PyVista within a Docker Container

You can use pyvista from within a docker container with jupyterlab. To create a local docker image install docker and be sure you’ve logged into docker by following the directions at Configuring Docker for use with GitHub Packages)

Next pull and run the image with:

docker run -p 8888:8888 docker.pkg.github.com/pyvista/pyvista/pyvista-jupyterlab:v0.27.0

Finally, open the link that shows up from the terminal output and start playing around with pyvista in jupyterlab! For example:

To access the notebook, open this file in a browser:
    file:///home/jovyan/.local/share/jupyter/runtime/nbserver-6-open.html
Or copy and paste one of these URLs:
    http://861c873f6352:8888/?token=b3ac1f6397188944fb21e1f58b673b5b4e6f1ede1a84787b
 or http://127.0.0.1:8888/?token=b3ac1f6397188944fb21e1f58b673b5b4e6f1ede1a84787b

Create your own Docker Container with pyvista

Clone pyvista and cd into this directory to create your own customized docker image.

git clone https://github.com/pyvista/pyvista
cd pyvista/docker
IMAGE=my-pyvista-jupyterlab:v0.1.0
docker build -t $IMAGE .
docker push $IMAGE

If you wish to have off-screen GPU support when rending on jupyterlab, see the the notes about building with EGL at Building VTK, or use the custom, pre-built wheels at Release 0.27.0. Install that customized vtk wheel onto your docker image by modifying the docker image at pyvista/docker/Dockerfile with:

COPY vtk-9.0.20201105-cp38-cp38-linux_x86_64.whl /tmp/
RUN pip install /tmp/vtk-9.0.20201105-cp38-cp38-linux_x86_64.whl

Additionally, you must install GPU drivers on the docker image of the same version running on the host machine. For example, if you are running on Azure Kubernetes Service and the GPU nodes on the kubernetes cluster is running 450.51.06, you must install the same version on your image. Since you will be using the underlying kernel module, there’s no reason to build it on the container (and trying will only result in an error).

COPY NVIDIA-Linux-x86_64-450.51.06.run nvidia_drivers.run
RUN sudo apt-get install kmod libglvnd-dev pkg-config -yq
RUN ./NVIDIA-Linux-x86_64-450.51.06.run -s --no-kernel-module

To verify that you’re rendering on a GPU, first check the output of nvidia-smi. You should get something like:

$ nvidia-smi
Sun Nov  8 05:48:46 2020
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 450.51.06    Driver Version: 450.51.06    CUDA Version: 11.0     |
|-------------------------------+----------------------+----------------------+
| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |
| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |
|                               |                      |               MIG M. |
|===============================+======================+======================|
|   0  Tesla K80           Off  | 00000001:00:00.0 Off |                    0 |
| N/A   34C    P8    32W / 149W |   1297MiB / 11441MiB |      0%      Default |
|                               |                      |                  N/A |
+-------------------------------+----------------------+----------------------+

Note the driver version (which is actually the kernel driver version), and verify it matches the version you installed on your docker image.

Finally, check that your render window is using NVIDIA by running ReportCapabilities:

>>> import pyvista
>>> pl = pyvista.Plotter()
>>> print(pl.ren_win.ReportCapabilities())

OpenGL vendor string:  NVIDIA Corporation
OpenGL renderer string:  Tesla K80/PCIe/SSE2
OpenGL version string:  4.6.0 NVIDIA 450.51.06
OpenGL extensions:
  GL_AMD_multi_draw_indirect
  GL_AMD_seamless_cubemap_per_texture
  GL_ARB_arrays_of_arrays
  GL_ARB_base_instance
  GL_ARB_bindless_texture

If you get display id not set, then your environment is likely not setup correctly.