Cluster Guide

Clusters are a great way to speed up training and evaluation of learning algorithms. While the Isaac Lab Docker image can be used to run jobs on a cluster, many clusters only support singularity images. This is because singularity is designed for ease-of-use on shared multi-user systems and high performance computing (HPC) environments. It does not require root privileges to run containers and can be used to run user-defined containers.

Singularity is compatible with all Docker images. In this section, we describe how to convert the Isaac Lab Docker image into a singularity image and use it to submit jobs to a cluster.

Attention

Cluster setup varies across different institutions. The following instructions have been tested on the ETH Zurich Euler cluster (which uses the SLURM workload manager), and the IIT Genoa Franklin cluster (which uses PBS workload manager).

The instructions may need to be adapted for other clusters. If you have successfully adapted the instructions for another cluster, please consider contributing to the documentation.

Setup Instructions

In order to export the Docker Image to a singularity image, apptainer is required. A detailed overview of the installation procedure for apptainer can be found in its documentation. For convenience, we summarize the steps here for a local installation:

sudo apt update
sudo apt install -y software-properties-common
sudo add-apt-repository -y ppa:apptainer/ppa
sudo apt update
sudo apt install -y apptainer

For simplicity, we recommend that an SSH connection is set up between the local development machine and the cluster. Such a connection will simplify the file transfer and prevent the user cluster password from being requested multiple times.

Attention

The workflow has been tested with:

• apptainer version 1.2.5-1.el7 and docker version 24.0.7

• apptainer version 1.3.4 and docker version 27.3.1

In the case of issues, please try to switch to those versions.

Configuring the cluster parameters

First, you need to configure the cluster-specific parameters in docker/cluster/.env.cluster file. The following describes the parameters that need to be configured:

Parameter	Description
CLUSTER_JOB_SCHEDULER	The job scheduler/workload manager used by your cluster. Currently, we support ‘SLURM’ and ‘PBS’ workload managers.
CLUSTER_ISAAC_SIM_CACHE_DIR	The directory on the cluster where the Isaac Sim cache is stored. This directory has to end on docker-isaac-sim. It will be copied to the compute node and mounted into the singularity container. This should increase the speed of starting the simulation.
CLUSTER_ISAACLAB_DIR	The directory on the cluster where the Isaac Lab logs are stored. This directory has to end on isaaclab. It will be copied to the compute node and mounted into the singularity container. When a job is submitted, the latest local changes will be copied to the cluster to a new directory in the format ${CLUSTER_ISAACLAB_DIR}_${datetime} with the date and time of the job submission. This allows to run multiple jobs with different code versions at the same time.
CLUSTER_LOGIN	The login to the cluster. Typically, this is the user and cluster names, e.g., your_user@euler.ethz.ch.
CLUSTER_SIF_PATH	The path on the cluster where the singularity image will be stored. The image will be copied to the compute node but not uploaded again to the cluster when a job is submitted.
REMOVE_CODE_COPY_AFTER_JOB	Whether the copied code should be removed after the job is finished or not. The logs from the job will not be deleted as these are saved under the permanent CLUSTER_ISAACLAB_DIR. This feature is useful to save disk space on the cluster. If set to true, the code copy will be removed.
CLUSTER_PYTHON_EXECUTABLE	The path within Isaac Lab to the Python executable that should be executed in the submitted job.

When a job is submitted, it will also use variables defined in docker/.env.base, though these should be correct by default.

Exporting to singularity image

Next, we need to export the Docker image to a singularity image and upload it to the cluster. This step is only required once when the first job is submitted or when the Docker image is updated. For instance, due to an upgrade of the Isaac Sim version, or additional requirements for your project.

To export to a singularity image, execute the following command:

./docker/cluster/cluster_interface.sh push [profile]

This command will create a singularity image under docker/exports directory and upload it to the defined location on the cluster. It requires that you have previously built the image with the container.py interface. Be aware that creating the singularity image can take a while. [profile] is an optional argument that specifies the container profile to be used. If no profile is specified, the default profile base will be used.

Note

By default, the singularity image is created without root access by providing the --fakeroot flag to the apptainer build command. In case the image creation fails, you can try to create it with root access by removing the flag in docker/cluster/cluster_interface.sh.

Defining the job parameters

The job parameters need to be defined based on the job scheduler used by your cluster. You only need to update the appropriate script for the scheduler available to you.

• For SLURM, update the parameters in docker/cluster/submit_job_slurm.sh.

• For PBS, update the parameters in docker/cluster/submit_job_pbs.sh.

For SLURM

The job parameters are defined inside the docker/cluster/submit_job_slurm.sh. A typical SLURM operation requires specifying the number of CPUs and GPUs, the memory, and the time limit. For more information, please check the SLURM documentation.

The default configuration is as follows:

#SBATCH --cpus-per-task=8
#SBATCH --gpus=rtx_3090:1
#SBATCH --time=23:00:00
#SBATCH --mem-per-cpu=4048
#SBATCH --mail-type=END
#SBATCH --mail-user=name@mail
#SBATCH --job-name="training-$(date +"%Y-%m-%dT%H:%M")"

An essential requirement for the cluster is that the compute node has access to the internet at all times. This is required to load assets from the Nucleus server. For some cluster architectures, extra modules must be loaded to allow internet access.

For instance, on ETH Zurich Euler cluster, the eth_proxy module needs to be loaded. This can be done by adding the following line to the submit_job_slurm.sh script:

# in the case you need to load specific modules on the cluster, add them here
# e.g., `module load eth_proxy`

For PBS

The job parameters are defined inside the docker/cluster/submit_job_pbs.sh. A typical PBS operation requires specifying the number of CPUs and GPUs, and the time limit. For more information, please check the PBS Official Site.

The default configuration is as follows:

#PBS -l select=1:ncpus=8:mpiprocs=1:ngpus=1
#PBS -l walltime=01:00:00
#PBS -j oe
#PBS -q gpu
#PBS -N isaaclab
#PBS -m bea -M "user@mail"

Submitting a job

To submit a job on the cluster, the following command can be used:

./docker/cluster/cluster_interface.sh job [profile] "argument1" "argument2" ...

This command will copy the latest changes in your code to the cluster and submit a job. Please ensure that your Python executable’s output is stored under isaaclab/logs as this directory is synced between the compute node and CLUSTER_ISAACLAB_DIR.

[profile] is an optional argument that specifies which singularity image corresponding to the container profile will be used. If no profile is specified, the default profile base will be used. The profile has be defined directlty after the job command. All other arguments are passed to the Python executable. If no profile is defined, all arguments are passed to the Python executable.

The training arguments are passed to the Python executable. As an example, the standard ANYmal rough terrain locomotion training can be executed with the following command:

./docker/cluster/cluster_interface.sh job --task Isaac-Velocity-Rough-Anymal-C-v0 --headless --video --enable_cameras

The above will, in addition, also render videos of the training progress and store them under isaaclab/logs directory.