Device is not actually using gpu

Describe the bug

I train the model with device_name == ‘cuda’ or left as default to use the gpu but trains on cpu instead. What is the current behavior? I train the model with device_name == ‘cuda’ or left as default to use the gpu but trains on cpu instead. It finds Device used : cuda but doesn’t seem to use the gpu

If the current behavior is a bug, please provide the steps to reproduce.

from pytorch_tabnet.multitask import TabNetMultiTaskClassifier
import optuna
import pandas as pd
import numpy as np

import utils

utils.get_latest_round_data(tournament_only=False)

train_data = utils.load_data('training')
valid_data = utils.load_data('validation')

train_data = train_data.dropna()
valid_data = valid_data.dropna()

targets, features = utils.get_targets_features(train_data)

X_train = train_data[features].values
Y_train = (train_data[targets].values*12).astype('int')

X_valid = valid_data[features].values
Y_valid = (valid_data[targets].values*12).astype('int')

clf = TabNetMultiTaskClassifier(device_name = 'cuda') 
clf.fit(X_train, Y_train,
        eval_set=[(X_train, Y_train), (X_valid, Y_valid)])

Expected behavior

It should train on gpu Screenshots

Other relevant information:

Additional context

[dzheng-pitt]

Yes, like you said it was a bit faster, and I saw the GPU utilization increase to a couple percent.

[dzheng-pitt]

Yes! It was all good now. Thank you!