Good morning, fellow deep learning enthusiasts!
Are you struggling to utilize Google Colab’s GPU for training NeuralForecast’s AutoLSTM? You’re not alone! I’ve been in your shoes, and I’m excited to share a straightforward solution to get you up and running.
## The Problem
Google Colab provides an incredible platform for machine learning and deep learning experiments, but specifying the GPU for AutoLSTM can be a bit tricky. The default setup might not automatically detect the GPU, leaving you wondering what’s going on.
## The Solution
Fear not, my friends! With a few tweaks to your code, you can harness the power of Colab’s GPU for faster and more efficient training. Here’s the modified code snippet:
import torch
device = "cuda" if torch.cuda.is_available() else "cpu"
print(device)
trainer_kwargs = {
'accelerator': 'gpu' if device == 'cuda' else 'cpu',
'devices': 1 if device == 'cuda' else None
}
from neuralforecast import NeuralForecast
from neuralforecast.auto import AutoLSTM
models = [AutoLSTM(h=h, num_samples=30)]
model = NeuralForecast(models=models, freq='D')## What’s Happening Behind the Scenes?
The code snippet above checks for the availability of a CUDA device (GPU) using `torch.cuda.is_available()`. If the GPU is detected, it sets the `device` variable to `’cuda’`, otherwise, it defaults to `’cpu’`. The `trainer_kwargs` dictionary is then used to specify the accelerator and devices for the NeuralForecast model.
## Conclusion
By following these simple steps, you should now be able to utilize Google Colab’s GPU for training AutoLSTM models. This will significantly speed up your training process, allowing you to focus on more interesting aspects of your project.
Happy training, and don’t hesitate to reach out if you have any further questions!