---
title: Scaleway Docker AI images
description: Discover detailed information about Scaleway's Docker images for AI development.
tags: docker docker-image tensorflow pytorch jax rapids
dates:
  validation: 2025-07-28
  posted: 2022-03-25
---

Scaleway offers a range of ready-to-use AI Docker images. These Docker images can be used with all GPU Instance OS images.

You can pull the images from our Container Registry as follows:

```bash
docker pull rg.fr-par.scw.cloud/scw-ai/<IMAGE:TAG>
```

Our tag names follow the pattern `:YYMM-py3`, where `YY` and `MM` refer to the year and month the image was built. One exception to this is the tag used for the Tensorflow image, which is `:YYMM-tf2-py3`.

The latest images are also tagged with the `:latest` tag.

Initial tags will start with 2204-py3 (April 2022). Check out the [Scaleway Changelog](/changelog/) to be informed of Docker AI images updates.

All our images are based on CUDA 12.2 and cuDNN8.

<Message type="tip">
  When using the “Ubuntu Jammy GPU OS 12” image with your Instance, you can omit the *NVIDIA runtime* option.
</Message>

The following commands show how to launch a container based on each of our various Docker images:

## Tensorflow

```bash
docker run --runtime=nvidia -it --rm -p 8888:8888 -p 6006:6006 rg.fr-par.scw.cloud/scw-ai/tensorflow:latest /bin/bash
```

The main libraries included in the Tensorflow image are Tensorflow 2, Tensorboard, Autokeras, Numpy, Scikit-Learn, Scipy, Pandas, Matplotlib, Plotly, Bokeh, Seaborn, CatBoost, XGBoost, Pillow, ONNX, Spacy, Nvidia Dali, Optuna, HuggingFace Transformers and HuggingFace-Hub, Weights and Biases, JupyterLab, Code Server and JupyterLab Nvidia Dashboard.

## Pytorch

```bash
docker run --runtime=nvidia -it --rm -p 8888:8888 -p 6006:6006 rg.fr-par.scw.cloud/scw-ai/pytorch:latest /bin/bash
```

The main libraries included in the Pytorch image are Pytorch, Torch Audio, Torch Vision, Fast AI, Pytorch Lightning, Tensorboard, Numpy, Scikit-Learn, Scipy, Pandas, Matplotlib, Plotly, Bokeh, Seaborn, CatBoost, XGBoost, Pillow, Plotly, ONNX, Spacy, Nvidia Dali, Optuna, HuggingFace Transformers and HuggingFace-Hub, Weights and Biases, JupyterLab, Code Server and JupyterLab Nvidia Dashboard.

## Jax

```bash
docker run --runtime=nvidia -it --rm -p 8888:8888 -p 6006:6006 rg.fr-par.scw.cloud/scw-ai/jax:latest /bin/bash
```

The main libraries included in the Jax image are Jax, Numpy, Scikit-Learn, Scipy, Pandas, Matplotlib, Plotly, Bokeh, Seaborn, CatBoost, XGBoost, Pillow, ONNX, JupyterLab, Code Server and JupyterLab Nvidia Dashboard.

## RAPIDS

<Message type="note">
  This image is built on top of the official RAPIDS Docker image, which relies on Anaconda.
</Message>

```bash
docker run --runtime=nvidia -it --rm -p 8888:8888 -p 6006:6006 rg.fr-par.scw.cloud/scw-ai/rapids:latest /bin/bash

```

The main libraries included in the RAPIDS image are cuDF, cuML, cuGraph, cuxfilter, cuspatial, cusignal, Dask, Numpy, Scikit-Learn, Scipy, Pandas, Matplotlib, Plotly, Bokeh, Seaborn, CatBoost, XGBoost, Pillow, ONNX, JupyterLab and JupyterLab Nvidia Dashboard.


<Message type="note">
  As the official RAPIDS Docker image uses Anaconda, make sure the "rapids" conda environment is activated to use Rapids libraries:

  ```
  jovyan@561f14e37a44:~/ai$ conda activate rapids
  (rapids) jovyan@561f14e37a44:~/ai$ jupyter lab
  ```
</Message>

## "All" (Experimental)

If there are no dependency issues when building the image, the **all** image will try to include all the above-listed libraries into a single Docker image (except RAPIDS).

```bash
docker run --runtime=nvidia -it --rm -p 8888:8888 -p 6006:6006 rg.fr-par.scw.cloud/scw-ai/all:latest /bin/bash
```