Running on HPC Clusters

Many HPC clusters run containers through singularity_, which supports shared systems without requiring root privileges. Use the workflow below to run Isaac Lab training and evaluation jobs on these clusters.

Convert the Isaac Lab Docker image to a Singularity image, upload it, and submit a job to your cluster.

Prerequisites

Install apptainer to export the image. The steps below install it locally; see its documentation for other installation options.

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

Set up SSH access from your development machine to the cluster to simplify file transfers and avoid repeated password prompts.

Cluster configuration

Configure the cluster-specific parameters in docker/cluster/.env.cluster. Each parameter is documented inline in the file:

###
# Cluster specific settings
###
# Job scheduler used by cluster.
# Currently supports PBS and SLURM
CLUSTER_JOB_SCHEDULER=SLURM
# Docker cache dir for Isaac Sim (has to end on docker-isaac-sim)
# e.g. /cluster/scratch/$USER/docker-isaac-sim
CLUSTER_ISAAC_SIM_CACHE_DIR=/some/path/on/cluster/docker-isaac-sim
# Isaac Lab directory on the cluster (has to end on isaaclab)
# e.g. /cluster/home/$USER/isaaclab
CLUSTER_ISAACLAB_DIR=/some/path/on/cluster/isaaclab
# Cluster login
CLUSTER_LOGIN=username@cluster_ip
# Cluster scratch directory to store the SIF file
# e.g. /cluster/scratch/$USER
CLUSTER_SIF_PATH=/some/path/on/cluster/
# Remove the temporary isaaclab code copy after the job is done
REMOVE_CODE_COPY_AFTER_JOB=false
# Python executable within Isaac Lab directory to run with the submitted job
CLUSTER_PYTHON_EXECUTABLE=scripts/reinforcement_learning/train.py

A few constraints are worth calling out:

• CLUSTER_ISAAC_SIM_CACHE_DIR must end in docker-isaac-sim. It is copied to the compute node and mounted into the singularity container, which speeds up simulation startup.
• CLUSTER_ISAACLAB_DIR must end in isaaclab. On submission, the latest local changes are copied to ${CLUSTER_ISAACLAB_DIR}_${datetime}, so several jobs can run different code versions at once. Logs are written back to the permanent CLUSTER_ISAACLAB_DIR.
• REMOVE_CODE_COPY_AFTER_JOB deletes only that code copy, never the logs. Enable it to save disk space on the cluster.

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

Exporting the Apptainer image

Export and upload the image before your first job, then repeat this step whenever the Docker image changes, such as after an Isaac Sim or dependency update.

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.

Defining 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.

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`

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 directly 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 --rl_library rsl_rl --task IsaacContrib-Velocity-Rough-AnymalC --video

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

Submitting to OSMO

NVIDIA OSMO is a cloud-native orchestration platform for scheduling robotics workloads. Unlike the SLURM and PBS workflows above, it runs the Isaac Lab Docker image directly, so no singularity conversion is needed and there is no code copy step — the image is the unit of deployment.

The workflow definition lives in docker/cluster/osmo_multi_gpu_workflow.yaml. It requests a single node and scales training across the GPUs on that node through the train_multigpu command. The following parameters can be overridden at submission time:

To submit a job, use the osmo CLI:

osmo workflow submit docker/cluster/osmo_multi_gpu_workflow.yaml \
  --set num_gpu=4 \
  --set args="--task Isaac-Reorient-KukaAllegro --num_envs 4096"