| # metadata specialised for each experiment | |
| core: | |
| version: ${get_flowmm_version:} | |
| tags: | |
| - ${now:%Y-%m-%d} | |
| logging: | |
| # log frequency | |
| val_check_interval: 5 | |
| wandb: | |
| project: ${model.target_distribution}-${hydra:runtime.choices.data} | |
| entity: null | |
| log_model: True | |
| mode: 'cloud' | |
| experiment_name: flowmm | |
| wandb_watch: | |
| log: all | |
| log_freq: 500 | |
| lr_monitor: | |
| logging_interval: step | |
| log_momentum: False | |
| optim: | |
| # RFM | |
| optimizer: | |
| _target_: torch.optim.AdamW | |
| lr: 0.0003 | |
| lr_diff: False | |
| lr_backbone: 0.00003 | |
| lr_head: 0.0003 | |
| weight_decay: 0.0 | |
| lr_scheduler: | |
| _target_: torch.optim.lr_scheduler.CosineAnnealingLR | |
| T_max: ${data.train_max_epochs} | |
| eta_min: 1e-5 | |
| interval: epoch | |
| ema_decay: 0.999 | |
| train: | |
| # reproducibility | |
| deterministic: warn | |
| random_seed: 42 | |
| # training | |
| pl_trainer: | |
| fast_dev_run: False # Enable this for debug purposes | |
| strategy: ddp | |
| num_nodes: 1 | |
| devices: 1 | |
| accelerator: gpu | |
| precision: 32 | |
| # max_steps: 10000 | |
| max_epochs: ${data.train_max_epochs} | |
| accumulate_grad_batches: 1 | |
| num_sanity_val_steps: 1 | |
| gradient_clip_val: 0.5 | |
| gradient_clip_algorithm: value | |
| profiler: simple | |
| monitor_metric: "val/loss" # "val/nll" | |
| monitor_metric_mode: min | |
| # early_stopping: | |
| # patience: ${data.early_stopping_patience} | |
| # verbose: False | |
| model_checkpoints: | |
| save_top_k: 1 | |
| verbose: False | |
| save_last: False | |
| every_n_epochs_checkpoint: | |
| every_n_epochs: 100 | |
| save_top_k: -1 | |
| verbose: False | |
| save_last: False | |
| val: | |
| compute_nll: false | |
| test: | |
| compute_nll: false | |
| compute_loss: true | |
| integrate: | |
| div_mode: rademacher # "exact" is an alternative | |
| method: euler | |
| num_steps: 1_000 | |
| normalize_loglik: True # this is normalized by dimension | |
| inference_anneal_slope: 0.0 | |
| inference_anneal_offset: 0.0 | |
| defaults: | |
| - _self_ | |
| - data: perov | |
| - model: null_nonsym | |
| - vectorfield: rfm_cspnet | |
| - hydra: trash | |
| base_distribution_from_data: False | |