看不到训练进程

[cheemsbaby]

训练的时候卡在这一步，看不到训练进程 (mvp) root@autodl-container-a34a11a952-3cb61709:~/autodl-tmp/absa/multi-view-prompting# bash scripts/run_unified.sh

• export CUDA_VISIBLE_DEVICES=0
• CUDA_VISIBLE_DEVICES=0
• cd src
• for SEED in 5 10 15 20 25
• K=5
• INFER_PATH=5
• CTRL_TOKEN=post
• TASK=unified
• OUT_DIR=../outputs/unified/top5_seed5
• mkdir -p ../outputs/unified/top5_seed5
• python main.py --data_path ../data/ --dataset seed5 --model_name_or_path t5-base --output_dir ../outputs/unified/top5_seed5 --num_train_epochs 20 --save_top_k 0 --task unified --top_k 5 --ctrl_token post --multi_path --num_path 5 --seed 5 --train_batch_size 16 --gradient_accumulation_steps 1 --learning_rate 1e-4 --lowercase --sort_label --data_ratio 1.0 --check_val_every_n_epoch 10 --agg_strategy vote --eval_batch_size 64 --constrained_decode --multi_task --do_train Some weights of the model checkpoint at t5-base were not used when initializing MyT5ForConditionalGeneration: ['decoder.block.0.layer.1.EncDecAttention.relative_attention_bias.weight']
• This IS expected if you are initializing MyT5ForConditionalGeneration from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).

• This IS NOT expected if you are initializing MyT5ForConditionalGeneration from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model). Some weights of MyT5ForConditionalGeneration were not initialized from the model checkpoint at t5-base and are newly initialized: ['encoder.embed_tokens.weight', 'lm_head.weight', 'decoder.embed_tokens.weight'] You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. GPU available: True (cuda), used: True TPU available: False, using: 0 TPU cores IPU available: False, using: 0 IPUs HPU available: False, using: 0 HPUs /root/miniconda3/envs/mvp/lib/python3.8/site-packages/pytorch_lightning/trainer/connectors/logger_connector/logger_connector.py:67: UserWarning: Starting from v1.9.0, tensorboardX has been removed as a dependency of the pytorch_lightning package, due to potential conflicts with other packages in the ML ecosystem. For this reason, logger=True will use CSVLogger as the default logger, unless the tensorboard or tensorboardX packages are found. Please pip install lightning[extra] or one of them to enable TensorBoard support by default warning_cache.warn( You are using a CUDA device ('NVIDIA GeForce RTX 3090') that has Tensor Cores. To properly utilize them, you should set torch.set_float32_matmul_precision('medium' | 'high') which will trade-off precision for performance. For more details, read https://pytorch.org/docs/stable/generated/torch.set_float32_matmul_precision.html#torch.set_float32_matmul_precision LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]

  | Name | Type | Params

  0 | model | MyT5ForConditionalGeneration | 222 M

  222 M Trainable params 0 Non-trainable params 222 M Total params 891.614 Total estimated model params size (MB)

[beeevita]

训练进程在输出目录的 log 文件里

[cheemsbaby]

但是我看不到训练多少了，是我自己的问题么，只有我自己卡在这样，每次ctrl c中止之后才能在log中看到训练多少

[ZubinGou]

You can use tee as in this bash script.

[inksheep]

但是我看不到训练多少了，是我自己的问题么，只有我自己卡在这样，每次ctrl c中止之后才能在log中看到训练多少

你好，我想获得这篇论文用gdown下载的模型，自己使用gdown下载失败，想问问能发我邮箱吗，我的邮箱是775415315@qq.com。非常感谢！