training fails on VPN dataset with a ValueError

[gsankara]

I see a ValueError: Please pass features or at least one example when writing data` at the end of train_cnn when run on VPN dataset. I have not modified the code. I faced NaN error and set under sampling to False. Then I encountered this one.

Here is the detailed output ``Trainer(val_check_interval=1.0)` was configured so validation will run at the end of the training epoch..

| Name | Type | Params

0 | conv1 | Sequential | 1.0 K 1 | conv2 | Sequential | 200 K 2 | max_pool | MaxPool1d | 0 3 | fc1 | Sequential | 9.9 M 4 | fc2 | Sequential | 20.1 K 5 | fc3 | Sequential | 5.0 K 6 | out | Linear | 867

10.1 M Trainable params 0 Non-trainable params 10.1 M Total params 40.430 Total estimated model params size (MB) Using custom data configuration train.parquet-2c3be5e9d214c057 Downloading and preparing dataset parquet/train.parquet to /home/rvn/.cache/huggingface/datasets/parquet/train.parquet-2c3be5e9d214c057/0.0.0/2a3b91fbd88a2c90d1dbbb32b460cf621d31bd5b05b934492fdef7d8d6f236ec... Downloading data files: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 3663.15it/s] Extracting data files: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 565.27it/s] Traceback (most recent call last): File "/home/rvn/Deep-Packet/train_cnn.py", line 33, in main() File "/home/rvn/miniconda3/envs/deep_packet/lib/python3.10/site-packages/click/core.py", line 1130, in call return self.main(*args, **kwargs) File "/home/rvn/miniconda3/envs/deep_packet/lib/python3.10/site-packages/click/core.py", line 1055, in main rv = self.invoke(ctx) File "/home/rvn/miniconda3/envs/deep_packet/lib/python3.10/site-packages/click/core.py", line 1404, in invoke return ctx.invoke(self.callback, **ctx.params) File "/home/rvn/miniconda3/envs/deep_packet/lib/python3.10/site-packages/click/core.py", line 760, in invoke return __callback(*args, **kwargs) File "/home/rvn/Deep-Packet/train_cnn.py", line 25, in main train_application_classification_cnn_model(data_path, model_path) File "/home/rvn/Deep-Packet/ml/utils.py", line 117, in train_application_classification_cnn_model train_cnn( File "/home/rvn/Deep-Packet/ml/utils.py", line 58, in train_cnn trainer.fit(model) File "/home/rvn/miniconda3/envs/deep_packet/lib/python3.10/site-packages/pytorch_lightning/trainer/trainer.py", line 696, in fit self._call_and_handle_interrupt( File "/home/rvn/miniconda3/envs/deep_packet/lib/python3.10/site-packages/pytorch_lightning/trainer/trainer.py", line 650, in _call_and_handle_interrupt return trainer_fn(*args, **kwargs) File "/home/rvn/miniconda3/envs/deep_packet/lib/python3.10/site-packages/pytorch_lightning/trainer/trainer.py", line 735, in _fit_impl results = self._run(model, ckpt_path=self.ckpt_path) File "/home/rvn/miniconda3/envs/deep_packet/lib/python3.10/site-packages/pytorch_lightning/trainer/trainer.py", line 1166, in _run results = self._run_stage() File "/home/rvn/miniconda3/envs/deep_packet/lib/python3.10/site-packages/pytorch_lightning/trainer/trainer.py", line 1252, in _run_stage return self._run_train() File "/home/rvn/miniconda3/envs/deep_packet/lib/python3.10/site-packages/pytorch_lightning/trainer/trainer.py", line 1283, in _run_train self.fit_loop.run() File "/home/rvn/miniconda3/envs/deep_packet/lib/python3.10/site-packages/pytorch_lightning/loops/loop.py", line 195, in run self.on_run_start(*args, **kwargs) File "/home/rvn/miniconda3/envs/deep_packet/lib/python3.10/site-packages/pytorch_lightning/loops/fit_loop.py", line 211, in on_run_start self.trainer.reset_train_dataloader(self.trainer.lightning_module) File "/home/rvn/miniconda3/envs/deep_packet/lib/python3.10/site-packages/pytorch_lightning/trainer/trainer.py", line 1812, in reset_train_dataloader self.train_dataloader = self._data_connector._request_dataloader(RunningStage.TRAINING) File "/home/rvn/miniconda3/envs/deep_packet/lib/python3.10/site-packages/pytorch_lightning/trainer/connectors/data_connector.py", line 453, in _request_dataloader dataloader = source.dataloader() File "/home/rvn/miniconda3/envs/deep_packet/lib/python3.10/site-packages/pytorch_lightning/trainer/connectors/data_connector.py", line 526, in dataloader return self.instance.trainer._call_lightning_module_hook(self.name, pl_module=self.instance) File "/home/rvn/miniconda3/envs/deep_packet/lib/python3.10/site-packages/pytorch_lightning/trainer/trainer.py", line 1550, in _call_lightning_module_hook output = fn(*args, **kwargs) File "/home/rvn/Deep-Packet/ml/model.py", line 101, in train_dataloader dataset_dict = datasets.load_dataset(self.hparams.data_path) File "/home/rvn/miniconda3/envs/deep_packet/lib/python3.10/site-packages/datasets/load.py", line 1698, in load_dataset builder_instance.download_and_prepare( File "/home/rvn/miniconda3/envs/deep_packet/lib/python3.10/site-packages/datasets/builder.py", line 807, in download_and_prepare self._download_and_prepare( File "/home/rvn/miniconda3/envs/deep_packet/lib/python3.10/site-packages/datasets/builder.py", line 898, in _download_and_prepare self._prepare_split(split_generator, **prepare_split_kwargs) File "/home/rvn/miniconda3/envs/deep_packet/lib/python3.10/site-packages/datasets/builder.py", line 1516, in _prepare_split num_examples, num_bytes = writer.finalize() File "/home/rvn/miniconda3/envs/deep_packet/lib/python3.10/site-packages/datasets/arrow_writer.py", line 559, in finalize raise ValueError("Please pass features or at least one example when writing data") ValueError: Please pass features or at least one example when writing data`