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_finetune8431.out
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==========================================
SLURM_JOB_ID = 8431
SLURM_NODELIST = virya3
==========================================
Tue Feb 13 18:30:23 2024
+---------------------------------------------------------------------------------------+
| NVIDIA-SMI 535.86.10 Driver Version: 535.86.10 CUDA Version: 12.2 |
|-----------------------------------------+----------------------+----------------------+
| GPU Name Persistence-M | Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap | Memory-Usage | GPU-Util Compute M. |
| | | MIG M. |
|=========================================+======================+======================|
| 0 NVIDIA A100-SXM4-40GB Off | 00000000:C1:00.0 Off | On |
| N/A 24C P0 41W / 400W | 74MiB / 40960MiB | N/A Default |
| | | Enabled |
+-----------------------------------------+----------------------+----------------------+
+---------------------------------------------------------------------------------------+
| MIG devices: |
+------------------+--------------------------------+-----------+-----------------------+
| GPU GI CI MIG | Memory-Usage | Vol| Shared |
| ID ID Dev | BAR1-Usage | SM Unc| CE ENC DEC OFA JPG |
| | | ECC| |
|==================+================================+===========+=======================|
| 0 1 0 0 | 37MiB / 19968MiB | 42 0 | 3 0 2 0 0 |
| | 0MiB / 32767MiB | | |
+------------------+--------------------------------+-----------+-----------------------+
+---------------------------------------------------------------------------------------+
| Processes: |
| GPU GI CI PID Type Process name GPU Memory |
| ID ID Usage |
|=======================================================================================|
| No running processes found |
+---------------------------------------------------------------------------------------+
Task: IC-MP
Model: ESM-1b
--------------------------------------------------
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Some weights of EsmForSequenceClassification were not initialized from the model checkpoint at facebook/esm1b_t33_650M_UR50S and are newly initialized: ['classifier.dense.weight', 'classifier.out_proj.weight', 'classifier.dense.bias', 'classifier.out_proj.bias']
You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.
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wandb: $ pip install wandb --upgrade
wandb: Tracking run with wandb version 0.16.1
wandb: Run data is saved locally in /home/h_ghazik/toot_bert_cnn_c/wandb/run-20240213_183154-mbifj3tq
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wandb: Syncing run golden-fuse-413
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Some weights of EsmForSequenceClassification were not initialized from the model checkpoint at facebook/esm1b_t33_650M_UR50S and are newly initialized: ['classifier.dense.weight', 'classifier.out_proj.weight', 'classifier.dense.bias', 'classifier.out_proj.bias']
You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.
{'loss': 0.2364, 'learning_rate': 1e-05, 'epoch': 1.37}
{'loss': 0.025, 'learning_rate': 2e-05, 'epoch': 2.75}
{'loss': 0.0103, 'learning_rate': 2.995e-05, 'epoch': 4.12}
{'train_runtime': 5657.2572, 'train_samples_per_second': 8.241, 'train_steps_per_second': 0.128, 'train_loss': 0.07720347815546497, 'epoch': 4.98}
Task: IT-MP
Model: ESM-1b
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{'loss': 0.3636, 'learning_rate': 1e-05, 'epoch': 1.32}
{'loss': 0.0377, 'learning_rate': 1.995e-05, 'epoch': 2.64}
{'loss': 0.0132, 'learning_rate': 2.9900000000000002e-05, 'epoch': 3.96}
{'train_runtime': 5800.8344, 'train_samples_per_second': 8.348, 'train_steps_per_second': 0.13, 'train_loss': 0.11202687443486901, 'epoch': 4.99}
Task: IC-IT
Model: ESM-2
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Some weights of EsmForSequenceClassification were not initialized from the model checkpoint at facebook/esm2_t33_650M_UR50D and are newly initialized: ['classifier.dense.weight', 'classifier.out_proj.weight', 'classifier.dense.bias', 'classifier.out_proj.bias']
You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.
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{'train_runtime': 836.367, 'train_samples_per_second': 7.18, 'train_steps_per_second': 0.108, 'train_loss': 0.6165419260660807, 'epoch': 4.8}
wandb:
wandb: Run history:
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: train/epoch 4.8
wandb: train/global_step 90
wandb: train/learning_rate 3e-05
wandb: train/loss 0.0132
wandb: train/total_flos 1.1790888140033088e+16
wandb: train/train_loss 0.61654
wandb: train/train_runtime 836.367
wandb: train/train_samples_per_second 7.18
wandb: train/train_steps_per_second 0.108
wandb:
wandb: 🚀 View run golden-fuse-413 at: https://wandb.ai/bioinformatics-group/huggingface/runs/mbifj3tq
wandb: ️⚡ View job at https://wandb.ai/bioinformatics-group/huggingface/jobs/QXJ0aWZhY3RDb2xsZWN0aW9uOjEzOTU4NzI0MQ==/version_details/v0
wandb: Synced 6 W&B file(s), 0 media file(s), 2 artifact file(s) and 0 other file(s)
wandb: Find logs at: ./wandb/run-20240213_183154-mbifj3tq/logs