scripts/training/training.sh

agent="hydra_pe_temporal"
# train without cache, good for debugging model
cache=null
# run cache_dataset.sh first, good for training
#cache="your cache path"


# use navtrain : train split
config="default_training"
# use navtrain : train split + val split
#config="competition_training"

bs=8
lr=0.0001
epoch=20
dir=${agent}_ckpt
#
##git pull;
#python ${NAVSIM_DEVKIT_ROOT}/navsim/planning/script/run_training.py \
#        --config-name $config \
#        agent=$agent \
#        ~trainer.params.strategy \
#        experiment_name=$dir \
#        cache_path=null\
#        agent.config.ckpt_path=$dir \
#        split=trainval \
#        trainer.params.max_epochs=$epoch \
#        dataloader.params.batch_size=$bs \
#        agent.lr=$lr \
#        scene_filter=navtrain
#agent="hydra_pe"
#bs=8
#lr=0.0001
#cache=null
#resume="epoch19.ckpt"
#config="competition_training"
#sync_bn=False
#epoch=25
#replicas=8
#dir=${agent}_vov_sine_bs${bs}x${replicas}_ckpt
#python \${NAVSIM_DEVKIT_ROOT}/navsim/planning/script/run_training.py \
#            --config-name $config \
#            agent=$agent \
#            +resume_ckpt_path=\${NAVSIM_EXP_ROOT}/$dir/$resume \
#            trainer.params.num_nodes=$replicas \
#            trainer.params.max_epochs=$epoch \
#            +trainer.params.sync_batchnorm=$sync_bn \
#            ~trainer.params.strategy \
#            dataloader.params.batch_size=$bs \
#            experiment_name=$dir \
#            cache_path=$cache \
#            agent.config.ckpt_path=$dir \
#            agent.lr=$lr \
#            split=trainval \
#            scene_filter=navtrain;
#git pull;
python ${NAVSIM_DEVKIT_ROOT}/navsim/planning/script/run_training.py \
          --config-name=tiny_training \
          cache_path=null \
          experiment_name=debug \
          agent.config.ckpt_path=debug \
          agent=hydra_pe_temporal \
          agent.pdm_split=tiny \
          split=tiny \
          scene_filter=navtiny \
          dataloader.params.batch_size=2 \
          dataloader.params.num_workers=0 \
          dataloader.params.pin_memory=false \
          dataloader.params.prefetch_factor=null \
          ~trainer.params.strategy