wandb: Downloading large artifact dataset:latest, 179.17MB. 1 files... wandb: 1 of 1 files downloaded. Done. 0:0:0.3 /opt/conda/lib/python3.11/site-packages/anndata/_core/aligned_df.py:68: ImplicitModificationWarning: Transforming to str index. wandb: WARNING Calling wandb.login() after wandb.init() has no effect. wandb: Downloading large artifact human_state_dict:latest, 939.29MB. 1 files... wandb: 1 of 1 files downloaded. Done. 0:0:0.7 /opt/conda/lib/python3.11/site-packages/grelu/model/models.py:771: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature. GPU available: True (cuda), used: True TPU available: False, using: 0 TPU cores HPU available: False, using: 0 HPUs /opt/conda/lib/python3.11/site-packages/pytorch_lightning/loggers/wandb.py:397: UserWarning: There is a wandb run already in progress and newly created instances of `WandbLogger` will reuse this run. If this is not desired, call `wandb.finish()` before instantiating `WandbLogger`. LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [1] Validation DataLoader 0: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 24/24 [00:08<00:00, 2.84it/s] LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [1] | Name | Type | Params | Mode ----------------------------------------------------------------- 0 | model | EnformerPretrainedModel | 72.1 M | train 1 | loss | BCEWithLogitsLoss | 0 | train 2 | val_metrics | MetricCollection | 0 | train 3 | test_metrics | MetricCollection | 0 | train 4 | transform | Identity | 0 | train ----------------------------------------------------------------- 72.1 M Trainable params 0 Non-trainable params 72.1 M Total params 288.279 Total estimated model params size (MB) 240 Modules in train mode 0 Modules in eval mode Epoch 9: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 319/319 [03:28<00:00, 1.53it/s, v_num=t24e, train_loss_step=0.118, train_loss_epoch=0.143] Testing DataLoader 0: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 284/284 [00:09<00:00, 28.44it/s] `Trainer.fit` stopped: `max_epochs=10` reached. wandb: WARNING Calling wandb.login() after wandb.init() has no effect. wandb: Downloading large artifact human_state_dict:latest, 939.29MB. 1 files... wandb: 1 of 1 files downloaded. Done. 0:0:0.7 /opt/conda/lib/python3.11/site-packages/grelu/model/models.py:771: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature. GPU available: True (cuda), used: True TPU available: False, using: 0 TPU cores HPU available: False, using: 0 HPUs LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [1] CPU times: user 13.7 s, sys: 1.66 s, total: 15.4 s Wall time: 15.7 s /opt/conda/lib/python3.11/site-packages/plotnine/stats/stat_bin.py:109: PlotnineWarning: 'stat_bin()' using 'bins = 19'. Pick better value with 'binwidth'. GPU available: True (cuda), used: True TPU available: False, using: 0 TPU cores HPU available: False, using: 0 HPUs LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [1] /opt/conda/lib/python3.11/site-packages/pytorch_lightning/trainer/connectors/data_connector.py:425: PossibleUserWarning: The 'predict_dataloader' does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` to `num_workers=255` in the `DataLoader` to improve performance. Predicting DataLoader 0: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 71/71 [00:04<00:00, 14.21it/s]