1. Home
  2. Containers
  3. Using enroot and pyxis in Bright Cluster Manager
  1. Home
  2. Machine Learning
  3. Using enroot and pyxis in Bright Cluster Manager
  1. Home
  3. Using enroot and pyxis in Bright Cluster Manager
  1. Home
  2. Third Party Software
  3. Using enroot and pyxis in Bright Cluster Manager
  1. Home
  2. Workload Management
  3. Using enroot and pyxis in Bright Cluster Manager

Using enroot and pyxis in Bright Cluster Manager

This article will explain the steps that are needed to install NVIDIA’s enroot tool on a Bright cluster. Enroot allows container images to be executed without a container runtime engine such as Docker. In addition we will show how the pyxis Slurm plugin can be deployed so that enroot can be used from within Slurm jobs. We will then show how an arbitrary Docker image can be extended with the Jupyter Enterprise Gateway endpoint that will allow the container image to be used in JupyterLab setups that are commonly found on Bright clusters. Lastly, we show how a Jupyter kernel template can be set up that can be instantiated by users to create a kernel that when selected from the Jupyter launcher, will run the desired Docker image through Slurm (leveraging pyxis and enroot), allowing the user to access the running container through a Jupyter notebook.

Configure user namespaces.

The kernel parameters on head node(s) and compute nodes need to be changed in order
to support user spaces.

  • The operating system should support user-owned namespaces:
    namespace.unpriv_enable=1 and user_namespace.enable=1
  • Also, two sysctl options:
    user.max_mnt_namespaces and user.max_user_namespaces
    describe the number of namespaces that are available to users.
sed -i -e 's/\(GRUB_CMDLINE_LINUX=\)\(.*\)"/\1\2 namespace.unpriv_enable=1 user_namespace.enable=1"/' /etc/default/grub
cmsh -c "softwareimage; use ${IMAGE_NAME}; append kernelparameters namespace.unpriv_enable=1 user_namespace.enable=1; commit"
echo -e "user.max_mnt_namespaces = 32" > /etc/sysctl.d/10-enroot.conf
echo -e "user.max_user_namespaces = 32" >> /etc/sysctl.d/10-enroot.conf
cp /etc/sysctl.d/10-enroot.conf /cm/images/${IMAGE_NAME}/etc/sysctl.d/10-enroot.conf
cmsh -c "device; reboot -m ${IMAGE_NAME}"

The head node(s) also need(s) to be rebooted.

Download and install Enroot

The latest releases of enroot is available here (https://github.com/NVIDIA/enroot/releases)

wget https://github.com/NVIDIA/enroot/releases/download/v3.2.0/enroot-3.2.0-1.el7.x86_64.rpm
wget https://github.com/NVIDIA/enroot/releases/download/v3.2.0/enroot+caps-3.2.0-1.el7.x86_64.rpm
yum install -y ./enroot-3.2.0-1.el7.x86_64.rpm ./enroot+caps-3.2.0-1.el7.x86_64.rpm
yum install -y --installroot=/cm/images/${IMAGE_NAME} ./enroot-3.2.0-1.el7.x86_64.rpm ./enroot+caps-3.2.0-1.el7.x86_64.rpm
echo 'ENROOT_RUNTIME_PATH        ${HOME}/enroot' >> /etc/enroot/enroot.conf
\cp -f /etc/enroot/enroot.conf /cm/images/${IMAGE_NAME}/etc/enroot/
cmsh -c "device; imageupdate -m ${IMAGE_NAME} -w"

Check that enroot is working

Commands should work both with privileged and with unprivileged users.

enroot import docker://gcr.io#google-containers/debian-base:v2.0.0
enroot create google-containers+debian-base+v2.0.0.sqsh
enroot start google-containers+debian-base+v2.0.0 cat /etc/os-release

Download, build and install pyxis

Slurm should be installed to build pyxis.
The latest pyxis releases are available here (https://github.com/NVIDIA/pyxis/releases)

git clone https://github.com/NVIDIA/pyxis.git
pushd pyxis
git checkout fce2847
module load slurm
make rpm
yum install -y ./nvslurm-plugin-pyxis-0.9.1-1.el7.x86_64.rpm
yum install -y --installroot=/cm/images/${IMAGE_NAME} ./nvslurm-plugin-pyxis-0.9.1-1.el7.x86_64.rpm
cmsh -c "device; imageupdate -m ${IMAGE_NAME} -w"

Slumr now needs to be made aware about the new SPANK module provided by pyxis:

module load slurm
mkdir $(dirname ${SLURM_CONF})/plugstack.conf.d
echo "include $(dirname ${SLURM_CONF})/plugstack.conf.d/*" >> $(dirname ${SLURM_CONF})/plugstack.conf
echo "required /usr/lib64/slurm/spank_pyxis.so remap_root=1 execute_entrypoint=0" \
        > $(dirname ${SLURM_CONF})/plugstack.conf.d/pyxis.conf

By default, pyxis stores unpacked images under /run, which is mapped to memory. If the nodes do not have enough RAM, then the storage path can be pointed to /tmp instead:

echo "required /usr/lib64/slurm/spank_pyxis.so runtime_path=/tmp remap_root=1 execute_entrypoint=0" \
    > $(dirname ${SLURM_CONF})/plugstack.conf.d/pyxis.conf

Running a Jupyter kernel


In order to build a custom image for a Jupyter kernel we will be using docker and a local docker registry. They can be installed with cm-docker-setup and cm-docker-registry-setup. Also enroot has to be configured to allow use of the local registry.

mkdir -p /cm/images/${IMAGE_NAME}/etc/docker/certs.d
echo "SSL_CERT_DIR    /etc/docker/certs.d/master:5000" >> /etc/enroot/enroot.conf
\cp -f /etc/enroot/enroot.conf /cm/images/${IMAGE_NAME}/etc/enroot/
cmsh -c "device; imageupdate -m ${IMAGE_NAME} -w"

Please note that in this example the hostname of the registry node is master (which on a Bright cluster always leads to the active head node).

– Customize docker image.

In this setup only Python kernels are supported. In this example the official Jupyter image is being used. Feel free to try other images in the FROM section. For example, from the NGC registry.

mkdir jupyter-docker-image
pushd jupyter-docker-image
echo "FROM jupyter/datascience-notebook" > Dockerfile
echo "RUN pip install cm-jupyter-eg-kernel-wlm==2.0.0" >> Dockerfile
docker build --network=host -t master:5000/local/datascience-notebook .
docker push master:5000/local/datascience-notebook:latest

To check if the image can be used in enroot+pyxis.

srun --container-image master:5000#local/datascience-notebook:latest cat /etc/os-release

It might take up to 20 minutes to download and unpack the image. To reduce the cached image data it is possible to give an image a name.

    srun --container-image master:5000#local/datascience-notebook:latest \
        --container-name=master:5000#local_datascience-notebook:latest \
        cat /etc/os-release

In this case the unpacked image will be stored under ~/.local/share/enroot/ and the user should carry out a manual cleanup to save space.

– Setting up Jupyter Kernel templates

A sample kernel template is available here (https://support.brightcomputing.com/kb-articles/enroot-pyxis/jupyter-eg-kernel-slurm-pyxis-py37.tar.gz).

It slightly diverges from a standard Jupyter kernel template. Another parameter is added in meta.yaml to allow the user to choose an image to load and run. Also batch job templates are changed accordingly in kernel.json.j2 to use Pyxis.

wget https://support.brightcomputing.com/kb-articles/enroot-pyxis/jupyter-eg-kernel-slurm-pyxis-py37.tar.gz
tar -xzf jupyter-eg-kernel-slurm-pyxis-py37.tar.gz
cp -pr jupyter-eg-kernel-slurm-pyxis-py37 /cm/shared/apps/jupyter/current/lib/python3.7/site-packages/cm_jupyter_kernel_creator/kerneltemplates/

On a first run the kernel installation could timeout because downloading and unpacking image takes up to 30 minutes.

Updated on April 8, 2021

Was this article helpful?

Related Articles

Leave a Comment