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_cnnnew376.out
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_cnnnew376.out
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Interactive jobs timeout is 24hrs
==========================================
SLURM_JOB_ID = 376
SLURM_NODELIST = virya3
==========================================
Tue Feb 20 14:24:45 2024
+---------------------------------------------------------------------------------------+
| NVIDIA-SMI 545.23.08 Driver Version: 545.23.08 CUDA Version: 12.3 |
|-----------------------------------------+----------------------+----------------------+
| 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 | 00000000:81:00.0 Off | On |
| N/A 23C 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 |
+---------------------------------------------------------------------------------------+
Some weights of EsmModel were not initialized from the model checkpoint at facebook/esm2_t33_650M_UR50D and are newly initialized: ['esm.pooler.dense.weight', 'esm.pooler.dense.bias']
You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.
/home/h_ghazik/toot_bert_cnn_c/cnn_cv_generate_rep_new.py:136: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
X_train_fold = [torch.tensor(x, dtype=torch.float) for x in X_train_fold]
/home/h_ghazik/toot_bert_cnn_c/cnn_cv_generate_rep_new.py:138: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
X_val_fold = [torch.tensor(x, dtype=torch.float) for x in X_val_fold]
/home/h_ghazik/toot_bert_cnn_c/cnn_cv_generate_rep_new.py:136: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
X_train_fold = [torch.tensor(x, dtype=torch.float) for x in X_train_fold]
/home/h_ghazik/toot_bert_cnn_c/cnn_cv_generate_rep_new.py:138: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
X_val_fold = [torch.tensor(x, dtype=torch.float) for x in X_val_fold]
/home/h_ghazik/toot_bert_cnn_c/cnn_cv_generate_rep_new.py:136: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
X_train_fold = [torch.tensor(x, dtype=torch.float) for x in X_train_fold]
/home/h_ghazik/toot_bert_cnn_c/cnn_cv_generate_rep_new.py:138: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
X_val_fold = [torch.tensor(x, dtype=torch.float) for x in X_val_fold]
/home/h_ghazik/toot_bert_cnn_c/cnn_cv_generate_rep_new.py:136: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
X_train_fold = [torch.tensor(x, dtype=torch.float) for x in X_train_fold]
/home/h_ghazik/toot_bert_cnn_c/cnn_cv_generate_rep_new.py:138: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
X_val_fold = [torch.tensor(x, dtype=torch.float) for x in X_val_fold]
/home/h_ghazik/toot_bert_cnn_c/cnn_cv_generate_rep_new.py:136: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
X_train_fold = [torch.tensor(x, dtype=torch.float) for x in X_train_fold]
/home/h_ghazik/toot_bert_cnn_c/cnn_cv_generate_rep_new.py:138: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
X_val_fold = [torch.tensor(x, dtype=torch.float) for x in X_val_fold]
Model saved to ./final_models/final_model_generated_rep_IC_IT.pt