Comments (4)
Not sure if it fits your needs but I added it to my repo https://github.com/pabloppp/pytorch-tools that you can install using pip (the instructions are in the readme) and I try to keep it up to date with the original implementation.
Of course, I created my repo mainly for personal use so I have other tools that I personally find interesting, so you might be adding things that you don't need to your project.
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Thanks for the suggestion, I'll look into it
from ranger-deep-learning-optimizer.
Hi @mpariente,
Thanks for the idea and sorry for the delay in my reply.
Yes, it would be great to make it a proper python package for pip install.
I think the better way would be to do the PR to install a python package.
I'm not familiar with that process but if you can help on that front that would be great.
I have plans to try and integrate Ranger regular and RangerQH into one optimizer where the user can toggle with a param which mode to run in and that would make it easy for people to test both Rangers for their specific dataset.
Thanks!
from ranger-deep-learning-optimizer.
Looking forward to this optimizer, thank you for your work and thanks for merging the PR.
from ranger-deep-learning-optimizer.
Related Issues (20)
- Is there a publication of Ranger? HOT 2
- It makes sense to use it on a batch of 1? HOT 3
- Benchmarck Adaptive Scheduling of Stochastic Gradients
- Gradient centralization was updated HOT 4
- [question] Why Ranger is not available as a pip package HOT 3
- What the "GC operations" mean?
- TypeError in GC operation for Conv layers and FC layers
- Not able to save the model_state_dict. HOT 1
- Does it works well for transformer?
- The results I tested on the cifar10 dataset are as follows. Ranger's results look strange HOT 1
- RangerVA with GC
- This overload of addcmul_ is deprecated: addcmul_(Number value, Tensor tensor1, Tensor tensor2) HOT 5
- How to use ranger in keras? Please help me. HOT 1
- Stochastic Weight Averaging support HOT 1
- Loss stuck after 1 epoch HOT 1
- Is adabelief the best optimizer? HOT 7
- best result : flat learning rate for 75% it means ranger optimizer is not sensitive to lr? HOT 1
- Please note in the documentation (or in the constructor) that closures must be enabled
- ranger and cosine annealing LR leads to different schedule than SGD optimizer? o_O
- Collate pip package so that it picks up from main repo. HOT 2
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