File size: 1,371 Bytes
da2e2ac |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 |
agent="hydra_offset"
bs=8
lr=0.0002
cache=null
resume="epoch09.ckpt"
config="competition_training"
epoch=20
replicas=8
dir=${agent}_vov_fixedpading_bs${bs}x${replicas}_ckpt
ngc batch run \
-in dgx1v.32g.8.norm \
--ace nv-us-west-2 \
--label _wl___computer_vision \
-n ml-model.lkl_train._wl___computer_vision \
--result /result \
-i nvcr.io/nvidian/swaiinf/lzx-navsim \
--workspace q-2TlPKESo62ktTxOc8rYg:/zhenxinl_nuplan \
--port 6007 \
--array-type "MPI" \
--replicas $replicas \
--total-runtime "4D" \
--commandline "
mpirun --allow-run-as-root -np $replicas -npernode 1 bash -c '
git pull;
pip install --upgrade diffusers[torch];
MASTER_PORT=29500 MASTER_ADDR=launcher-svc-\${NGC_JOB_ID} WORLD_SIZE=\${NGC_ARRAY_SIZE} NODE_RANK=\${NGC_ARRAY_INDEX} \
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.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;
'
" |