51 lines
1.4 KiB
YAML
51 lines
1.4 KiB
YAML
# @package _global_
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# specify here default training configuration
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defaults:
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- _self_
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- trainer: default
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- optimizer: adamw
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- scheduler: null
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- task: sequence-model
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- model: null
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- datamodule: null
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- callbacks: default # set this to null if you don't want to use callbacks
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- metrics: null
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- logger: null # set logger here or use command line (e.g. `python run.py logger=wandb`)
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- mode: default
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- experiment: null
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- hparams_search: null
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# enable color logging
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- override hydra/hydra_logging: colorlog
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- override hydra/job_logging: colorlog
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# path to original working directory
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# hydra hijacks working directory by changing it to the current log directory,
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# so it's useful to have this path as a special variable
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# https://hydra.cc/docs/next/tutorials/basic/running_your_app/working_directory
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work_dir: ${hydra:runtime.cwd}
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# path to folder with data
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data_dir: ${work_dir}/data/
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# pretty print config at the start of the run using Rich library
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print_config: True
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# disable python warnings if they annoy you
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ignore_warnings: True
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# check performance on test set, using the best model achieved during training
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# lightning chooses best model based on metric specified in checkpoint callback
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test_after_training: True
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resume: False
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# seed for random number generators in pytorch, numpy and python.random
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seed: null
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# name of the run, accessed by loggers
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name: null
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