When training machine learning models, what techniques or strategies do you use to speed up the process without sacrificing accuracy or performance? Do you have any tips for making training more efficient while maintaining robust results?
Optimizing Hardware Usage:
How do you leverage GPUs, TPUs, or multi-core CPUs to speed up training? Do you have experience using distributed training techniques, and if so, how has that impacted your training times?
Batch Size and Learning Rate Adjustments:
Do you experiment with increasing batch sizes or adjusting the learning rate to achieve faster convergence? How do you find the right balance to avoid overfitting or underfitting?
Model Pruning and Quantization:
Have you implemented model pruning or quantization techniques to reduce model complexity during or after training? How have these methods affected training time and overall model performance?
Efficient Data Pipeline:
What tricks do you use to optimize the data loading pipeline? For example, do you use techniques like data augmentation, prefetching, or multi-threaded data loading to prevent bottlenecks during training?
Transfer Learning:
Do you often use transfer learning or pre-trained models to jump-start the training process? How do you decide when it’s more efficient to fine-tune an existing model versus training one from scratch?
Mixed Precision Training:
Have you tried using mixed precision training to reduce memory usage and speed up computations without compromising accuracy? What frameworks or tools do you find best for this technique?
Early Stopping and Checkpointing:
Do you use early stopping or checkpointing to prevent over-training and save time by halting the process when performance stops improving?
One additional technique to speed up machine learning model training without compromising results is the use of asynchronous gradient updates in distributed training. In synchronous training, all workers must wait for the slowest worker to finish computing the gradient before updating model parameters, which can introduce delays. By allowing workers to update model parameters independently (asynchronously), training can progress without waiting for all updates, particularly in cases of heterogeneous hardware or uneven computational loads. However, this introduces challenges such as stale gradients, which can be mitigated by techniques like gradient clipping or momentum correction to maintain the integrity of the learning process. Additionally, pairing this approach with adaptive optimization algorithms like Adam or AdaGrad can help ensure that even with gradient asynchrony, convergence remains stable and training times are significantly reduced.
In theory the batch size should be as large as the model can fit in the GPU memory. This is because we’re using stochastic gradient descent, and we are hoping the batch gradient to be close to the full gradient, i.e., the gradient computed using the whole training set. So, having a larger batch size will give us a more accurate estimated gradient. If the model is trained better with smaller batch size, it’s grossly over fitted. Using a smaller model and feeding it more data are both better choices than using a larger batch size.
The learning rate is better fun. A larger learning rate at the beginning of the training can help us get to the bottom faster. With a lot of luck, it may also help bumping through local minima. But since the gradient is inaccurate in SGD, a larger learning rate will keep us bouncing around and never converging to a good enough minimum. So we need to reduce it towards the end of the training. Now the timing becomes very tricky, we want to reduce the learning rate as we get close to the bottom, but we never know if we are even close. So a general way out is to use adaptive learning rate scheduler, which decreases the learning rate when the lost plateaus. But even with the scheduler, it’s still tricky to set the condition for decreasing the learning rate. Since the descent is stochastic, it means the optimizer can wander around on the plateau for unknown amount of time and suddenly find the hole to actually start descending. How to set the learning rate is probably one of the many unanswerable questions in deep learning. Using the defaults maybe the easiest way out.
In summary,
1, choose the latest popular model (fixed the model size)
2, use the largest batch size that can fit into the GPU
3, use ADAM with default parameters (It’s almost always better than SGD, the number of citations is its warranty.) (Also, if the performance of a model is drastically affected by the optimizer, it’s probably a good idea to just use another one. Playing with the optimizer doesn’t worth the time.)
4, choose the amount of training time until you can’t stand it.
5, train multiple times with different seeds, keep the best one. (This is necessary because where a SGD send you roughly follows the Gibbs distribution, i.e., it’s only more LIKELY to land you at a place with a lower cost. So multiple shots may well yield better results.) (The lost is a good indicator. But keep in mind that it’s just a arbitrary heuristic. So a good practice would be keeping several trained models with low lost, evaluate them on some toy examples, see the results yourself, and make your arbitrary judgement, which is the correct yet hard to evaluate lost.)
Pruning and quantization are techniques more commonly used after training. They deal minimal brain damage to a model so that it can fit on a smaller chip. They both try to find a balance between accuracy and model size.
Using them for training sounds like a fun research topic but not quite ready for industrial deployment.