[examples] consistently use --gpus, instead of --n_gpu (#6315)
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@ -55,7 +55,7 @@ Here are the results on the *test* sets for 6 of the languages available in XNLI
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## Setup
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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`.
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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`.
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**Important note:** The training scripts have been updated to support PyTorch v1.2.0 (there are breakings changes compared to v1.1.0).
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@ -161,7 +161,7 @@ python -m torch.distributed.launch \
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--master_port $MASTER_PORT \
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train.py \
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--force \
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--n_gpu $WORLD_SIZE \
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--gpus $WORLD_SIZE \
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--student_type distilbert \
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--student_config training_configs/distilbert-base-uncased.json \
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--teacher_type bert \
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@ -210,7 +210,7 @@ def main():
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help="For fp16: Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']."
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"See details at https://nvidia.github.io/apex/amp.html",
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)
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parser.add_argument("--n_gpu", type=int, default=1, help="Number of GPUs in the node.")
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parser.add_argument("--gpus", type=int, default=1, help="Number of GPUs in the node.")
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parser.add_argument("--local_rank", type=int, default=-1, help="Distributed training - Local rank")
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parser.add_argument("--seed", type=int, default=56, help="Random seed")
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@ -249,7 +249,7 @@ The results are the following:
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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.
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Pass `--n_gpu` flag to change the number of GPUs. Default uses 1. At the end, the expected results are:
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Pass `--gpus` flag to change the number of GPUs. Default uses 1. At the end, the expected results are:
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```
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TEST RESULTS {'val_loss': tensor(0.0707), 'precision': 0.852427800698191, 'recall': 0.869537067011978, 'f1': 0.8608974358974358}
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@ -294,7 +294,3 @@ Training with the previously defined hyper-parameters yields the following resul
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```bash
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acc = 0.7093812375249501
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```
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@ -134,7 +134,7 @@ On the test dataset the following results could be achieved:
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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.
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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}`
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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}`
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#### Run the Tensorflow 2 version
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