Installation

Choose the path that matches what you want to install and how you want to run it. Start with the automatic uv setup unless you need to manage your own environment, use a downloaded Isaac Sim package, or deploy to Docker or the cloud. Each card jumps to complete instructions on this page.

Choose an installation path

Contents

• Automatic setup with uv — Run from the Isaac Lab checkout while uv creates and manages the environment. Recommended for most users.
• isaaclab.sh installer (legacy) — Use the legacy installer script to select packages in a virtual environment.
• Python environment with Isaac Sim — Manage a uv, venv, or conda environment and install Isaac Sim with pip.
• Isaac Lab Python package — Install the released Isaac Lab package as a dependency of your own project.
• Downloaded Isaac Sim package — Download Isaac Sim and use the Python interpreter included with it.
• Build Isaac Sim from source — Build or modify Isaac Sim itself. This is an advanced workflow.
• Docker and HPC clusters — Develop in a container or submit containerized jobs to an HPC cluster.
• Cloud workstations — Provision a remote GPU workstation on a supported cloud provider.

System requirements

Full Isaac Sim workflows require Python 3.12 on Ubuntu 22.04+ or Windows 11. Use a recent NVIDIA production driver and a workstation with at least 32 GB RAM and 16 GB GPU VRAM. Rendering can require additional VRAM. Confirm your machine against the Isaac Sim system requirements and Omniverse technical requirements.

Isaac Sim 5.1 and older are not supported. Use Isaac Sim 6.2 with Python 3.12. The Isaac Sim wheels require GLIBC 2.35 or newer on Linux.

The CUDA 13.0 PyTorch build requires NVIDIA driver 580.65.06 or newer on Linux and 580.88 or newer on Windows, as documented in the PyTorch CUDA 13.0 requirements. CUDA 13.0 wheels support Blackwell GPUs.

Use the latest NVIDIA production branch driver. Version 580.95.05 or later is recommended on Linux x86_64 and aarch64, 580.142 on DGX Spark, and 581.42.00 on Windows. If a new GPU or driver issue requires a newer release, use the production driver from the Unix Driver Archive. On Linux, the Isaac Sim Compatibility Checker and Linux troubleshooting guide can identify unsupported host configurations.

DGX Spark requires CUDA 13 or newer and the corresponding PyTorch build. Install the build prerequisites before installing Isaac Lab:

sudo apt install python3.12-dev libgl1-mesa-dev libx11-dev libxcursor-dev \
   libxi-dev libxinerama-dev libxrandr-dev

SkillGen, XR teleoperation, livestream, Hub Workstation Cache, Cosmos Transfer1, and RLinf are not currently supported or validated on DGX Spark. SkillGen depends on native CUDA/C++ extensions whose toolchain has not been validated on DGX Spark, while XR remains limited by unvalidated encoding performance.

Automatic setup with uv (recommended)

Use this path for the fastest setup from an Isaac Lab checkout. uv resolves the project environment on each invocation, so you do not need to create or activate an environment manually.

Install uv, clone Isaac Lab, and start a workflow:

curl -LsSf https://astral.sh/uv/install.sh | sh

git clone git@github.com:isaac-sim/IsaacLab.git --branch release/3.0.0
cd IsaacLab

# Newton backend without Isaac Sim
uv run isaaclab train --rl_library rsl_rl \
   --task Isaac-Cartpole-Direct physics=newton_mjwarp
# OV PhysX backend
uv run --extra ovphysx isaaclab train --rl_library rsl_rl \
   --task Isaac-Cartpole-Direct physics=ovphysx
# Full Isaac Sim support
uv run --extra isaacsim isaaclab train --rl_library rsl_rl \
   --task Isaac-Cartpole-Direct physics=isaacsim_physx
# Play a policy
uv run isaaclab play --rl_library rsl_rl --task Isaac-Cartpole-Direct --viz newton_gl

uv run installs the core dependencies automatically. The --extra <name> option includes the selected optional integration in the command’s environment. Place it before isaaclab; for example, --extra ov installs both ovphysx and ovrtx backends. Pass a comma-separated list or repeat --extra. No extras conflict, so any combination resolves into one environment. The --extra all shortcut installs the curated ov, rl-games, sb3, skrl, rsl-rl, rerun, and viser extras. It does not include Isaac Sim or the specialized skrl-jax, rlinf, mimic, teleop, tetrahedralization, video, and leapp extras; request them by name:

uv run --extra all isaaclab train --rl_library rsl_rl \
   --task Isaac-Cartpole-Direct physics=ovphysx

See Optional extras for the available extras.

uv run --extra <name> <command> syncs the selected extra into the project environment and then runs the command.

The source checkout selects PyTorch’s CUDA 13.0 build on Linux x86_64, Linux aarch64, and Windows. No additional command flags are needed. The aggregate wheel pins the PyTorch versions using registry-compatible metadata. Its published-wheel commands select the CUDA 13.0 build at install time. The wheel supports Python 3.12, matching the source checkout.

Head over to the /source/setup/quickstart, which starts with your first task and introduces the available commands, RL libraries, backends, and visualizers.

isaaclab.sh installer (legacy)

isaaclab.sh installer (legacy)

Kit-less installation uses a Python 3.12 environment and does not install Isaac Sim. Clone Isaac Lab, create and activate an environment, then install the default source packages and dependencies. Install uv or conda before starting:

git clone git@github.com:isaac-sim/IsaacLab.git --branch release/3.0.0
cd IsaacLab

uv venv --python 3.12 --seed env_isaaclab
source env_isaaclab/bin/activate
uv pip install --upgrade pip
./isaaclab.sh -i

-i always installs the core source packages. With no value, it also installs the optional mimic and teleop submodules plus the default Newton, RL, and visualizer dependencies. It does not install tetrahedralization, contrib, ov, or Isaac Sim; request those explicitly when needed.

Use -i core for core packages only. Otherwise, pass a comma-separated list of selectors:

For example:

# Core packages only
./isaaclab.sh -i core
# Newton, RSL-RL, and the Newton visualizer
./isaaclab.sh -i 'newton,rl[rsl-rl],visualizer[newton]'
# OVRTX runtime dependencies
./isaaclab.sh -i 'ov[ovrtx]'

Python environment with Isaac Sim

Use this path when you want an editable Isaac Lab checkout with full Isaac Sim support and a Python environment you manage yourself. Create and activate the environment before installing Isaac Sim or Isaac Lab. Isaac Sim’s pip packages require GLIBC 2.35 or newer on Linux. Enable Windows long-path support before installing on Windows.

Create and activate a Python 3.12 environment:

uv venv --python 3.12 --seed env_isaaclab
source env_isaaclab/bin/activate

Install Isaac Sim and the CUDA 13.0 PyTorch build for your platform:

uv pip install "isaacsim[all,extscache]==6.2.0.0" --extra-index-url https://pypi.nvidia.com --index-strategy unsafe-best-match --prerelease=allow

uv pip install -U torch==2.11.0 torchvision==0.26.0 --index-url https://download.pytorch.org/whl/cu130

The first launch asks you to accept the NVIDIA Omniverse EULA. For non-interactive environments, set OMNI_KIT_ACCEPT_EULA=yes. Verify Isaac Sim with isaacsim.

With the environment still active, clone, install, and verify Isaac Lab:

git clone git@github.com:isaac-sim/IsaacLab.git --branch release/3.0.0
cd IsaacLab

sudo apt install cmake build-essential
./isaaclab.sh -i
./isaaclab.sh -p scripts/tutorials/00_sim/create_empty.py --viz kit

The verification command should open a black simulator viewport. The initial launch can take over ten minutes while Isaac Sim downloads extensions.

Isaac Lab Python package

Use this path when Isaac Lab is a dependency of an external Python project. The released isaaclab package includes the unified train, play, zero_agent, random_agent, benchmark, train_multigpu, demo, and example commands. Downstream projects can register their task package through the isaaclab.tasks Python package entry-point group; projects created by the template generator configure this automatically.

To create a project built on Isaac Lab, see Build your own project or task.

Installing an unreleased Git revision

The aggregate package can also be built directly from an Isaac Lab Git revision. Point uv at the tools/wheel_builder subdirectory so it uses the same dependency metadata and packaged runtime resources as a released wheel:

[project]
dependencies = ["isaaclab"]
[tool.uv.sources]
isaaclab = { git = "https://github.com/isaac-sim/IsaacLab.git", rev = "<git-revision>", subdirectory = "tools/wheel_builder" }

Use a commit hash or release tag for reproducible environments. A branch name is accepted, but updating the lockfile can then select a newer Isaac Lab revision and dependency set.

Installing the published wheel

Use NVIDIA’s package index for the |isaaclab_wheel_version| prerelease. Do not point Isaac Lab at the Git source from the previous workflow when installing the published wheel.

Run the published package directly or install it into a persistent environment. All workflows start with the base isaaclab package; add optional capabilities only when your project needs them.

uvx --index https://pypi.nvidia.com \
   --torch-backend cu130 \
   --from "isaaclab==3.0.0rc1" \
   isaaclab demo zoo

The project workflow records the dependencies, package indexes, and lockfile for an application you maintain; the commands make those project-file changes, so no manual editing is required. Use uvx for one-off commands or a standalone environment for exploratory work that needs an activated Python environment. Pass the same index strategy when adding or upgrading dependencies in the project.

Optional extras

Add extras only when your project needs them. Most extras work with uv pip install "isaaclab[<extra>]" in a standalone environment or uv add "isaaclab[<extra>]" in a uv project. The importers and isaacsim extras have dedicated commands below.

Use all for the curated list above. Isaac Sim, standalone importers, specialized extras (skrl-jax, rlinf, mimic, teleop, tetrahedralization, video, leapp), and the developer test tooling remain opt-in.

Installing the importers extra

Installing the importers extra

Install this extra to convert URDF and MJCF files without Isaac Sim.

uv pip install "isaaclab[importers]==3.0.0rc1" \
  --overrides "https://raw.githubusercontent.com/isaac-sim/IsaacLab/v3.0.0-EA/tools/wheel_builder/uv-overrides.txt" \
  --index https://pypi.nvidia.com \
  --torch-backend cu130 \
  --index-strategy unsafe-best-match

Installing the isaacsim extra

Installing the isaacsim extra

Isaac Sim 6.2 pins dependencies that conflict with Isaac Lab. Install the isaacsim extra with the tested overrides:

uv pip install "isaaclab[isaacsim]==3.0.0rc1" \
  --overrides "https://raw.githubusercontent.com/isaac-sim/IsaacLab/v3.0.0-EA/tools/wheel_builder/uv-overrides.txt" \
  --extra-index-url https://pypi.nvidia.com \
  --torch-backend cu130 \
  --index-strategy unsafe-best-match

Add other extras inside the brackets when needed; for example, use isaaclab[isaacsim,all] to include the curated all list.

Installing CUDA-enabled PyTorch

The published-wheel commands above select the CUDA 13.0 PyTorch build during dependency resolution. To update an existing standalone environment that was installed without the CUDA index, use the command for your platform:

uv pip install -U torch==2.11.0 torchvision==0.26.0 --index-url https://download.pytorch.org/whl/cu130

If you installed the isaacsim extra, verify it before running your project:

isaacsim

The first launch downloads Isaac Sim extensions and can take more than ten minutes. It also asks you to accept the NVIDIA Omniverse EULA; set OMNI_KIT_ACCEPT_EULA=yes for a non-interactive environment. Run a project script with python my_script.py.

Generate VS Code or Cursor settings for the current workspace with:

uv run isaaclab --editor

Downloaded Isaac Sim package

Use this path when you prefer a downloaded Isaac Sim package instead of pip. Download and extract the Isaac Sim pre-built package. Binary installs must use Isaac Sim’s bundled Python; combining them with conda, uv, or venv is unsupported. If you need a dedicated Python environment, use Python environment with Isaac Sim instead.

The commands below assume the package was extracted to ${HOME}/isaacsim on Linux or C:\isaacsim on Windows. Set the installation paths and verify the simulator:

export ISAACSIM_PATH="${HOME}/isaacsim"
export ISAACSIM_PYTHON_EXE="${ISAACSIM_PATH}/python.sh"
${ISAACSIM_PATH}/isaac-sim.sh
${ISAACSIM_PYTHON_EXE} -c "print('Isaac Sim configuration is now complete.')"
${ISAACSIM_PYTHON_EXE} ${ISAACSIM_PATH}/standalone_examples/api/isaacsim.core.experimental.api/add_cubes.py

Clone Isaac Lab, create the _isaac_sim link, install, and verify:

git clone git@github.com:isaac-sim/IsaacLab.git --branch release/3.0.0
cd IsaacLab

cd IsaacLab
ln -s ${ISAACSIM_PATH} _isaac_sim
sudo apt install cmake build-essential
./isaaclab.sh -i
./isaaclab.sh -p scripts/tutorials/00_sim/create_empty.py --viz kit

The tutorial command should open a black simulator viewport. If either verification command fails, consult the Isaac Sim Linux troubleshooting guide or the Isaac Sim forums.

Build Isaac Sim from source

Build Isaac Sim from source only when you need to modify it or test a nightly revision. Building requires Ubuntu 22.04 or newer on Linux. For driver requirements, see the technical requirements. On Windows, enable long-path support before building.

Choose how to connect the Isaac Sim source build to Isaac Lab:

git clone https://github.com/isaac-sim/IsaacSim.git ../IsaacSim
uv run isaaclab --isaacsim_source ../IsaacSim

uv run isaaclab train --rl_library rsl_rl --task Isaac-Cartpole-Direct physics=isaacsim_physx

Docker and HPC clusters

Install Docker Engine, Docker Compose, and the NVIDIA Container Toolkit. Place the Isaac Lab checkout under /home when Docker was installed with Snap.

Clone Isaac Lab, then build, start, and enter the development container:

git clone git@github.com:isaac-sim/IsaacLab.git --branch release/3.0.0
cd IsaacLab

./docker/container.py start
./docker/container.py enter base

The container uses /isaac-sim/python.sh and mounts the repository’s source and docs directories for live editing. Use ./docker/container.py stop to stop it and ./docker/container.py copy to retrieve logs, data, and documentation artifacts.

For HPC, build the image on a machine with Docker, convert it to an Apptainer/Singularity image, and submit it with the cluster’s SLURM or PBS workflow. Keep cluster-specific paths and scheduler settings outside the base image.

See Running Isaac Lab in Docker for volume management, X11, image extensions, pre-built containers, worked examples, and complete cluster instructions.

Cloud workstations

Isaac Automator provisions GPU workstations on AWS, GCP, Azure, and Alibaba Cloud. Install Docker, then clone and build Isaac Automator:

git clone https://github.com/isaac-sim/IsaacAutomator.git
cd IsaacAutomator
./build
./run ./deploy-aws

Replace deploy-aws with deploy-gcp, deploy-azure, or deploy-alicloud. Use --isaaclab and --isaacsim to select Git revisions. Connection details for SSH, noVNC, and NoMachine are stored in state/<deployment-name>/info.txt.

Manage the workstation from the Automator container:

./stop <deployment-name>
./start <deployment-name>
./upload <deployment-name>
./download <deployment-name>
./destroy <deployment-name>

Preserve the state directory because it contains the deployment metadata.

See Cloud Deployment for credentials, provider options, connection methods, data transfer, and the complete workstation lifecycle.

Next steps

Continue with the quickstart to run a task and train a policy. For asset caching, Nucleus migration, and regional asset services, see /source/how-to/manage_asset_downloads. For setup, launch, and performance problems, use the troubleshooting reference .