Update articles/machine-learning/how-to-train-distributed-gpu.md

Author: Jak-MS

articles/machine-learning/how-to-train-distributed-gpu.md

@@ -258,7 +258,7 @@ run = Experiment(ws, 'experiment_name').submit(run_config)
 For single-node training (including single-node multi-GPU), you can run your code on Azure ML without needing to specify a `distributed_job_config`. 
 To run an experiment using multiple nodes with multiple GPUs, there are 2 options:
 
-- Using PyTorch configuration (recommended): Define `PyTorchConfiguration` and specify `communication_backend="Nccl"`, `node_count`, and `process_count` (note that this is the total number of processes, ie, `num_nodes * process_count_per_node`). In Lightning Trainer module, specify both `num_nodes` and `gpus` to be consistent with `PyTorchConfiguration`, ie, `num_nodes = node_count` and `gpus = process_count_per_node`.
+- Using PyTorch configuration (recommended): Define `PyTorchConfiguration` and specify `communication_backend="Nccl"`, `node_count`, and `process_count` (note that this is the total number of processes, ie, `num_nodes * process_count_per_node`). In Lightning Trainer module, specify both `num_nodes` and `gpus` to be consistent with `PyTorchConfiguration`. For example, `num_nodes = node_count` and `gpus = process_count_per_node`.
 
 - Using MPI Configuration: