Benchmark Colab does not work
https://colab.research.google.com/github/huggingface/notebooks/blob/master/transformers_doc/tensorflow/benchmarks.ipynb
(https://huggingface.co/transformers/benchmarks.html?highlight=benchmark)

Running the above Colab on a CPU crashes with:

```
1 / 1
Process killed. Error in Process
Process killed. Error in Process
---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-4-df0caab4d791> in <module>()
----> 1 results = benchmark.run()
      2 print(results)

/usr/local/lib/python3.7/dist-packages/transformers/benchmark/benchmark_utils.py in run(self)
    705                     if self.args.inference:
    706                         if self.args.memory:
--> 707                             memory, inference_summary = self.inference_memory(model_name, batch_size, sequence_length)
    708                             inference_result_memory[model_name]["result"][batch_size][sequence_length] = memory
    709                         if self.args.speed:

ValueError: too many values to unpack (expected 2)
```
This issue is related directly to Google Colab environment. I ran same code on my local machine (CPU only) and whole benchmark has passed.

```{python}
from transformers import TensorFlowBenchmark, TensorFlowBenchmarkArguments
args = TensorFlowBenchmarkArguments(models=["bert-base-uncased"], batch_sizes=[8], sequence_lengths=[8, 32, 128, 512])
benchmark = TensorFlowBenchmark(args)

results = benchmark.run()
print(results)
```

```
====================       INFERENCE - SPEED - RESULT       ====================
--------------------------------------------------------------------------------
          Model Name             Batch Size     Seq Length     Time in s   
--------------------------------------------------------------------------------
      bert-base-uncased              8               8             0.139     
      bert-base-uncased              8               32            0.369     
      bert-base-uncased              8              128            1.319     
      bert-base-uncased              8              512            5.523     
--------------------------------------------------------------------------------

====================      INFERENCE - MEMORY - RESULT       ====================
--------------------------------------------------------------------------------
          Model Name             Batch Size     Seq Length    Memory in MB 
--------------------------------------------------------------------------------
      bert-base-uncased              8               8              1089     
      bert-base-uncased              8               32             1212     
      bert-base-uncased              8              128             1535     
      bert-base-uncased              8              512             1956     
--------------------------------------------------------------------------------
```<|||||>It seems that colab is killing your process, which may be due to a lack of resources. Does it still happen if you use a tiny model on small batch sizes & sequence lengths?<|||||>I have found out that there is a problem with the multiprocess benchmark on Colab. I added  **multi_process = False** in args and the benchmark has passed.
```
args = TensorFlowBenchmarkArguments(models=["bert-base-uncased"], batch_sizes=[8], sequence_lengths=[8, 32, 128, 512],
 multi_process = False)
```

The default value for the multi_process flag is True. I don't know why Colab is killing those processes. <|||||>This issue has been automatically marked as stale because it has not had recent activity. If you think this still needs to be addressed please comment on this thread.

Please note that issues that do not follow the [contributing guidelines](https://github.com/huggingface/transformers/blob/master/CONTRIBUTING.md) are likely to be ignored.