Updates multi-node training commands to also support Spark (#3978)

# Description

Removes rendezvous backend for multi-node training since it doesn't seem
to be necessary and prevents multi-node setup on the DGX Spark.


## Type of change

- Documentation update


## Checklist

- [x] I have read and understood the [contribution
guidelines](https://isaac-sim.github.io/IsaacLab/main/source/refs/contributing.html)
- [x] I have run the [`pre-commit` checks](https://pre-commit.com/) with
`./isaaclab.sh --format`
- [x] I have made corresponding changes to the documentation
- [x] My changes generate no new warnings
- [ ] I have added tests that prove my fix is effective or that my
feature works
- [ ] I have updated the changelog and the corresponding version in the
extension's `config/extension.toml` file
- [ ] I have added my name to the `CONTRIBUTORS.md` or my name already
exists there

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---------

Signed-off-by: Kelly Guo <kellyg@nvidia.com>
Co-authored-by: greptile-apps[bot] <165735046+greptile-apps[bot]@users.noreply.github.com>

Author: kellyguo11

docs/source/features/multi_gpu.rst

@@ -141,14 +141,14 @@ For the master node, use the following command, where ``--nproc_per_node`` repre
 
         .. code-block:: shell
 
-            python -m torch.distributed.run --nproc_per_node=2 --nnodes=2 --node_rank=0 --rdzv_id=123 --rdzv_backend=c10d --rdzv_endpoint=localhost:5555 scripts/reinforcement_learning/rl_games/train.py --task=Isaac-Cartpole-v0 --headless --distributed
+            python -m torch.distributed.run --nproc_per_node=2 --nnodes=2 --node_rank=0 --master_addr=<ip_of_master> --master_port=5555 scripts/reinforcement_learning/rl_games/train.py --task=Isaac-Cartpole-v0 --headless --distributed
 
     .. tab-item:: rsl_rl
         :sync: rsl_rl
 
         .. code-block:: shell
 
-            python -m torch.distributed.run --nproc_per_node=2 --nnodes=2 --node_rank=0 --rdzv_id=123 --rdzv_backend=c10d --rdzv_endpoint=localhost:5555 scripts/reinforcement_learning/rsl_rl/train.py --task=Isaac-Cartpole-v0 --headless --distributed
+            python -m torch.distributed.run --nproc_per_node=2 --nnodes=2 --node_rank=0 --master_addr=<ip_of_master> --master_port=5555 scripts/reinforcement_learning/rsl_rl/train.py --task=Isaac-Cartpole-v0 --headless --distributed
 
     .. tab-item:: skrl
         :sync: skrl
@@ -160,7 +160,7 @@ For the master node, use the following command, where ``--nproc_per_node`` repre
 
                 .. code-block:: shell
 
-                    python -m torch.distributed.run --nproc_per_node=2 --nnodes=2 --node_rank=0 --rdzv_id=123 --rdzv_backend=c10d --rdzv_endpoint=localhost:5555 scripts/reinforcement_learning/skrl/train.py --task=Isaac-Cartpole-v0 --headless --distributed
+                    python -m torch.distributed.run --nproc_per_node=2 --nnodes=2 --node_rank=0 --master_addr=<ip_of_master> --master_port=5555 scripts/reinforcement_learning/skrl/train.py --task=Isaac-Cartpole-v0 --headless --distributed
 
             .. tab-item:: JAX
                 :sync: jax
@@ -181,14 +181,14 @@ For non-master nodes, use the following command, replacing ``--node_rank`` with
 
         .. code-block:: shell
 
-            python -m torch.distributed.run --nproc_per_node=2 --nnodes=2 --node_rank=1 --rdzv_id=123 --rdzv_backend=c10d --rdzv_endpoint=ip_of_master_machine:5555 scripts/reinforcement_learning/rl_games/train.py --task=Isaac-Cartpole-v0 --headless --distributed
+            python -m torch.distributed.run --nproc_per_node=2 --nnodes=2 --node_rank=1 --master_addr=<ip_of_master> --master_port=5555 scripts/reinforcement_learning/rl_games/train.py --task=Isaac-Cartpole-v0 --headless --distributed
 
     .. tab-item:: rsl_rl
         :sync: rsl_rl
 
         .. code-block:: shell
 
-            python -m torch.distributed.run --nproc_per_node=2 --nnodes=2 --node_rank=1 --rdzv_id=123 --rdzv_backend=c10d --rdzv_endpoint=ip_of_master_machine:5555 scripts/reinforcement_learning/rsl_rl/train.py --task=Isaac-Cartpole-v0 --headless --distributed
+            python -m torch.distributed.run --nproc_per_node=2 --nnodes=2 --node_rank=1 --master_addr=<ip_of_master> --master_port=5555 scripts/reinforcement_learning/rsl_rl/train.py --task=Isaac-Cartpole-v0 --headless --distributed
 
     .. tab-item:: skrl
         :sync: skrl
@@ -200,7 +200,7 @@ For non-master nodes, use the following command, replacing ``--node_rank`` with
 
                 .. code-block:: shell
 
-                    python -m torch.distributed.run --nproc_per_node=2 --nnodes=2 --node_rank=1 --rdzv_id=123 --rdzv_backend=c10d --rdzv_endpoint=ip_of_master_machine:5555 scripts/reinforcement_learning/skrl/train.py --task=Isaac-Cartpole-v0 --headless --distributed
+                    python -m torch.distributed.run --nproc_per_node=2 --nnodes=2 --node_rank=1 --master_addr=<ip_of_master> --master_port=5555 scripts/reinforcement_learning/skrl/train.py --task=Isaac-Cartpole-v0 --headless --distributed
 
             .. tab-item:: JAX
                 :sync: jax

docs/source/setup/installation/index.rst

@@ -89,8 +89,6 @@ Other notable limitations with respect to Isaac Lab include...
 
 #. Livestream and Hub Workstation Cache are not supported on the DGX spark.
 
-#. Multi-node training may require direct connections between Spark machines or additional network configurations.
-
 #. :ref:`Running Cosmos Transfer1 <running-cosmos>` is not currently supported on the DGX Spark.
 
 Troubleshooting