Code: https://github.com/BackpropTools/BackpropTools

Paper: https://arxiv.org/abs/2306.03530

Live Demo (browser): https://backprop.tools

Interactive Tutorial: run-on-binder


BackpropTools stands for Backpropagation Tools paying tribute to the Backpropagation algorithm allowing the efficient calculation of gradients of functions with many inputs (neural network parameters are essentially inputs as well). Hence the Backpropagation algorithm is sitting at the core of the deep learning revolution. BackpropTools started out as a library for training and inference for small, fully-connected neural networks that can be tightly integrated with fast simulators running on GPUs to facilitate fast Reinforcement Learning (RL).

GPUs are based on massively parallel architectures consisting of thousands of small processing units which are usually Turing complete but relatively limited in their general computing capabilities (e.g. usually slow for code that is branching a lot). In the future, deep learning accelerators might not even be Turing complete anymore because they become more specialized for deep learning workloads. For simulations of e.g. fluid or robot dynamics, turing completeness is essential though. Hence, BackpropTools is designed to be tightly integrated with simulators on GPUs to run in a SIMD fashion. GPU kernels are written in C/C++ and compiled through e.g. Nvidia CUDA with very limited support for existing libraries (not even the C++ Standard Library is fully supported), hence BackpropTools is designed to be a dependency-free, header-only, pure C++ library.

BackpropTools makes heavy use of the template metaprogramming capabilities of recent C++ standards (C++17 in particular). This allows the code to be as generic as possible through the static multiple dispatch paradigm described in the accompanying paper while allowing for maximum performance (7-13 times faster than other popular RL libraries at the time of writing). The outstanding performance is achieved through template metaprogramming which allows the size of all containers and loops to be known at compile-time and hence allows the compiler to heavily optimize the code through inlining and loop unrolling. We observed the unrolling of loops with up to 100 iterations in the case of nvcc (the CUDA compiler).

Key features#

Over time, BackpropTools has grown into a complete library for deep supervised and reinforcement learning:

Deep Learning#

BackpropTools provides deep learning functionality for fully-connected neural networks. In the future we plan to include further architectures like recurrent neural networks but for now our focus on fully connected architectures is supported by our analysis of the deep reinforcement learning landscape (in the paper). There we find that in deep RL for continuous control relatively small, fully-connected neural networks are by far the most commonly used architecture.


BackpropTools includes a fast dynamics simulator for a pendulum (equivalent to Pendulum-v1 from the gym/gymnasium suite). The pendulum simulator is an example/template for the integration of other dynamics simulators. Future simulators we are planning on implementing include multirotor drones and racing cars. Furthermore we provide a high-performance, low-level MuJoCo interface (about 25% faster than the state of the art EnvPool)

Reinforcement Learning#

BackpropTools tightly integrates the deep learning and simulation components to provide highly performant reinforcement learning routines. We implement state of the art on- and off-policy RL algorithms in the form of PPO and TD3 and demonstrate that BackpropTools enables faster training than other popular RL libraries in case of the pendulum and the MuJoCo Ant-v4 task (learning to walk a quadruped) (see paper).

About this documentation#

This documentation is structured as a series of interactive Jupyter notebooks using the C/C++ interpreter Cling. The notebooks can be run on Binder using the links at the top of each one. Note that starting notebooks on Binder is convenient because all that is needed is a browser but they can take a long time or even fail to start. Alternatively you can also easily run this tutorial on you computer using Docker. Given that you have Docker installed and running you can clone this repository at a location of your choice:

git clone https://github.com/BackpropTools/documentation.git
cd documentation

Then we can build the Docker image. Note that as a pre-caution we are using –no-cache to make sure the clone of BackpropTools as well as package indices etc. are up to date. If you know what you are doing you can omit this flag to speed up repeated/incremental builds.

docker build . -t backprop_tools_docs --no-cache

Finally we can run the image and start a Jupyter server:

docker run -it --rm --platform linux/amd64 -p 8888:8888 backprop_tools_docs jupyter lab --ip

Open the link that is displayed in the CLI ( in your browser and enjoy tinkering with the tutorial!