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add allen_cahn_default.yaml
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hydra:
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run:
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# dynamic output directory according to running time and override name
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dir: outputs_allen_cahn_default/${now:%Y-%m-%d}/${now:%H-%M-%S}/${hydra.job.override_dirname}
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job:
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name: ${mode} # name of logfile
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chdir: false # keep current working direcotry unchaned
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config:
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override_dirname:
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exclude_keys:
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- TRAIN.checkpoint_path
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- TRAIN.pretrained_model_path
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- EVAL.pretrained_model_path
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- INFER.pretrained_model_path
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- mode
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- output_dir
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- log_freq
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callbacks:
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init_callback:
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_target_: ppsci.utils.callbacks.InitCallback
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sweep:
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# output directory for multirun
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dir: ${hydra.run.dir}
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subdir: ./
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# general settings
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mode: train # running mode: train/eval
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seed: 42
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output_dir: ${hydra:run.dir}
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log_freq: 100
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use_tbd: true
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DATA_PATH: ./dataset/allen_cahn.mat
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# model settings
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MODEL:
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input_keys: [t, x]
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output_keys: [u]
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num_layers: 4
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hidden_size: 256
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activation: tanh
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periods:
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x: [2.0, False]
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fourier:
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dim: 256
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scale: 1.0
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random_weight:
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mean: 0.5
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std: 0.1
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# training settings
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TRAIN:
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epochs: 200
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iters_per_epoch: 1000
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save_freq: 10
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eval_during_train: true
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eval_freq: 1
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lr_scheduler:
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epochs: ${TRAIN.epochs}
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iters_per_epoch: ${TRAIN.iters_per_epoch}
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learning_rate: 1.0e-3
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gamma: 0.9
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decay_steps: 2000
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by_epoch: false
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batch_size: 4096
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pretrained_model_path: null
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checkpoint_path: null
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causal:
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n_chunks: 32
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tol: 1.0
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grad_norm:
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update_freq: 1000
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momentum: 0.9
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# evaluation settings
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EVAL:
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pretrained_model_path: null
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eval_with_no_grad: true
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batch_size: 4096
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# inference settings
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INFER:
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pretrained_model_path: https://paddle-org.bj.bcebos.com/paddlescience/models/AllenCahn/allen_cahn_plain_pretrained.pdparams
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export_path: ./inference/allen_cahn
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pdmodel_path: ${INFER.export_path}.pdmodel
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pdpiparams_path: ${INFER.export_path}.pdiparams
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onnx_path: ${INFER.export_path}.onnx
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device: gpu
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engine: native
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precision: fp32
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ir_optim: true
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min_subgraph_size: 5
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gpu_mem: 2000
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gpu_id: 0
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max_batch_size: 1024
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num_cpu_threads: 10
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batch_size: 1024

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