[examples] consistently use --gpus, instead of --n_gpu (#6315)

examples/distillation/README.md

@@ -55,7 +55,7 @@ Here are the results on the *test* sets for 6 of the languages available in XNLI
 
 ## Setup
 
-This part of the library has only be tested with Python3.6+. There are few specific dependencies to install before launching a distillation, you can install them with the command `pip install -r requirements.txt`. 
+This part of the library has only be tested with Python3.6+. There are few specific dependencies to install before launching a distillation, you can install them with the command `pip install -r requirements.txt`.
 
 **Important note:** The training scripts have been updated to support PyTorch v1.2.0 (there are breakings changes compared to v1.1.0).
 
@@ -161,7 +161,7 @@ python -m torch.distributed.launch \
     --master_port $MASTER_PORT \
     train.py \
         --force \
-        --n_gpu $WORLD_SIZE \
+        --gpus $WORLD_SIZE \
         --student_type distilbert \
         --student_config training_configs/distilbert-base-uncased.json \
         --teacher_type bert \

examples/distillation/train.py

@@ -210,7 +210,7 @@ def main():
         help="For fp16: Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']."
         "See details at https://nvidia.github.io/apex/amp.html",
     )
-    parser.add_argument("--n_gpu", type=int, default=1, help="Number of GPUs in the node.")
+    parser.add_argument("--gpus", type=int, default=1, help="Number of GPUs in the node.")
     parser.add_argument("--local_rank", type=int, default=-1, help="Distributed training - Local rank")
     parser.add_argument("--seed", type=int, default=56, help="Random seed")
 

examples/text-classification/README.md

@@ -249,7 +249,7 @@ The results  are the following:
 
 Run `bash run_pl.sh` from the `glue` directory. This will also install `pytorch-lightning` and the requirements in `examples/requirements.txt`. It is a shell pipeline that will automatically download, pre-process the data and run the specified models. Logs are saved in `lightning_logs` directory.
 
-Pass `--n_gpu` flag to change the number of GPUs. Default uses 1. At the end, the expected results are: 
+Pass `--gpus` flag to change the number of GPUs. Default uses 1. At the end, the expected results are:
 
 ```
 TEST RESULTS {'val_loss': tensor(0.0707), 'precision': 0.852427800698191, 'recall': 0.869537067011978, 'f1': 0.8608974358974358}
@@ -294,7 +294,3 @@ Training with the previously defined hyper-parameters yields the following resul
 ```bash
 acc = 0.7093812375249501
 ```
-
-
-
-

examples/token-classification/README.md

@@ -134,7 +134,7 @@ On the test dataset the following results could be achieved:
 
 Run `bash run_pl.sh` from the `ner` directory. This would also install `pytorch-lightning` and the `examples/requirements.txt`. It is a shell pipeline which would automatically download, pre-process the data and run the models in `germeval-model` directory. Logs are saved in `lightning_logs` directory.
 
-Pass `--n_gpu` flag to change the number of GPUs. Default uses 1. At the end, the expected results are: `TEST RESULTS {'val_loss': tensor(0.0707), 'precision': 0.852427800698191, 'recall': 0.869537067011978, 'f1': 0.8608974358974358}`
+Pass `--gpus` flag to change the number of GPUs. Default uses 1. At the end, the expected results are: `TEST RESULTS {'val_loss': tensor(0.0707), 'precision': 0.852427800698191, 'recall': 0.869537067011978, 'f1': 0.8608974358974358}`
 
 
 #### Run the Tensorflow 2 version