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+ #! /bin/bash
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+
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+ # Assumes that versions has been done first to build snapbug/qqa:sha-cf0e269
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+
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+ nvidia-docker run -itd --name gpu snapbug/qqa:sha-cf0e269 python main.py model.gpu --paper-ext-feats --num_threads=1 --cuda
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+ nvidia-docker run -itd --name gpu.nocudnn snapbug/qqa:sha-cf0e269 python main.py model.gpu.nocudnn --paper-ext-feats --num_threads=1 --cuda --nocudnn
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+
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+ nvidia-docker wait gpu gpu.nocudnn
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+
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+ nvidia-docker logs gpu > gpu.log
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+ nvidia-docker cp gpu:/castorini/castor/sm_cnn/model.gpu .
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+ nvidia-docker rm gpu
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+
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+ nvidia-docker logs gpu.nocudnn > gpu.nocudnn.log
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+ nvidia-docker cp gpu.nocudnn:/castorini/castor/sm_cnn/model.gpu.nocudnn .
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+ nvidia-docker rm gpu.nocudnn
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+ #! /bin/bash
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+
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+ cd /castorini/data/TrecQA
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+ python parse.py
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+ python overlap_features.py
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+ python build_vocab.py
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+ python build_qrels.py
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+
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+ cd /castorini/castor/idf_baseline
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+ python qa-data-only-idf.py ../../data/TrecQA TrecQA
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+
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+ ../sm_cnn/trec_eval-8.0/trec_eval ../../data/TrecQA/raw-test.qrel TrecQA.raw-test.idfsim
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#! /bin/sh
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+ for SHA in $( git -C ~ /castorini/castor/sm_cnn/ log --pretty=%h __init__.py model.py main.py train.py utils.py )
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+ do
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+ nvidia-docker build -t snapbug/qqa:sha-${SHA} --build-arg sha=${SHA} .
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+ done
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+
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for SHA in $( git -C ~ /castorini/castor/sm_cnn/ log --pretty=%h __init__.py model.py main.py train.py utils.py )
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do
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# Train the model
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while [ ${reps} -lt 200 ]
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do
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seed=$(( RANDOM ))
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+
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if [ ! -f model.cpu.seed.${seed} .log ]
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then
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echo ${seed} runs
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