Use gpu to speed up training

[kkckk1110]

Description

Hello, I am using TFT to train a forecast model. I wonder how can I use GPU to speed up my training process? It seems unclear in the document.

from neuralforecast import NeuralForecast from neuralforecast.losses.pytorch import MQLoss, DistributionLoss, GMM, PMM from neuralforecast.tsdataset import TimeSeriesDataset from neuralforecast.utils import AirPassengers, AirPassengersPanel, AirPassengersStatic import pandas as pd import pytorch_lightning as pl import matplotlib.pyplot as plt

from neuralforecast import NeuralForecast from neuralforecast.models import TFT from neuralforecast.losses.pytorch import MQLoss, DistributionLoss, GMM, PMM from neuralforecast.tsdataset import TimeSeriesDataset from neuralforecast.utils import AirPassengers, AirPassengersPanel, AirPassengersStatic

nf = NeuralForecast( models=[TFT(h=h, input_size=6, hidden_size=20, loss=DistributionLoss(distribution='StudentT', level=[80, 90]), learning_rate=0.005,

stat_exog_list=['airline1'],

            futr_exog_list=futr_exog_list,
            #hist_exog_list=['trend'],
            max_steps=500,
            val_check_steps=10,
            early_stop_patience_steps=10,
            scaler_type='robust',
            windows_batch_size=None,
            enable_progress_bar=True),
],
freq='MS'

) nf.fit(df=train, val_size=12) Y_hat_df = nf.predict(futr_df=Y_test_df)

Link

No response

[flight505]

If you have CUDA installed, then neuralforecast will automatically leverage your GPU to train the models

[kkckk1110]

Thanks! But I came across an error when training.

Stacktrace

```python ProcessRaisedException Traceback (most recent call last) Cell In[23], line 29 8 from neuralforecast.models import TFT 10 nf = NeuralForecast( 11 models=[TFT(h=h, input_size=6, 12 hidden_size=20, (...) 27 freq='MS' 28 ) ---> 29 nf.fit(df=train, val_size=12) File ~/anaconda3/envs/app/lib/python3.9/site-packages/neuralforecast/core.py:274, in NeuralForecast.fit(self, df, static_df, val_size, sort_df, use_init_models, verbose) 271 print("WARNING: Deleting previously fitted models.") 273 for model in self.models: --> 274 model.fit(self.dataset, val_size=val_size) 276 self._fitted = True File ~/anaconda3/envs/app/lib/python3.9/site-packages/neuralforecast/common/_base_windows.py:734, in BaseWindows.fit(self, dataset, val_size, test_size, random_seed) 731 self.trainer_kwargs["check_val_every_n_epoch"] = None 733 trainer = pl.Trainer(**self.trainer_kwargs) --> 734 trainer.fit(self, datamodule=datamodule) File ~/anaconda3/envs/app/lib/python3.9/site-packages/pytorch_lightning/trainer/trainer.py:520, in Trainer.fit(self, model, train_dataloaders, val_dataloaders, datamodule, ckpt_path) 518 model = _maybe_unwrap_optimized(model) 519 self.strategy._lightning_module = model --> 520 call._call_and_handle_interrupt( 521 self, self._fit_impl, model, train_dataloaders, val_dataloaders, datamodule, ckpt_path 522 ) File ~/anaconda3/envs/app/lib/python3.9/site-packages/pytorch_lightning/trainer/call.py:42, in _call_and_handle_interrupt(trainer, trainer_fn, *args, **kwargs) 40 try: 41 if trainer.strategy.launcher is not None: ---> 42 return trainer.strategy.launcher.launch(trainer_fn, *args, trainer=trainer, **kwargs) 43 else: 44 return trainer_fn(*args, **kwargs) File ~/anaconda3/envs/app/lib/python3.9/site-packages/pytorch_lightning/strategies/launchers/multiprocessing.py:124, in _MultiProcessingLauncher.launch(self, function, trainer, *args, **kwargs) 116 process_context = mp.start_processes( 117 self._wrapping_function, 118 args=process_args, (...) 121 join=False, # we will join ourselves to get the process references 122 ) 123 self.procs = process_context.processes --> 124 while not process_context.join(): 125 pass 127 worker_output = return_queue.get() File ~/anaconda3/envs/app/lib/python3.9/site-packages/torch/multiprocessing/spawn.py:160, in ProcessContext.join(self, timeout) 158 msg = "\n\n-- Process %d terminated with the following error:\n" % error_index 159 msg += original_trace --> 160 raise ProcessRaisedException(msg, error_index, failed_process.pid) ProcessRaisedException: -- Process 2 terminated with the following error: Traceback (most recent call last): File "/home/ckj/anaconda3/envs/app/lib/python3.9/site-packages/torch/multiprocessing/spawn.py", line 69, in _wrap fn(i, *args) File "/home/ckj/anaconda3/envs/app/lib/python3.9/site-packages/pytorch_lightning/strategies/launchers/multiprocessing.py", line 147, in _wrapping_function results = function(*args, **kwargs) File "/home/ckj/anaconda3/envs/app/lib/python3.9/site-packages/pytorch_lightning/trainer/trainer.py", line 559, in _fit_impl self._run(model, ckpt_path=ckpt_path) File "/home/ckj/anaconda3/envs/app/lib/python3.9/site-packages/pytorch_lightning/trainer/trainer.py", line 893, in _run self.strategy.setup_environment() File "/home/ckj/anaconda3/envs/app/lib/python3.9/site-packages/pytorch_lightning/strategies/ddp.py", line 143, in setup_environment super().setup_environment() File "/home/ckj/anaconda3/envs/app/lib/python3.9/site-packages/pytorch_lightning/strategies/strategy.py", line 127, in setup_environment self.accelerator.setup_device(self.root_device) File "/home/ckj/anaconda3/envs/app/lib/python3.9/site-packages/pytorch_lightning/accelerators/cuda.py", line 43, in setup_device _check_cuda_matmul_precision(device) File "/home/ckj/anaconda3/envs/app/lib/python3.9/site-packages/lightning_fabric/accelerators/cuda.py", line 345, in _check_cuda_matmul_precision major, _ = torch.cuda.get_device_capability(device) File "/home/ckj/anaconda3/envs/app/lib/python3.9/site-packages/torch/cuda/__init__.py", line 381, in get_device_capability prop = get_device_properties(device) File "/home/ckj/anaconda3/envs/app/lib/python3.9/site-packages/torch/cuda/__init__.py", line 395, in get_device_properties _lazy_init() # will define _get_device_properties File "/home/ckj/anaconda3/envs/app/lib/python3.9/site-packages/torch/cuda/__init__.py", line 235, in _lazy_init raise RuntimeError( RuntimeError: Cannot re-initialize CUDA in forked subprocess. To use CUDA with multiprocessing, you must use the 'spawn' start method

[jmoralez]

@kkckk1110 do you have multiple GPUs? pytorch lightning will try to use all of them by default if you do, can you try setting devices=[0] in your model constructor?

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