I love a good deep learning model. I do.
However, I am no stranger to the challenges of training neural networks.
There's one problem that constantly nagged at me: The excruciatingly slow training times.
It was like watching a snail in a world of cheetahs, and I knew I needed a solution.
Why we train models in the real world
The situation got even worse when I started working on bigger projects that involved satellite data, radar data from trains, or what I do today, improving weather forecasts using machine learning.
The real world and meteorology is a field where timely predictions can save lives and property, and every minute counts.
Traditional neural network training methods can really hold us back there, making it feel like we are running in the opposite direction of progress.
Finding solutions to a hard problem
My very first paper back in the day was about a game-changing concept – transfer learning.
It was like finding a hidden treasure chest amid a desert.
The idea that I could leverage pre-trained models to jumpstart our model still fascinates me.
Then, I learned how to speed up training even more.
We can efficiently utilise GPUs and tensor cores or fancy other accelerators if you work for Google.
We can stop the training early when the model is converged and use model pruning.
But we have even more tools nowadays!
Pytorch has the
torch.compile(model) tool for just-in-time (JIT) compilation for a significant speed-up in many training setups.
Or we can go all the way (which is a bit harder, honestly) and use Microsoft Deepspeed or Huggingface Accelerate to squeeze even more performance out of our model training pipeline.
Looking ahead with machine learning
I saw a future where we could significantly reduce the time it took to train our models for weather forecasting.
Faster training means we can experiment with better models, different variables, and other architectures.
In my job today, that means we can prepare communities for severe weather events and potentially save lives.
I love the vision of a world where machine learning plays a vital role in disaster preparedness. (The people at the Focus Group for AI for Natural Disaster Management do too, which is cool!)
By implementing transfer learning and the other tricks and tools becomes the key to unlocking this positive future.
We started using pre-trained models that had already learned intricate patterns from vast datasets.
When we cut down our training time significantly, it is like having a shortcut in a maze, leading us straight to our goal.
In conclusion, the pain of slow neural network training in the real world led me to discover the power of transfer learning.
These intriguing concepts not only revolutionise training but also open the doors to a brighter, more efficient future in weather forecasting.
So, the next time you face the challenge of sluggish training times, consider this solution your ticket to a faster, more effective machine learning journey.