Ranger - a synergistic optimizer using RAdam (Rectified Adam) and LookAhead in one codebase

pip install asranger==0.0.5



Install with

pip install pytorch_ranger

Ranger - a synergistic optimizer combining RAdam (Rectified Adam) and LookAhead in one codebase.

Latest version 9.3.19 - full refactoring for slow weights and one pass handling (vs two before). Refactor should eliminate any random save/load issues regarding memory.


Beta Version -

For anyone who wants to try this out early, this version changes from RAdam to using calibrated anistropic adaptive learning rate per this paper:

"Empirical studies support our observation of the anisotropic A-LR and show that the proposed methods outperform existing AGMs and generalize even better than S-Momentum in multiple deep learning tasks."

Initial testing looks very good for training stabilization. Any feedback in comparsion with current Ranger (9.3.19) is welcome!


Medium article with more info:

Multiple updates: 1 - Ranger is the optimizer we used to beat the high scores for 12 different categories on the FastAI leaderboards! (Previous records all held with AdamW optimizer).

2 - Highly recommend combining Ranger with: Mish activation function, and flat+ cosine anneal training curve.

3 - Based on that, also found .95 is better than .90 for beta1 (momentum) param (ala betas=(0.95, 0.999)).

Fixes: 1 - Differential Group learning rates now supported. This was fix in RAdam and ported here thanks to @sholderbach. 2 - save and then load may leave first run weights stranded in memory, slowing down future runs = fixed.


Clone the repo, cd into it and install it in editable mode (-e option). That way, these is no more need to re-install the package after modification.

git clone
cd Ranger-Deep-Learning-Optimizer
pip install -e . 


from pytorch_ranger import Ranger  # this is from
from pytorch_ranger import RangerVA  # this is from
from pytorch_ranger import RangerQH  # this is from

# Define your model
model = ...
# Each of the Ranger, RangerVA, RangerQH have different parameters.
optimizer = Ranger(model.parameters(), **kwargs)

Usage and notebook to test are available here: