slurm submission log: 2022-12-22 23:07:26.299599
created following sbatch script: 

###############################

#!/bin/bash

#SBATCH --account=nlp
#SBATCH --cpus-per-task=2
#SBATCH --exclude=john0,john1,john2,john3,john4,john5,john6,john7,john8,john9,john10,john11,jagupard15
#SBATCH --gres=gpu:1
#SBATCH --job-name=lxuechen-job-3319645
#SBATCH --mem=32G
#SBATCH --open-mode=append
#SBATCH --output=/nlp/scr/lxuechen/human-feedback/tldr_pretrain/gpt2-xl-1e-05-3/log.out
#SBATCH --partition=sphinx
#SBATCH --time=3-0

# activate your desired anaconda environment
. /u/nlp/anaconda/main/anaconda3/envs/lxuechen-torch-stable/etc/profile.d/conda.sh ; conda activate lxuechen-torch-stable

# cd to working directory
cd .

# launch commands
srun --unbuffered run_as_child_processes 'python tldr_pretrain.py   --fp16 True   --half_precision_backend "cuda_amp"   --model_name_or_path "gpt2-xl"   --output_dir "/nlp/scr/lxuechen/human-feedback/tldr_pretrain/gpt2-xl-1e-05-3"   --num_train_epochs 3   --per_device_train_batch_size 2   --per_device_eval_batch_size 4   --gradient_accumulation_steps 64   --evaluation_strategy "steps"   --eval_steps 500   --save_strategy "steps"   --save_steps 500   --save_total_limit 1   --learning_rate 1e-05   --warmup_ratio 0.03   --lr_scheduler_type "cosine"   --logging_steps 100'

###############################

submission to slurm complete!


###############################
slurm submission output

Submitted batch job 5410770



###############################

/var/lib/slurm/slurmd/job5410770/slurm_script: line 15: /u/nlp/anaconda/main/anaconda3/envs/lxuechen-torch-stable/etc/profile.d/conda.sh: No such file or directory

CommandNotFoundError: Your shell has not been properly configured to use 'conda activate'.
To initialize your shell, run

    $ conda init <SHELL_NAME>

Currently supported shells are:
  - bash
  - fish
  - tcsh
  - xonsh
  - zsh
  - powershell

See 'conda init --help' for more information and options.

IMPORTANT: You may need to close and restart your shell after running 'conda init'.


###############################
start time: 2022-12-22 23:07:29.715547
machine: sphinx7
conda env: lxuechen-torch-stable
###############################
running following processes

	python tldr_pretrain.py   --fp16 True   --half_precision_backend "cuda_amp"   --model_name_or_path "gpt2-xl"   --output_dir "/nlp/scr/lxuechen/human-feedback/tldr_pretrain/gpt2-xl-1e-05-3"   --num_train_epochs 3   --per_device_train_batch_size 2   --per_device_eval_batch_size 4   --gradient_accumulation_steps 64   --evaluation_strategy "steps"   --eval_steps 500   --save_strategy "steps"   --save_steps 500   --save_total_limit 1   --learning_rate 1e-05   --warmup_ratio 0.03   --lr_scheduler_type "cosine"   --logging_steps 100


###############################
command outputs: 


Using cuda_amp half precision backend
/u/nlp/anaconda/main/anaconda3/envs/lxuechen-torch-stable/lib/python3.10/site-packages/transformers/optimization.py:306: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning
  warnings.warn(
***** Running training *****
  Num examples = 116722
  Num Epochs = 3
  Instantaneous batch size per device = 2
  Total train batch size (w. parallel, distributed & accumulation) = 128
  Gradient Accumulation steps = 64
  Total optimization steps = 2733
  Number of trainable parameters = 1557612800
Automatic Weights & Biases logging enabled, to disable set os.environ["WANDB_DISABLED"] = "true"
wandb: Currently logged in as: lxuechen. Use `wandb login --relogin` to force relogin
wandb: Tracking run with wandb version 0.13.7
wandb: Run data is saved locally in /sailhome/lxuechen/software/human-feedback/summ/wandb/run-20221222_230947-1aawy2qo
wandb: Run `wandb offline` to turn off syncing.
wandb: Syncing run /nlp/scr/lxuechen/human-feedback/tldr_pretrain/gpt2-xl-1e-05-3
wandb: ⭐️ View project at https://wandb.ai/lxuechen/tldr_pretrain
wandb: 🚀 View run at https://wandb.ai/lxuechen/tldr_pretrain/runs/1aawy2qo

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{'loss': 2.3408, 'learning_rate': 9.998862506715891e-06, 'epoch': 0.11}

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{'loss': 2.2705, 'learning_rate': 9.951193630803286e-06, 'epoch': 0.22}

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{'loss': 2.2454, 'learning_rate': 9.834073214196232e-06, 'epoch': 0.33}

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{'loss': 2.2308, 'learning_rate': 9.649144132125768e-06, 'epoch': 0.44}

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{'loss': 2.222, 'learning_rate': 9.399000427690736e-06, 'epoch': 0.55}

 18%|█▊        | 500/2733 [1:19:58<6:00:10,  9.68s/it]***** Running Evaluation *****
  Num examples = 6447
  Batch size = 4


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{'eval_loss': 2.1795129776000977, 'eval_runtime': 134.9568, 'eval_samples_per_second': 47.771, 'eval_steps_per_second': 11.945, 'epoch': 0.55}

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                                                   Saving model checkpoint to /nlp/scr/lxuechen/human-feedback/tldr_pretrain/gpt2-xl-1e-05-3/checkpoint-500
Configuration saved in /nlp/scr/lxuechen/human-feedback/tldr_pretrain/gpt2-xl-1e-05-3/checkpoint-500/config.json
Model weights saved in /nlp/scr/lxuechen/human-feedback/tldr_pretrain/gpt2-xl-1e-05-3/checkpoint-500/pytorch_model.bin

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{'loss': 2.2111, 'learning_rate': 9.087150924613952e-06, 'epoch': 0.66}

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{'loss': 2.2157, 'learning_rate': 8.717970008158547e-06, 'epoch': 0.77}

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{'loss': 2.1904, 'learning_rate': 8.296636264611935e-06, 'epoch': 0.88}

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{'loss': 2.1934, 'learning_rate': 7.829059840055622e-06, 'epoch': 0.99}

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{'loss': 2.1122, 'learning_rate': 7.321799537376213e-06, 'epoch': 1.1}

 37%|███▋      | 1000/2733 [2:51:40<4:34:41,  9.51s/it]***** Running Evaluation *****
  Num examples = 6447
  Batch size = 4


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{'eval_loss': 2.1545722484588623, 'eval_runtime': 135.1839, 'eval_samples_per_second': 47.691, 'eval_steps_per_second': 11.924, 'epoch': 1.1}

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                                                   Saving model checkpoint to /nlp/scr/lxuechen/human-feedback/tldr_pretrain/gpt2-xl-1e-05-3/checkpoint-1000
Configuration saved in /nlp/scr/lxuechen/human-feedback/tldr_pretrain/gpt2-xl-1e-05-3/checkpoint-1000/config.json
Model weights saved in /nlp/scr/lxuechen/human-feedback/tldr_pretrain/gpt2-xl-1e-05-3/checkpoint-1000/pytorch_model.bin
Deleting older checkpoint [/nlp/scr/lxuechen/human-feedback/tldr_pretrain/gpt2-xl-1e-05-3/checkpoint-500] due to args.save_total_limit

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{'loss': 2.0764, 'learning_rate': 6.781970814417049e-06, 'epoch': 1.21}

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{'loss': 2.0787, 'learning_rate': 6.217145973801762e-06, 'epoch': 1.32}

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{'loss': 2.0785, 'learning_rate': 5.635247944490186e-06, 'epoch': 1.43}

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{'loss': 2.0739, 'learning_rate': 5.044439145017379e-06, 'epoch': 1.54}

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{'loss': 2.0817, 'learning_rate': 4.453006987356882e-06, 'epoch': 1.65}

 55%|█████▍    | 1500/2733 [4:17:07<3:18:21,  9.65s/it]***** Running Evaluation *****
  Num examples = 6447
  Batch size = 4


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{'eval_loss': 2.139988422393799, 'eval_runtime': 135.0241, 'eval_samples_per_second': 47.747, 'eval_steps_per_second': 11.939, 'epoch': 1.65}

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                                                   Saving model checkpoint to /nlp/scr/lxuechen/human-feedback/tldr_pretrain/gpt2-xl-1e-05-3/checkpoint-1500
Configuration saved in /nlp/scr/lxuechen/human-feedback/tldr_pretrain/gpt2-xl-1e-05-3/checkpoint-1500/config.json
Model weights saved in /nlp/scr/lxuechen/human-feedback/tldr_pretrain/gpt2-xl-1e-05-3/checkpoint-1500/pytorch_model.bin
Deleting older checkpoint [/nlp/scr/lxuechen/human-feedback/tldr_pretrain/gpt2-xl-1e-05-3/checkpoint-1000] due to args.save_total_limit

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{'loss': 2.0758, 'learning_rate': 3.869247627472021e-06, 'epoch': 1.76}

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{'loss': 2.0758, 'learning_rate': 3.3013495932132446e-06, 'epoch': 1.87}

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{'loss': 2.0732, 'learning_rate': 2.7572789219399587e-06, 'epoch': 1.97}

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{'loss': 2.0324, 'learning_rate': 2.2446674190680336e-06, 'epoch': 2.09}

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{'loss': 2.0075, 'learning_rate': 1.7707056049662669e-06, 'epoch': 2.2}

 73%|███████▎  | 2000/2733 [5:43:01<1:58:00,  9.66s/it]***** Running Evaluation *****
  Num examples = 6447
  Batch size = 4


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{'eval_loss': 2.139112949371338, 'eval_runtime': 135.0198, 'eval_samples_per_second': 47.749, 'eval_steps_per_second': 11.939, 'epoch': 2.2}

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                                                   Saving model checkpoint to /nlp/scr/lxuechen/human-feedback/tldr_pretrain/gpt2-xl-1e-05-3/checkpoint-2000
Configuration saved in /nlp/scr/lxuechen/human-feedback/tldr_pretrain/gpt2-xl-1e-05-3/checkpoint-2000/config.json
Model weights saved in /nlp/scr/lxuechen/human-feedback/tldr_pretrain/gpt2-xl-1e-05-3/checkpoint-2000/pytorch_model.bin
Deleting older checkpoint [/nlp/scr/lxuechen/human-feedback/tldr_pretrain/gpt2-xl-1e-05-3/checkpoint-1500] due to args.save_total_limit

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{'loss': 1.9987, 'learning_rate': 1.3420418518604827e-06, 'epoch': 2.3}

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{'loss': 2.0033, 'learning_rate': 9.646891255752928e-07, 'epoch': 2.41}

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{'loss': 2.0071, 'learning_rate': 6.439406402687365e-07, 'epoch': 2.52}

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{'loss': 1.9992, 'learning_rate': 3.8429560929037044e-07, 'epoch': 2.63}

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{'loss': 2.0045, 'learning_rate': 1.893961336727451e-07, 'epoch': 2.74}

 91%|█████████▏| 2500/2733 [7:08:21<37:13,  9.59s/it]***** Running Evaluation *****
  Num examples = 6447
  Batch size = 4


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{'eval_loss': 2.137310743331909, 'eval_runtime': 134.9818, 'eval_samples_per_second': 47.762, 'eval_steps_per_second': 11.942, 'epoch': 2.74}

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                                                   Saving model checkpoint to /nlp/scr/lxuechen/human-feedback/tldr_pretrain/gpt2-xl-1e-05-3/checkpoint-2500
Configuration saved in /nlp/scr/lxuechen/human-feedback/tldr_pretrain/gpt2-xl-1e-05-3/checkpoint-2500/config.json
Model weights saved in /nlp/scr/lxuechen/human-feedback/tldr_pretrain/gpt2-xl-1e-05-3/checkpoint-2500/pytorch_model.bin
Deleting older checkpoint [/nlp/scr/lxuechen/human-feedback/tldr_pretrain/gpt2-xl-1e-05-3/checkpoint-2000] due to args.save_total_limit

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{'loss': 1.9924, 'learning_rate': 6.197611353601918e-08, 'epoch': 2.85}

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{'loss': 1.997, 'learning_rate': 3.822899037286276e-09, 'epoch': 2.96}

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Training completed. Do not forget to share your model on huggingface.co/models =)



                                                     
{'train_runtime': 28254.5864, 'train_samples_per_second': 12.393, 'train_steps_per_second': 0.097, 'train_loss': 2.1056736130614966, 'epoch': 3.0}

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Saving model checkpoint to /nlp/scr/lxuechen/human-feedback/tldr_pretrain/gpt2-xl-1e-05-3
Configuration saved in /nlp/scr/lxuechen/human-feedback/tldr_pretrain/gpt2-xl-1e-05-3/config.json
Model weights saved in /nlp/scr/lxuechen/human-feedback/tldr_pretrain/gpt2-xl-1e-05-3/pytorch_model.bin
wandb: Waiting for W&B process to finish... (success).
wandb: 
wandb: Run history:
wandb:                      eval/loss █▄▁▁▁
wandb:                   eval/runtime ▁█▃▃▂
wandb:        eval/samples_per_second █▁▆▆▇
wandb:          eval/steps_per_second █▁▆▆▇
wandb:                    train/epoch ▁▁▂▂▂▂▂▃▃▃▃▃▄▄▄▄▅▅▅▅▆▆▆▆▆▇▇▇▇▇███
wandb:              train/global_step ▁▁▂▂▂▂▂▃▃▃▃▃▄▄▄▄▅▅▅▅▆▆▆▆▆▇▇▇▇▇███
wandb:            train/learning_rate █████▇▇▇▆▆▆▅▅▅▄▄▃▃▃▂▂▂▁▁▁▁▁
wandb:                     train/loss █▇▆▆▆▅▅▅▅▃▃▃▃▃▃▃▃▃▂▁▁▁▁▁▁▁▁
wandb:               train/total_flos ▁
wandb:               train/train_loss ▁
wandb:            train/train_runtime ▁
wandb: train/train_samples_per_second ▁
wandb:   train/train_steps_per_second ▁
wandb: 
wandb: Run summary:
wandb:                      eval/loss 2.13731
wandb:                   eval/runtime 134.9818
wandb:        eval/samples_per_second 47.762
wandb:          eval/steps_per_second 11.942
wandb:                    train/epoch 3.0
wandb:              train/global_step 2733
wandb:            train/learning_rate 0.0
wandb:                     train/loss 1.997
wandb:               train/total_flos 1.3361242101108096e+18
wandb:               train/train_loss 2.10567
wandb:            train/train_runtime 28254.5864
wandb: train/train_samples_per_second 12.393
wandb:   train/train_steps_per_second 0.097
wandb: 
wandb: Synced /nlp/scr/lxuechen/human-feedback/tldr_pretrain/gpt2-xl-1e-05-3: https://wandb.ai/lxuechen/tldr_pretrain/runs/1aawy2qo
wandb: Synced 6 W&B file(s), 0 media file(s), 0 artifact file(s) and 1 other file(s)
wandb: Find logs at: ./wandb/run-20221222_230947-1aawy2qo/logs
###############################
end time: 2022-12-23 07:01:47.540629
elapsed time: 7:54:17.825082