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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 |