportThe coverage rate went from
Diff Coverage details (click to unfold)src/renate/defaults.py
src/renate/benchmark/experimentation.py
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wistuba
approved these changes
Jun 14, 2023
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Large datasets' buffers can be too big and result in storage issues (noticed while training CLEAR). This flag eliminates backing up intermediate results and overwrites the same folder/files in each update.
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