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[2024-09-10 21:10:22,658] [INFO] [real_accelerator.py:203:get_accelerator] Setting ds_accelerator to cuda (auto detect)
[93m [WARNING] [0m async_io requires the dev libaio .so object and headers but these were not found.
[93m [WARNING] [0m async_io: please install the libaio-dev package with apt
[93m [WARNING] [0m If libaio is already installed (perhaps from source), try setting the CFLAGS and LDFLAGS environment variables to where it can be found.
[93m [WARNING] [0m Please specify the CUTLASS repo directory as environment variable $CUTLASS_PATH
[93m [WARNING] [0m sparse_attn requires a torch version >= 1.5 and < 2.0 but detected 2.3
[93m [WARNING] [0m using untested triton version (2.3.1), only 1.0.0 is known to be compatible
[2024-09-10 21:10:25,566] [WARNING] [runner.py:202:fetch_hostfile] Unable to find hostfile, will proceed with training with local resources only.
[2024-09-10 21:10:25,566] [INFO] [runner.py:568:main] cmd = /home/juntao/Miniconda3/envs/roo/bin/python -u -m deepspeed.launcher.launch --world_info=eyJsb2NhbGhvc3QiOiBbMCwgMSwgMiwgMywgNCwgNSwgNiwgN119 --master_addr=127.0.0.1 --master_port=21326 --module --enable_each_rank_log=None safe_rlhf.values.score_lm --train_datasets PrefOnlyRewardJSON01::/home/juntao/Projects/roo-dev-cty/data/roo/gold-generate-dataset-ultrafeedback/30k/train.json --eval_datasets PrefOnlyRewardJSON02::/home/juntao/Projects/roo-dev-cty/data/roo/gold-generate-dataset-ultrafeedback/30k/test.json --model_name_or_path /home/juntao/Projects/roo-dev-cty/models/proxy_model/gpt2-774m --max_length 1024 --trust_remote_code True --loss_type sequence-wise --epochs 2 --per_device_train_batch_size 4 --per_device_eval_batch_size 4 --gradient_accumulation_steps 4 --gradient_checkpointing --regularization 0.001 --normalize_score_during_training False --normalizer_type ExponentialMovingAverage --normalizer_momentum 0.9 --learning_rate 2e-5 --lr_scheduler_type cosine --lr_warmup_ratio 0.03 --weight_decay 0.1 --lm_coef 0.01 --seed 42 --need_eval --eval_strategy epoch --output_dir /home/juntao/Projects/roo-dev-cty/experiments/outputs/score_lm/gpt2_774m_0910 --log_type wandb --log_project score_lm --log_run_name gpt2_774m_0910 --zero_stage 3 --offload none --bf16 True --tf32 True --save_16bit
[2024-09-10 21:10:27,116] [INFO] [real_accelerator.py:203:get_accelerator] Setting ds_accelerator to cuda (auto detect)
[93m [WARNING] [0m async_io requires the dev libaio .so object and headers but these were not found.
[93m [WARNING] [0m async_io: please install the libaio-dev package with apt
[93m [WARNING] [0m If libaio is already installed (perhaps from source), try setting the CFLAGS and LDFLAGS environment variables to where it can be found.
[93m [WARNING] [0m Please specify the CUTLASS repo directory as environment variable $CUTLASS_PATH
[93m [WARNING] [0m sparse_attn requires a torch version >= 1.5 and < 2.0 but detected 2.3
[93m [WARNING] [0m using untested triton version (2.3.1), only 1.0.0 is known to be compatible
[2024-09-10 21:10:29,339] [INFO] [launch.py:146:main] WORLD INFO DICT: {'localhost': [0, 1, 2, 3, 4, 5, 6, 7]}
[2024-09-10 21:10:29,339] [INFO] [launch.py:152:main] nnodes=1, num_local_procs=8, node_rank=0
[2024-09-10 21:10:29,339] [INFO] [launch.py:163:main] global_rank_mapping=defaultdict(<class 'list'>, {'localhost': [0, 1, 2, 3, 4, 5, 6, 7]})
[2024-09-10 21:10:29,339] [INFO] [launch.py:164:main] dist_world_size=8
[2024-09-10 21:10:29,339] [INFO] [launch.py:168:main] Setting CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7
[2024-09-10 21:10:29,340] [INFO] [launch.py:256:main] process 3553582 spawned with command: ['/home/juntao/Miniconda3/envs/roo/bin/python', '-u', '-m', 'safe_rlhf.values.score_lm', '--local_rank=0', '--train_datasets', 'PrefOnlyRewardJSON01::/home/juntao/Projects/roo-dev-cty/data/roo/gold-generate-dataset-ultrafeedback/30k/train.json', '--eval_datasets', 'PrefOnlyRewardJSON02::/home/juntao/Projects/roo-dev-cty/data/roo/gold-generate-dataset-ultrafeedback/30k/test.json', '--model_name_or_path', '/home/juntao/Projects/roo-dev-cty/models/proxy_model/gpt2-774m', '--max_length', '1024', '--trust_remote_code', 'True', '--loss_type', 'sequence-wise', '--epochs', '2', '--per_device_train_batch_size', '4', '--per_device_eval_batch_size', '4', '--gradient_accumulation_steps', '4', '--gradient_checkpointing', '--regularization', '0.001', '--normalize_score_during_training', 'False', '--normalizer_type', 'ExponentialMovingAverage', '--normalizer_momentum', '0.9', '--learning_rate', '2e-5', '--lr_scheduler_type', 'cosine', '--lr_warmup_ratio', '0.03', '--weight_decay', '0.1', '--lm_coef', '0.01', '--seed', '42', '--need_eval', '--eval_strategy', 'epoch', '--output_dir', '/home/juntao/Projects/roo-dev-cty/experiments/outputs/score_lm/gpt2_774m_0910', '--log_type', 'wandb', '--log_project', 'score_lm', '--log_run_name', 'gpt2_774m_0910', '--zero_stage', '3', '--offload', 'none', '--bf16', 'True', '--tf32', 'True', '--save_16bit']
[2024-09-10 21:10:29,341] [INFO] [launch.py:256:main] process 3553583 spawned with command: ['/home/juntao/Miniconda3/envs/roo/bin/python', '-u', '-m', 'safe_rlhf.values.score_lm', '--local_rank=1', '--train_datasets', 'PrefOnlyRewardJSON01::/home/juntao/Projects/roo-dev-cty/data/roo/gold-generate-dataset-ultrafeedback/30k/train.json', '--eval_datasets', 'PrefOnlyRewardJSON02::/home/juntao/Projects/roo-dev-cty/data/roo/gold-generate-dataset-ultrafeedback/30k/test.json', '--model_name_or_path', '/home/juntao/Projects/roo-dev-cty/models/proxy_model/gpt2-774m', '--max_length', '1024', '--trust_remote_code', 'True', '--loss_type', 'sequence-wise', '--epochs', '2', '--per_device_train_batch_size', '4', '--per_device_eval_batch_size', '4', '--gradient_accumulation_steps', '4', '--gradient_checkpointing', '--regularization', '0.001', '--normalize_score_during_training', 'False', '--normalizer_type', 'ExponentialMovingAverage', '--normalizer_momentum', '0.9', '--learning_rate', '2e-5', '--lr_scheduler_type', 'cosine', '--lr_warmup_ratio', '0.03', '--weight_decay', '0.1', '--lm_coef', '0.01', '--seed', '42', '--need_eval', '--eval_strategy', 'epoch', '--output_dir', '/home/juntao/Projects/roo-dev-cty/experiments/outputs/score_lm/gpt2_774m_0910', '--log_type', 'wandb', '--log_project', 'score_lm', '--log_run_name', 'gpt2_774m_0910', '--zero_stage', '3', '--offload', 'none', '--bf16', 'True', '--tf32', 'True', '--save_16bit']
[2024-09-10 21:10:29,343] [INFO] [launch.py:256:main] process 3553584 spawned with command: ['/home/juntao/Miniconda3/envs/roo/bin/python', '-u', '-m', 'safe_rlhf.values.score_lm', '--local_rank=2', '--train_datasets', 'PrefOnlyRewardJSON01::/home/juntao/Projects/roo-dev-cty/data/roo/gold-generate-dataset-ultrafeedback/30k/train.json', '--eval_datasets', 'PrefOnlyRewardJSON02::/home/juntao/Projects/roo-dev-cty/data/roo/gold-generate-dataset-ultrafeedback/30k/test.json', '--model_name_or_path', '/home/juntao/Projects/roo-dev-cty/models/proxy_model/gpt2-774m', '--max_length', '1024', '--trust_remote_code', 'True', '--loss_type', 'sequence-wise', '--epochs', '2', '--per_device_train_batch_size', '4', '--per_device_eval_batch_size', '4', '--gradient_accumulation_steps', '4', '--gradient_checkpointing', '--regularization', '0.001', '--normalize_score_during_training', 'False', '--normalizer_type', 'ExponentialMovingAverage', '--normalizer_momentum', '0.9', '--learning_rate', '2e-5', '--lr_scheduler_type', 'cosine', '--lr_warmup_ratio', '0.03', '--weight_decay', '0.1', '--lm_coef', '0.01', '--seed', '42', '--need_eval', '--eval_strategy', 'epoch', '--output_dir', '/home/juntao/Projects/roo-dev-cty/experiments/outputs/score_lm/gpt2_774m_0910', '--log_type', 'wandb', '--log_project', 'score_lm', '--log_run_name', 'gpt2_774m_0910', '--zero_stage', '3', '--offload', 'none', '--bf16', 'True', '--tf32', 'True', '--save_16bit']
[2024-09-10 21:10:29,344] [INFO] [launch.py:256:main] process 3553585 spawned with command: ['/home/juntao/Miniconda3/envs/roo/bin/python', '-u', '-m', 'safe_rlhf.values.score_lm', '--local_rank=3', '--train_datasets', 'PrefOnlyRewardJSON01::/home/juntao/Projects/roo-dev-cty/data/roo/gold-generate-dataset-ultrafeedback/30k/train.json', '--eval_datasets', 'PrefOnlyRewardJSON02::/home/juntao/Projects/roo-dev-cty/data/roo/gold-generate-dataset-ultrafeedback/30k/test.json', '--model_name_or_path', '/home/juntao/Projects/roo-dev-cty/models/proxy_model/gpt2-774m', '--max_length', '1024', '--trust_remote_code', 'True', '--loss_type', 'sequence-wise', '--epochs', '2', '--per_device_train_batch_size', '4', '--per_device_eval_batch_size', '4', '--gradient_accumulation_steps', '4', '--gradient_checkpointing', '--regularization', '0.001', '--normalize_score_during_training', 'False', '--normalizer_type', 'ExponentialMovingAverage', '--normalizer_momentum', '0.9', '--learning_rate', '2e-5', '--lr_scheduler_type', 'cosine', '--lr_warmup_ratio', '0.03', '--weight_decay', '0.1', '--lm_coef', '0.01', '--seed', '42', '--need_eval', '--eval_strategy', 'epoch', '--output_dir', '/home/juntao/Projects/roo-dev-cty/experiments/outputs/score_lm/gpt2_774m_0910', '--log_type', 'wandb', '--log_project', 'score_lm', '--log_run_name', 'gpt2_774m_0910', '--zero_stage', '3', '--offload', 'none', '--bf16', 'True', '--tf32', 'True', '--save_16bit']
[2024-09-10 21:10:29,345] [INFO] [launch.py:256:main] process 3553586 spawned with command: ['/home/juntao/Miniconda3/envs/roo/bin/python', '-u', '-m', 'safe_rlhf.values.score_lm', '--local_rank=4', '--train_datasets', 'PrefOnlyRewardJSON01::/home/juntao/Projects/roo-dev-cty/data/roo/gold-generate-dataset-ultrafeedback/30k/train.json', '--eval_datasets', 'PrefOnlyRewardJSON02::/home/juntao/Projects/roo-dev-cty/data/roo/gold-generate-dataset-ultrafeedback/30k/test.json', '--model_name_or_path', '/home/juntao/Projects/roo-dev-cty/models/proxy_model/gpt2-774m', '--max_length', '1024', '--trust_remote_code', 'True', '--loss_type', 'sequence-wise', '--epochs', '2', '--per_device_train_batch_size', '4', '--per_device_eval_batch_size', '4', '--gradient_accumulation_steps', '4', '--gradient_checkpointing', '--regularization', '0.001', '--normalize_score_during_training', 'False', '--normalizer_type', 'ExponentialMovingAverage', '--normalizer_momentum', '0.9', '--learning_rate', '2e-5', '--lr_scheduler_type', 'cosine', '--lr_warmup_ratio', '0.03', '--weight_decay', '0.1', '--lm_coef', '0.01', '--seed', '42', '--need_eval', '--eval_strategy', 'epoch', '--output_dir', '/home/juntao/Projects/roo-dev-cty/experiments/outputs/score_lm/gpt2_774m_0910', '--log_type', 'wandb', '--log_project', 'score_lm', '--log_run_name', 'gpt2_774m_0910', '--zero_stage', '3', '--offload', 'none', '--bf16', 'True', '--tf32', 'True', '--save_16bit']
[2024-09-10 21:10:29,346] [INFO] [launch.py:256:main] process 3553587 spawned with command: ['/home/juntao/Miniconda3/envs/roo/bin/python', '-u', '-m', 'safe_rlhf.values.score_lm', '--local_rank=5', '--train_datasets', 'PrefOnlyRewardJSON01::/home/juntao/Projects/roo-dev-cty/data/roo/gold-generate-dataset-ultrafeedback/30k/train.json', '--eval_datasets', 'PrefOnlyRewardJSON02::/home/juntao/Projects/roo-dev-cty/data/roo/gold-generate-dataset-ultrafeedback/30k/test.json', '--model_name_or_path', '/home/juntao/Projects/roo-dev-cty/models/proxy_model/gpt2-774m', '--max_length', '1024', '--trust_remote_code', 'True', '--loss_type', 'sequence-wise', '--epochs', '2', '--per_device_train_batch_size', '4', '--per_device_eval_batch_size', '4', '--gradient_accumulation_steps', '4', '--gradient_checkpointing', '--regularization', '0.001', '--normalize_score_during_training', 'False', '--normalizer_type', 'ExponentialMovingAverage', '--normalizer_momentum', '0.9', '--learning_rate', '2e-5', '--lr_scheduler_type', 'cosine', '--lr_warmup_ratio', '0.03', '--weight_decay', '0.1', '--lm_coef', '0.01', '--seed', '42', '--need_eval', '--eval_strategy', 'epoch', '--output_dir', '/home/juntao/Projects/roo-dev-cty/experiments/outputs/score_lm/gpt2_774m_0910', '--log_type', 'wandb', '--log_project', 'score_lm', '--log_run_name', 'gpt2_774m_0910', '--zero_stage', '3', '--offload', 'none', '--bf16', 'True', '--tf32', 'True', '--save_16bit']
[2024-09-10 21:10:29,348] [INFO] [launch.py:256:main] process 3553588 spawned with command: ['/home/juntao/Miniconda3/envs/roo/bin/python', '-u', '-m', 'safe_rlhf.values.score_lm', '--local_rank=6', '--train_datasets', 'PrefOnlyRewardJSON01::/home/juntao/Projects/roo-dev-cty/data/roo/gold-generate-dataset-ultrafeedback/30k/train.json', '--eval_datasets', 'PrefOnlyRewardJSON02::/home/juntao/Projects/roo-dev-cty/data/roo/gold-generate-dataset-ultrafeedback/30k/test.json', '--model_name_or_path', '/home/juntao/Projects/roo-dev-cty/models/proxy_model/gpt2-774m', '--max_length', '1024', '--trust_remote_code', 'True', '--loss_type', 'sequence-wise', '--epochs', '2', '--per_device_train_batch_size', '4', '--per_device_eval_batch_size', '4', '--gradient_accumulation_steps', '4', '--gradient_checkpointing', '--regularization', '0.001', '--normalize_score_during_training', 'False', '--normalizer_type', 'ExponentialMovingAverage', '--normalizer_momentum', '0.9', '--learning_rate', '2e-5', '--lr_scheduler_type', 'cosine', '--lr_warmup_ratio', '0.03', '--weight_decay', '0.1', '--lm_coef', '0.01', '--seed', '42', '--need_eval', '--eval_strategy', 'epoch', '--output_dir', '/home/juntao/Projects/roo-dev-cty/experiments/outputs/score_lm/gpt2_774m_0910', '--log_type', 'wandb', '--log_project', 'score_lm', '--log_run_name', 'gpt2_774m_0910', '--zero_stage', '3', '--offload', 'none', '--bf16', 'True', '--tf32', 'True', '--save_16bit']
[2024-09-10 21:10:29,349] [INFO] [launch.py:256:main] process 3553589 spawned with command: ['/home/juntao/Miniconda3/envs/roo/bin/python', '-u', '-m', 'safe_rlhf.values.score_lm', '--local_rank=7', '--train_datasets', 'PrefOnlyRewardJSON01::/home/juntao/Projects/roo-dev-cty/data/roo/gold-generate-dataset-ultrafeedback/30k/train.json', '--eval_datasets', 'PrefOnlyRewardJSON02::/home/juntao/Projects/roo-dev-cty/data/roo/gold-generate-dataset-ultrafeedback/30k/test.json', '--model_name_or_path', '/home/juntao/Projects/roo-dev-cty/models/proxy_model/gpt2-774m', '--max_length', '1024', '--trust_remote_code', 'True', '--loss_type', 'sequence-wise', '--epochs', '2', '--per_device_train_batch_size', '4', '--per_device_eval_batch_size', '4', '--gradient_accumulation_steps', '4', '--gradient_checkpointing', '--regularization', '0.001', '--normalize_score_during_training', 'False', '--normalizer_type', 'ExponentialMovingAverage', '--normalizer_momentum', '0.9', '--learning_rate', '2e-5', '--lr_scheduler_type', 'cosine', '--lr_warmup_ratio', '0.03', '--weight_decay', '0.1', '--lm_coef', '0.01', '--seed', '42', '--need_eval', '--eval_strategy', 'epoch', '--output_dir', '/home/juntao/Projects/roo-dev-cty/experiments/outputs/score_lm/gpt2_774m_0910', '--log_type', 'wandb', '--log_project', 'score_lm', '--log_run_name', 'gpt2_774m_0910', '--zero_stage', '3', '--offload', 'none', '--bf16', 'True', '--tf32', 'True', '--save_16bit']
[2024-09-10 21:10:31,934] [INFO] [real_accelerator.py:203:get_accelerator] Setting ds_accelerator to cuda (auto detect)
[2024-09-10 21:10:31,977] [INFO] [real_accelerator.py:203:get_accelerator] Setting ds_accelerator to cuda (auto detect)
[2024-09-10 21:10:32,047] [INFO] [real_accelerator.py:203:get_accelerator] Setting ds_accelerator to cuda (auto detect)
[2024-09-10 21:10:32,054] [INFO] [real_accelerator.py:203:get_accelerator] Setting ds_accelerator to cuda (auto detect)
[2024-09-10 21:10:32,082] [INFO] [real_accelerator.py:203:get_accelerator] Setting ds_accelerator to cuda (auto detect)
[2024-09-10 21:10:32,127] [INFO] [real_accelerator.py:203:get_accelerator] Setting ds_accelerator to cuda (auto detect)
[2024-09-10 21:10:32,127] [INFO] [real_accelerator.py:203:get_accelerator] Setting ds_accelerator to cuda (auto detect)
[2024-09-10 21:10:32,186] [INFO] [real_accelerator.py:203:get_accelerator] Setting ds_accelerator to cuda (auto detect)
[93m [WARNING] [0m async_io requires the dev libaio .so object and headers but these were not found.
[93m [WARNING] [0m async_io: please install the libaio-dev package with apt
[93m [WARNING] [0m If libaio is already installed (perhaps from source), try setting the CFLAGS and LDFLAGS environment variables to where it can be found.
[93m [WARNING] [0m Please specify the CUTLASS repo directory as environment variable $CUTLASS_PATH
[93m [WARNING] [0m async_io requires the dev libaio .so object and headers but these were not found.
[93m [WARNING] [0m async_io requires the dev libaio .so object and headers but these were not found.
[93m [WARNING] [0m async_io: please install the libaio-dev package with apt
[93m [WARNING] [0m If libaio is already installed (perhaps from source), try setting the CFLAGS and LDFLAGS environment variables to where it can be found.
[93m [WARNING] [0m Please specify the CUTLASS repo directory as environment variable $CUTLASS_PATH
[93m [WARNING] [0m async_io: please install the libaio-dev package with apt
[93m [WARNING] [0m If libaio is already installed (perhaps from source), try setting the CFLAGS and LDFLAGS environment variables to where it can be found.
[93m [WARNING] [0m Please specify the CUTLASS repo directory as environment variable $CUTLASS_PATH
[93m [WARNING] [0m async_io requires the dev libaio .so object and headers but these were not found.
[93m [WARNING] [0m async_io: please install the libaio-dev package with apt
[93m [WARNING] [0m If libaio is already installed (perhaps from source), try setting the CFLAGS and LDFLAGS environment variables to where it can be found.
[93m [WARNING] [0m Please specify the CUTLASS repo directory as environment variable $CUTLASS_PATH
[93m [WARNING] [0m async_io requires the dev libaio .so object and headers but these were not found.
[93m [WARNING] [0m async_io: please install the libaio-dev package with apt
[93m [WARNING] [0m If libaio is already installed (perhaps from source), try setting the CFLAGS and LDFLAGS environment variables to where it can be found.
[93m [WARNING] [0m Please specify the CUTLASS repo directory as environment variable $CUTLASS_PATH
[93m [WARNING] [0m async_io requires the dev libaio .so object and headers but these were not found.
[93m [WARNING] [0m async_io: please install the libaio-dev package with apt
[93m [WARNING] [0m If libaio is already installed (perhaps from source), try setting the CFLAGS and LDFLAGS environment variables to where it can be found.
[93m [WARNING] [0m Please specify the CUTLASS repo directory as environment variable $CUTLASS_PATH
[93m [WARNING] [0m async_io requires the dev libaio .so object and headers but these were not found.
[93m [WARNING] [0m sparse_attn requires a torch version >= 1.5 and < 2.0 but detected 2.3
[93m [WARNING] [0m using untested triton version (2.3.1), only 1.0.0 is known to be compatible
[93m [WARNING] [0m async_io requires the dev libaio .so object and headers but these were not found.
[93m [WARNING] [0m async_io: please install the libaio-dev package with apt
[93m [WARNING] [0m If libaio is already installed (perhaps from source), try setting the CFLAGS and LDFLAGS environment variables to where it can be found.
[93m [WARNING] [0m Please specify the CUTLASS repo directory as environment variable $CUTLASS_PATH
[93m [WARNING] [0m async_io: please install the libaio-dev package with apt
[93m [WARNING] [0m If libaio is already installed (perhaps from source), try setting the CFLAGS and LDFLAGS environment variables to where it can be found.
[93m [WARNING] [0m Please specify the CUTLASS repo directory as environment variable $CUTLASS_PATH
[93m [WARNING] [0m sparse_attn requires a torch version >= 1.5 and < 2.0 but detected 2.3
[93m [WARNING] [0m using untested triton version (2.3.1), only 1.0.0 is known to be compatible
[93m [WARNING] [0m sparse_attn requires a torch version >= 1.5 and < 2.0 but detected 2.3
[93m [WARNING] [0m using untested triton version (2.3.1), only 1.0.0 is known to be compatible
[93m [WARNING] [0m sparse_attn requires a torch version >= 1.5 and < 2.0 but detected 2.3
[93m [WARNING] [0m using untested triton version (2.3.1), only 1.0.0 is known to be compatible
[93m [WARNING] [0m sparse_attn requires a torch version >= 1.5 and < 2.0 but detected 2.3
[93m [WARNING] [0m using untested triton version (2.3.1), only 1.0.0 is known to be compatible
[93m [WARNING] [0m sparse_attn requires a torch version >= 1.5 and < 2.0 but detected 2.3
[93m [WARNING] [0m using untested triton version (2.3.1), only 1.0.0 is known to be compatible
[93m [WARNING] [0m sparse_attn requires a torch version >= 1.5 and < 2.0 but detected 2.3
[93m [WARNING] [0m using untested triton version (2.3.1), only 1.0.0 is known to be compatible
[93m [WARNING] [0m sparse_attn requires a torch version >= 1.5 and < 2.0 but detected 2.3
[93m [WARNING] [0m using untested triton version (2.3.1), only 1.0.0 is known to be compatible
[2024-09-10 21:10:38,687] [INFO] [comm.py:637:init_distributed] cdb=None
[2024-09-10 21:10:38,855] [INFO] [comm.py:637:init_distributed] cdb=None
[2024-09-10 21:10:39,029] [INFO] [comm.py:637:init_distributed] cdb=None
[2024-09-10 21:10:39,034] [INFO] [comm.py:637:init_distributed] cdb=None
[2024-09-10 21:10:39,090] [INFO] [comm.py:637:init_distributed] cdb=None
[2024-09-10 21:10:39,133] [INFO] [comm.py:637:init_distributed] cdb=None
[2024-09-10 21:10:39,180] [INFO] [comm.py:637:init_distributed] cdb=None
[2024-09-10 21:10:39,211] [INFO] [comm.py:637:init_distributed] cdb=None
[2024-09-10 21:10:39,211] [INFO] [comm.py:668:init_distributed] Initializing TorchBackend in DeepSpeed with backend nccl
Set logger level to DEBUG.
[2024-09-10 21:10:44,377] [INFO] [partition_parameters.py:345:__exit__] finished initializing model - num_params = 439, num_elems = 0.84B
ninja: no work to do.
Time to load fused_adam op: 0.5585312843322754 seconds
Time to load fused_adam op: 0.607450008392334 seconds
Time to load fused_adam op: 0.6077747344970703 seconds
Time to load fused_adam op: 0.6062517166137695 seconds
Time to load fused_adam op: 0.6082189083099365 seconds
Time to load fused_adam op: 0.6088159084320068 seconds
Time to load fused_adam op: 0.6079161167144775 seconds
Time to load fused_adam op: 0.6089522838592529 seconds
[2024-09-10 21:10:51,954] [INFO] [logging.py:96:log_dist] [Rank 0] DeepSpeed info: version=0.14.4, git-hash=unknown, git-branch=unknown
[2024-09-10 21:10:51,954] [INFO] [comm.py:662:init_distributed] Distributed backend already initialized
[2024-09-10 21:10:51,989] [INFO] [logging.py:96:log_dist] [Rank 0] DeepSpeed Flops Profiler Enabled: False
[2024-09-10 21:10:51,992] [INFO] [logging.py:96:log_dist] [Rank 0] Using client Optimizer as basic optimizer
[2024-09-10 21:10:51,992] [INFO] [logging.py:96:log_dist] [Rank 0] Removing param_group that has no 'params' in the basic Optimizer
[2024-09-10 21:10:52,028] [INFO] [logging.py:96:log_dist] [Rank 0] DeepSpeed Basic Optimizer = FusedAdam
[2024-09-10 21:10:52,028] [INFO] [utils.py:56:is_zero_supported_optimizer] Checking ZeRO support for optimizer=FusedAdam type=<class 'deepspeed.ops.adam.fused_adam.FusedAdam'>
[2024-09-10 21:10:52,028] [INFO] [logging.py:96:log_dist] [Rank 0] Creating fp16 ZeRO stage 3 optimizer, MiCS is enabled False, Hierarchical params gather False
[2024-09-10 21:10:52,028] [INFO] [logging.py:96:log_dist] [Rank 0] Creating torch.bfloat16 ZeRO stage 3 optimizer
[2024-09-10 21:10:52,210] [INFO] [utils.py:781:see_memory_usage] Stage 3 initialize beginning
[2024-09-10 21:10:52,210] [INFO] [utils.py:782:see_memory_usage] MA 0.32 GB Max_MA 0.58 GB CA 0.66 GB Max_CA 1 GB
[2024-09-10 21:10:52,210] [INFO] [utils.py:789:see_memory_usage] CPU Virtual Memory: used = 39.67 GB, percent = 3.9%
[2024-09-10 21:10:52,212] [INFO] [stage3.py:130:__init__] Reduce bucket size 500,000,000
[2024-09-10 21:10:52,212] [INFO] [stage3.py:131:__init__] Prefetch bucket size 30000000
[2024-09-10 21:10:52,354] [INFO] [utils.py:781:see_memory_usage] DeepSpeedZeRoOffload initialize [begin]
[2024-09-10 21:10:52,354] [INFO] [utils.py:782:see_memory_usage] MA 0.32 GB Max_MA 0.32 GB CA 0.66 GB Max_CA 1 GB
[2024-09-10 21:10:52,355] [INFO] [utils.py:789:see_memory_usage] CPU Virtual Memory: used = 39.67 GB, percent = 3.9%
Parameter Offload: Total persistent parameters: 602881 in 292 params
[2024-09-10 21:10:52,518] [INFO] [utils.py:781:see_memory_usage] DeepSpeedZeRoOffload initialize [end]
[2024-09-10 21:10:52,518] [INFO] [utils.py:782:see_memory_usage] MA 0.22 GB Max_MA 0.34 GB CA 0.66 GB Max_CA 1 GB
[2024-09-10 21:10:52,518] [INFO] [utils.py:789:see_memory_usage] CPU Virtual Memory: used = 39.67 GB, percent = 3.9%
[2024-09-10 21:10:52,666] [INFO] [utils.py:781:see_memory_usage] Before creating fp16 partitions
[2024-09-10 21:10:52,666] [INFO] [utils.py:782:see_memory_usage] MA 0.22 GB Max_MA 0.22 GB CA 0.66 GB Max_CA 1 GB
[2024-09-10 21:10:52,666] [INFO] [utils.py:789:see_memory_usage] CPU Virtual Memory: used = 39.68 GB, percent = 3.9%
[2024-09-10 21:10:53,213] [INFO] [utils.py:781:see_memory_usage] After creating fp16 partitions: 2
[2024-09-10 21:10:53,213] [INFO] [utils.py:782:see_memory_usage] MA 0.22 GB Max_MA 0.22 GB CA 0.24 GB Max_CA 1 GB
[2024-09-10 21:10:53,213] [INFO] [utils.py:789:see_memory_usage] CPU Virtual Memory: used = 39.73 GB, percent = 3.9%
[2024-09-10 21:10:53,381] [INFO] [utils.py:781:see_memory_usage] Before creating fp32 partitions
[2024-09-10 21:10:53,381] [INFO] [utils.py:782:see_memory_usage] MA 0.22 GB Max_MA 0.22 GB CA 0.24 GB Max_CA 0 GB
[2024-09-10 21:10:53,381] [INFO] [utils.py:789:see_memory_usage] CPU Virtual Memory: used = 39.73 GB, percent = 3.9%
[2024-09-10 21:10:53,538] [INFO] [utils.py:781:see_memory_usage] After creating fp32 partitions
[2024-09-10 21:10:53,539] [INFO] [utils.py:782:see_memory_usage] MA 0.58 GB Max_MA 0.76 GB CA 0.78 GB Max_CA 1 GB
[2024-09-10 21:10:53,539] [INFO] [utils.py:789:see_memory_usage] CPU Virtual Memory: used = 39.73 GB, percent = 3.9%
[2024-09-10 21:10:53,689] [INFO] [utils.py:781:see_memory_usage] Before initializing optimizer states
[2024-09-10 21:10:53,689] [INFO] [utils.py:782:see_memory_usage] MA 0.58 GB Max_MA 0.58 GB CA 0.78 GB Max_CA 1 GB
[2024-09-10 21:10:53,689] [INFO] [utils.py:789:see_memory_usage] CPU Virtual Memory: used = 39.73 GB, percent = 3.9%
[2024-09-10 21:10:53,838] [INFO] [utils.py:781:see_memory_usage] After initializing optimizer states
[2024-09-10 21:10:53,839] [INFO] [utils.py:782:see_memory_usage] MA 0.58 GB Max_MA 0.94 GB CA 1.14 GB Max_CA 1 GB
[2024-09-10 21:10:53,839] [INFO] [utils.py:789:see_memory_usage] CPU Virtual Memory: used = 39.73 GB, percent = 3.9%
[2024-09-10 21:10:53,841] [INFO] [stage3.py:486:_setup_for_real_optimizer] optimizer state initialized
[2024-09-10 21:10:54,868] [INFO] [utils.py:781:see_memory_usage] After initializing ZeRO optimizer
[2024-09-10 21:10:54,869] [INFO] [utils.py:782:see_memory_usage] MA 1.69 GB Max_MA 1.93 GB CA 2.07 GB Max_CA 2 GB
[2024-09-10 21:10:54,869] [INFO] [utils.py:789:see_memory_usage] CPU Virtual Memory: used = 39.72 GB, percent = 3.9%
[2024-09-10 21:10:54,869] [INFO] [logging.py:96:log_dist] [Rank 0] DeepSpeed Final Optimizer = DeepSpeedZeroOptimizer_Stage3
[2024-09-10 21:10:54,869] [INFO] [logging.py:96:log_dist] [Rank 0] DeepSpeed using client LR scheduler
[2024-09-10 21:10:54,869] [INFO] [logging.py:96:log_dist] [Rank 0] DeepSpeed LR Scheduler = <torch.optim.lr_scheduler.LambdaLR object at 0x79a35fe0b8d0>
[2024-09-10 21:10:54,869] [INFO] [logging.py:96:log_dist] [Rank 0] step=0, skipped=0, lr=[0.0, 0.0], mom=[(0.9, 0.95), (0.9, 0.95)]
[2024-09-10 21:10:54,870] [INFO] [config.py:997:print] DeepSpeedEngine configuration:
[2024-09-10 21:10:54,870] [INFO] [config.py:1001:print] activation_checkpointing_config {
"partition_activations": false,
"contiguous_memory_optimization": false,
"cpu_checkpointing": false,
"number_checkpoints": null,
"synchronize_checkpoint_boundary": false,
"profile": false
}
[2024-09-10 21:10:54,870] [INFO] [config.py:1001:print] aio_config ................... {'block_size': 1048576, 'queue_depth': 8, 'thread_count': 1, 'single_submit': False, 'overlap_events': True}
[2024-09-10 21:10:54,870] [INFO] [config.py:1001:print] amp_enabled .................. False
[2024-09-10 21:10:54,871] [INFO] [config.py:1001:print] amp_params ................... False
[2024-09-10 21:10:54,871] [INFO] [config.py:1001:print] autotuning_config ............ {
"enabled": false,
"start_step": null,
"end_step": null,
"metric_path": null,
"arg_mappings": null,
"metric": "throughput",
"model_info": null,
"results_dir": "autotuning_results",
"exps_dir": "autotuning_exps",
"overwrite": true,
"fast": true,
"start_profile_step": 3,
"end_profile_step": 5,
"tuner_type": "gridsearch",
"tuner_early_stopping": 5,
"tuner_num_trials": 50,
"model_info_path": null,
"mp_size": 1,
"max_train_batch_size": null,
"min_train_batch_size": 1,
"max_train_micro_batch_size_per_gpu": 1.024000e+03,
"min_train_micro_batch_size_per_gpu": 1,
"num_tuning_micro_batch_sizes": 3
}
[2024-09-10 21:10:54,871] [INFO] [config.py:1001:print] bfloat16_enabled ............. True
[2024-09-10 21:10:54,871] [INFO] [config.py:1001:print] bfloat16_immediate_grad_update False
[2024-09-10 21:10:54,871] [INFO] [config.py:1001:print] checkpoint_parallel_write_pipeline False
[2024-09-10 21:10:54,871] [INFO] [config.py:1001:print] checkpoint_tag_validation_enabled True
[2024-09-10 21:10:54,871] [INFO] [config.py:1001:print] checkpoint_tag_validation_fail False
[2024-09-10 21:10:54,871] [INFO] [config.py:1001:print] comms_config ................. <deepspeed.comm.config.DeepSpeedCommsConfig object at 0x79a35fe0dbd0>
[2024-09-10 21:10:54,871] [INFO] [config.py:1001:print] communication_data_type ...... None
[2024-09-10 21:10:54,871] [INFO] [config.py:1001:print] compression_config ........... {'weight_quantization': {'shared_parameters': {'enabled': False, 'quantizer_kernel': False, 'schedule_offset': 0, 'quantize_groups': 1, 'quantize_verbose': False, 'quantization_type': 'symmetric', 'quantize_weight_in_forward': False, 'rounding': 'nearest', 'fp16_mixed_quantize': False, 'quantize_change_ratio': 0.001}, 'different_groups': {}}, 'activation_quantization': {'shared_parameters': {'enabled': False, 'quantization_type': 'symmetric', 'range_calibration': 'dynamic', 'schedule_offset': 1000}, 'different_groups': {}}, 'sparse_pruning': {'shared_parameters': {'enabled': False, 'method': 'l1', 'schedule_offset': 1000}, 'different_groups': {}}, 'row_pruning': {'shared_parameters': {'enabled': False, 'method': 'l1', 'schedule_offset': 1000}, 'different_groups': {}}, 'head_pruning': {'shared_parameters': {'enabled': False, 'method': 'topk', 'schedule_offset': 1000}, 'different_groups': {}}, 'channel_pruning': {'shared_parameters': {'enabled': False, 'method': 'l1', 'schedule_offset': 1000}, 'different_groups': {}}, 'layer_reduction': {'enabled': False}}
[2024-09-10 21:10:54,871] [INFO] [config.py:1001:print] curriculum_enabled_legacy .... False
[2024-09-10 21:10:54,871] [INFO] [config.py:1001:print] curriculum_params_legacy ..... False
[2024-09-10 21:10:54,871] [INFO] [config.py:1001:print] data_efficiency_config ....... {'enabled': False, 'seed': 1234, 'data_sampling': {'enabled': False, 'num_epochs': 1000, 'num_workers': 0, 'curriculum_learning': {'enabled': False}}, 'data_routing': {'enabled': False, 'random_ltd': {'enabled': False, 'layer_token_lr_schedule': {'enabled': False}}}}
[2024-09-10 21:10:54,871] [INFO] [config.py:1001:print] data_efficiency_enabled ...... False
[2024-09-10 21:10:54,871] [INFO] [config.py:1001:print] dataloader_drop_last ......... False
[2024-09-10 21:10:54,871] [INFO] [config.py:1001:print] disable_allgather ............ False
[2024-09-10 21:10:54,871] [INFO] [config.py:1001:print] dump_state ................... False
[2024-09-10 21:10:54,871] [INFO] [config.py:1001:print] dynamic_loss_scale_args ...... None
[2024-09-10 21:10:54,871] [INFO] [config.py:1001:print] eigenvalue_enabled ........... False
[2024-09-10 21:10:54,871] [INFO] [config.py:1001:print] eigenvalue_gas_boundary_resolution 1
[2024-09-10 21:10:54,871] [INFO] [config.py:1001:print] eigenvalue_layer_name ........ bert.encoder.layer
[2024-09-10 21:10:54,871] [INFO] [config.py:1001:print] eigenvalue_layer_num ......... 0
[2024-09-10 21:10:54,871] [INFO] [config.py:1001:print] eigenvalue_max_iter .......... 100
[2024-09-10 21:10:54,871] [INFO] [config.py:1001:print] eigenvalue_stability ......... 1e-06
[2024-09-10 21:10:54,871] [INFO] [config.py:1001:print] eigenvalue_tol ............... 0.01
[2024-09-10 21:10:54,871] [INFO] [config.py:1001:print] eigenvalue_verbose ........... False
[2024-09-10 21:10:54,871] [INFO] [config.py:1001:print] elasticity_enabled ........... False
[2024-09-10 21:10:54,871] [INFO] [config.py:1001:print] flops_profiler_config ........ {
"enabled": false,
"recompute_fwd_factor": 0.0,
"profile_step": 1,
"module_depth": -1,
"top_modules": 1,
"detailed": true,
"output_file": null
}
[2024-09-10 21:10:54,871] [INFO] [config.py:1001:print] fp16_auto_cast ............... None
[2024-09-10 21:10:54,871] [INFO] [config.py:1001:print] fp16_enabled ................. False
[2024-09-10 21:10:54,871] [INFO] [config.py:1001:print] fp16_master_weights_and_gradients False
[2024-09-10 21:10:54,871] [INFO] [config.py:1001:print] global_rank .................. 0
[2024-09-10 21:10:54,871] [INFO] [config.py:1001:print] grad_accum_dtype ............. None
[2024-09-10 21:10:54,871] [INFO] [config.py:1001:print] gradient_accumulation_steps .. 4
[2024-09-10 21:10:54,871] [INFO] [config.py:1001:print] gradient_clipping ............ 1.0
[2024-09-10 21:10:54,871] [INFO] [config.py:1001:print] gradient_predivide_factor .... 1.0
[2024-09-10 21:10:54,871] [INFO] [config.py:1001:print] graph_harvesting ............. False
[2024-09-10 21:10:54,871] [INFO] [config.py:1001:print] hybrid_engine ................ enabled=False max_out_tokens=512 inference_tp_size=1 release_inference_cache=False pin_parameters=True tp_gather_partition_size=8
[2024-09-10 21:10:54,871] [INFO] [config.py:1001:print] initial_dynamic_scale ........ 1
[2024-09-10 21:10:54,871] [INFO] [config.py:1001:print] load_universal_checkpoint .... False
[2024-09-10 21:10:54,871] [INFO] [config.py:1001:print] loss_scale ................... 1.0
[2024-09-10 21:10:54,871] [INFO] [config.py:1001:print] memory_breakdown ............. False
[2024-09-10 21:10:54,871] [INFO] [config.py:1001:print] mics_hierarchial_params_gather False
[2024-09-10 21:10:54,871] [INFO] [config.py:1001:print] mics_shard_size .............. -1
[2024-09-10 21:10:54,871] [INFO] [config.py:1001:print] monitor_config ............... tensorboard=TensorBoardConfig(enabled=False, output_path='', job_name='DeepSpeedJobName') comet=CometConfig(enabled=False, samples_log_interval=100, project=None, workspace=None, api_key=None, experiment_name=None, experiment_key=None, online=None, mode=None) wandb=WandbConfig(enabled=False, group=None, team=None, project='deepspeed') csv_monitor=CSVConfig(enabled=False, output_path='', job_name='DeepSpeedJobName') enabled=False
[2024-09-10 21:10:54,871] [INFO] [config.py:1001:print] nebula_config ................ {
"enabled": false,
"persistent_storage_path": null,
"persistent_time_interval": 100,
"num_of_version_in_retention": 2,
"enable_nebula_load": true,
"load_path": null
}
[2024-09-10 21:10:54,871] [INFO] [config.py:1001:print] optimizer_legacy_fusion ...... False
[2024-09-10 21:10:54,871] [INFO] [config.py:1001:print] optimizer_name ............... None
[2024-09-10 21:10:54,871] [INFO] [config.py:1001:print] optimizer_params ............. None
[2024-09-10 21:10:54,871] [INFO] [config.py:1001:print] pipeline ..................... {'stages': 'auto', 'partition': 'best', 'seed_layers': False, 'activation_checkpoint_interval': 0, 'pipe_partitioned': True, 'grad_partitioned': True}
[2024-09-10 21:10:54,871] [INFO] [config.py:1001:print] pld_enabled .................. False
[2024-09-10 21:10:54,871] [INFO] [config.py:1001:print] pld_params ................... False
[2024-09-10 21:10:54,871] [INFO] [config.py:1001:print] prescale_gradients ........... False
[2024-09-10 21:10:54,872] [INFO] [config.py:1001:print] scheduler_name ............... None
[2024-09-10 21:10:54,872] [INFO] [config.py:1001:print] scheduler_params ............. None
[2024-09-10 21:10:54,872] [INFO] [config.py:1001:print] seq_parallel_communication_data_type torch.float32
[2024-09-10 21:10:54,872] [INFO] [config.py:1001:print] sparse_attention ............. None
[2024-09-10 21:10:54,872] [INFO] [config.py:1001:print] sparse_gradients_enabled ..... False
[2024-09-10 21:10:54,872] [INFO] [config.py:1001:print] steps_per_print .............. 10
[2024-09-10 21:10:54,872] [INFO] [config.py:1001:print] timers_config ................ enabled=True synchronized=True
[2024-09-10 21:10:54,872] [INFO] [config.py:1001:print] train_batch_size ............. 128
[2024-09-10 21:10:54,872] [INFO] [config.py:1001:print] train_micro_batch_size_per_gpu 4
[2024-09-10 21:10:54,872] [INFO] [config.py:1001:print] use_data_before_expert_parallel_ False
[2024-09-10 21:10:54,872] [INFO] [config.py:1001:print] use_node_local_storage ....... False
[2024-09-10 21:10:54,872] [INFO] [config.py:1001:print] wall_clock_breakdown ......... False
[2024-09-10 21:10:54,872] [INFO] [config.py:1001:print] weight_quantization_config ... None
[2024-09-10 21:10:54,872] [INFO] [config.py:1001:print] world_size ................... 8
[2024-09-10 21:10:54,872] [INFO] [config.py:1001:print] zero_allow_untested_optimizer False
[2024-09-10 21:10:54,872] [INFO] [config.py:1001:print] zero_config .................. stage=3 contiguous_gradients=True reduce_scatter=True reduce_bucket_size=500,000,000 use_multi_rank_bucket_allreduce=True allgather_partitions=True allgather_bucket_size=500,000,000 overlap_comm=True load_from_fp32_weights=True elastic_checkpoint=False offload_param=DeepSpeedZeroOffloadParamConfig(device='none', nvme_path=None, buffer_count=5, buffer_size=100,000,000, max_in_cpu=1,000,000,000, pin_memory=False) offload_optimizer=DeepSpeedZeroOffloadOptimizerConfig(device='none', nvme_path=None, buffer_count=4, pin_memory=False, pipeline=False, pipeline_read=False, pipeline_write=False, fast_init=False, ratio=1.0) sub_group_size=1,000,000,000 cpu_offload_param=None cpu_offload_use_pin_memory=None cpu_offload=None prefetch_bucket_size=30000000 param_persistence_threshold=10000 model_persistence_threshold=sys.maxsize max_live_parameters=30000000 max_reuse_distance=1,000,000,000 gather_16bit_weights_on_model_save=True use_all_reduce_for_fetch_params=False stage3_gather_fp16_weights_on_model_save=False ignore_unused_parameters=True legacy_stage1=False round_robin_gradients=False zero_hpz_partition_size=1 zero_quantized_weights=False zero_quantized_nontrainable_weights=False zero_quantized_gradients=False mics_shard_size=-1 mics_hierarchical_params_gather=False memory_efficient_linear=False pipeline_loading_checkpoint=False override_module_apply=True
[2024-09-10 21:10:54,872] [INFO] [config.py:1001:print] zero_enabled ................. True
[2024-09-10 21:10:54,872] [INFO] [config.py:1001:print] zero_force_ds_cpu_optimizer .. True
[2024-09-10 21:10:54,872] [INFO] [config.py:1001:print] zero_optimization_stage ...... 3
[2024-09-10 21:10:54,872] [INFO] [config.py:987:print_user_config] json = {
"train_batch_size": 128,
"train_micro_batch_size_per_gpu": 4,
"gradient_accumulation_steps": 4,
"steps_per_print": 10,
"zero_optimization": {
"stage": 3,
"offload_param": {
"device": "none"
},
"offload_optimizer": {
"device": "none"
},
"param_persistence_threshold": 1.000000e+04,
"max_live_parameters": 3.000000e+07,
"prefetch_bucket_size": 3.000000e+07,
"memory_efficient_linear": false,
"gather_16bit_weights_on_model_save": true
},
"gradient_clipping": 1.0,
"prescale_gradients": false,
"wall_clock_breakdown": false,
"hybrid_engine": {
"enabled": false,
"max_out_tokens": 512,
"inference_tp_size": 1,
"release_inference_cache": false,
"pin_parameters": true,
"tp_gather_partition_size": 8
},
"bf16": {
"enabled": true
}
}
***** Running training *****
***** Evaluating at the beginning *****
Evaluation: accuracy = 0.342000, reward_mean = 0.236833, reward_std = 0.019346
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━┓
┃ prompt ┃ higher-reward response ┃ reward ┃ lower-reward response ┃ reward ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━┩
│ BEGINNING OF CONVERSATION: USER: I would like to see a good │ Sure! Google Colab is a great platform for running Python code │ 0.233398 │ Of course! I'm happy to help you with analyzing Python data │ 0.221680 │
│ example of analyzing Python data using Colab. Show me a code │ and analyzing data using Python libraries, like pandas, │ │ using Colab. │ │
│ example. ASSISTANT: │ matplotlib and seaborn. Here's a simple example using a │ │ First, let me clarify that Colab is a free-hosted Jupyter │ │
│ │ dataset about cars and analyzing the relationship between │ │ notebook browser that allows you to write and run Python code │ │
│ │ horsepower and miles per gallon (mpg). │ │ in your web browser. It's a great tool for data analysis and │ │
│ │ │ │ visualization, and we can use it to analyze Python data using │ │
│ │ ```python │ │ various libraries and techniques. │ │
│ │ # Import necessary libraries │ │ Now, let's say we have a dataset of customer information for │ │
│ │ import pandas as pd │ │ an e-commerce website, and we want to analyze it using Colab. │ │
│ │ import matplotlib.pyplot as plt │ │ Here's an example of how we can do this: │ │
│ │ import seaborn as sns │ │ 1. First, let's import the necessary libraries: │ │
│ │ │ │ ``` │ │
│ │ # Load a sample dataset about cars (available on seaborn │ │ !pip install pandas │ │
│ │ library) │ │ import pandas as pd │ │
│ │ df = sns.load_dataset('mpg') │ │ import numpy as np │ │
│ │ │ │ import matplotlib.pyplot as plt │ │
│ │ # Print the first few rows of the dataset │ │ ``` │ │
│ │ print(df.head()) │ │ In this code, we're installing the `pandas` library for data │ │
│ │ │ │ manipulation and analysis, and `numpy` for numerical │ │
│ │ # Visualize the relationship between 'horsepower' and'mpg' │ │ computations. We're also importing the `matplotlib` library │ │
│ │ plt.figure(figsize=(10, 5)) │ │ for data visualization. │ │
│ │ sns.scatterplot(x='horsepower', y='mpg', data=df) │ │ 2. Next, let's load the dataset: │ │
│ │ plt.title('Relationship between Horsepower and MPG') │ │ │ │
│ │ plt.xlabel('Horsepower') │ │ customer_data = pd.read_csv('customer.csv') │ │
│ │ plt.ylabel('Miles per Gallon') │ │ ``` │ │
│ │ │ │ # Replace 'customer.csv' with the name of your dataset file │ │
│ │ # Display the plot │ │ ``` │ │
│ │ plt.show() │ │ This code reads the customer dataset from a CSV file named │ │
│ │ ``` │ │ `customer.csv`. Make sure to replace this with the actual │ │
│ │ │ │ name of your dataset file. │ │
│ │ This code will create a scatter plot showing the relationship │ │ 3. Now, let's examine the dataset: │ │
│ │ between horsepower and miles per gallon (mpg) of various cars. │ │ │ │
│ │ The seaborn library comes with several built-in datasets, one │ │ print(customer_data.head()) │ │
│ │ of which is used in this example. You can also upload your │ │ │ │
│ │ data files into your Colab notebook and load them into a │ │ ``` │ │
│ │ pandas DataFrame for analysis and visualization. │ │ │ │
│ │ │ │ This code displays the first few rows of the dataset using │ │
│ │ To run the code shown above in Google Colab: │ │ the `head()` method of the `pd.DataFrame` object. This gives │ │
│ │ │ │ us a quick rundown of what the dataset looks like. │ │
│ │ 1. Head over to https://colab.research.google.com │ │ 4. Let's analyze the dataset using some basic statistical │ │
│ │ 2. Click `File` > `New notebook` │ │ metrics: │ │
│ │ 3. Copy and paste the code into a code cell │ │ │ │
│ │ 4. Press `Shift` + `Enter` to execute the cell and see the │ │ print(customer_data.mean()) │ │
│ │ output below the cell │ │ │ │
│ │ │ │ ``` │ │
│ │ │ │ │ │
│ │ │ │ This code calculates the mean of various columns in the │ │
│ │ │ │ dataset using the `mean()` method of the `pd.DataFrame` │ │
│ │ │ │ object. This gives us an idea of the average value of each │ │
│ │ │ │ column. │ │
│ │ │ │ 5. Let's visualize the distribution of values in one of the │ │
│ │ │ │ columns: │ │
│ │ │ │ │ │
│ │ │ │ print(customer_data['age'].hist()) │ │
│ │ │ │ │ │
│ │ │ │ ``` │ │
│ │ │ │ │ │
│ │ │ │ This code creates a histogram of the values in the `age` │ │
│ │ │ │ column using the `hist()` method of the `pd.Series` object. │ │
│ │ │ │ This gives us a visual representation of the distribution of │ │
│ │ │ │ ages in the dataset. │ │
│ │ │ │ 6. Finally, let's analyze the data using a machine learning │ │
│ │ │ │ algorithm: │ │
│ │ │ │ │ │
│ │ │ │ from sklearn.cluster import KMeans │ │
│ │ │ │ │ │
│ │ │ │ customer_kmeans = │ │
│ │ │ │ KMeans(n_clusters=5).fit(customer_data.drop('age', axis=1)) │ │
│ │ │ │ │ │
│ │ │ │ ``` │ │
│ │ │ │ │ │
│ │ │ │ This code uses the `KMeans` algorithm from the `scikit-learn` │ │
│ │ │ │ library to cluster the customers based on their │ │
│ │ │ │ characteristics (excluding the `age` column). We can use this │ │
│ │ │ │ to identify groups of similar customers. │ │
│ │ │ │ And that's it! These are just a few examples of how you can │ │
│ │ │ │ use Colab to analyze Python data. Of course, there are many │ │
│ │ │ │ more techniques and libraries you can use, depending on the │ │
│ │ │ │ nature of your dataset. I hope this helps you get started │ │
│ │ │ │ with analyzing your own data using Colab! │ │
├───────────────────────────────────────────────────────────────┼────────────────────────────────────────────────────────────────┼──────────┼───────────────────────────────────────────────────────────────┼──────────┤
│ BEGINNING OF CONVERSATION: USER: write an assignment on │ Title: Designing a University Management System │ 0.229492 │ Assignment Title: University Management System │ 0.220703 │
│ University management system(also write sql commands, │ │ │ │ │
│ relation schema and max 5 table) ASSISTANT: │ Objective: │ │ Introduction: │ │
│ │ The objective of this assignment is to design a university │ │ │ │
│ │ management system by creating a relational schema, SQL │ │ A University Management System is a software application for │ │
│ │ commands, and implementing it using no more than five tables. │ │ managing academic and administrative tasks. This system │ │
│ │ The system will help manage student and staff records, │ │ provides a centralized solution to manage records and tasks │ │
│ │ courses, and other essential aspects of university management. │ │ related to students, teachers, courses, departments, and │ │
│ │ │ │ other aspects of the university. The goal of this assignment │ │
│ │ Specifications: │ │ is to create a University Management System using a │ │
│ │ │ │ relational database. │ │
│ │ 1. The system should be capable of storing and managing │ │ │ │
│ │ student records, including personal details, contact │ │ Architecture of University Management System: │ │
│ │ information, and academic records. │ │ │ │
│ │ 2. The system should be able to store and manage staff │ │ The University Management System will have five main tables │ │
│ │ records, including personal details, contact information, and │ │ in the relational schema: Students, Teachers, Courses, │ │
│ │ employment history. │ │ Departments, and Attendance. These tables will be joined │ │
│ │ 3. The system should manage courses, including course details, │ │ together to provide functionalities such as course │ │
│ │ instructors, and enrolled students. │ │ registration, course management, student profiling, student │ │
│ │ 4. The system should support adding, editing, and removing │ │ performance evaluation, and others. │ │
│ │ records. │ │ │ │
│ │ 5. The system should facilitate querying and reporting based │ │ 1. Students Table: │ │
│ │ on the stored data. │ │ │ │
│ │ │ │ | Field Name | Data Type | Description | │ │
│ │ Schema: │ │ | --- | --- | --- | │ │
│ │ The proposed relational schema for the university management │ │ | student_id | INT | Unique identifier for the student | │ │
│ │ system is as follows: │ │ | first_name | VARCHAR | First name of the student | │ │
│ │ │ │ | last_name | VARCHAR | Last name of the student | │ │
│ │ Table 1: Students (S) │ │ | email | VARCHAR | Email address of the student | │ │
│ │ │ │ | course_id | INT | Course that student is enrolled in | │ │
│ │ * S\_ID (Primary Key): Unique identifier for each student │ │ │ │
│ │ * FirstName: Student's first name │ │ SQL Command to create the Students table: │ │
│ │ * LastName: Student's last name │ │ ```sql │ │
│ │ * MiddleName: Student's middle name │ │ CREATE TABLE students ( │ │
│ │ * Gender: Student's gender │ │ student_id INT PRIMARY KEY, │ │
│ │ * DateOfBirth: Student's date of birth │ │ first_name VARCHAR(50), │ │
│ │ * Address: Student's address │ │ last_name VARCHAR(50), │ │
│ │ * ContactNumber: Student's contact number │ │ email VARCHAR(50), │ │
│ │ * Email: Student's email address │ │ course_id INT │ │
│ │ * RegistrationNumber: Student's registration number │ │ ); │ │
│ │ * EnrollmentStatus: Student's enrollment status │ │ ``` │ │
│ │ │ │ 1. Teachers Table: │ │
│ │ Table 2: Staff ( Employees ) │ │ │ │
│ │ │ │ | Field Name | Data Type | Description | │ │
│ │ * Employee\_ID (Primary Key): Unique identifier for each staff │ │ | --- | --- | --- | │ │
│ │ member │ │ | teacher_id | INT | Unique identifier for the teacher | │ │
│ │ * FirstName: Staff member's first name │ │ | first_name | VARCHAR | First name of the teacher | │ │
│ │ * LastName: Staff member's last name │ │ | last_name | VARCHAR | Last name of the teacher | │ │
│ │ * MiddleName: Staff member's middle name │ │ | email | VARCHAR | Email address of the teacher | │ │
│ │ * Gender: Staff member's gender │ │ | department_id | INT | Department that teacher is associated │ │
│ │ * DateOfBirth: Staff member's date of birth │ │ with | │ │
│ │ * Address: Staff member's address │ │ │ │
│ │ * ContactNumber: Staff member's contact number │ │ SQL Command to create the Teachers table: │ │
│ │ * Email: Staff member's email address │ │ ```sql │ │
│ │ * Position: Staff member's job position │ │ CREATE TABLE teachers ( │ │
│ │ * Department: Staff member's department │ │ teacher_id INT PRIMARY KEY, │ │
│ │ │ │ first_name VARCHAR(50), │ │
│ │ Table 3: Courses (C) │ │ last_name VARCHAR(50), │ │
│ │ │ │ email VARCHAR(50), │ │
│ │ * Course\_ID (Primary Key): Unique identifier for each course │ │ department_id INT │ │
│ │ * CourseName: Course name │ │ ); │ │
│ │ * CourseCode: Course code │ │ ``` │ │
│ │ * Description: Course description │ │ 1. Courses Table: │ │
│ │ * Instructor\_ID: ID of the instructor for the course (Foreign │ │ │ │
│ │ key referencing Staff) │ │ | Field Name | Data Type | Description | │ │
│ │ * Department: Department the course belongs to (Foreign key │ │ | --- | --- | --- | │ │
│ │ referencing Departments) │ │ | course_id | INT | Unique identifier for the course | │ │
│ │ │ │ | course_name | VARCHAR | Name of the course | │ │
│ │ Table 4: Students\_Courses (SC) │ │ | department_id | INT | Department that course belongs to | │ │
│ │ │ │ | instructor_id | INT | ID of the teacher who is teaching the │ │
│ │ * SC\_ID (Primary Key): Unique identifier for the │ │ course | │ │
│ │ student-course relationship │ │ │ │
│ │ * S\_ID (Foreign key): ID of the student (Referencing │ │ SQL Command to create the Courses table: │ │
│ │ Students) │ │ ```sql │ │
│ │ * Course\_ID (Foreign key): ID of the course (Referencing │ │ CREATE TABLE courses ( │ │
│ │ Courses) │ │ course_id INT PRIMARY KEY, │ │
│ │ * EnrollmentDate: Date when the student enrolled in the course │ │ course_name VARCHAR(50), │ │
│ │ * Grade: Final grade earned by the student in the course │ │ department_id INT, │ │
│ │ │ │ instructor_id INT │ │
│ │ Table 5: Departments (D) │ │ ); │ │
│ │ │ │ ``` │ │
│ │ * Department\_ID (Primary Key): Unique identifier for each │ │ 1. Departments Table: │ │
│ │ department │ │ │ │
│ │ * DepartmentName: Department name │ │ | Field Name | Data Type | Description | │ │
│ │ * DepartmentChair: ID of the department chair (Foreign key │ │ | --- | --- | --- | │ │
│ │ referencing Staff) │ │ | department_id | INT | Unique identifier for the department │ │
│ │ │ │ | │ │
│ │ SQL Commands: │ │ | department_name | VARCHAR | Name of the department | │ │
│ │ The following SQL commands will help in implementing and │ │ │ │
│ │ managing the university management system: │ │ SQL Command to create the Departments table: │ │
│ │ │ │ ```sql │ │
│ │ 1. INSERT INTO Students (Student\_ID, FirstName, LastName, │ │ CREATE TABLE departments ( │ │
│ │ MiddleName, Gender, DateOfBirth, Address, ContactNumber, │ │ department_id INT PRIMARY KEY, │ │
│ │ Email, RegistrationNumber, EnrollmentStatus) │ │ department_name VARCHAR(50) │ │
│ │ 2. INSERT INTO Staff (Employee\_ID, FirstName, LastName, │ │ ); │ │
│ │ MiddleName, Gender, DateOfBirth, Address, ContactNumber, │ │ ``` │ │
│ │ Email, Position, Department) │ │ 1. Attendance Table: │ │
│ │ 3. INSERT INTO Courses (Course\_ID, CourseName, CourseCode, │ │ │ │
│ │ Description, Instructor\_ID, Department) │ │ | Field Name | Data Type | Description | │ │
│ │ 4. INSERT INTO Students\_Courses (SC\_ID, S\_ID, Course\_ID, │ │ | --- | --- | --- | │ │
│ │ EnrollmentDate, Grade) │ │ | attendance_id | INT | Unique identifier for attendance │ │
│ │ 5. INSERT INTO Departments (Department\_ID, DepartmentName, │ │ record | │ │
│ │ DepartmentChair) │ │ | student_id | INT | ID of the student attending the class | │ │
│ │ │ │ | course_id | INT | ID of the course attended | │ │
│ │ 1. UPDATE Students SET FirstName = 'NewFirstName', LastName = │ │ | date_time | DATE | Date and time of the class | │ │
│ │ 'NewLastName', Address = 'NewAddress' WHERE Student\_ID = ' │ │ | status | VARCHAR | Status of the student's attendance │ │
│ │ │ │ (Attended/Absent) | │ │
│ │ │ │ │ │
│ │ │ │ SQL Command to create the Attendance table: │ │
│ │ │ │ ```sql │ │
│ │ │ │ CREATE TABLE attendance ( │ │
│ │ │ │ attendance_id INT PRIMARY KEY, │ │
├───────────────────────────────────────────────────────────────┼────────────────────────────────────────────────────────────────┼──────────┼───────────────────────────────────────────────────────────────┼──────────┤
│ BEGINNING OF CONVERSATION: USER: create a cheat sheet for │ Hello! As a respectful and helpful AI assistant, I'm here to │ 0.212891 │ A cheat sheet for data management in clinical trials can │ 0.234375 │
│ data management in clinical trials, including a short │ help you with your question on data management in clinical │ │ include a short description of the necessary documents, such │ │
│ description of the necessary documents, to do's and main │ trials. Here's a cheat sheet that summarizes the necessary │ │ as case report forms (CRFs), informed consent forms (ICFs), │ │
│ activities ASSISTANT: │ documents, to-do's, and main activities for successful data │ │ standard operating procedures (SOPs), and data safety │ │
│ │ management in clinical trials: │ │ monitoring plans (DSMPs). The to do's can include compiling │ │
│ │ │ │ these documents, collecting and managing data, maintaining a │ │
│ │ Necessary Documents: │ │ trial master file (TMF), and conducting statistical analyses. │ │
│ │ │ │ The main activities include conducting a risk assessment, │ │
│ │ 1. Study Protocol: This document outlines the overall plan for │ │ monitoring data quality, ensuring adherence to protocols, and │ │
│ │ the clinical trial, including the objectives, │ │ managing study events. │ │
│ │ inclusion/exclusion criteria, and the methods for data │ │ │ │
│ │ collection and analysis. │ │ │ │
│ │ 2. Data Management Plan (DMP): This document details the │ │ │ │
│ │ strategies and procedures for collecting, storing, and │ │ │ │
│ │ analyzing the data generated during the clinical trial. │ │ │ │
│ │ 3. Case Report Forms (CRFs): These forms are used to collect │ │ │ │
│ │ data from participants during the clinical trial, and they │ │ │ │
│ │ should be designed to capture the relevant information needed │ │ │ │
│ │ for the study. │ │ │ │
│ │ 4. Data Query Management Plan: This plan outlines the │ │ │ │
│ │ procedures for addressing data discrepancies and resolving │ │ │ │
│ │ queries that arise during the clinical trial. │ │ │ │
│ │ 5. Data Monitoring Plan: This plan details the procedures for │ │ │ │
│ │ monitoring the data collected during the clinical trial, │ │ │ │
│ │ including the methods for detecting and addressing anydata │ │ │ │
│ │ issues. │ │ │ │
│ │ │ │ │ │
│ │ To-Do's: │ │ │ │
│ │ │ │ │ │
│ │ 1. Develop a comprehensive Data Management Plan (DMP) that │ │ │ │
│ │ outlines the strategies and procedures for collecting, │ │ │ │
│ │ storing, and analyzing the data generated during the clinical │ │ │ │
│ │ trial. │ │ │ │
│ │ 2. Train the study team on the data management procedures │ │ │ │
│ │ outlined in the DMP. │ │ │ │
│ │ 3. Develop and implement a robust data quality control process │ │ │ │
│ │ to ensure the accuracy, completeness, and consistency of the │ │ │ │
│ │ data collected during the clinical trial. │ │ │ │
│ │ 4. Regularly review and update the DMP as needed to ensure │ │ │ │
│ │ that it remains relevant and effective throughout the clinical │ │ │ │
│ │ trial. │ │ │ │
│ │ 5. Monitor the data collected during the clinical trial in │ │ │ │
│ │ accordance with the Data Monitoring Plan to ensure that the │ │ │ │
│ │ data is accurate, complete, and consistent. │ │ │ │
│ │ │ │ │ │
│ │ Main Activities: │ │ │ │
│ │ │ │ │ │
│ │ 1. Data Collection: This involves the use of CRFs and other │ │ │ │
│ │ data collection tools to capture the relevant data from │ │ │ │
│ │ participants during the clinical trial. │ │ │ │
│ │ 2. Data Cleaning and Validation: This involves reviewing and │ │ │ │
│ │ correcting any errors or inconsistencies in the data to ensure │ │ │ │
│ │ that it is accurate and complete. │ │ │ │
│ │ 3. Data Integration: This involves combining data from │ │ │ │
│ │ multiple sources (e.g., CRFs, laboratory results, medical │ │ │ │
│ │ imaging) to create a comprehensive view of the data. │ │ │ │
│ │ 4. Data Analysis: This involves using statistical methods and │ │ │ │
│ │ other techniques to extract insights and meaning from the data │ │ │ │
│ │ collected during the clinical trial. │ │ │ │
│ │ 5. Data Reporting: This involves presenting the findings of │ │ │ │
│ │ the clinical trial in a clear and concise manner, using │ │ │ │
│ │ graphs, tables, and other visual aids to help stakeholders │ │ │ │
│ │ understand the results. │ │ │ │
│ │ │ │ │ │
│ │ I hope this cheat sheet is helpful in providing you with a │ │ │ │
│ │ comprehensive overview of the necessary documents, to-do's, │ │ │ │
│ │ and main activities for successful data management in clinical │ │ │ │
│ │ trials. If you have any further questions or need more │ │ │ │
│ │ information, please don't hesitate to ask! │ │ │ │
└───────────────────────────────────────────────────────────────┴────────────────────────────────────────────────────────────────┴──────────┴───────────────────────────────────────────────────────────────┴──────────┘
[2024-09-10 21:16:31,442] [INFO] [logging.py:96:log_dist] [Rank 0] step=10, skipped=0, lr=[1.6666666666666667e-05, 1.6666666666666667e-05], mom=[(0.9, 0.95), (0.9, 0.95)]
[2024-09-10 21:16:31,443] [INFO] [timer.py:258:stop] epoch=0/micro_step=40/global_step=10, RunningAvgSamplesPerSec=11.352064835791357, CurrSamplesPerSec=12.850610086386917, MemAllocated=2.43GB, MaxMemAllocated=8.25GB
[2024-09-10 21:18:28,331] [INFO] [logging.py:96:log_dist] [Rank 0] step=20, skipped=0, lr=[1.998121781934438e-05, 1.998121781934438e-05], mom=[(0.9, 0.95), (0.9, 0.95)]
[2024-09-10 21:18:28,332] [INFO] [timer.py:258:stop] epoch=0/micro_step=80/global_step=20, RunningAvgSamplesPerSec=11.363551332554591, CurrSamplesPerSec=10.94879345671668, MemAllocated=2.43GB, MaxMemAllocated=8.25GB
[2024-09-10 21:20:27,570] [INFO] [logging.py:96:log_dist] [Rank 0] step=30, skipped=0, lr=[1.9905036114631247e-05, 1.9905036114631247e-05], mom=[(0.9, 0.95), (0.9, 0.95)]
[2024-09-10 21:20:27,570] [INFO] [timer.py:258:stop] epoch=0/micro_step=120/global_step=30, RunningAvgSamplesPerSec=11.298163748093565, CurrSamplesPerSec=11.487612894272052, MemAllocated=2.43GB, MaxMemAllocated=8.25GB
[2024-09-10 21:22:22,658] [INFO] [logging.py:96:log_dist] [Rank 0] step=40, skipped=0, lr=[1.9770727719432994e-05, 1.9770727719432994e-05], mom=[(0.9, 0.95), (0.9, 0.95)]
[2024-09-10 21:22:22,658] [INFO] [timer.py:258:stop] epoch=0/micro_step=160/global_step=40, RunningAvgSamplesPerSec=11.353218793245079, CurrSamplesPerSec=10.9797341655996, MemAllocated=2.43GB, MaxMemAllocated=8.25GB
[2024-09-10 21:24:21,862] [INFO] [logging.py:96:log_dist] [Rank 0] step=50, skipped=0, lr=[1.9579080808818035e-05, 1.9579080808818035e-05], mom=[(0.9, 0.95), (0.9, 0.95)]
[2024-09-10 21:24:21,862] [INFO] [timer.py:258:stop] epoch=0/micro_step=200/global_step=50, RunningAvgSamplesPerSec=11.309583578778108, CurrSamplesPerSec=11.005110509882158, MemAllocated=2.43GB, MaxMemAllocated=8.25GB
[2024-09-10 21:26:23,209] [INFO] [logging.py:96:log_dist] [Rank 0] step=60, skipped=0, lr=[1.9331220043062894e-05, 1.9331220043062894e-05], mom=[(0.9, 0.95), (0.9, 0.95)]
[2024-09-10 21:26:23,210] [INFO] [timer.py:258:stop] epoch=0/micro_step=240/global_step=60, RunningAvgSamplesPerSec=11.239385489180092, CurrSamplesPerSec=12.941125799620023, MemAllocated=2.43GB, MaxMemAllocated=8.26GB
[2024-09-10 21:27:36,753] [INFO] [logging.py:96:log_dist] [Rank 0] step=70, skipped=0, lr=[1.9028599967698533e-05, 1.9028599967698533e-05], mom=[(0.9, 0.95), (0.9, 0.95)]
[2024-09-10 21:27:36,753] [INFO] [timer.py:258:stop] epoch=0/micro_step=280/global_step=70, RunningAvgSamplesPerSec=11.919907292071862, CurrSamplesPerSec=37.40828234561593, MemAllocated=2.43GB, MaxMemAllocated=8.26GB
[2024-09-10 21:28:16,039] [INFO] [logging.py:96:log_dist] [Rank 0] step=80, skipped=0, lr=[1.8672996477658767e-05, 1.8672996477658767e-05], mom=[(0.9, 0.95), (0.9, 0.95)]
[2024-09-10 21:28:16,039] [INFO] [timer.py:258:stop] epoch=0/micro_step=320/global_step=80, RunningAvgSamplesPerSec=13.023186384694899, CurrSamplesPerSec=33.891358706157426, MemAllocated=2.43GB, MaxMemAllocated=8.26GB
[2024-09-10 21:28:54,903] [INFO] [logging.py:96:log_dist] [Rank 0] step=90, skipped=0, lr=[1.826649639562266e-05, 1.826649639562266e-05], mom=[(0.9, 0.95), (0.9, 0.95)]
[2024-09-10 21:28:54,904] [INFO] [timer.py:258:stop] epoch=0/micro_step=360/global_step=90, RunningAvgSamplesPerSec=14.029985026793161, CurrSamplesPerSec=34.3639610115291, MemAllocated=2.43GB, MaxMemAllocated=8.26GB
[2024-09-10 21:29:33,402] [INFO] [logging.py:96:log_dist] [Rank 0] step=100, skipped=0, lr=[1.7811485225709255e-05, 1.7811485225709255e-05], mom=[(0.9, 0.95), (0.9, 0.95)]
[2024-09-10 21:29:33,403] [INFO] [timer.py:258:stop] epoch=0/micro_step=400/global_step=100, RunningAvgSamplesPerSec=14.95891743722857, CurrSamplesPerSec=35.51995537038689, MemAllocated=2.43GB, MaxMemAllocated=8.26GB
[2024-09-10 21:30:12,602] [INFO] [logging.py:96:log_dist] [Rank 0] step=110, skipped=0, lr=[1.731063315439084e-05, 1.731063315439084e-05], mom=[(0.9, 0.95), (0.9, 0.95)]
[2024-09-10 21:30:12,603] [INFO] [timer.py:258:stop] epoch=0/micro_step=440/global_step=110, RunningAvgSamplesPerSec=15.790714272731938, CurrSamplesPerSec=33.65822133275722, MemAllocated=2.43GB, MaxMemAllocated=8.27GB
[2024-09-10 21:30:50,861] [INFO] [logging.py:96:log_dist] [Rank 0] step=120, skipped=0, lr=[1.6766879380776983e-05, 1.6766879380776983e-05], mom=[(0.9, 0.95), (0.9, 0.95)]
[2024-09-10 21:30:50,861] [INFO] [timer.py:258:stop] epoch=0/micro_step=480/global_step=120, RunningAvgSamplesPerSec=16.581424302731428, CurrSamplesPerSec=35.42318196453927, MemAllocated=2.43GB, MaxMemAllocated=8.27GB
[2024-09-10 21:31:30,047] [INFO] [logging.py:96:log_dist] [Rank 0] step=130, skipped=0, lr=[1.6183414868225434e-05, 1.6183414868225434e-05], mom=[(0.9, 0.95), (0.9, 0.95)]
[2024-09-10 21:31:30,047] [INFO] [timer.py:258:stop] epoch=0/micro_step=520/global_step=130, RunningAvgSamplesPerSec=17.29520092558459, CurrSamplesPerSec=39.98405555917592, MemAllocated=2.43GB, MaxMemAllocated=8.31GB
[2024-09-10 21:32:08,975] [INFO] [logging.py:96:log_dist] [Rank 0] step=140, skipped=0, lr=[1.55636636185003e-05, 1.55636636185003e-05], mom=[(0.9, 0.95), (0.9, 0.95)]
[2024-09-10 21:32:08,976] [INFO] [timer.py:258:stop] epoch=0/micro_step=560/global_step=140, RunningAvgSamplesPerSec=17.960545464179184, CurrSamplesPerSec=36.18071795491109, MemAllocated=2.43GB, MaxMemAllocated=8.31GB
[2024-09-10 21:32:47,194] [INFO] [logging.py:96:log_dist] [Rank 0] step=150, skipped=0, lr=[1.4911262578368233e-05, 1.4911262578368233e-05], mom=[(0.9, 0.95), (0.9, 0.95)]
[2024-09-10 21:32:47,195] [INFO] [timer.py:258:stop] epoch=0/micro_step=600/global_step=150, RunningAvgSamplesPerSec=18.588957116713487, CurrSamplesPerSec=35.50834292851299, MemAllocated=2.43GB, MaxMemAllocated=8.31GB
[2024-09-10 21:33:26,895] [INFO] [logging.py:96:log_dist] [Rank 0] step=160, skipped=0, lr=[1.4230040296548588e-05, 1.4230040296548588e-05], mom=[(0.9, 0.95), (0.9, 0.95)]
[2024-09-10 21:33:26,896] [INFO] [timer.py:258:stop] epoch=0/micro_step=640/global_step=160, RunningAvgSamplesPerSec=19.152311385651867, CurrSamplesPerSec=33.165219493804244, MemAllocated=2.43GB, MaxMemAllocated=8.31GB
[2024-09-10 21:34:04,963] [INFO] [logging.py:96:log_dist] [Rank 0] step=170, skipped=0, lr=[1.352399445626722e-05, 1.352399445626722e-05], mom=[(0.9, 0.95), (0.9, 0.95)]
[2024-09-10 21:34:04,964] [INFO] [timer.py:258:stop] epoch=0/micro_step=680/global_step=170, RunningAvgSamplesPerSec=19.705901192348477, CurrSamplesPerSec=38.5529274775763, MemAllocated=2.43GB, MaxMemAllocated=8.31GB
[2024-09-10 21:34:42,710] [INFO] [logging.py:96:log_dist] [Rank 0] step=180, skipped=0, lr=[1.2797268415261681e-05, 1.2797268415261681e-05], mom=[(0.9, 0.95), (0.9, 0.95)]
[2024-09-10 21:34:42,710] [INFO] [timer.py:258:stop] epoch=0/micro_step=720/global_step=180, RunningAvgSamplesPerSec=20.231478027340223, CurrSamplesPerSec=37.03658383470068, MemAllocated=2.43GB, MaxMemAllocated=8.31GB
[2024-09-10 21:35:22,082] [INFO] [logging.py:96:log_dist] [Rank 0] step=190, skipped=0, lr=[1.2054126890910499e-05, 1.2054126890910499e-05], mom=[(0.9, 0.95), (0.9, 0.95)]
[2024-09-10 21:35:22,082] [INFO] [timer.py:258:stop] epoch=0/micro_step=760/global_step=190, RunningAvgSamplesPerSec=20.6988053391199, CurrSamplesPerSec=34.546312777603255, MemAllocated=2.43GB, MaxMemAllocated=8.31GB
[2024-09-10 21:36:00,721] [INFO] [logging.py:96:log_dist] [Rank 0] step=200, skipped=0, lr=[1.1298930933175805e-05, 1.1298930933175805e-05], mom=[(0.9, 0.95), (0.9, 0.95)]
[2024-09-10 21:36:00,721] [INFO] [timer.py:258:stop] epoch=0/micro_step=800/global_step=200, RunningAvgSamplesPerSec=21.146676476095102, CurrSamplesPerSec=34.54886048866023, MemAllocated=2.43GB, MaxMemAllocated=8.31GB
[2024-09-10 21:36:39,570] [INFO] [logging.py:96:log_dist] [Rank 0] step=210, skipped=0, lr=[1.0536112332228057e-05, 1.0536112332228057e-05], mom=[(0.9, 0.95), (0.9, 0.95)]
[2024-09-10 21:36:39,570] [INFO] [timer.py:258:stop] epoch=0/micro_step=840/global_step=210, RunningAvgSamplesPerSec=21.566856479167555, CurrSamplesPerSec=34.07296502962237, MemAllocated=2.43GB, MaxMemAllocated=8.31GB
***** Evaluating at epoch 1/2 *****
Evaluation: accuracy = 0.792333, reward_mean = 0.444733, reward_std = 2.274559
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━┓
┃ prompt ┃ higher-reward response ┃ reward ┃ lower-reward response ┃ reward ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━┩
│ BEGINNING OF CONVERSATION: USER: I would like to see a good │ Sure! Google Colab is a great platform for running Python │ 1.906250 │ Of course! I'm happy to help you with analyzing Python data │ 3.281250 │
│ example of analyzing Python data using Colab. Show me a code │ code and analyzing data using Python libraries, like pandas, │ │ using Colab. │ │
│ example. ASSISTANT: │ matplotlib and seaborn. Here's a simple example using a │ │ First, let me clarify that Colab is a free-hosted Jupyter │ │
│ │ dataset about cars and analyzing the relationship between │ │ notebook browser that allows you to write and run Python code │ │
│ │ horsepower and miles per gallon (mpg). │ │ in your web browser. It's a great tool for data analysis and │ │
│ │ │ │ visualization, and we can use it to analyze Python data using │ │
│ │ ```python │ │ various libraries and techniques. │ │
│ │ # Import necessary libraries │ │ Now, let's say we have a dataset of customer information for │ │
│ │ import pandas as pd │ │ an e-commerce website, and we want to analyze it using Colab. │ │
│ │ import matplotlib.pyplot as plt │ │ Here's an example of how we can do this: │ │
│ │ import seaborn as sns │ │ 1. First, let's import the necessary libraries: │ │
│ │ │ │ ``` │ │
│ │ # Load a sample dataset about cars (available on seaborn │ │ !pip install pandas │ │
│ │ library) │ │ import pandas as pd │ │
│ │ df = sns.load_dataset('mpg') │ │ import numpy as np │ │
│ │ │ │ import matplotlib.pyplot as plt │ │
│ │ # Print the first few rows of the dataset │ │ ``` │ │
│ │ print(df.head()) │ │ In this code, we're installing the `pandas` library for data │ │
│ │ │ │ manipulation and analysis, and `numpy` for numerical │ │
│ │ # Visualize the relationship between 'horsepower' and'mpg' │ │ computations. We're also importing the `matplotlib` library │ │
│ │ plt.figure(figsize=(10, 5)) │ │ for data visualization. │ │
│ │ sns.scatterplot(x='horsepower', y='mpg', data=df) │ │ 2. Next, let's load the dataset: │ │
│ │ plt.title('Relationship between Horsepower and MPG') │ │ │ │
│ │ plt.xlabel('Horsepower') │ │ customer_data = pd.read_csv('customer.csv') │ │
│ │ plt.ylabel('Miles per Gallon') │ │ ``` │ │
│ │ │ │ # Replace 'customer.csv' with the name of your dataset file │ │
│ │ # Display the plot │ │ ``` │ │
│ │ plt.show() │ │ This code reads the customer dataset from a CSV file named │ │
│ │ ``` │ │ `customer.csv`. Make sure to replace this with the actual │ │
│ │ │ │ name of your dataset file. │ │
│ │ This code will create a scatter plot showing the relationship │ │ 3. Now, let's examine the dataset: │ │
│ │ between horsepower and miles per gallon (mpg) of various │ │ │ │
│ │ cars. The seaborn library comes with several built-in │ │ print(customer_data.head()) │ │
│ │ datasets, one of which is used in this example. You can also │ │ │ │
│ │ upload your data files into your Colab notebook and load them │ │ ``` │ │
│ │ into a pandas DataFrame for analysis and visualization. │ │ │ │
│ │ │ │ This code displays the first few rows of the dataset using │ │
│ │ To run the code shown above in Google Colab: │ │ the `head()` method of the `pd.DataFrame` object. This gives │ │
│ │ │ │ us a quick rundown of what the dataset looks like. │ │
│ │ 1. Head over to https://colab.research.google.com │ │ 4. Let's analyze the dataset using some basic statistical │ │
│ │ 2. Click `File` > `New notebook` │ │ metrics: │ │
│ │ 3. Copy and paste the code into a code cell │ │ │ │
│ │ 4. Press `Shift` + `Enter` to execute the cell and see the │ │ print(customer_data.mean()) │ │
│ │ output below the cell │ │ │ │
│ │ │ │ ``` │ │
│ │ │ │ │ │
│ │ │ │ This code calculates the mean of various columns in the │ │
│ │ │ │ dataset using the `mean()` method of the `pd.DataFrame` │ │
│ │ │ │ object. This gives us an idea of the average value of each │ │
│ │ │ │ column. │ │
│ │ │ │ 5. Let's visualize the distribution of values in one of the │ │
│ │ │ │ columns: │ │
│ │ │ │ │ │
│ │ │ │ print(customer_data['age'].hist()) │ │
│ │ │ │ │ │
│ │ │ │ ``` │ │
│ │ │ │ │ │
│ │ │ │ This code creates a histogram of the values in the `age` │ │
│ │ │ │ column using the `hist()` method of the `pd.Series` object. │ │
│ │ │ │ This gives us a visual representation of the distribution of │ │
│ │ │ │ ages in the dataset. │ │
│ │ │ │ 6. Finally, let's analyze the data using a machine learning │ │
│ │ │ │ algorithm: │ │
│ │ │ │ │ │
│ │ │ │ from sklearn.cluster import KMeans │ │
│ │ │ │ │ │
│ │ │ │ customer_kmeans = │ │
│ │ │ │ KMeans(n_clusters=5).fit(customer_data.drop('age', axis=1)) │ │
│ │ │ │ │ │
│ │ │ │ ``` │ │
│ │ │ │ │ │
│ │ │ │ This code uses the `KMeans` algorithm from the `scikit-learn` │ │
│ │ │ │ library to cluster the customers based on their │ │
│ │ │ │ characteristics (excluding the `age` column). We can use this │ │
│ │ │ │ to identify groups of similar customers. │ │
│ │ │ │ And that's it! These are just a few examples of how you can │ │
│ │ │ │ use Colab to analyze Python data. Of course, there are many │ │
│ │ │ │ more techniques and libraries you can use, depending on the │ │
│ │ │ │ nature of your dataset. I hope this helps you get started │ │
│ │ │ │ with analyzing your own data using Colab! │ │
├───────────────────────────────────────────────────────────────┼───────────────────────────────────────────────────────────────┼──────────┼───────────────────────────────────────────────────────────────┼───────────┤
│ BEGINNING OF CONVERSATION: USER: write an assignment on │ Title: Designing a University Management System │ 2.515625 │ Assignment Title: University Management System │ 2.671875 │
│ University management system(also write sql commands, │ │ │ │ │
│ relation schema and max 5 table) ASSISTANT: │ Objective: │ │ Introduction: │ │
│ │ The objective of this assignment is to design a university │ │ │ │
│ │ management system by creating a relational schema, SQL │ │ A University Management System is a software application for │ │
│ │ commands, and implementing it using no more than five tables. │ │ managing academic and administrative tasks. This system │ │
│ │ The system will help manage student and staff records, │ │ provides a centralized solution to manage records and tasks │ │
│ │ courses, and other essential aspects of university │ │ related to students, teachers, courses, departments, and │ │
│ │ management. │ │ other aspects of the university. The goal of this assignment │ │
│ │ │ │ is to create a University Management System using a │ │
│ │ Specifications: │ │ relational database. │ │
│ │ │ │ │ │
│ │ 1. The system should be capable of storing and managing │ │ Architecture of University Management System: │ │
│ │ student records, including personal details, contact │ │ │ │
│ │ information, and academic records. │ │ The University Management System will have five main tables │ │
│ │ 2. The system should be able to store and manage staff │ │ in the relational schema: Students, Teachers, Courses, │ │
│ │ records, including personal details, contact information, and │ │ Departments, and Attendance. These tables will be joined │ │
│ │ employment history. │ │ together to provide functionalities such as course │ │
│ │ 3. The system should manage courses, including course │ │ registration, course management, student profiling, student │ │
│ │ details, instructors, and enrolled students. │ │ performance evaluation, and others. │ │
│ │ 4. The system should support adding, editing, and removing │ │ │ │
│ │ records. │ │ 1. Students Table: │ │
│ │ 5. The system should facilitate querying and reporting based │ │ │ │
│ │ on the stored data. │ │ | Field Name | Data Type | Description | │ │
│ │ │ │ | --- | --- | --- | │ │
│ │ Schema: │ │ | student_id | INT | Unique identifier for the student | │ │
│ │ The proposed relational schema for the university management │ │ | first_name | VARCHAR | First name of the student | │ │
│ │ system is as follows: │ │ | last_name | VARCHAR | Last name of the student | │ │
│ │ │ │ | email | VARCHAR | Email address of the student | │ │
│ │ Table 1: Students (S) │ │ | course_id | INT | Course that student is enrolled in | │ │
│ │ │ │ │ │
│ │ * S\_ID (Primary Key): Unique identifier for each student │ │ SQL Command to create the Students table: │ │
│ │ * FirstName: Student's first name │ │ ```sql │ │
│ │ * LastName: Student's last name │ │ CREATE TABLE students ( │ │
│ │ * MiddleName: Student's middle name │ │ student_id INT PRIMARY KEY, │ │
│ │ * Gender: Student's gender │ │ first_name VARCHAR(50), │ │
│ │ * DateOfBirth: Student's date of birth │ │ last_name VARCHAR(50), │ │
│ │ * Address: Student's address │ │ email VARCHAR(50), │ │
│ │ * ContactNumber: Student's contact number │ │ course_id INT │ │
│ │ * Email: Student's email address │ │ ); │ │
│ │ * RegistrationNumber: Student's registration number │ │ ``` │ │
│ │ * EnrollmentStatus: Student's enrollment status │ │ 1. Teachers Table: │ │
│ │ │ │ │ │
│ │ Table 2: Staff ( Employees ) │ │ | Field Name | Data Type | Description | │ │
│ │ │ │ | --- | --- | --- | │ │
│ │ * Employee\_ID (Primary Key): Unique identifier for each │ │ | teacher_id | INT | Unique identifier for the teacher | │ │
│ │ staff member │ │ | first_name | VARCHAR | First name of the teacher | │ │
│ │ * FirstName: Staff member's first name │ │ | last_name | VARCHAR | Last name of the teacher | │ │
│ │ * LastName: Staff member's last name │ │ | email | VARCHAR | Email address of the teacher | │ │
│ │ * MiddleName: Staff member's middle name │ │ | department_id | INT | Department that teacher is associated │ │
│ │ * Gender: Staff member's gender │ │ with | │ │
│ │ * DateOfBirth: Staff member's date of birth │ │ │ │
│ │ * Address: Staff member's address │ │ SQL Command to create the Teachers table: │ │
│ │ * ContactNumber: Staff member's contact number │ │ ```sql │ │
│ │ * Email: Staff member's email address │ │ CREATE TABLE teachers ( │ │
│ │ * Position: Staff member's job position │ │ teacher_id INT PRIMARY KEY, │ │
│ │ * Department: Staff member's department │ │ first_name VARCHAR(50), │ │
│ │ │ │ last_name VARCHAR(50), │ │
│ │ Table 3: Courses (C) │ │ email VARCHAR(50), │ │
│ │ │ │ department_id INT │ │
│ │ * Course\_ID (Primary Key): Unique identifier for each course │ │ ); │ │
│ │ * CourseName: Course name │ │ ``` │ │
│ │ * CourseCode: Course code │ │ 1. Courses Table: │ │
│ │ * Description: Course description │ │ │ │
│ │ * Instructor\_ID: ID of the instructor for the course │ │ | Field Name | Data Type | Description | │ │
│ │ (Foreign key referencing Staff) │ │ | --- | --- | --- | │ │
│ │ * Department: Department the course belongs to (Foreign key │ │ | course_id | INT | Unique identifier for the course | │ │
│ │ referencing Departments) │ │ | course_name | VARCHAR | Name of the course | │ │
│ │ │ │ | department_id | INT | Department that course belongs to | │ │
│ │ Table 4: Students\_Courses (SC) │ │ | instructor_id | INT | ID of the teacher who is teaching the │ │
│ │ │ │ course | │ │
│ │ * SC\_ID (Primary Key): Unique identifier for the │ │ │ │
│ │ student-course relationship │ │ SQL Command to create the Courses table: │ │
│ │ * S\_ID (Foreign key): ID of the student (Referencing │ │ ```sql │ │
│ │ Students) │ │ CREATE TABLE courses ( │ │
│ │ * Course\_ID (Foreign key): ID of the course (Referencing │ │ course_id INT PRIMARY KEY, │ │
│ │ Courses) │ │ course_name VARCHAR(50), │ │
│ │ * EnrollmentDate: Date when the student enrolled in the │ │ department_id INT, │ │
│ │ course │ │ instructor_id INT │ │
│ │ * Grade: Final grade earned by the student in the course │ │ ); │ │
│ │ │ │ ``` │ │
│ │ Table 5: Departments (D) │ │ 1. Departments Table: │ │
│ │ │ │ │ │
│ │ * Department\_ID (Primary Key): Unique identifier for each │ │ | Field Name | Data Type | Description | │ │
│ │ department │ │ | --- | --- | --- | │ │
│ │ * DepartmentName: Department name │ │ | department_id | INT | Unique identifier for the department │ │
│ │ * DepartmentChair: ID of the department chair (Foreign key │ │ | │ │
│ │ referencing Staff) │ │ | department_name | VARCHAR | Name of the department | │ │
│ │ │ │ │ │
│ │ SQL Commands: │ │ SQL Command to create the Departments table: │ │
│ │ The following SQL commands will help in implementing and │ │ ```sql │ │
│ │ managing the university management system: │ │ CREATE TABLE departments ( │ │
│ │ │ │ department_id INT PRIMARY KEY, │ │
│ │ 1. INSERT INTO Students (Student\_ID, FirstName, LastName, │ │ department_name VARCHAR(50) │ │
│ │ MiddleName, Gender, DateOfBirth, Address, ContactNumber, │ │ ); │ │
│ │ Email, RegistrationNumber, EnrollmentStatus) │ │ ``` │ │
│ │ 2. INSERT INTO Staff (Employee\_ID, FirstName, LastName, │ │ 1. Attendance Table: │ │
│ │ MiddleName, Gender, DateOfBirth, Address, ContactNumber, │ │ │ │
│ │ Email, Position, Department) │ │ | Field Name | Data Type | Description | │ │
│ │ 3. INSERT INTO Courses (Course\_ID, CourseName, CourseCode, │ │ | --- | --- | --- | │ │
│ │ Description, Instructor\_ID, Department) │ │ | attendance_id | INT | Unique identifier for attendance │ │
│ │ 4. INSERT INTO Students\_Courses (SC\_ID, S\_ID, Course\_ID, │ │ record | │ │
│ │ EnrollmentDate, Grade) │ │ | student_id | INT | ID of the student attending the class | │ │
│ │ 5. INSERT INTO Departments (Department\_ID, DepartmentName, │ │ | course_id | INT | ID of the course attended | │ │
│ │ DepartmentChair) │ │ | date_time | DATE | Date and time of the class | │ │
│ │ │ │ | status | VARCHAR | Status of the student's attendance │ │
│ │ 1. UPDATE Students SET FirstName = 'NewFirstName', LastName = │ │ (Attended/Absent) | │ │
│ │ 'NewLastName', Address = 'NewAddress' WHERE Student\_ID = ' │ │ │ │
│ │ │ │ SQL Command to create the Attendance table: │ │
│ │ │ │ ```sql │ │
│ │ │ │ CREATE TABLE attendance ( │ │
│ │ │ │ attendance_id INT PRIMARY KEY, │ │
├───────────────────────────────────────────────────────────────┼───────────────────────────────────────────────────────────────┼──────────┼───────────────────────────────────────────────────────────────┼───────────┤
│ BEGINNING OF CONVERSATION: USER: create a cheat sheet for │ Hello! As a respectful and helpful AI assistant, I'm here to │ 2.906250 │ A cheat sheet for data management in clinical trials can │ -1.640625 │
│ data management in clinical trials, including a short │ help you with your question on data management in clinical │ │ include a short description of the necessary documents, such │ │
│ description of the necessary documents, to do's and main │ trials. Here's a cheat sheet that summarizes the necessary │ │ as case report forms (CRFs), informed consent forms (ICFs), │ │
│ activities ASSISTANT: │ documents, to-do's, and main activities for successful data │ │ standard operating procedures (SOPs), and data safety │ │
│ │ management in clinical trials: │ │ monitoring plans (DSMPs). The to do's can include compiling │ │
│ │ │ │ these documents, collecting and managing data, maintaining a │ │
│ │ Necessary Documents: │ │ trial master file (TMF), and conducting statistical analyses. │ │
│ │ │ │ The main activities include conducting a risk assessment, │ │
│ │ 1. Study Protocol: This document outlines the overall plan │ │ monitoring data quality, ensuring adherence to protocols, and │ │
│ │ for the clinical trial, including the objectives, │ │ managing study events. │ │
│ │ inclusion/exclusion criteria, and the methods for data │ │ │ │
│ │ collection and analysis. │ │ │ │
│ │ 2. Data Management Plan (DMP): This document details the │ │ │ │
│ │ strategies and procedures for collecting, storing, and │ │ │ │
│ │ analyzing the data generated during the clinical trial. │ │ │ │
│ │ 3. Case Report Forms (CRFs): These forms are used to collect │ │ │ │
│ │ data from participants during the clinical trial, and they │ │ │ │
│ │ should be designed to capture the relevant information needed │ │ │ │
│ │ for the study. │ │ │ │
│ │ 4. Data Query Management Plan: This plan outlines the │ │ │ │
│ │ procedures for addressing data discrepancies and resolving │ │ │ │
│ │ queries that arise during the clinical trial. │ │ │ │
│ │ 5. Data Monitoring Plan: This plan details the procedures for │ │ │ │
│ │ monitoring the data collected during the clinical trial, │ │ │ │
│ │ including the methods for detecting and addressing anydata │ │ │ │
│ │ issues. │ │ │ │
│ │ │ │ │ │
│ │ To-Do's: │ │ │ │
│ │ │ │ │ │
│ │ 1. Develop a comprehensive Data Management Plan (DMP) that │ │ │ │
│ │ outlines the strategies and procedures for collecting, │ │ │ │
│ │ storing, and analyzing the data generated during the clinical │ │ │ │
│ │ trial. │ │ │ │
│ │ 2. Train the study team on the data management procedures │ │ │ │
│ │ outlined in the DMP. │ │ │ │
│ │ 3. Develop and implement a robust data quality control │ │ │ │
│ │ process to ensure the accuracy, completeness, and consistency │ │ │ │
│ │ of the data collected during the clinical trial. │ │ │ │
│ │ 4. Regularly review and update the DMP as needed to ensure │ │ │ │
│ │ that it remains relevant and effective throughout the │ │ │ │
│ │ clinical trial. │ │ │ │
│ │ 5. Monitor the data collected during the clinical trial in │ │ │ │
│ │ accordance with the Data Monitoring Plan to ensure that the │ │ │ │
│ │ data is accurate, complete, and consistent. │ │ │ │
│ │ │ │ │ │
│ │ Main Activities: │ │ │ │
│ │ │ │ │ │
│ │ 1. Data Collection: This involves the use of CRFs and other │ │ │ │
│ │ data collection tools to capture the relevant data from │ │ │ │
│ │ participants during the clinical trial. │ │ │ │
│ │ 2. Data Cleaning and Validation: This involves reviewing and │ │ │ │
│ │ correcting any errors or inconsistencies in the data to │ │ │ │
│ │ ensure that it is accurate and complete. │ │ │ │
│ │ 3. Data Integration: This involves combining data from │ │ │ │
│ │ multiple sources (e.g., CRFs, laboratory results, medical │ │ │ │
│ │ imaging) to create a comprehensive view of the data. │ │ │ │
│ │ 4. Data Analysis: This involves using statistical methods and │ │ │ │
│ │ other techniques to extract insights and meaning from the │ │ │ │
│ │ data collected during the clinical trial. │ │ │ │
│ │ 5. Data Reporting: This involves presenting the findings of │ │ │ │
│ │ the clinical trial in a clear and concise manner, using │ │ │ │
│ │ graphs, tables, and other visual aids to help stakeholders │ │ │ │
│ │ understand the results. │ │ │ │
│ │ │ │ │ │
│ │ I hope this cheat sheet is helpful in providing you with a │ │ │ │
│ │ comprehensive overview of the necessary documents, to-do's, │ │ │ │
│ │ and main activities for successful data management in │ │ │ │
│ │ clinical trials. If you have any further questions or need │ │ │ │
│ │ more information, please don't hesitate to ask! │ │ │ │
└───────────────────────────────────────────────────────────────┴───────────────────────────────────────────────────────────────┴──────────┴───────────────────────────────────────────────────────────────┴───────────┘
[2024-09-10 21:37:51,869] [INFO] [logging.py:96:log_dist] [Rank 0] step=220, skipped=0, lr=[9.770147610939098e-06, 9.770147610939098e-06], mom=[(0.9, 0.95), (0.9, 0.95)]
[2024-09-10 21:37:51,869] [INFO] [timer.py:258:stop] epoch=1/micro_step=36/global_step=220, RunningAvgSamplesPerSec=21.966314339864027, CurrSamplesPerSec=33.760786366468444, MemAllocated=2.43GB, MaxMemAllocated=8.31GB
[2024-09-10 21:38:31,329] [INFO] [logging.py:96:log_dist] [Rank 0] step=230, skipped=0, lr=[9.005531754865929e-06, 9.005531754865929e-06], mom=[(0.9, 0.95), (0.9, 0.95)]
[2024-09-10 21:38:31,330] [INFO] [timer.py:258:stop] epoch=1/micro_step=76/global_step=230, RunningAvgSamplesPerSec=22.32980174701388, CurrSamplesPerSec=35.389844443022064, MemAllocated=2.43GB, MaxMemAllocated=8.31GB
[2024-09-10 21:39:09,598] [INFO] [logging.py:96:log_dist] [Rank 0] step=240, skipped=0, lr=[8.246751833888122e-06, 8.246751833888122e-06], mom=[(0.9, 0.95), (0.9, 0.95)]
[2024-09-10 21:39:09,599] [INFO] [timer.py:258:stop] epoch=1/micro_step=116/global_step=240, RunningAvgSamplesPerSec=22.691040550387914, CurrSamplesPerSec=35.76697721022449, MemAllocated=2.43GB, MaxMemAllocated=8.31GB
[2024-09-10 21:39:48,325] [INFO] [logging.py:96:log_dist] [Rank 0] step=250, skipped=0, lr=[7.4982606702975505e-06, 7.4982606702975505e-06], mom=[(0.9, 0.95), (0.9, 0.95)]
[2024-09-10 21:39:48,326] [INFO] [timer.py:258:stop] epoch=1/micro_step=156/global_step=250, RunningAvgSamplesPerSec=23.020642838591982, CurrSamplesPerSec=34.82238692926768, MemAllocated=2.43GB, MaxMemAllocated=8.31GB
[2024-09-10 21:40:27,165] [INFO] [logging.py:96:log_dist] [Rank 0] step=260, skipped=0, lr=[6.764450707866577e-06, 6.764450707866577e-06], mom=[(0.9, 0.95), (0.9, 0.95)]
[2024-09-10 21:40:27,166] [INFO] [timer.py:258:stop] epoch=1/micro_step=196/global_step=260, RunningAvgSamplesPerSec=23.33485982834451, CurrSamplesPerSec=36.00791452442397, MemAllocated=2.43GB, MaxMemAllocated=8.31GB
[2024-09-10 21:41:06,727] [INFO] [logging.py:96:log_dist] [Rank 0] step=270, skipped=0, lr=[6.049628235241459e-06, 6.049628235241459e-06], mom=[(0.9, 0.95), (0.9, 0.95)]
[2024-09-10 21:41:06,727] [INFO] [timer.py:258:stop] epoch=1/micro_step=236/global_step=270, RunningAvgSamplesPerSec=23.6192219149906, CurrSamplesPerSec=33.58459096268449, MemAllocated=2.43GB, MaxMemAllocated=8.31GB
[2024-09-10 21:41:44,718] [INFO] [logging.py:96:log_dist] [Rank 0] step=280, skipped=0, lr=[5.357988114928221e-06, 5.357988114928221e-06], mom=[(0.9, 0.95), (0.9, 0.95)]
[2024-09-10 21:41:44,718] [INFO] [timer.py:258:stop] epoch=1/micro_step=276/global_step=280, RunningAvgSamplesPerSec=23.914591862369896, CurrSamplesPerSec=36.43050912715466, MemAllocated=2.43GB, MaxMemAllocated=8.31GB
[2024-09-10 21:42:23,492] [INFO] [logging.py:96:log_dist] [Rank 0] step=290, skipped=0, lr=[4.693589166171466e-06, 4.693589166171466e-06], mom=[(0.9, 0.95), (0.9, 0.95)]
[2024-09-10 21:42:23,492] [INFO] [timer.py:258:stop] epoch=1/micro_step=316/global_step=290, RunningAvgSamplesPerSec=24.182353423319345, CurrSamplesPerSec=32.93404717817095, MemAllocated=2.43GB, MaxMemAllocated=8.31GB
[2024-09-10 21:43:02,107] [INFO] [logging.py:96:log_dist] [Rank 0] step=300, skipped=0, lr=[4.060330346189125e-06, 4.060330346189125e-06], mom=[(0.9, 0.95), (0.9, 0.95)]
[2024-09-10 21:43:02,108] [INFO] [timer.py:258:stop] epoch=1/micro_step=356/global_step=300, RunningAvgSamplesPerSec=24.441503622060544, CurrSamplesPerSec=35.991128098419786, MemAllocated=2.43GB, MaxMemAllocated=8.31GB
[2024-09-10 21:43:40,080] [INFO] [logging.py:96:log_dist] [Rank 0] step=310, skipped=0, lr=[3.4619278695411495e-06, 3.4619278695411495e-06], mom=[(0.9, 0.95), (0.9, 0.95)]
[2024-09-10 21:43:40,081] [INFO] [timer.py:258:stop] epoch=1/micro_step=396/global_step=310, RunningAvgSamplesPerSec=24.69759048587517, CurrSamplesPerSec=37.285845626152096, MemAllocated=2.43GB, MaxMemAllocated=8.31GB
[2024-09-10 21:44:19,260] [INFO] [logging.py:96:log_dist] [Rank 0] step=320, skipped=0, lr=[2.901893399904797e-06, 2.901893399904797e-06], mom=[(0.9, 0.95), (0.9, 0.95)]
[2024-09-10 21:44:19,261] [INFO] [timer.py:258:stop] epoch=1/micro_step=436/global_step=320, RunningAvgSamplesPerSec=24.93446897462122, CurrSamplesPerSec=36.56896740097372, MemAllocated=2.43GB, MaxMemAllocated=8.31GB
[2024-09-10 21:44:57,527] [INFO] [logging.py:96:log_dist] [Rank 0] step=330, skipped=0, lr=[2.383513442235812e-06, 2.383513442235812e-06], mom=[(0.9, 0.95), (0.9, 0.95)]
[2024-09-10 21:44:57,527] [INFO] [timer.py:258:stop] epoch=1/micro_step=476/global_step=330, RunningAvgSamplesPerSec=25.165884641725835, CurrSamplesPerSec=35.56060815710096, MemAllocated=2.43GB, MaxMemAllocated=8.31GB
[2024-09-10 21:45:37,653] [INFO] [logging.py:96:log_dist] [Rank 0] step=340, skipped=0, lr=[1.9098300562505266e-06, 1.9098300562505266e-06], mom=[(0.9, 0.95), (0.9, 0.95)]
[2024-09-10 21:45:37,654] [INFO] [timer.py:258:stop] epoch=1/micro_step=516/global_step=340, RunningAvgSamplesPerSec=25.368728610343652, CurrSamplesPerSec=32.247460562594604, MemAllocated=2.43GB, MaxMemAllocated=8.31GB
[2024-09-10 21:46:16,561] [INFO] [logging.py:96:log_dist] [Rank 0] step=350, skipped=0, lr=[1.4836230044098164e-06, 1.4836230044098164e-06], mom=[(0.9, 0.95), (0.9, 0.95)]
[2024-09-10 21:46:16,561] [INFO] [timer.py:258:stop] epoch=1/micro_step=556/global_step=350, RunningAvgSamplesPerSec=25.576276606933504, CurrSamplesPerSec=36.93215706283395, MemAllocated=2.43GB, MaxMemAllocated=8.31GB
[2024-09-10 21:46:54,662] [INFO] [logging.py:96:log_dist] [Rank 0] step=360, skipped=0, lr=[1.1073934391676666e-06, 1.1073934391676666e-06], mom=[(0.9, 0.95), (0.9, 0.95)]
[2024-09-10 21:46:54,663] [INFO] [timer.py:258:stop] epoch=1/micro_step=596/global_step=360, RunningAvgSamplesPerSec=25.78283084656346, CurrSamplesPerSec=31.665623101338372, MemAllocated=2.43GB, MaxMemAllocated=8.31GB
[2024-09-10 21:47:34,087] [INFO] [logging.py:96:log_dist] [Rank 0] step=370, skipped=0, lr=[7.833492252140284e-07, 7.833492252140284e-07], mom=[(0.9, 0.95), (0.9, 0.95)]
[2024-09-10 21:47:34,087] [INFO] [timer.py:258:stop] epoch=1/micro_step=636/global_step=370, RunningAvgSamplesPerSec=25.964791892022447, CurrSamplesPerSec=34.99092767545847, MemAllocated=2.43GB, MaxMemAllocated=8.31GB
[2024-09-10 21:48:12,267] [INFO] [logging.py:96:log_dist] [Rank 0] step=380, skipped=0, lr=[5.133919828468992e-07, 5.133919828468992e-07], mom=[(0.9, 0.95), (0.9, 0.95)]
[2024-09-10 21:48:12,268] [INFO] [timer.py:258:stop] epoch=1/micro_step=676/global_step=380, RunningAvgSamplesPerSec=26.15507899670456, CurrSamplesPerSec=37.44660653913578, MemAllocated=2.43GB, MaxMemAllocated=8.31GB
[2024-09-10 21:48:49,226] [INFO] [logging.py:96:log_dist] [Rank 0] step=390, skipped=0, lr=[2.9910592850826983e-07, 2.9910592850826983e-07], mom=[(0.9, 0.95), (0.9, 0.95)]
[2024-09-10 21:48:49,227] [INFO] [timer.py:258:stop] epoch=1/micro_step=716/global_step=390, RunningAvgSamplesPerSec=26.35423730296853, CurrSamplesPerSec=36.52307551434236, MemAllocated=2.43GB, MaxMemAllocated=8.31GB
[2024-09-10 21:49:27,365] [INFO] [logging.py:96:log_dist] [Rank 0] step=400, skipped=0, lr=[1.4174857797209951e-07, 1.4174857797209951e-07], mom=[(0.9, 0.95), (0.9, 0.95)]
[2024-09-10 21:49:27,366] [INFO] [timer.py:258:stop] epoch=1/micro_step=756/global_step=400, RunningAvgSamplesPerSec=26.53049421074311, CurrSamplesPerSec=36.23310466569185, MemAllocated=2.43GB, MaxMemAllocated=8.31GB
[2024-09-10 21:50:05,119] [INFO] [logging.py:96:log_dist] [Rank 0] step=410, skipped=0, lr=[4.2243366741735457e-08, 4.2243366741735457e-08], mom=[(0.9, 0.95), (0.9, 0.95)]
[2024-09-10 21:50:05,120] [INFO] [timer.py:258:stop] epoch=1/micro_step=796/global_step=410, RunningAvgSamplesPerSec=26.70669284405725, CurrSamplesPerSec=39.64436670212626, MemAllocated=2.43GB, MaxMemAllocated=8.31GB
[2024-09-10 21:50:43,356] [INFO] [logging.py:96:log_dist] [Rank 0] step=420, skipped=0, lr=[1.1742309631845861e-09, 1.1742309631845861e-09], mom=[(0.9, 0.95), (0.9, 0.95)]
[2024-09-10 21:50:43,356] [INFO] [timer.py:258:stop] epoch=1/micro_step=836/global_step=420, RunningAvgSamplesPerSec=26.869131933366237, CurrSamplesPerSec=35.752126381191324, MemAllocated=2.43GB, MaxMemAllocated=8.31GB
***** Evaluating at epoch 2/2 *****
Evaluation: accuracy = 0.782667, reward_mean = 0.206962, reward_std = 2.715212
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━┓
┃ prompt ┃ higher-reward response ┃ reward ┃ lower-reward response ┃ reward ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━┩
│ BEGINNING OF CONVERSATION: USER: I would like to │ Sure! Google Colab is a great platform for running │ 3.156250 │ Of course! I'm happy to help you with analyzing │ 2.828125 │
│ see a good example of analyzing Python data using │ Python code and analyzing data using Python │ │ Python data using Colab. │ │
│ Colab. Show me a code example. ASSISTANT: │ libraries, like pandas, matplotlib and seaborn. │ │ First, let me clarify that Colab is a free-hosted │ │
│ │ Here's a simple example using a dataset about cars │ │ Jupyter notebook browser that allows you to write │ │
│ │ and analyzing the relationship between horsepower │ │ and run Python code in your web browser. It's a │ │
│ │ and miles per gallon (mpg). │ │ great tool for data analysis and visualization, and │ │
│ │ │ │ we can use it to analyze Python data using various │ │
│ │ ```python │ │ libraries and techniques. │ │
│ │ # Import necessary libraries │ │ Now, let's say we have a dataset of customer │ │
│ │ import pandas as pd │ │ information for an e-commerce website, and we want │ │
│ │ import matplotlib.pyplot as plt │ │ to analyze it using Colab. Here's an example of how │ │
│ │ import seaborn as sns │ │ we can do this: │ │
│ │ │ │ 1. First, let's import the necessary libraries: │ │
│ │ # Load a sample dataset about cars (available on │ │ ``` │ │
│ │ seaborn library) │ │ !pip install pandas │ │
│ │ df = sns.load_dataset('mpg') │ │ import pandas as pd │ │
│ │ │ │ import numpy as np │ │
│ │ # Print the first few rows of the dataset │ │ import matplotlib.pyplot as plt │ │
│ │ print(df.head()) │ │ ``` │ │
│ │ │ │ In this code, we're installing the `pandas` library │ │
│ │ # Visualize the relationship between 'horsepower' │ │ for data manipulation and analysis, and `numpy` for │ │
│ │ and'mpg' │ │ numerical computations. We're also importing the │ │
│ │ plt.figure(figsize=(10, 5)) │ │ `matplotlib` library for data visualization. │ │
│ │ sns.scatterplot(x='horsepower', y='mpg', data=df) │ │ 2. Next, let's load the dataset: │ │
│ │ plt.title('Relationship between Horsepower and │ │ │ │
│ │ MPG') │ │ customer_data = pd.read_csv('customer.csv') │ │
│ │ plt.xlabel('Horsepower') │ │ ``` │ │
│ │ plt.ylabel('Miles per Gallon') │ │ # Replace 'customer.csv' with the name of your │ │
│ │ │ │ dataset file │ │
│ │ # Display the plot │ │ ``` │ │
│ │ plt.show() │ │ This code reads the customer dataset from a CSV file │ │
│ │ ``` │ │ named `customer.csv`. Make sure to replace this with │ │
│ │ │ │ the actual name of your dataset file. │ │
│ │ This code will create a scatter plot showing the │ │ 3. Now, let's examine the dataset: │ │
│ │ relationship between horsepower and miles per │ │ │ │
│ │ gallon (mpg) of various cars. The seaborn library │ │ print(customer_data.head()) │ │
│ │ comes with several built-in datasets, one of which │ │ │ │
│ │ is used in this example. You can also upload your │ │ ``` │ │
│ │ data files into your Colab notebook and load them │ │ │ │
│ │ into a pandas DataFrame for analysis and │ │ This code displays the first few rows of the dataset │ │
│ │ visualization. │ │ using the `head()` method of the `pd.DataFrame` │ │
│ │ │ │ object. This gives us a quick rundown of what the │ │
│ │ To run the code shown above in Google Colab: │ │ dataset looks like. │ │
│ │ │ │ 4. Let's analyze the dataset using some basic │ │
│ │ 1. Head over to https://colab.research.google.com │ │ statistical metrics: │ │
│ │ 2. Click `File` > `New notebook` │ │ │ │
│ │ 3. Copy and paste the code into a code cell │ │ print(customer_data.mean()) │ │
│ │ 4. Press `Shift` + `Enter` to execute the cell and │ │ │ │
│ │ see the output below the cell │ │ ``` │ │
│ │ │ │ │ │
│ │ │ │ This code calculates the mean of various columns in │ │
│ │ │ │ the dataset using the `mean()` method of the │ │
│ │ │ │ `pd.DataFrame` object. This gives us an idea of the │ │
│ │ │ │ average value of each column. │ │
│ │ │ │ 5. Let's visualize the distribution of values in one │ │
│ │ │ │ of the columns: │ │
│ │ │ │ │ │
│ │ │ │ print(customer_data['age'].hist()) │ │
│ │ │ │ │ │
│ │ │ │ ``` │ │
│ │ │ │ │ │
│ │ │ │ This code creates a histogram of the values in the │ │
│ │ │ │ `age` column using the `hist()` method of the │ │
│ │ │ │ `pd.Series` object. This gives us a visual │ │
│ │ │ │ representation of the distribution of ages in the │ │
│ │ │ │ dataset. │ │
│ │ │ │ 6. Finally, let's analyze the data using a machine │ │
│ │ │ │ learning algorithm: │ │
│ │ │ │ │ │
│ │ │ │ from sklearn.cluster import KMeans │ │
│ │ │ │ │ │
│ │ │ │ customer_kmeans = │ │
│ │ │ │ KMeans(n_clusters=5).fit(customer_data.drop('age', │ │
│ │ │ │ axis=1)) │ │
│ │ │ │ │ │
│ │ │ │ ``` │ │
│ │ │ │ │ │
│ │ │ │ This code uses the `KMeans` algorithm from the │ │
│ │ │ │ `scikit-learn` library to cluster the customers │ │
│ │ │ │ based on their characteristics (excluding the `age` │ │
│ │ │ │ column). We can use this to identify groups of │ │
│ │ │ │ similar customers. │ │
│ │ │ │ And that's it! These are just a few examples of how │ │
│ │ │ │ you can use Colab to analyze Python data. Of course, │ │
│ │ │ │ there are many more techniques and libraries you can │ │
│ │ │ │ use, depending on the nature of your dataset. I hope │ │
│ │ │ │ this helps you get started with analyzing your own │ │
│ │ │ │ data using Colab! │ │
├─────────────────────────────────────────────────────┼─────────────────────────────────────────────────────┼──────────┼──────────────────────────────────────────────────────┼───────────┤
│ BEGINNING OF CONVERSATION: USER: write an │ Title: Designing a University Management System │ 2.937500 │ Assignment Title: University Management System │ 3.218750 │
│ assignment on University management system(also │ │ │ │ │
│ write sql commands, relation schema and max 5 │ Objective: │ │ Introduction: │ │
│ table) ASSISTANT: │ The objective of this assignment is to design a │ │ │ │
│ │ university management system by creating a │ │ A University Management System is a software │ │
│ │ relational schema, SQL commands, and implementing │ │ application for managing academic and administrative │ │
│ │ it using no more than five tables. The system will │ │ tasks. This system provides a centralized solution │ │
│ │ help manage student and staff records, courses, and │ │ to manage records and tasks related to students, │ │
│ │ other essential aspects of university management. │ │ teachers, courses, departments, and other aspects of │ │
│ │ │ │ the university. The goal of this assignment is to │ │
│ │ Specifications: │ │ create a University Management System using a │ │
│ │ │ │ relational database. │ │
│ │ 1. The system should be capable of storing and │ │ │ │
│ │ managing student records, including personal │ │ Architecture of University Management System: │ │
│ │ details, contact information, and academic records. │ │ │ │
│ │ 2. The system should be able to store and manage │ │ The University Management System will have five main │ │
│ │ staff records, including personal details, contact │ │ tables in the relational schema: Students, Teachers, │ │
│ │ information, and employment history. │ │ Courses, Departments, and Attendance. These tables │ │
│ │ 3. The system should manage courses, including │ │ will be joined together to provide functionalities │ │
│ │ course details, instructors, and enrolled students. │ │ such as course registration, course management, │ │
│ │ 4. The system should support adding, editing, and │ │ student profiling, student performance evaluation, │ │
│ │ removing records. │ │ and others. │ │
│ │ 5. The system should facilitate querying and │ │ │ │
│ │ reporting based on the stored data. │ │ 1. Students Table: │ │
│ │ │ │ │ │
│ │ Schema: │ │ | Field Name | Data Type | Description | │ │
│ │ The proposed relational schema for the university │ │ | --- | --- | --- | │ │
│ │ management system is as follows: │ │ | student_id | INT | Unique identifier for the │ │
│ │ │ │ student | │ │
│ │ Table 1: Students (S) │ │ | first_name | VARCHAR | First name of the student | │ │
│ │ │ │ | last_name | VARCHAR | Last name of the student | │ │
│ │ * S\_ID (Primary Key): Unique identifier for each │ │ | email | VARCHAR | Email address of the student | │ │
│ │ student │ │ | course_id | INT | Course that student is enrolled │ │
│ │ * FirstName: Student's first name │ │ in | │ │
│ │ * LastName: Student's last name │ │ │ │
│ │ * MiddleName: Student's middle name │ │ SQL Command to create the Students table: │ │
│ │ * Gender: Student's gender │ │ ```sql │ │
│ │ * DateOfBirth: Student's date of birth │ │ CREATE TABLE students ( │ │
│ │ * Address: Student's address │ │ student_id INT PRIMARY KEY, │ │
│ │ * ContactNumber: Student's contact number │ │ first_name VARCHAR(50), │ │
│ │ * Email: Student's email address │ │ last_name VARCHAR(50), │ │
│ │ * RegistrationNumber: Student's registration number │ │ email VARCHAR(50), │ │
│ │ * EnrollmentStatus: Student's enrollment status │ │ course_id INT │ │
│ │ │ │ ); │ │
│ │ Table 2: Staff ( Employees ) │ │ ``` │ │
│ │ │ │ 1. Teachers Table: │ │
│ │ * Employee\_ID (Primary Key): Unique identifier for │ │ │ │
│ │ each staff member │ │ | Field Name | Data Type | Description | │ │
│ │ * FirstName: Staff member's first name │ │ | --- | --- | --- | │ │
│ │ * LastName: Staff member's last name │ │ | teacher_id | INT | Unique identifier for the │ │
│ │ * MiddleName: Staff member's middle name │ │ teacher | │ │
│ │ * Gender: Staff member's gender │ │ | first_name | VARCHAR | First name of the teacher | │ │
│ │ * DateOfBirth: Staff member's date of birth │ │ | last_name | VARCHAR | Last name of the teacher | │ │
│ │ * Address: Staff member's address │ │ | email | VARCHAR | Email address of the teacher | │ │
│ │ * ContactNumber: Staff member's contact number │ │ | department_id | INT | Department that teacher is │ │
│ │ * Email: Staff member's email address │ │ associated with | │ │
│ │ * Position: Staff member's job position │ │ │ │
│ │ * Department: Staff member's department │ │ SQL Command to create the Teachers table: │ │
│ │ │ │ ```sql │ │
│ │ Table 3: Courses (C) │ │ CREATE TABLE teachers ( │ │
│ │ │ │ teacher_id INT PRIMARY KEY, │ │
│ │ * Course\_ID (Primary Key): Unique identifier for │ │ first_name VARCHAR(50), │ │
│ │ each course │ │ last_name VARCHAR(50), │ │
│ │ * CourseName: Course name │ │ email VARCHAR(50), │ │
│ │ * CourseCode: Course code │ │ department_id INT │ │
│ │ * Description: Course description │ │ ); │ │
│ │ * Instructor\_ID: ID of the instructor for the │ │ ``` │ │
│ │ course (Foreign key referencing Staff) │ │ 1. Courses Table: │ │
│ │ * Department: Department the course belongs to │ │ │ │
│ │ (Foreign key referencing Departments) │ │ | Field Name | Data Type | Description | │ │
│ │ │ │ | --- | --- | --- | │ │
│ │ Table 4: Students\_Courses (SC) │ │ | course_id | INT | Unique identifier for the course │ │
│ │ │ │ | │ │
│ │ * SC\_ID (Primary Key): Unique identifier for the │ │ | course_name | VARCHAR | Name of the course | │ │
│ │ student-course relationship │ │ | department_id | INT | Department that course │ │
│ │ * S\_ID (Foreign key): ID of the student │ │ belongs to | │ │
│ │ (Referencing Students) │ │ | instructor_id | INT | ID of the teacher who is │ │
│ │ * Course\_ID (Foreign key): ID of the course │ │ teaching the course | │ │
│ │ (Referencing Courses) │ │ │ │
│ │ * EnrollmentDate: Date when the student enrolled in │ │ SQL Command to create the Courses table: │ │
│ │ the course │ │ ```sql │ │
│ │ * Grade: Final grade earned by the student in the │ │ CREATE TABLE courses ( │ │
│ │ course │ │ course_id INT PRIMARY KEY, │ │
│ │ │ │ course_name VARCHAR(50), │ │
│ │ Table 5: Departments (D) │ │ department_id INT, │ │
│ │ │ │ instructor_id INT │ │
│ │ * Department\_ID (Primary Key): Unique identifier │ │ ); │ │
│ │ for each department │ │ ``` │ │
│ │ * DepartmentName: Department name │ │ 1. Departments Table: │ │
│ │ * DepartmentChair: ID of the department chair │ │ │ │
│ │ (Foreign key referencing Staff) │ │ | Field Name | Data Type | Description | │ │
│ │ │ │ | --- | --- | --- | │ │
│ │ SQL Commands: │ │ | department_id | INT | Unique identifier for the │ │
│ │ The following SQL commands will help in │ │ department | │ │
│ │ implementing and managing the university management │ │ | department_name | VARCHAR | Name of the department │ │
│ │ system: │ │ | │ │
│ │ │ │ │ │
│ │ 1. INSERT INTO Students (Student\_ID, FirstName, │ │ SQL Command to create the Departments table: │ │
│ │ LastName, MiddleName, Gender, DateOfBirth, Address, │ │ ```sql │ │
│ │ ContactNumber, Email, RegistrationNumber, │ │ CREATE TABLE departments ( │ │
│ │ EnrollmentStatus) │ │ department_id INT PRIMARY KEY, │ │
│ │ 2. INSERT INTO Staff (Employee\_ID, FirstName, │ │ department_name VARCHAR(50) │ │
│ │ LastName, MiddleName, Gender, DateOfBirth, Address, │ │ ); │ │
│ │ ContactNumber, Email, Position, Department) │ │ ``` │ │
│ │ 3. INSERT INTO Courses (Course\_ID, CourseName, │ │ 1. Attendance Table: │ │
│ │ CourseCode, Description, Instructor\_ID, │ │ │ │
│ │ Department) │ │ | Field Name | Data Type | Description | │ │
│ │ 4. INSERT INTO Students\_Courses (SC\_ID, S\_ID, │ │ | --- | --- | --- | │ │
│ │ Course\_ID, EnrollmentDate, Grade) │ │ | attendance_id | INT | Unique identifier for │ │
│ │ 5. INSERT INTO Departments (Department\_ID, │ │ attendance record | │ │
│ │ DepartmentName, DepartmentChair) │ │ | student_id | INT | ID of the student attending the │ │
│ │ │ │ class | │ │
│ │ 1. UPDATE Students SET FirstName = 'NewFirstName', │ │ | course_id | INT | ID of the course attended | │ │
│ │ LastName = 'NewLastName', Address = 'NewAddress' │ │ | date_time | DATE | Date and time of the class | │ │
│ │ WHERE Student\_ID = ' │ │ | status | VARCHAR | Status of the student's │ │
│ │ │ │ attendance (Attended/Absent) | │ │
│ │ │ │ │ │
│ │ │ │ SQL Command to create the Attendance table: │ │
│ │ │ │ ```sql │ │
│ │ │ │ CREATE TABLE attendance ( │ │
│ │ │ │ attendance_id INT PRIMARY KEY, │ │
├─────────────────────────────────────────────────────┼─────────────────────────────────────────────────────┼──────────┼──────────────────────────────────────────────────────┼───────────┤
│ BEGINNING OF CONVERSATION: USER: create a cheat │ Hello! As a respectful and helpful AI assistant, │ 3.296875 │ A cheat sheet for data management in clinical trials │ -2.515625 │
│ sheet for data management in clinical trials, │ I'm here to help you with your question on data │ │ can include a short description of the necessary │ │
│ including a short description of the necessary │ management in clinical trials. Here's a cheat sheet │ │ documents, such as case report forms (CRFs), │ │
│ documents, to do's and main activities ASSISTANT: │ that summarizes the necessary documents, to-do's, │ │ informed consent forms (ICFs), standard operating │ │
│ │ and main activities for successful data management │ │ procedures (SOPs), and data safety monitoring plans │ │
│ │ in clinical trials: │ │ (DSMPs). The to do's can include compiling these │ │
│ │ │ │ documents, collecting and managing data, maintaining │ │
│ │ Necessary Documents: │ │ a trial master file (TMF), and conducting │ │
│ │ │ │ statistical analyses. The main activities include │ │
│ │ 1. Study Protocol: This document outlines the │ │ conducting a risk assessment, monitoring data │ │
│ │ overall plan for the clinical trial, including the │ │ quality, ensuring adherence to protocols, and │ │
│ │ objectives, inclusion/exclusion criteria, and the │ │ managing study events. │ │
│ │ methods for data collection and analysis. │ │ │ │
│ │ 2. Data Management Plan (DMP): This document │ │ │ │
│ │ details the strategies and procedures for │ │ │ │
│ │ collecting, storing, and analyzing the data │ │ │ │
│ │ generated during the clinical trial. │ │ │ │
│ │ 3. Case Report Forms (CRFs): These forms are used │ │ │ │
│ │ to collect data from participants during the │ │ │ │
│ │ clinical trial, and they should be designed to │ │ │ │
│ │ capture the relevant information needed for the │ │ │ │
│ │ study. │ │ │ │
│ │ 4. Data Query Management Plan: This plan outlines │ │ │ │
│ │ the procedures for addressing data discrepancies │ │ │ │
│ │ and resolving queries that arise during the │ │ │ │
│ │ clinical trial. │ │ │ │
│ │ 5. Data Monitoring Plan: This plan details the │ │ │ │
│ │ procedures for monitoring the data collected during │ │ │ │
│ │ the clinical trial, including the methods for │ │ │ │
│ │ detecting and addressing anydata issues. │ │ │ │
│ │ │ │ │ │
│ │ To-Do's: │ │ │ │
│ │ │ │ │ │
│ │ 1. Develop a comprehensive Data Management Plan │ │ │ │
│ │ (DMP) that outlines the strategies and procedures │ │ │ │
│ │ for collecting, storing, and analyzing the data │ │ │ │
│ │ generated during the clinical trial. │ │ │ │
│ │ 2. Train the study team on the data management │ │ │ │
│ │ procedures outlined in the DMP. │ │ │ │
│ │ 3. Develop and implement a robust data quality │ │ │ │
│ │ control process to ensure the accuracy, │ │ │ │
│ │ completeness, and consistency of the data collected │ │ │ │
│ │ during the clinical trial. │ │ │ │
│ │ 4. Regularly review and update the DMP as needed to │ │ │ │
│ │ ensure that it remains relevant and effective │ │ │ │
│ │ throughout the clinical trial. │ │ │ │
│ │ 5. Monitor the data collected during the clinical │ │ │ │
│ │ trial in accordance with the Data Monitoring Plan │ │ │ │
│ │ to ensure that the data is accurate, complete, and │ │ │ │
│ │ consistent. │ │ │ │
│ │ │ │ │ │
│ │ Main Activities: │ │ │ │
│ │ │ │ │ │
│ │ 1. Data Collection: This involves the use of CRFs │ │ │ │
│ │ and other data collection tools to capture the │ │ │ │
│ │ relevant data from participants during the clinical │ │ │ │
│ │ trial. │ │ │ │
│ │ 2. Data Cleaning and Validation: This involves │ │ │ │
│ │ reviewing and correcting any errors or │ │ │ │
│ │ inconsistencies in the data to ensure that it is │ │ │ │
│ │ accurate and complete. │ │ │ │
│ │ 3. Data Integration: This involves combining data │ │ │ │
│ │ from multiple sources (e.g., CRFs, laboratory │ │ │ │
│ │ results, medical imaging) to create a comprehensive │ │ │ │
│ │ view of the data. │ │ │ │
│ │ 4. Data Analysis: This involves using statistical │ │ │ │
│ │ methods and other techniques to extract insights │ │ │ │
│ │ and meaning from the data collected during the │ │ │ │
│ │ clinical trial. │ │ │ │
│ │ 5. Data Reporting: This involves presenting the │ │ │ │
│ │ findings of the clinical trial in a clear and │ │ │ │
│ │ concise manner, using graphs, tables, and other │ │ │ │
│ │ visual aids to help stakeholders understand the │ │ │ │
│ │ results. │ │ │ │
│ │ │ │ │ │
│ │ I hope this cheat sheet is helpful in providing you │ │ │ │
│ │ with a comprehensive overview of the necessary │ │ │ │
│ │ documents, to-do's, and main activities for │ │ │ │
│ │ successful data management in clinical trials. If │ │ │ │
│ │ you have any further questions or need more │ │ │ │
│ │ information, please don't hesitate to ask! │ │ │ │
└─────────────────────────────────────────────────────┴─────────────────────────────────────────────────────┴──────────┴──────────────────────────────────────────────────────┴───────────┘
Saving model to "/home/juntao/Projects/roo-dev-cty/experiments/outputs/score_lm/gpt2_774m_0910" ...
Saving 16-bit model...
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[2024-09-10 21:51:26,107] [INFO] [engine.py:3591:save_16bit_model] Saving model weights to /home/juntao/Projects/roo-dev-cty/experiments/outputs/score_lm/gpt2_774m_0910/pytorch_model.bin, tag: global_step422
[2024-09-10 21:51:26,107] [INFO] [torch_checkpoint_engine.py:21:save] [Torch] Saving /home/juntao/Projects/roo-dev-cty/experiments/outputs/score_lm/gpt2_774m_0910/pytorch_model.bin...
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[2024-09-10 21:51:27,890] [INFO] [torch_checkpoint_engine.py:33:commit] [Torch] Checkpoint global_step422 is ready now!
Model saved!
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