How can I reduce computational cost while training large generative models

Question

With the help of proper code explanation, can you tell me How I can reduce computational cost while training large generative models?

Answer 1

To reduce computational cost while training large generative models, you can follow the following:

• Use Mixed Precision Training: Use lower precision (16-bit) arithmetic to speed up training and reduce memory usage.
• Gradient Accumulation: Accumulate gradients over multiple mini-batches to simulate a larger batch size without increasing memory.
• Model Pruning: Reduce the number of parameters in your model by pruning low-importance connections.
• Efficient Data Loading: Use optimized data pipelines to minimize data loading overhead.

Here is the code snippet you can refer to:

In the above code we are using the following:

• Mixed Precision: Use mixed_float16 to reduce memory and computational cost.
• Gradient Accumulation: Accumulate gradients over multiple steps, especially for large batch sizes.
• Pruning: Use pruning to reduce model size and computation.
• Efficient Data Pipeline: Optimize data loading with tf.data or multi-threading.

Hence, these methods help speed up training and reduce resource consumption for large generative models.