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GPU available: True (cuda), used: True |
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TPU available: False, using: 0 TPU cores |
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IPU available: False, using: 0 IPUs |
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HPU available: False, using: 0 HPUs |
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Downloading and preparing dataset json/default to /home/ace14459tv/t5maru/cache/json/default-76e405bf2a5f1b35/0.0.0/e347ab1c932092252e717ff3f949105a4dd28b27e842dd53157d2f72e276c2e4... |
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Extracting data files: 100%|██████████| 1/1 [00:00<00:00, 374.29it/s] |
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Generating train split: 0 examples [00:00, ? examples/s]
Dataset json downloaded and prepared to /home/ace14459tv/t5maru/cache/json/default-76e405bf2a5f1b35/0.0.0/e347ab1c932092252e717ff3f949105a4dd28b27e842dd53157d2f72e276c2e4. Subsequent calls will reuse this data. |
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Map (num_proc=4): 3%|▎ | 94/2880 [00:00<00:15, 175.45 examples/s]
Map (num_proc=4): 13%|█▎ | 384/2880 [00:00<00:03, 655.72 examples/s]
Map (num_proc=4): 32%|███▏ | 913/2880 [00:00<00:01, 1620.08 examples/s]
Map (num_proc=4): 58%|█████▊ | 1672/2880 [00:00<00:00, 2983.67 examples/s]
Map (num_proc=4): 86%|████████▌ | 2467/2880 [00:01<00:00, 4206.04 examples/s]
Downloading and preparing dataset json/default to /home/ace14459tv/t5maru/cache/json/default-8eccec914afb5393/0.0.0/e347ab1c932092252e717ff3f949105a4dd28b27e842dd53157d2f72e276c2e4... |
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Extracting data files: 100%|██████████| 1/1 [00:00<00:00, 426.86it/s] |
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Generating train split: 0 examples [00:00, ? examples/s]
Dataset json downloaded and prepared to /home/ace14459tv/t5maru/cache/json/default-8eccec914afb5393/0.0.0/e347ab1c932092252e717ff3f949105a4dd28b27e842dd53157d2f72e276c2e4. Subsequent calls will reuse this data. |
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Map (num_proc=4): 16%|█▌ | 96/618 [00:01<00:05, 94.84 examples/s]
LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0] |
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| Name | Type | Params |
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------------------------------------------ |
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0 | model | OptimizedModule | 300 M |
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------------------------------------------ |
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300 M Trainable params |
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0 Non-trainable params |
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300 M Total params |
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1,200.707 Total estimated model params size (MB) |
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[2023-06-30 20:54:37,264] torch._inductor.utils: [WARNING] using triton random, expect difference from eager |
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Metric val_loss improved. New best score: 1.139 |
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Metric val_loss improved by 0.743 >= min_delta = 0.0. New best score: 0.397 |
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Metric val_loss improved by 0.178 >= min_delta = 0.0. New best score: 0.219 |
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Metric val_loss improved by 0.058 >= min_delta = 0.0. New best score: 0.161 |
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Metric val_loss improved by 0.027 >= min_delta = 0.0. New best score: 0.134 |
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Metric val_loss improved by 0.020 >= min_delta = 0.0. New best score: 0.115 |
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Metric val_loss improved by 0.012 >= min_delta = 0.0. New best score: 0.103 |
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Metric val_loss improved by 0.005 >= min_delta = 0.0. New best score: 0.098 |
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Metric val_loss improved by 0.011 >= min_delta = 0.0. New best score: 0.087 |
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Metric val_loss improved by 0.000 >= min_delta = 0.0. New best score: 0.086 |
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Metric val_loss improved by 0.004 >= min_delta = 0.0. New best score: 0.083 |
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Metric val_loss improved by 0.006 >= min_delta = 0.0. New best score: 0.077 |
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Metric val_loss improved by 0.002 >= min_delta = 0.0. New best score: 0.075 |
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Monitored metric val_loss did not improve in the last 3 records. Best score: 0.075. Signaling Trainer to stop. |
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{"log": "trained", "date": "2023-06-30T20:53:45", "elapsed": "00:05:22", "model": "google/mt5-small", "max_length": 128, "target_max_length": 128, "batch_size": 32, "gradient_accumulation_steps": 1, "train_steps": 2700, "accelerator": "gpu", "devices": "auto", "precision": 32, "strategy": "auto", "gradient_clip_val": 1.0, "compile": true, "solver": "adamw", "lr": 0.0003, "warmup_steps": 1, "training_steps": 100000, "adam_epsilon": 1e-08, "weight_decay": 0.0, "epoch": 17, "step": 1530, "saved": "0630_mT5"} |
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😊 testing /home/ace14459tv/t5maru/error_data/0630/error_3_0630_test.jsonl on cuda |
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Downloading and preparing dataset generator/default to /home/ace14459tv/t5maru/cache/generator/default-f9a3d4be341e4e78/0.0.0... |
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Generating train split: 0 examples [00:00, ? examples/s]
Dataset generator downloaded and prepared to /home/ace14459tv/t5maru/cache/generator/default-f9a3d4be341e4e78/0.0.0. Subsequent calls will reuse this data. |
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Map (num_proc=4): 0%| | 0/617 [00:00<?, ? examples/s]
Map (num_proc=4): 25%|██▌ | 155/617 [00:00<00:01, 267.27 examples/s]
😊 Tested 617 items. See 0630_mT5/error_3_0630_tested.jsonl |
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