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

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Stas Bekman
2020-08-07 07:36:32 -07:00
committed by GitHub
parent 0e36e51515
commit 6695450a23
4 changed files with 5 additions and 9 deletions
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2
examples/distillation/README.md
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@@ -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 \

2
examples/distillation/train.py
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@@ -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")

6
examples/text-classification/README.md
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@@ -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
```

2
examples/token-classification/README.md
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@@ -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