| hydra: | |
| run: | |
| dir: ${output_dir} | |
| output_subdir: ${output_dir}/code/hydra # Store hydra's config breakdown here for debugging | |
| searchpath: # Only <exp_dir> in these paths are discoverable | |
| - det_map/config/defaults | |
| - det_map/config | |
| - det_map/config/splits | |
| - det_map/config/agent | |
| # - pkg://navsim.planning.script.config.training | |
| defaults: | |
| - default_common | |
| - default_evaluation | |
| - default_train_val_test_log_split | |
| - agent: map_agent | |
| - scene_filter: det_all_scenes | |
| split: mini | |
| dataloader: | |
| params: | |
| batch_size: 32 # number of samples per batch | |
| num_workers: 4 # number of workers for data loading | |
| pin_memory: true # pin memory for faster GPU transfer | |
| prefetch_factor: 1 | |
| trainer: | |
| params: | |
| max_epochs: 20 # maximum number of training epochs | |
| check_val_every_n_epoch: 1 # run validation set every n training epochs | |
| val_check_interval: 1.0 # [%] run validation set every X% of training set | |
| limit_train_batches: 1.0 # how much of training dataset to check (float = fraction, int = num_batches) | |
| limit_val_batches: 1.0 # how much of validation dataset to check (float = fraction, int = num_batches) | |
| accelerator: gpu # distribution method | |
| strategy: ddp | |
| precision: 32 # floating point precision | |
| num_nodes: 1 # Number of nodes used for training | |
| num_sanity_val_steps: 0 # number of validation steps to run before training begins | |
| fast_dev_run: false # runs 1 batch of train/val/test for sanity | |
| accumulate_grad_batches: 1 # accumulates gradients every n batches | |
| # track_grad_norm: -1 # logs the p-norm for inspection | |
| gradient_clip_val: 0.0 # value to clip gradients | |
| gradient_clip_algorithm: norm # [value, norm] method to clip gradients |