| # |
| # Licensed to the Apache Software Foundation (ASF) under one |
| # or more contributor license agreements. See the NOTICE file |
| # distributed with this work for additional information |
| # regarding copyright ownership. The ASF licenses this file |
| # to you under the Apache License, Version 2.0 (the |
| # "License"); you may not use this file except in compliance |
| # with the License. You may obtain a copy of the License at |
| # |
| # http://www.apache.org/licenses/LICENSE-2.0 |
| # |
| # Unless required by applicable law or agreed to in writing, software |
| # distributed under the License is distributed on an "AS IS" BASIS, |
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. |
| # See the License for the specific language governing permissions and |
| # limitations under the License. |
| # |
| |
| export PYTHONPATH=$PYTHONPATH:./internal/ml/model_selection |
| conda activate trails |
| |
| |
| |
| # default setting. |
| python ./internal/ml/model_selection/exps/nas_bench_tabular/0.train_one_model.py \ |
| --log_name=baseline_train_based \ |
| --search_space=mlp_sp \ |
| --base_dir=../exp_data/ \ |
| --num_labels=2 \ |
| --device=cuda:0 \ |
| --batch_size=1024 \ |
| --lr=0.001 \ |
| --epoch=5 \ |
| --iter_per_epoch=2000 \ |
| --dataset=criteo \ |
| --nfeat=2100000 \ |
| --nfield=39 \ |
| --nemb=10 \ |
| --workers=0 \ |
| --result_dir=./internal/ml/model_selection/exp_result/ \ |
| --log_folder=log_criteo_train_tune >criteo_5.log & |
| |
| |
| python ./internal/ml/model_selection/exps/nas_bench_tabular/0.train_one_model.py \ |
| --log_name=baseline_train_based \ |
| --search_space=mlp_sp \ |
| --base_dir=../exp_data/ \ |
| --num_labels=2 \ |
| --device=cuda:0 \ |
| --batch_size=1024 \ |
| --lr=0.001 \ |
| --epoch=10 \ |
| --iter_per_epoch=2000 \ |
| --dataset=criteo \ |
| --nfeat=2100000 \ |
| --nfield=39 \ |
| --nemb=10 \ |
| --workers=0 \ |
| --result_dir=./internal/ml/model_selection/exp_result/ \ |
| --log_folder=log_criteo_train_tune >criteo_10.log & |
| |
| |
| |
| python ./internal/ml/model_selection/exps/nas_bench_tabular/0.train_one_model.py \ |
| --log_name=baseline_train_based \ |
| --search_space=mlp_sp \ |
| --base_dir=../exp_data/ \ |
| --num_labels=2 \ |
| --device=cuda:1 \ |
| --batch_size=1024 \ |
| --lr=0.001 \ |
| --epoch=20 \ |
| --iter_per_epoch=2000 \ |
| --dataset=criteo \ |
| --nfeat=2100000 \ |
| --nfield=39 \ |
| --nemb=10 \ |
| --workers=0 \ |
| --result_dir=./internal/ml/model_selection/exp_result/ \ |
| --log_folder=log_criteo_train_tune >criteo_20.log & |
| |
| |
| |
| |
| python ./internal/ml/model_selection/exps/nas_bench_tabular/0.train_one_model.py \ |
| --log_name=baseline_train_based \ |
| --search_space=mlp_sp \ |
| --base_dir=../exp_data/ \ |
| --num_labels=2 \ |
| --device=cuda:2 \ |
| --batch_size=1024 \ |
| --lr=0.001 \ |
| --epoch=40 \ |
| --iter_per_epoch=2000 \ |
| --dataset=criteo \ |
| --nfeat=2100000 \ |
| --nfield=39 \ |
| --nemb=10 \ |
| --workers=0 \ |
| --result_dir=./internal/ml/model_selection/exp_result/ \ |
| --log_folder=log_criteo_train_tune >criteo_40.log & |
| |
| |
| |
| python ./internal/ml/model_selection/exps/nas_bench_tabular/0.train_one_model.py \ |
| --log_name=baseline_train_based \ |
| --search_space=mlp_sp \ |
| --base_dir=../exp_data/ \ |
| --num_labels=2 \ |
| --device=cuda:3 \ |
| --batch_size=1024 \ |
| --lr=0.001 \ |
| --epoch=60 \ |
| --iter_per_epoch=2000 \ |
| --dataset=criteo \ |
| --nfeat=2100000 \ |
| --nfield=39 \ |
| --nemb=10 \ |
| --workers=0 \ |
| --result_dir=./internal/ml/model_selection/exp_result/ \ |
| --log_folder=log_criteo_train_tune >criteo_60.log & |
| |
| |
| |
| python ./internal/ml/model_selection/exps/nas_bench_tabular/0.train_one_model.py \ |
| --log_name=baseline_train_based \ |
| --search_space=mlp_sp \ |
| --base_dir=../exp_data/ \ |
| --num_labels=2 \ |
| --device=cuda:4 \ |
| --batch_size=1024 \ |
| --lr=0.001 \ |
| --epoch=80 \ |
| --iter_per_epoch=2000 \ |
| --dataset=criteo \ |
| --nfeat=2100000 \ |
| --nfield=39 \ |
| --nemb=10 \ |
| --workers=0 \ |
| --result_dir=./internal/ml/model_selection/exp_result/ \ |
| --log_folder=log_criteo_train_tune >criteo_80.log & |
| |
| |
| |
| python ./internal/ml/model_selection/exps/nas_bench_tabular/0.train_one_model.py \ |
| --log_name=baseline_train_based \ |
| --search_space=mlp_sp \ |
| --base_dir=../exp_data/ \ |
| --num_labels=2 \ |
| --device=cuda:5 \ |
| --batch_size=1024 \ |
| --lr=0.001 \ |
| --epoch=100 \ |
| --iter_per_epoch=2000 \ |
| --dataset=criteo \ |
| --nfeat=2100000 \ |
| --nfield=39 \ |
| --nemb=10 \ |
| --workers=0 \ |
| --result_dir=./internal/ml/model_selection/exp_result/ \ |
| --log_folder=log_criteo_train_tune >criteo_100.log & |
| |