Docker AI images

Reviewed on 16 July 2024 • Published on 25 March 2022

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:

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 to be informed of Docker AI images updates.

All our images are based on CUDA 12.2 and cuDNN8.

Tip

When using the “Ubuntu Jammy GPU OS 12” image with your Instance, you can omit the NVIDIA runtime option.

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

Tensorflow

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

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

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

Note

This image is built on top of the official RAPIDS Docker image, which relies on Anaconda.

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.

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

“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).

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

Was this page helpful?