318 points by tanh-user 1 year ago flag hide 16 comments
user1 1 year ago next
Interesting case study. I've seen similar issues before with parallelized neural network training.
user1 1 year ago next
Yes, the case study presents several techniques to improve data distribution. Personally, I've found that increasing the batch size helps with inconsistent scaling.
user1 1 year ago next
Yes, the study also discusses using specific normalization techniques, such as Layer Normalization and Batch Normalization. Additionally, they suggest using synchronization algorithms to keep the gradients consistent.
user2 1 year ago prev next
I think the key is to make sure the data is distributed evenly. Any solutions discussed in the case study?
user2 1 year ago next
Ah, I'll have to try that. I've been dealing with this issue for a while now. Any other methods discussed?
user5 1 year ago next
Yes, they did mention using a combination of data parallelism and model parallelism as an effective solution. Even gradient checkspointing was briefly discussed.
new_user 1 year ago prev next
I've always wondered, why not use a single GPU with large RAM instead of parallelizing the process? Wouldn't that solve the problem?
user3 1 year ago next
That can work for smaller datasets, but for large datasets or models, it's still beneficial to parallelize. Plus, the cost of large GPUs is substantial.
user4 1 year ago prev next
Single GPU might also become a bottleneck as the model's complexity increases. Parallelization is still useful for larger projects.
user6 1 year ago prev next
Thanks! I'll give it a read and review the different parallelization techniques.
user7 1 year ago prev next
Has anyone tried implementing these techniques in TensorFlow? Are the improvements noticeable?
user8 1 year ago next
Yes, I've tried using a few of these techniques with TensorFlow and the improvements were significant! Especially when combining data parallelism and model parallelism.
user9 1 year ago next
Working with large models is much less of a headache now. Glad I found this case study.
user10 1 year ago prev next
Were there any drawbacks or limitations you encountered when implementing these solutions in TensorFlow?
user8 1 year ago next
I had some issues with the communication overhead between the GPUs, but it was mostly due to my specific setup. In general, these methods work well with TensorFlow.
user11 1 year ago prev next
Thanks for sharing your experience! Have you tried any improvements for communication overhead?