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11 changes: 9 additions & 2 deletions docs/source/how-to/simulation_performance.rst
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Simulation Performance
=======================
Simulation Performance and Tuning
====================================

The performance of the simulation can be affected by various factors, including the number of objects in the scene,
the complexity of the physics simulation, and the hardware being used. Here are some tips to improve performance:
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Additional Performance Guides
-----------------------------

There are many ways to "tune" the performance of the simulation, but the way you choose largely depends on what you are trying to simulate. In general, the first place
you will want to look for performance gains is with the `physics engine <https://docs.omniverse.nvidia.com/kit/docs/omni_physics/107.3/dev_guide/guides.html>`_. Next to rendering
and running deep learning models, the physics engine is the most computationally costly. Tuning the physics sim to limit the scope to only the task of interest is a great place to
start hunting for performance gains.

We have recently released a new `gripper tuning guide <https://docs.omniverse.nvidia.com/kit/docs/omni_physics/107.3/dev_guide/guides/gripper_tuning_example.html>`_ , specific to contact and grasp tuning. Please check it first if you intend to use robot grippers. For additional details, you should also checkout these guides!

* `Isaac Sim Performance Optimization Handbook <https://docs.isaacsim.omniverse.nvidia.com/latest/reference_material/sim_performance_optimization_handbook.html>`_
* `Omni Physics Simulation Performance Guide <https://docs.omniverse.nvidia.com/kit/docs/omni_physics/latest/dev_guide/guides/physics-performance.html>`_
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.. tab-item:: Conda Environment

To install conda, please follow the instructions `here <https://docs.conda.io/projects/conda/en/latest/user-guide/install/index.html>__`.
To install conda, please follow the instructions `here <https://docs.conda.io/projects/conda/en/latest/user-guide/install/index.html>`__.
You can create the Isaac Lab environment using the following commands.

We recommend using `Miniconda <https://www.anaconda.com/docs/getting-started/miniconda/main/>`_,
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19 changes: 19 additions & 0 deletions docs/source/setup/installation/index.rst
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driver from the `Unix Driver Archive <https://www.nvidia.com/en-us/drivers/unix/>`_
using the ``.run`` installer.

DGX Spark: details and limitations
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

The DGX spark is a standalone machine learning device with aarch64 architecture. As a consequence, some
features of Isaac Lab are not currently supported on the DGX spark. The most noteworthy is that the architecture *requires* CUDA ≥ 13, and thus the cu13 build of PyTorch or newer.
Other notable limitations with respect to Isaac Lab include...

#. `SkillGen <https://isaac-sim.github.io/IsaacLab/main/source/overview/imitation-learning/skillgen.html>`_ is not explicitly supported out of the box. This
is because cuRobo builds native CUDA/C++ extensions that requires specific tooling and library versions which may not have been explicitly documented
and validated for use with DGX spark. Use at your own risk!

#. Extended reality teleoperation tools such as `OpenXR <https://isaac-sim.github.io/IsaacLab/release/2.3.0/source/api/lab/isaaclab.devices.html#openxr>`_ is not supported. This is due
to encoding performance limitations that have not yet been fully investigated.

#. SKRL training with `JAX <https://docs.jax.dev/en/latest/notebooks/thinking_in_jax.html>`_ has not been explicitly validated or tested in Isaac Lab on the DGX spark, and so this functionality may be limited or
different than expected out of the box.

#. Livestream and Hub Workstation Cache are not supported on the DGX spark.

Troubleshooting
~~~~~~~~~~~~~~~

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