fluidgym 0.0.2

Plug-and-Play Benchmarking of Reinforcement Learning Algorithms for Large-Scale Flow Control

Installation | Getting Started | Documentation | License & Citation

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Installation

📦 Installation from PyPi

1. Ensure the correct PyTorch version is installed (compatible with CUDA 12.8):

pip install torch --index-url https://download.pytorch.org/whl/cu128

2. Install

pip install fluidgym

🐳 Using Docker (coming soon)

Instead of installing FluidGym you can use one of our Docker containers:

• Python 3.10
• Python 3.11
• Python 3.12
• Python 3.13

🧱 Build from Source (GitHub)

1. Create a new conda environment and activate it:

conda create -n fluidgym python=3.10
conda activate fluidgym

2. Install gcc:

conda install pip "gcc_linux-64>=6.0,<=11.5" "gxx_linux-64>=6.0,<=11.5"

3. Install the latest Pytorch for CUDA 12.8 via pip:

pip install torch --index-url https://download.pytorch.org/whl/cu128

4. Install the matching cuda toolkit via conda:

conda install cuda-toolkit=12.8 -c nvidia/label/cuda-12.8.1

5. Clone the repository and enter the directory, then compile the custom CUDA kernels and install the package (this might take several minutes):

make install

Getting Started

For an easy start refer to our documentation and the examples directory. FluidGym provides a gymnasium-like interface that can be used as follows:

import fluidgym

env = fluidgym.make(
    "JetCylinder2D-easy-v0",
)
obs, info = env.reset(seed=42)

for _ in range(50):
    action = env.sample_action()
    obs, reward, term, trunc, info = env.step(action)
    env.render()

    if term or trunc:
        break

License & Citation

This repository is published under the MIT license.