"Cannot re-initialize CUDA in forked subprocess." error when running "transformers/notebooks/05-benchmark.ipynb" notebook

Environment info

I am getting this error on a server, but also on Collab, so giving the Collab specs:

• transformers version: 3.3.1
• Platform: Linux-4.19.112±x86_64-with-Ubuntu-18.04-bionic
• Python version: 3.6.9
• PyTorch version (GPU?): 1.6.0+cu101 (True)
• Tensorflow version (GPU?): 2.3.0 (True)
• Using GPU in script?: Yes
• Using distributed or parallel set-up in script?: No?

And on my server:

• transformers version: 3.3.1
• Platform: Linux-4.19.0-11-amd64-x86_64-with-debian-10.6
• Python version: 3.6.12
• PyTorch version (GPU?): 1.4.0 (True)
• Tensorflow version (GPU?): not installed (NA)
• Using GPU in script?: Yes
• Using distributed or parallel set-up in script?: No?

Who can help

@patrickvonplaten

Information

Model I am using (Bert, XLNet …): Any model.

The problem arises when using:

• the official example scripts: (give details below)
• my own modified scripts: (give details below)

The tasks I am working on is:

• an official GLUE/SQUaD task: (give the name)
• my own task or dataset: (give details below)

To reproduce

Steps to reproduce the behavior:

1. Run the colab on benchmarking provided on the transformers GitHub.

2020-10-15 14:20:38.078717: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudart.so.10.1
1 / 5
Cannot re-initialize CUDA in forked subprocess. To use CUDA with multiprocessing, you must use the 'spawn' start method
Cannot re-initialize CUDA in forked subprocess. To use CUDA with multiprocessing, you must use the 'spawn' start method
Traceback (most recent call last):
  File "run_benchmark.py", line 47, in <module>
    main()
  File "run_benchmark.py", line 43, in main
    benchmark.run()
  File "/usr/local/lib/python3.6/dist-packages/transformers/benchmark/benchmark_utils.py", line 674, in run
    memory, inference_summary = self.inference_memory(model_name, batch_size, sequence_length)
ValueError: too many values to unpack (expected 2)

Expected behavior

[dhkim0225]

Try this. You can check other parameters for PyTorchBenchmarkArguments at here.

# main.py
def main():
    from transformers import PyTorchBenchmark, PyTorchBenchmarkArguments

    args = PyTorchBenchmarkArguments(models=["bert-base-uncased"],
                                     batch_sizes=[8],
                                     sequence_lengths=[8, 32, 128, 512],
                                     multi_process=False)
    print(args.do_multi_processing)
    benchmark = PyTorchBenchmark(args)
    results = benchmark.run()
    print(results)


if __name__ == '__main__':
    main()

CUDA_VISIBLE_DEVICES=0 python main.py

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