[QUESTION][REQUEST] Way to move model to a specific device

In torch you can move model to run on a specific device via my_model.to(device) Is there a way currently to do that with Darts?

It can be especially useful for m1 macs, as it does not use CUDA and you need to specify an mps backend. In pure torch it’s very easy to do, but I couldn’t find a way to do that with Darts.

[skeenan]

Got this working. Solution to get num_loader_workers working was to wrap the code execution logic into an
if name == ‘main’ guard.

However, even with num_workers > 0 still very slow on GPU. 2seconds on CPU - I killed the still-running process after 30mins on GPU. I can only conclude there is some sort of issue with current state of M1 PyTorch implementation. Not worth bothering with this in the current state of support IMO. Hope this helps someone in future.

Complete code below.

import numpy as np
import pandas as pd

import matplotlib.pyplot as plt

from darts.dataprocessing.transformers import Scaler
from darts.models import RNNModel
from darts.metrics import mape
from darts.datasets import AirPassengersDataset

def main():
    # Read data:
    series = AirPassengersDataset().load()
    series = series.astype(np.float32)

    # Create training and validation sets:
    train, val = series.split_after(pd.Timestamp("19590101"))

    # Normalize the time series (note: we avoid fitting the transformer on the validation set)
    transformer = Scaler()
    train_transformed = transformer.fit_transform(train)
    val_transformed = transformer.transform(val)
    series_transformed = transformer.transform(series)


    my_model = RNNModel(
        model="RNN",
        hidden_dim=20,
        dropout=0,
        batch_size=40,
        n_epochs=300,
        optimizer_kwargs={"lr": 1e-3},
        model_name="Air_RNN",
        log_tensorboard=True,
        random_state=42,
        training_length=20,
        input_chunk_length=14,
        force_reset=True,
         pl_trainer_kwargs={
          "accelerator": "gpu",
          "devices": [0],
        },


    )

    my_model.fit(train_transformed, val_series=val_transformed, verbose=False, num_loader_workers=4)

if __name__ == '__main__':
    main()

[skeenan]

Using the latest nightly build for PyTorch. Have tried diving deeper, but no luck. If I ever resolve I’ll post.

Wondering if this is related https://github.com/Lightning-AI/lightning/issues/4289