jaxopt
torchopt
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| jaxopt | torchopt | |
|---|---|---|
| 1 | 1 | |
| 888 | 496 | |
| 1.6% | 5.0% | |
| 7.8 | 7.1 | |
| 2 days ago | 26 days ago | |
| Python | Python | |
| Apache License 2.0 | Apache License 2.0 |
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jaxopt
torchopt
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What is the most efficient approach to ensemble a pytorch actor-critic model?
I would suggest checking https://pytorch.org/functorch/ and https://github.com/metaopt/torchopt for efficient inference and training with ensembles (e.g., t be independent actors in a multi-agent setting or multiple critics).
What are some alternatives?
jax - Composable transformations of Python+NumPy programs: differentiate, vectorize, JIT to GPU/TPU, and more
Adan-pytorch - Implementation of the Adan (ADAptive Nesterov momentum algorithm) Optimizer in Pytorch
einops - Flexible and powerful tensor operations for readable and reliable code (for pytorch, jax, TF and others)
symbolicai - Compositional Differentiable Programming Library
pyprobml - Python code for "Probabilistic Machine learning" book by Kevin Murphy
theseus - A library for differentiable nonlinear optimization
datasets - TFDS is a collection of datasets ready to use with TensorFlow, Jax, ...
functorch - functorch is JAX-like composable function transforms for PyTorch.
PyNeuraLogic - PyNeuraLogic lets you use Python to create Differentiable Logic Programs
pennylane - PennyLane is a cross-platform Python library for differentiable programming of quantum computers. Train a quantum computer the same way as a neural network.