brax
jax
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brax | jax | |
---|---|---|
11 | 82 | |
2,058 | 27,936 | |
3.3% | 4.0% | |
5.2 | 10.0 | |
11 days ago | 5 days ago | |
Jupyter Notebook | Python | |
Apache License 2.0 | Apache License 2.0 |
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brax
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4000x Speedup in Reinforcement Learning with Jax
There is Brax with its Ant, Humanoid and other rigid body articulated Gym environments: https://github.com/google/brax
- Physic engine for 3D simulation: which one to use?
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Brax vs TDS for differentiable rigid body dynamics
I need differentiable rigid body dynamics because I want to do nonlinear MPC. One library that can do this is C++ is Tiny Differentiable Simulator https://github.com/erwincoumans/tiny-differentiable-simulator. As I understand it, this software uses a C++ auto-diff library and code generation to create CUDA kernels to compute fast derivatives in parallel. This seems pretty fast because it's C++. Another option is Brax https://github.com/google/brax. Brax uses JAX which I've never used, but from what I've seen online, JAX is popular for researchers and probably very good.
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Deep learning for robotics
I am doing a MSc on robotics with a focus on machine learning, especially attention based architectures. There is a lot simulation and reinforcement learning going on. I have a funding of ~2500$ for the hardware system (no flexibility here, cannot use it for cloud either). I used pcpartpicker.com to select compatible components, as shown below. I am not located in the western part of the world; which means I have difficulty accessing some components and prices are higher here than that of pcpartpicker.com. That is why I am aiming towards 2000 - 2200$ range in the pcpartpicker.com. - Overall, what do you think of my planned setup? - Since there is a lot of simulation planned including rigid body dynamics with contact (libraries like https://github.com/raisimTech/raisimLib, https://github.com/deepmind/mujoco), I need some powerful CPU to use these libraries. I know that Intel has MKL over AMD; however, I am not sure how relevant that is for my case. The robotics simulators are generally written with C++, uses Eigen or their own math libraries. I feel like there is a lot of linear algebra involved and Intel combined with MKL should give me less headache. I have chosen i9-12900K, but what about AMD Ryzen9 5950X for example? - There is a new generation of rigid body simulators which use GPU instead of CPU (https://github.com/google/brax, https://developer.nvidia.com/isaac-gym). I do not think they are as mature as the previously mentioned simulators. Perhaps I am mistaken. Shall I focus on them instead? In terms of hardware that means I can downgrade the CPU to Ryzen5, and upgrade to RTX3080, roughly. - Do you think this system is easy to upgrade in future? What can I change to make it easier for long-term use and upgrades? Thanks for any help!
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[D] Advice on Hardware Setup for Robotics
There is a new generation of rigid body simulators which use GPU instead of CPU (https://github.com/google/brax, https://developer.nvidia.com/isaac-gym). I do not think they are as mature as the previously mentioned simulators. Perhaps I am mistaken. Shall I focus on them instead? In terms of hardware that means I can downgrade the CPU to Ryzen5, and upgrade to RTX3080, roughly.
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DeepMind open-sourcing MuJoCo simulator
I wonder what this means for the future of Brax [1].
1. https://github.com/google/brax
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Any tutorial on how to create RL C++ environments?
If you want raw speed, parallel execution on GPU or TPU is best. Checkout out our Brax simulator, which uses the XLA compiler and JAX Python frontend: https://github.com/google/brax
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Best environment to train RL agents
Check out Brax, hardware accelerated RL training in a Google Jupyter Colab. It trains typical RL tasks in minutes on TPU, also on GPU or CPU. And it is free, you can train with just a browser: https://github.com/google/brax
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[N] Mujoco is free for everyone until October 31 2021
Anyone made any progress with Brax? That was sold as a massively-parallel Mujoco alternative but not sure if anyone's actually using it yet.
- [R] Brax: A Differentiable Physics Engine for Large Scale Rigid Body Simulation, with a focus on performance and parallelism on accelerators, written in JAX.
jax
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The Elements of Differentiable Programming
The dual numbers exist just as surely as the real numbers and have been used well over 100 years
https://en.m.wikipedia.org/wiki/Dual_number
Pytorch has had them for many years.
https://pytorch.org/docs/stable/generated/torch.autograd.for...
JAX implements them and uses them exactly as stated in this thread.
https://github.com/google/jax/discussions/10157#discussionco...
As you so eloquently stated, "you shouldn't be proclaiming things you don't actually know on a public forum," and doubly so when your claimed "corrections" are so demonstrably and totally incorrect.
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Julia GPU-based ODE solver 20x-100x faster than those in Jax and PyTorch
On your last point, as long as you jit the topmost level, it doesn't matter whether or not you have inner jitted functions. The end result should be the same.
Source: https://github.com/google/jax/discussions/5199#discussioncom...
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Apple releases MLX for Apple Silicon
The design of MLX is inspired by frameworks like NumPy, PyTorch, Jax, and ArrayFire.
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MLPerf training tests put Nvidia ahead, Intel close, and Google well behind
I'm still not totally sure what the issue is. Jax uses program transformations to compile programs to run on a variety of hardware, for example, using XLA for TPUs. It can also run cuda ops for Nvidia gpus without issue: https://jax.readthedocs.io/en/latest/installation.html
There is also support for custom cpp and cuda ops if that's what is needed: https://jax.readthedocs.io/en/latest/Custom_Operation_for_GP...
I haven't worked with float4, but can imagine that new numerical types would require some special handling. But I assume that's the case for any ml environment.
But really you probably mean fixed point 4bit integer types? Looks like that has had at least some work done in Jax: https://github.com/google/jax/issues/8566
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MatX: Efficient C++17 GPU numerical computing library with Python-like syntax
>
Are they even comparing apples to apples to claim that they see these improvements over NumPy?
> While the code complexity and length are roughly the same, the MatX version shows a 2100x over the Numpy version, and over 4x faster than the CuPy version on the same GPU.
NumPy doesn't use GPU by default unless you use something like Jax [1] to compile NumPy code to run on GPUs. I think more honest comparison will mainly compare MatX running on same CPU like NumPy as focus the GPU comparison against CuPy.
[1] https://github.com/google/jax
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JAX – NumPy on the CPU, GPU, and TPU, with great automatic differentiation
Actually that never changed. The README has always had an example of differentiating through native Python control flow:
https://github.com/google/jax/commit/948a8db0adf233f333f3e5f...
The constraints on control flow expressions come from jax.jit (because Python control flow can't be staged out) and jax.vmap (because we can't take multiple branches of Python control flow, which we might need to do for different batch elements). But autodiff of Python-native control flow works fine!
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Julia and Mojo (Modular) Mandelbrot Benchmark
For a similar "benchmark" (also Mandelbrot) but took place in Jax repo discussion: https://github.com/google/jax/discussions/11078#discussionco...
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Functional Programming 1
2. https://github.com/fantasyland/fantasy-land (A bit heavy on jargon)
Note there is a python version of Ramda available on pypi and there’s a lot of FP tidbits inside JAX:
3. https://pypi.org/project/ramda/ (Worth making your own version if you want to learn, though)
4. For nested data, JAX tree_util is epic: https://jax.readthedocs.io/en/latest/jax.tree_util.html and also their curry implementation is funny: https://github.com/google/jax/blob/4ac2bdc2b1d71ec0010412a32...
Anyway don’t put FP on a pedestal, main thing is to focus on the core principles of avoiding external mutation and making helper functions. Doesn’t always work because some languages like Rust don’t have legit support for currying (afaik in 2023 August), but in those cases you can hack it with builder methods to an extent.
Finally, if you want to understand the middle of the midwit meme, check out this wiki article and connect the free monoid to the Kleene star (0 or more copies of your pattern) and Kleene plus (1 or more copies of your pattern). Those are also in regex so it can help you remember the regex symbols. https://en.wikipedia.org/wiki/Free_monoid?wprov=sfti1
The simplest example might be {0}^* in which case
0: “” // because we use *
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Best Way to Learn JAX
Hello! I'm trying to learn JAX over the next couple of weeks. Ideally, I want to be comfortable with using it for projects after about 3 weeks to a month, although I understand that may not be realistic. I currently have experience with PyTorch and TensorFlow. How should I go about learning JAX? Is there a specific YouTube tutorial or online course I should use, or should I just use the tutorial on https://jax.readthedocs.io/? Any information, advice, or experience you can share would be much appreciated!
- Codon: Python Compiler
What are some alternatives?
mujoco - Multi-Joint dynamics with Contact. A general purpose physics simulator.
Numba - NumPy aware dynamic Python compiler using LLVM
pybullet-gym - Open-source implementations of OpenAI Gym MuJoCo environments for use with the OpenAI Gym Reinforcement Learning Research Platform.
functorch - functorch is JAX-like composable function transforms for PyTorch.
tiny-differentiable-simulator - Tiny Differentiable Simulator is a header-only C++ and CUDA physics library for reinforcement learning and robotics with zero dependencies.
julia - The Julia Programming Language
RustyNEAT - Rust implementation of NEAT algorithm (HyperNEAT + ES-HyperNEAT + NoveltySearch + CTRNN + L-systems)
Pytorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration
open_spiel - OpenSpiel is a collection of environments and algorithms for research in general reinforcement learning and search/planning in games.
Cython - The most widely used Python to C compiler
jax-windows-builder - A community supported Windows build for jax.