Add doc for multinode GPU training. (pytorch#5704)

* add doc for multinode traning.

* reworded ab it

* fix comments

* emphasize that cuda12+ is needed.

docs/pjrt.md

@@ -194,15 +194,61 @@ for more information.
 
 *Warning: GPU support is still highly experimental!*
 
+### Single-node GPU training
+
 To use GPUs with PJRT, simply set `PJRT_DEVICE=GPU` and configure
 `GPU_NUM_DEVICES` to the number of devices on the host. For example:
 
 ```
 PJRT_DEVICE=GPU GPU_NUM_DEVICES=4 python3 xla/test/test_train_mp_imagenet.py --fake_data --batch_size=128 --num_epochs=1
 ```
 
-Currently, only a single host is supported, and multi-host GPU cluster support
-will be added in an future release.
+You can also use `torchrun` to initiate the single-node multi-GPU training. For example,
+
+```
+PJRT_DEVICE=GPU torchrun --nnodes 1 --nproc-per-node ${NUM_GPU_DEVICES} xla/test/test_train_mp_imagenet.py --fake_data --batch_size=128 --num_epochs=1
+```
+
+In the above example, `--nnodes` means how many machines (physical machines or VMs) to be used (it is 1 since we do single-node training). `--nproc-per-node` means how many GPU devices to be used.
+
+### Multi-node GPU training
+
+**Note that this feature only works for cuda 12+**. Similar to how PyTorch uses multi-node training, you can run the command as below:
+
+```
+PJRT_DEVICE=GPU torchrun \
+--nnodes=${NUMBER_GPU_VM} \
+--node_rank=${CURRENT_NODE_RANK} \
+--nproc_per_node=${NUMBER_LOCAL_GPU_DEVICES} \
+--rdzv_endpoint=<internal_ip_address:port> multinode_training.py
+```
+
+- `--nnodes`: how many GPU machines to be used.
+- `--node_rank`: the index of the current GPU machines. The value can be 0, 1, ..., ${NUMBER_GPU_VM}-1.
+- `--nproc_per_node`: the number of GPU devices to be used on the current machine.
+- `--rdzv_endpoint`: the endpoint of the GPU machine with node_rank==0, in the form <host>:<port>. The `host` will be the internal IP address. The port can be any available port on the machine.
+
+For example, if you want to train on 2 GPU machines: machine_0 and machine_1, on the first GPU machine machine_0, run
+
+```
+# PJRT_DEVICE=GPU torchrun \
+--nnodes=2 \
+--node_rank=0 \
+--nproc_per_node=4 \
+--rdzv_endpoint="<MACHINE_0_IP_ADDRESS>:12355" pytorch/xla/test/test_train_mp_imagenet.py  --fake_data --pjrt_distributed --batch_size=128 --num_epochs=1
+```
+
+On the second GPU machine, run
+
+```
+# PJRT_DEVICE=GPU torchrun \
+--nnodes=2 \
+--node_rank=1 \
+--nproc_per_node=4 \
+--rdzv_endpoint="<MACHINE_0_IP_ADDRESS>:12355" pytorch/xla/test/test_train_mp_imagenet_torchrun.py  --fake_data --pjrt_distributed --batch_size=128 --num_epochs=1
+```
+
+the difference between the 2 commands above are `--node_rank` and potentially `--nproc_per_node` if you want to use different number of GPU devices on each machine. All the rest are identical.
 
 ## Differences from XRT