RAPIDS Demo Container
Get started with our preconfigured RAPIDS demo container, featuring several demo notebooks using cuDF, cuML, cuGraph, Dask, and XGBoost
- RAPIDS Demo Container
Current Version
RAPIDS 0.17 - 10 December 2020
Versions of libraries included in the 0.17
images:
cuDF
v0.17,cuML
v0.17,cuGraph
v0.17,RMM
v0.17,cuSpatial
v0.17,cuxfilter
v0.17,cuSignal
v0.17blazingsql
branch,xgboost
branch,dask-xgboost
branchdask-cuda
branch
Former Version
RAPIDS 0.16 - 20 October 2020
Versions of libraries included in the 0.16
images:
cuDF
v0.16,cuML
v0.16,cuGraph
v0.16,RMM
v0.16,cuSpatial
v0.16,cuxfilter
v0.16,cuSignal
v0.16xgboost
branch,dask-xgboost
branchdask-cuda
branch
Image Types
The RAPIDS images are based on nvidia/cuda, and are intended to be drop-in replacements for the corresponding CUDA images in order to make it easy to add RAPIDS libraries while maintaining support for existing CUDA applications.
RAPIDS images come in three types, distributed in two different repos:
The rapidsai/rapidsai repo contains the following:
base
- contains a RAPIDS environment ready for use.- TIP: Use this image if you want to use RAPIDS as a part of your pipeline.
runtime
- extends thebase
image by adding a notebook server and example notebooks.- TIP: Use this image if you want to explore RAPIDS through notebooks and examples.
The rapidsai/rapidsai-dev repo adds the following:
devel
- contains the full RAPIDS source tree, pre-built with all artifacts in place, and the compiler toolchain, the debugging tools, the headers and the static libraries for RAPIDS development.- TIP: Use this image to develop RAPIDS from source.
Image Tag Naming Scheme
The tag naming scheme for RAPIDS images incorporates key platform details into the tag as shown below:
0.17-cuda10.1-runtime-18.04-py3.7
^ ^ ^ ^ ^
| | type | python version
| | |
| cuda version |
| |
RAPIDS version linux version
To get the latest RAPIDS version of a specific platform combination, simply exclude the RAPIDS version. For example, to pull the latest version of RAPIDS for the runtime
image with support for CUDA 10.1, Python 3.7, and Ubuntu 18.04, use the following tag:
cuda10.1-runtime-18.04-py3.7
Many users do not need a specific platform combination but would like to ensure they’re getting the latest version of RAPIDS, so as an additional convenience, a tag named simply latest
is also provided which is equivalent to cuda10.1-runtime-ubuntu16.04-py3.7
.
Prerequisites
- NVIDIA Pascal™ GPU architecture or better
- CUDA 10.1/10.2/11.0 with a compatible NVIDIA driver
- Ubuntu 16.04/18.04 or CentOS 7
- Docker CE v18+
- nvidia-docker v2+
Usage
Start Container and Notebook Server
Preferred - Docker CE v19+ and nvidia-container-toolkit
$ docker pull rapidsai/rapidsai:cuda10.1-runtime-18.04-py3.7
$ docker run --gpus all --rm -it -p 8888:8888 -p 8787:8787 -p 8786:8786 \
rapidsai/rapidsai:cuda10.1-runtime-18.04-py3.7
NOTE: This will open a shell with JupyterLab running in the background on port 8888 on your host machine.
Legacy - Docker CE v18 and nvidia-docker2
$ docker pull rapidsai/rapidsai:cuda10.1-runtime-18.04-py3.7
$ docker run --runtime=nvidia --rm -it -p 8888:8888 -p 8787:8787 -p 8786:8786 \
rapidsai/rapidsai:cuda10.1-runtime-18.04-py3.7
NOTE: This will open a shell with JupyterLab running in the background on port 8888 on your host machine.
Use JupyterLab to Explore the Notebooks
Notebooks can be found in the following directories within the 0.17 container:
/rapids/notebooks/clx
- CLX demo notebooks/rapids/notebooks/cugraph
- cuGraph demo notebooks/rapids/notebooks/cuml
- cuML demo notebooks/rapids/notebooks/cusignal
- cuSignal demo notebooks/rapids/notebooks/cuxfilter
- cuXfilter demo notebooks/rapids/notebooks/cuxfilter
- BlazingSQL demo notebooks/blazing/Welcome_to_BlazingSQL_Notebooks
- XGBoost demo notebooks
For a full description of each notebook, see the README in the notebooks repository.
Custom Data and Advanced Usage
You are free to modify the above steps. For example, you can launch an interactive session with your own data:
Preferred - Docker CE v19+ and nvidia-container-toolkit
$ docker run --gpus all --rm -it -p 8888:8888 -p 8787:8787 -p 8786:8786 \
-v /path/to/host/data:/rapids/my_data \
rapidsai/rapidsai:cuda10.1-runtime-18.04-py3.7
Legacy - Docker CE v18 and nvidia-docker2
$ docker run --runtime=nvidia --rm -it -p 8888:8888 -p 8787:8787 -p 8786:8786 \
-v /path/to/host/data:/rapids/my_data \
rapidsai/rapidsai:cuda10.1-runtime-18.04-py3.7
This will map data from your host operating system to the container OS in the /rapids/my_data
directory. You may need to modify the provided notebooks for the new data paths.
Access Documentation within Notebooks
You can check the documentation for RAPIDS APIs inside the JupyterLab notebook using a ?
command, like this:
[1] ?cudf.read_csv
This prints the function signature and its usage documentation. If this is not enough, you can see the full code for the function using ??
:
[1] ??pygdf.read_csv
Check out the RAPIDS documentation for more detailed information and a RAPIDS cheat sheet.
More Information
Check out the RAPIDS and XGBoost API docs.
Learn how to setup a mult-node cuDF and XGBoost data preparation and distributed training environment by following the mortgage data example notebook and scripts.
Where can I get help or file bugs/requests?
Please submit issues with the container to this GitHub repository: https://github.com/rapidsai/docs
For issues with RAPIDS libraries like cuDF, cuML, RMM, or others file an issue in the related GitHub project.
Additional help can be found on Stack Overflow or Google Groups.