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llm2clip/run.sh

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MODEL=EVA02-CLIP-L-14
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MODEL=EVA02-CLIP-L-14-336
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PRETRAINED=eva_clip
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python -m torch.distributed.launch --nproc_per_node=2 \
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python -m torch.distributed.launch --nproc_per_node=8 \
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--use_env training/main.py \
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--enable-deepspeed \
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--grad-checkpointing \
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--name="T_vitl336_Rcc12mR_Rcc3m_4ep" \
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--save-frequency 1 \
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--zeroshot-frequency 1 \
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--report-to="" \
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--report-to="tensorboard, wandb" \
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--wandb-project-name="LLM2CLIP" \
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--wandb-notes="EVA02-CLIP-L-14-336" \
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--train-data-list "/home/aiscuser/LLM2CLIP/llm2clip/data/cc3m/{00..00004}.tar" \
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--train-num-samples-list 2873538 \
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--train-data-list "data/cc3m/cc3m-train-{00..0287}.tar;data/cc12m/cc12m-train-{00..1001}.tar" \
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--train-num-samples-list 2873538 10000225 \
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--eval-data-file=training/eval_datasets.yaml \
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--pretrained=${PRETRAINED} \
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--dataset-resampled \
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--precision "fp16" \
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--warmup 0 \
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--batch-size=16 \
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--batch-size=512 \
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--eval-batch-size=1024 \
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--log-every-n-steps 50 \
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--epochs=20 \
@@ -31,7 +32,7 @@ python -m torch.distributed.launch --nproc_per_node=2 \
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--visual-ld=0.85 \
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--grad-clip-norm=5.0 \
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--smoothing=0. \
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--workers=1 \
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--workers=8 \
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--model=${MODEL} \
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--seed 4096 \
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--gather-with-grad \

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