i have run two different experiments with several runs and sweeps and shared everything using GitHub: GitHub - teamtom/kaggle-vs-colab-speed: testing CPU/GPU speeds
this Github repo contains the Jupyter files and my pipeline code also my related W&B projects
The original slowdown were noticed with the Churn imbalanced dataset. I created a logistic regression model with a very simple network and tried to find a better threshold to improve the model with W&B sweeps.
I found that using GPU on either Kaggle or Colab the epoch time was way slower compared to running on CPU (on local machine, Kaggle and Colab)
eg. Colab GPU was 2 times slower than Colab CPU or my local CPU (I7, 10th gen Intel)
Kaggle GPU was better than Colab’s but still slower than my local CPU
I also run another experiment: it was MNIST dataset with a CNN to compare. This experiment contained solo runs and sweeps.
i observed no GPU slowdown, the acceleration proved to be ~3x using Kaggle; Colab was only ~2x
so the questions:
- why GPU proved way slower than CPU with the first experiment? Is it related to the problem itself?
- is there any error in my models, calculations or in the pipeline?
please help me to understand, thank you!