nablaDFT

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# Global variables
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name: DimeNet++
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dataset_name: dataset_train_tiny
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max_steps: 1000000
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job_type: train
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pretrained: null # name of pretrained split or 'null'
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ckpt_path: null # path to checkpoint for training resume or test run
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# Datamodule parameters
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root: ./datasets/nablaDFT/${.job_type}
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batch_size: 32
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num_workers: 8
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# Devices
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devices: [0]
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# Trainer parameters
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gradient_clip_algorithm: null
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gradient_clip_val: null
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# configs
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defaults:
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  - _self_
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  - datamodule: nablaDFT_pyg.yaml  # dataset config
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  - model: dimenetplusplus.yaml  # model config
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  - callbacks: default.yaml  # pl callbacks config
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  - loggers: wandb.yaml  # pl loggers config
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  - trainer: train.yaml  # trainer config
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# need this to set working dir as current dir
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hydra:
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  output_subdir: null
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  run:
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    dir: .
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original_work_dir: ${hydra:runtime.cwd}
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seed: 23
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