Metadata-Version: 2.4
Name: fluidgym
Version: 0.0.2
Summary: Plug-and-Play Benchmarking of Reinforcement Learning Algorithms for Large-Scale Flow Control
Author-email: Jannis Becktepe <jannis@becktepe.de>
License: MIT License
        
        Copyright (c) 2026 safe-autonomous-systems
        
        Permission is hereby granted, free of charge, to any person obtaining a copy
        of this software and associated documentation files (the "Software"), to deal
        in the Software without restriction, including without limitation the rights
        to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
        copies of the Software, and to permit persons to whom the Software is
        furnished to do so, subject to the following conditions:
        
        The above copyright notice and this permission notice shall be included in all
        copies or substantial portions of the Software.
        
        THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Science/Research
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Programming Language :: Python :: 3.13
Requires-Python: <3.14,>=3.10
Description-Content-Type: text/markdown
License-File: LICENSE
License-File: LICENSES/Apache-2.0.txt
Requires-Dist: torch==2.9.*
Requires-Dist: gymnasium
Requires-Dist: numpy
Requires-Dist: pandas
Requires-Dist: matplotlib<3.10,>=3.9
Requires-Dist: seaborn
Requires-Dist: huggingface_hub>=1.0.0
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Provides-Extra: experiments
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Dynamic: license-file

<p align="center">
    <a href="./docs/images/logo_lm.png#gh-light-mode-only">
        <img src="./docs/source/_static/img/logo_lm.png#gh-light-mode-only" alt="FluidGym Logo" width="50%"/>
    </a>
    <a href="./docs/images/logo_dm.png#gh-dark-mode-only">
        <img src="./docs/source/_static/img/logo_dm.png#gh-dark-mode-only" alt="FluidGym Logo" width="50%"/>
    </a>
</p>

<div align="center">
    
[![PyPI version](https://badge.fury.io/py/fluidgym.svg)](https://badge.fury.io/py/fluidgym)
![PyPI - Python Version](https://img.shields.io/pypi/pyversions/fluidgym)
![PyTorch](https://img.shields.io/badge/PyTorch-2.9-EE4C2C?logo=pytorch&logoColor=white)
![CUDA](https://img.shields.io/badge/CUDA-12.8-%2376B900)
![License](https://img.shields.io/badge/License-MIT-orange)
[![Linters](https://github.com/safe-autonomous-systems/fluidgym/actions/workflows/linters.yml/badge.svg?branch=main)](https://github.com/safe-autonomous-systems/fluidgym/actions/workflows/linters.yml)
    
</div>

<div align="center">
    <h3>
      <a href="#-installation">Installation</a> |
      <a href="#-getting-started">Getting Started</a> |
      <a href="https://safe-autonomous-systems.github.io/fluidgym">Documentation</a> | 
      <a href="#-license-&-citation">License & Citation</a>
    </h3>
</div>

---

## Installation

### 📦 Installation from PyPi

1. Ensure the correct PyTorch version is installed (compatible with CUDA 12.8):
```bash
pip install torch --index-url https://download.pytorch.org/whl/cu128
```

2. Install 

```bash
pip install fluidgym
```

### 🐳 Using Docker (coming soon)

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

- [Python 3.10](https://google.com)
- [Python 3.11](https://google.com)
- [Python 3.12](https://google.com)
- [Python 3.13](https://google.com)

### 🧱 Build from Source (GitHub)

1. Create a new conda environment and activate it:
```bash
conda create -n fluidgym python=3.10
conda activate fluidgym
```

2. Install gcc:
```bash
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:
```bash
pip install torch --index-url https://download.pytorch.org/whl/cu128
```

4. Install the matching cuda toolkit via conda:
```bash
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):
```bash
make install
```

## Getting Started

For an easy start refer to our [documentation](https://safe-autonomous-systems.github.io/fluidgym/) and the [`examples`](examples) directory.
FluidGym provides a ```gymnasium```-like interface that can be used as follows:

```python
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.
