<div align="center">
<img src="docs/img/qdax_logo.png" alt="qdax_logo" width="140"></img>
</div>
# QDax: Accelerated Quality-Diversity
[](https://qdax.readthedocs.io/en/latest/?badge=latest)
[](https://github.com/adaptive-intelligent-robotics/QDax/blob/main/LICENSE)
[](https://codecov.io/gh/adaptive-intelligent-robotics/QDax)
QDax is a tool to accelerate Quality-Diversity (QD) and neuro-evolution algorithms through hardware accelerators and massive parallelization. QD algorithms usually take days/weeks to run on large CPU clusters. With QDax, QD algorithms can now be run in minutes! ⏩ ⏩ 🕛
QDax has been developed as a research framework: it is flexible and easy to extend and build on and can be used for any problem setting. Get started with simple example and run a QD algorithm in minutes here! [](https://colab.research.google.com/github/adaptive-intelligent-robotics/QDax/blob/main/examples/mapelites.ipynb)
- QDax [paper](https://arxiv.org/abs/2202.01258)
- QDax [documentation](https://qdax.readthedocs.io/en/latest/)
## Installation
QDax is available on PyPI and can be installed with:
```bash
pip install qdax
```
To install QDax with CUDA 12 support, use:
```bash
pip install qdax[cuda12]
```
Alternatively, the latest commit of QDax can be installed directly from source with:
```bash
pip install git+https://github.com/adaptive-intelligent-robotics/QDax.git@main
```
Installing QDax via ```pip``` installs a CPU-only version of JAX by default. To use QDax with NVidia GPUs, you must first install [CUDA, CuDNN, and JAX with GPU support](https://github.com/google/jax#installation).
However, we also provide and recommend using either Docker or conda environments to use the repository which by default provides GPU support. Detailed steps to do so are available in the [documentation](https://qdax.readthedocs.io/en/latest/installation/).
## Basic API Usage
For a full and interactive example to see how QDax works, we recommend starting with the tutorial-style [Colab notebook](./examples/mapelites.ipynb). It is an example of the MAP-Elites algorithm used to evolve a population of controllers on a chosen Brax environment (Walker by default).
However, a summary of the main API usage is provided below:
```python
import jax
import functools
from qdax.core.map_elites import MAPElites
from qdax.core.containers.mapelites_repertoire import compute_euclidean_centroids
from qdax.tasks.arm import arm_scoring_function
from qdax.core.emitters.mutation_operators import isoline_variation
from qdax.core.emitters.standard_emitters import MixingEmitter
from qdax.utils.metrics import default_qd_metrics
seed = 42
num_param_dimensions = 100 # num DoF arm
init_batch_size = 100
batch_size = 1024
num_iterations = 50
grid_shape = (100, 100)
min_param = 0.0
max_param = 1.0
min_bd = 0.0
max_bd = 1.0
# Init a random key
random_key = jax.random.PRNGKey(seed)
# Init population of controllers
random_key, subkey = jax.random.split(random_key)
init_variables = jax.random.uniform(
subkey,
shape=(init_batch_size, num_param_dimensions),
minval=min_param,
maxval=max_param,
)
# Define emitter
variation_fn = functools.partial(
isoline_variation,
iso_sigma=0.05,
line_sigma=0.1,
minval=min_param,
maxval=max_param,
)
mixing_emitter = MixingEmitter(
mutation_fn=lambda x, y: (x, y),
variation_fn=variation_fn,
variation_percentage=1.0,
batch_size=batch_size,
)
# Define a metrics function
metrics_fn = functools.partial(
default_qd_metrics,
qd_offset=0.0,
)
# Instantiate MAP-Elites
map_elites = MAPElites(
scoring_function=arm_scoring_function,
emitter=mixing_emitter,
metrics_function=metrics_fn,
)
# Compute the centroids
centroids = compute_euclidean_centroids(
grid_shape=grid_shape,
minval=min_bd,
maxval=max_bd,
)
# Initializes repertoire and emitter state
repertoire, emitter_state, random_key = map_elites.init(init_variables, centroids, random_key)
# Run MAP-Elites loop
for i in range(num_iterations):
(repertoire, emitter_state, metrics, random_key,) = map_elites.update(
repertoire,
emitter_state,
random_key,
)
# Get contents of repertoire
repertoire.genotypes, repertoire.fitnesses, repertoire.descriptors
```
## QDax core algorithms
QDax currently supports the following algorithms:
| Algorithm | Example |
|-------------------------------------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| [MAP-Elites](https://arxiv.org/abs/1504.04909) | [](https://colab.research.google.com/github/adaptive-intelligent-robotics/QDax/blob/main/examples/mapelites.ipynb) |
| [CVT MAP-Elites](https://arxiv.org/abs/1610.05729) | [](https://colab.research.google.com/github/adaptive-intelligent-robotics/QDax/blob/main/examples/mapelites.ipynb) |
| [Policy Gradient Assisted MAP-Elites (PGA-ME)](https://hal.archives-ouvertes.fr/hal-03135723v2/file/PGA_MAP_Elites_GECCO.pdf) | [](https://colab.research.google.com/github/adaptive-intelligent-robotics/QDax/blob/main/examples/pgame.ipynb) |
| [DCRL-ME](https://arxiv.org/abs/2401.08632) | [](https://colab.research.google.com/github/adaptive-intelligent-robotics/QDax/blob/main/examples/dcrlme.ipynb) |
| [QDPG](https://arxiv.org/abs/2006.08505) | [](https://colab.research.google.com/github/adaptive-intelligent-robotics/QDax/blob/main/examples/qdpg.ipynb) |
| [CMA-ME](https://arxiv.org/pdf/1912.02400.pdf) | [](https://colab.research.google.com/github/adaptive-intelligent-robotics/QDax/blob/main/examples/cmame.ipynb) |
| [OMG-MEGA](https://arxiv.org/abs/2106.03894) | [](https://colab.research.google.com/github/adaptive-intelligent-robotics/QDax/blob/main/examples/omgmega.ipynb) |
| [CMA-MEGA](https://arxiv.org/abs/2106.03894) | [](https://colab.research.google.com/github/adaptive-intelligent-robotics/QDax/blob/main/examples/cmamega.ipynb) |
| [Multi-Objective MAP-Elites (MOME)](https://arxiv.org/abs/2202.03057) | [](https://colab.research.google.com/github/adaptive-intelligent-robotics/QDax/blob/main/examples/mome.ipynb) |
| [MAP-Elites Evolution Strategies (MEES)](https://dl.acm.org/doi/pdf/10.1145/3377930.3390217) | [](https://colab.research.google.com/github/adaptive-intelligent-robotics/QDax/blob/main/examples/mees.ipynb) |
| [MAP-Elites PBT (ME-PBT)](https://openreview.net/forum?id=CBfYffLqWqb) | [](https://colab.research.google.com/github/adaptive-intelligent-robotics/QDax/blob/main/examples/me_sac_pbt.ipynb) |
| [MAP-Elites Low-Spread (ME-LS)](https://dl.acm.org/doi/abs/10.1145/3583131.3590433) | [](https://colab.research.google.com/github/adaptive-intelligent-robotics/QDax/blob/main/examples/me_ls.ipynb) |
## QDax baseline algorithms
The QDax library also provides implementations for some useful baseline algorithms:
| Algorithm | Example |
| --- | --- |
| [DIAYN](https://arxiv.org/abs/1802.06070) | [](https://colab.research.google.com/github/adaptive-intelligent-robotics/QDax/blob/main/examples/diayn.ipynb) |
| [DADS](https://arxiv.org/abs/1907.01657) | [](https://colab.research.google.com/github/adaptive-intelligent-robotics/QDax/blob/main/examples/dads.ipynb) |
| [SMERL](https://arxiv.org/abs/2010.14484) | [](https://colab.research.google.com/github/adaptive-intelligent-robotics/QDax/blob/main/examples/smerl.ipynb) |
| [NSGA2](https://ieeexplore.ieee.org/document/996017) | [](https://colab.research.google.com/github/adaptive-intelligent-robotics/QDax/blob/main/examples/nsga2_spea2.ipynb) |
| [SPEA2](https://www.semanticscholar.org/paper/SPEA2%3A-Improving-the-strength-pareto-evolutionary-Zitzler-Laumanns/b13724cb54ae4171916f3f969d304b9e9752a57f) | [](https://colab.research.google.com/github/adaptive-intelligent-robotics/QDax/blob/main/examples/nsga2_spea2.ipynb) |
| [Population Based Training (PBT)](https://arxiv.org/abs/1711.09846) | [](https://colab.research.google.com/github/adaptive-intelligent-robotics/QDax/blob/main/examples/sac_pbt.ipynb) |
## QDax Tasks
The QDax library also provides numerous implementations for several standard Quality-Diversity tasks.
All those implementations, and their descriptions are provided in the [tasks directory](./qdax/tasks).
## Contributing
Issues and contributions are welcome. Please refer to the [contribution guide](https://qdax.readthedocs.io/en/latest/guides/CONTRIBUTING/) in the documentation for more details.
## Related Projects
- [EvoJAX: Hardware-Accelerated Neuroevolution](https://github.com/google/evojax). EvoJAX is a scalable, general purpose, hardware-accelerated neuroevolution toolkit. [Paper](https://arxiv.org/abs/2202.05008)
- [evosax: JAX-Based Evolution Strategies](https://github.com/RobertTLange/evosax)
## Citing QDax
If you use QDax in your research and want to cite it in your work, please use:
```
@article{chalumeau2024qdax,
title={Qdax: A library for quality-diversity and population-based algorithms with hardware acceleration},
author={Chalumeau, Felix and Lim, Bryan and Boige, Raphael and Allard, Maxime and Grillotti, Luca and Flageat, Manon and Mac{\'e}, Valentin and Richard, Guillaume and Flajolet, Arthur and Pierrot, Thomas and others},
journal={Journal of Machine Learning Research},
volume={25},
number={108},
pages={1--16},
year={2024}
}
```
## Contributors
QDax was developed and is maintained by the [Adaptive & Intelligent Robotics Lab (AIRL)](https://www.imperial.ac.uk/adaptive-intelligent-robotics/) and [InstaDeep](https://www.instadeep.com/).
<div align="center">
<img align="center" src="docs/img/AIRL_logo.png" alt="AIRL_Logo" width="220"/> <img align="center" src="docs/img/instadeep_logo.png" alt="InstaDeep_Logo" width="220"/>
</div>
<div align="center">
<a href="https://github.com/limbryan" title="Bryan Lim"><img src="https://github.com/limbryan.png" height="auto" width="50" style="border-radius:50%"></a>
<a href="https://github.com/maxiallard" title="Maxime Allard"><img src="https://github.com/maxiallard.png" height="auto" width="50" style="border-radius:50%"></a>
<a href="https://github.com/Lookatator" title="Luca Grilloti"><img src="https://github.com/Lookatator.png" height="auto" width="50" style="border-radius:50%"></a>
<a href="https://github.com/manon-but-yes" title="Manon Flageat"><img src="https://github.com/manon-but-yes.png" height="auto" width="50" style="border-radius:50%"></a>
<a href="https://github.com/Aneoshun" title="Antoine Cully"><img src="https://github.com/Aneoshun.png" height="auto" width="50" style="border-radius:50%"></a>
<a href="https://github.com/felixchalumeau" title="Felix Chalumeau"><img src="https://github.com/felixchalumeau.png" height="auto" width="50" style="border-radius:50%"></a>
<a href="https://github.com/ranzenTom" title="Thomas Pierrot"><img src="https://github.com/ranzenTom.png" height="auto" width="50" style="border-radius:50%"></a>
<a href="https://github.com/Egiob" title="Raphael Boige"><img src="https://github.com/Egiob.png" height="auto" width="50" style="border-radius:50%"></a>
<a href="https://github.com/valentinmace" title="Valentin Mace"><img src="https://github.com/valentinmace.png" height="auto" width="50" style="border-radius:50%"></a>
<a href="https://github.com/GRichard513" title="Guillaume Richard"><img src="https://github.com/GRichard513.png" height="auto" width="50" style="border-radius:50%"></a>
<a href="https://github.com/flajolet" title="Arthur Flajolet"><img src="https://github.com/flajolet.png" height="auto" width="50" style="border-radius:50%"></a>
<a href="https://github.com/remidebette" title="Rémi Debette"><img src="https://github.com/remidebette.png" height="auto" width="50" style="border-radius:50%"></a>
</div>
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"description": "<div align=\"center\">\n<img src=\"docs/img/qdax_logo.png\" alt=\"qdax_logo\" width=\"140\"></img>\n</div>\n\n\n# QDax: Accelerated Quality-Diversity\n\n[](https://qdax.readthedocs.io/en/latest/?badge=latest)\n\n[](https://github.com/adaptive-intelligent-robotics/QDax/blob/main/LICENSE)\n[](https://codecov.io/gh/adaptive-intelligent-robotics/QDax)\n\n\nQDax is a tool to accelerate Quality-Diversity (QD) and neuro-evolution algorithms through hardware accelerators and massive parallelization. QD algorithms usually take days/weeks to run on large CPU clusters. With QDax, QD algorithms can now be run in minutes! \u23e9 \u23e9 \ud83d\udd5b\n\nQDax has been developed as a research framework: it is flexible and easy to extend and build on and can be used for any problem setting. Get started with simple example and run a QD algorithm in minutes here! [](https://colab.research.google.com/github/adaptive-intelligent-robotics/QDax/blob/main/examples/mapelites.ipynb)\n\n- QDax [paper](https://arxiv.org/abs/2202.01258)\n- QDax [documentation](https://qdax.readthedocs.io/en/latest/)\n\n\n## Installation\nQDax is available on PyPI and can be installed with:\n```bash\npip install qdax\n```\n\nTo install QDax with CUDA 12 support, use:\n```bash\npip install qdax[cuda12]\n```\n\nAlternatively, the latest commit of QDax can be installed directly from source with:\n```bash\npip install git+https://github.com/adaptive-intelligent-robotics/QDax.git@main\n```\nInstalling QDax via ```pip``` installs a CPU-only version of JAX by default. To use QDax with NVidia GPUs, you must first install [CUDA, CuDNN, and JAX with GPU support](https://github.com/google/jax#installation).\n\nHowever, we also provide and recommend using either Docker or conda environments to use the repository which by default provides GPU support. Detailed steps to do so are available in the [documentation](https://qdax.readthedocs.io/en/latest/installation/).\n\n## Basic API Usage\nFor a full and interactive example to see how QDax works, we recommend starting with the tutorial-style [Colab notebook](./examples/mapelites.ipynb). It is an example of the MAP-Elites algorithm used to evolve a population of controllers on a chosen Brax environment (Walker by default).\n\nHowever, a summary of the main API usage is provided below:\n```python\nimport jax\nimport functools\nfrom qdax.core.map_elites import MAPElites\nfrom qdax.core.containers.mapelites_repertoire import compute_euclidean_centroids\nfrom qdax.tasks.arm import arm_scoring_function\nfrom qdax.core.emitters.mutation_operators import isoline_variation\nfrom qdax.core.emitters.standard_emitters import MixingEmitter\nfrom qdax.utils.metrics import default_qd_metrics\n\nseed = 42\nnum_param_dimensions = 100 # num DoF arm\ninit_batch_size = 100\nbatch_size = 1024\nnum_iterations = 50\ngrid_shape = (100, 100)\nmin_param = 0.0\nmax_param = 1.0\nmin_bd = 0.0\nmax_bd = 1.0\n\n# Init a random key\nrandom_key = jax.random.PRNGKey(seed)\n\n# Init population of controllers\nrandom_key, subkey = jax.random.split(random_key)\ninit_variables = jax.random.uniform(\n subkey,\n shape=(init_batch_size, num_param_dimensions),\n minval=min_param,\n maxval=max_param,\n)\n\n# Define emitter\nvariation_fn = functools.partial(\n isoline_variation,\n iso_sigma=0.05,\n line_sigma=0.1,\n minval=min_param,\n maxval=max_param,\n)\nmixing_emitter = MixingEmitter(\n mutation_fn=lambda x, y: (x, y),\n variation_fn=variation_fn,\n variation_percentage=1.0,\n batch_size=batch_size,\n)\n\n# Define a metrics function\nmetrics_fn = functools.partial(\n default_qd_metrics,\n qd_offset=0.0,\n)\n\n# Instantiate MAP-Elites\nmap_elites = MAPElites(\n scoring_function=arm_scoring_function,\n emitter=mixing_emitter,\n metrics_function=metrics_fn,\n)\n\n# Compute the centroids\ncentroids = compute_euclidean_centroids(\n grid_shape=grid_shape,\n minval=min_bd,\n maxval=max_bd,\n)\n\n# Initializes repertoire and emitter state\nrepertoire, emitter_state, random_key = map_elites.init(init_variables, centroids, random_key)\n\n# Run MAP-Elites loop\nfor i in range(num_iterations):\n (repertoire, emitter_state, metrics, random_key,) = map_elites.update(\n repertoire,\n emitter_state,\n random_key,\n )\n\n# Get contents of repertoire\nrepertoire.genotypes, repertoire.fitnesses, repertoire.descriptors\n```\n\n\n## QDax core algorithms\nQDax currently supports the following algorithms:\n\n\n| Algorithm | Example |\n|-------------------------------------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n| [MAP-Elites](https://arxiv.org/abs/1504.04909) | [](https://colab.research.google.com/github/adaptive-intelligent-robotics/QDax/blob/main/examples/mapelites.ipynb) |\n| [CVT MAP-Elites](https://arxiv.org/abs/1610.05729) | [](https://colab.research.google.com/github/adaptive-intelligent-robotics/QDax/blob/main/examples/mapelites.ipynb) |\n| [Policy Gradient Assisted MAP-Elites (PGA-ME)](https://hal.archives-ouvertes.fr/hal-03135723v2/file/PGA_MAP_Elites_GECCO.pdf) | [](https://colab.research.google.com/github/adaptive-intelligent-robotics/QDax/blob/main/examples/pgame.ipynb) |\n| [DCRL-ME](https://arxiv.org/abs/2401.08632) | [](https://colab.research.google.com/github/adaptive-intelligent-robotics/QDax/blob/main/examples/dcrlme.ipynb) |\n| [QDPG](https://arxiv.org/abs/2006.08505) | [](https://colab.research.google.com/github/adaptive-intelligent-robotics/QDax/blob/main/examples/qdpg.ipynb) |\n| [CMA-ME](https://arxiv.org/pdf/1912.02400.pdf) | [](https://colab.research.google.com/github/adaptive-intelligent-robotics/QDax/blob/main/examples/cmame.ipynb) |\n| [OMG-MEGA](https://arxiv.org/abs/2106.03894) | [](https://colab.research.google.com/github/adaptive-intelligent-robotics/QDax/blob/main/examples/omgmega.ipynb) |\n| [CMA-MEGA](https://arxiv.org/abs/2106.03894) | [](https://colab.research.google.com/github/adaptive-intelligent-robotics/QDax/blob/main/examples/cmamega.ipynb) |\n| [Multi-Objective MAP-Elites (MOME)](https://arxiv.org/abs/2202.03057) | [](https://colab.research.google.com/github/adaptive-intelligent-robotics/QDax/blob/main/examples/mome.ipynb) |\n| [MAP-Elites Evolution Strategies (MEES)](https://dl.acm.org/doi/pdf/10.1145/3377930.3390217) | [](https://colab.research.google.com/github/adaptive-intelligent-robotics/QDax/blob/main/examples/mees.ipynb) |\n| [MAP-Elites PBT (ME-PBT)](https://openreview.net/forum?id=CBfYffLqWqb) | [](https://colab.research.google.com/github/adaptive-intelligent-robotics/QDax/blob/main/examples/me_sac_pbt.ipynb) |\n| [MAP-Elites Low-Spread (ME-LS)](https://dl.acm.org/doi/abs/10.1145/3583131.3590433) | [](https://colab.research.google.com/github/adaptive-intelligent-robotics/QDax/blob/main/examples/me_ls.ipynb) |\n\n\n## QDax baseline algorithms\nThe QDax library also provides implementations for some useful baseline algorithms:\n\n| Algorithm | Example |\n| --- | --- |\n| [DIAYN](https://arxiv.org/abs/1802.06070) | [](https://colab.research.google.com/github/adaptive-intelligent-robotics/QDax/blob/main/examples/diayn.ipynb) |\n| [DADS](https://arxiv.org/abs/1907.01657) | [](https://colab.research.google.com/github/adaptive-intelligent-robotics/QDax/blob/main/examples/dads.ipynb) |\n| [SMERL](https://arxiv.org/abs/2010.14484) | [](https://colab.research.google.com/github/adaptive-intelligent-robotics/QDax/blob/main/examples/smerl.ipynb) |\n| [NSGA2](https://ieeexplore.ieee.org/document/996017) | [](https://colab.research.google.com/github/adaptive-intelligent-robotics/QDax/blob/main/examples/nsga2_spea2.ipynb) |\n| [SPEA2](https://www.semanticscholar.org/paper/SPEA2%3A-Improving-the-strength-pareto-evolutionary-Zitzler-Laumanns/b13724cb54ae4171916f3f969d304b9e9752a57f) | [](https://colab.research.google.com/github/adaptive-intelligent-robotics/QDax/blob/main/examples/nsga2_spea2.ipynb) |\n| [Population Based Training (PBT)](https://arxiv.org/abs/1711.09846) | [](https://colab.research.google.com/github/adaptive-intelligent-robotics/QDax/blob/main/examples/sac_pbt.ipynb) |\n\n## QDax Tasks\nThe QDax library also provides numerous implementations for several standard Quality-Diversity tasks.\n\nAll those implementations, and their descriptions are provided in the [tasks directory](./qdax/tasks).\n\n## Contributing\nIssues and contributions are welcome. Please refer to the [contribution guide](https://qdax.readthedocs.io/en/latest/guides/CONTRIBUTING/) in the documentation for more details.\n\n## Related Projects\n- [EvoJAX: Hardware-Accelerated Neuroevolution](https://github.com/google/evojax). EvoJAX is a scalable, general purpose, hardware-accelerated neuroevolution toolkit. [Paper](https://arxiv.org/abs/2202.05008)\n- [evosax: JAX-Based Evolution Strategies](https://github.com/RobertTLange/evosax)\n\n## Citing QDax\nIf you use QDax in your research and want to cite it in your work, please use:\n```\n@article{chalumeau2024qdax,\n title={Qdax: A library for quality-diversity and population-based algorithms with hardware acceleration},\n author={Chalumeau, Felix and Lim, Bryan and Boige, Raphael and Allard, Maxime and Grillotti, Luca and Flageat, Manon and Mac{\\'e}, Valentin and Richard, Guillaume and Flajolet, Arthur and Pierrot, Thomas and others},\n journal={Journal of Machine Learning Research},\n volume={25},\n number={108},\n pages={1--16},\n year={2024}\n}\n```\n\n## Contributors\n\nQDax was developed and is maintained by the [Adaptive & Intelligent Robotics Lab (AIRL)](https://www.imperial.ac.uk/adaptive-intelligent-robotics/) and [InstaDeep](https://www.instadeep.com/).\n\n<div align=\"center\">\n<img align=\"center\" src=\"docs/img/AIRL_logo.png\" alt=\"AIRL_Logo\" width=\"220\"/> <img align=\"center\" src=\"docs/img/instadeep_logo.png\" alt=\"InstaDeep_Logo\" width=\"220\"/>\n</div>\n\n<div align=\"center\">\n<a href=\"https://github.com/limbryan\" title=\"Bryan Lim\"><img src=\"https://github.com/limbryan.png\" height=\"auto\" width=\"50\" style=\"border-radius:50%\"></a>\n<a href=\"https://github.com/maxiallard\" title=\"Maxime Allard\"><img src=\"https://github.com/maxiallard.png\" height=\"auto\" width=\"50\" style=\"border-radius:50%\"></a>\n<a href=\"https://github.com/Lookatator\" title=\"Luca Grilloti\"><img src=\"https://github.com/Lookatator.png\" height=\"auto\" width=\"50\" style=\"border-radius:50%\"></a>\n<a href=\"https://github.com/manon-but-yes\" title=\"Manon Flageat\"><img src=\"https://github.com/manon-but-yes.png\" height=\"auto\" width=\"50\" style=\"border-radius:50%\"></a>\n<a href=\"https://github.com/Aneoshun\" title=\"Antoine Cully\"><img src=\"https://github.com/Aneoshun.png\" height=\"auto\" width=\"50\" style=\"border-radius:50%\"></a>\n<a href=\"https://github.com/felixchalumeau\" title=\"Felix Chalumeau\"><img src=\"https://github.com/felixchalumeau.png\" height=\"auto\" width=\"50\" style=\"border-radius:50%\"></a>\n<a href=\"https://github.com/ranzenTom\" title=\"Thomas Pierrot\"><img src=\"https://github.com/ranzenTom.png\" height=\"auto\" width=\"50\" style=\"border-radius:50%\"></a>\n<a href=\"https://github.com/Egiob\" title=\"Raphael Boige\"><img src=\"https://github.com/Egiob.png\" height=\"auto\" width=\"50\" style=\"border-radius:50%\"></a>\n<a href=\"https://github.com/valentinmace\" title=\"Valentin Mace\"><img src=\"https://github.com/valentinmace.png\" height=\"auto\" width=\"50\" style=\"border-radius:50%\"></a>\n<a href=\"https://github.com/GRichard513\" title=\"Guillaume Richard\"><img src=\"https://github.com/GRichard513.png\" height=\"auto\" width=\"50\" style=\"border-radius:50%\"></a>\n<a href=\"https://github.com/flajolet\" title=\"Arthur Flajolet\"><img src=\"https://github.com/flajolet.png\" height=\"auto\" width=\"50\" style=\"border-radius:50%\"></a>\n<a href=\"https://github.com/remidebette\" title=\"R\u00e9mi Debette\"><img src=\"https://github.com/remidebette.png\" height=\"auto\" width=\"50\" style=\"border-radius:50%\"></a>\n</div>\n",
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