Adding Docker images for transformers + notebooks
Docker images are as follow:

- transformers-cpu (PyTorch + TF)
- transformers-gpu (PyTorch + TF)
- transformers-pytorch-cpu
- transformers-pytorch-gpu
- transformers-tensorflow-cpu
- transformers-tensorflow-gpu

Images are tagged according to the version of the library they bring and always use the latest version of DL frameworks.

Notebooks introduce:

- How to use tokenizers
- Overall transformers overview
- How to use pipelines 

Currently notebooks are added to the repo, let's discuss internally if it's a good idea.
# [Codecov](https://codecov.io/gh/huggingface/transformers/pull/3051?src=pr&el=h1) Report
> Merging [#3051](https://codecov.io/gh/huggingface/transformers/pull/3051?src=pr&el=desc) into [master](https://codecov.io/gh/huggingface/transformers/commit/53ce3854a16ad2a715bc6ac8af3e30c18b5a1d11?src=pr&el=desc) will **not change** coverage.
> The diff coverage is `n/a`.

[![Impacted file tree graph](https://codecov.io/gh/huggingface/transformers/pull/3051/graphs/tree.svg?width=650&token=9qOlN6Hb1c&height=150&src=pr)](https://codecov.io/gh/huggingface/transformers/pull/3051?src=pr&el=tree)

```diff
@@          Coverage Diff           @@
##           master   #3051   +/-   ##
======================================
  Coverage    76.1%   76.1%           
======================================
  Files          98      98           
  Lines       15946   15946           
======================================
  Hits        12136   12136           
  Misses       3810    3810
```



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[Continue to review full report at Codecov](https://codecov.io/gh/huggingface/transformers/pull/3051?src=pr&el=continue).
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> `Δ = absolute <relative> (impact)`, `ø = not affected`, `? = missing data`
> Powered by [Codecov](https://codecov.io/gh/huggingface/transformers/pull/3051?src=pr&el=footer). Last update [53ce385...ff701d9](https://codecov.io/gh/huggingface/transformers/pull/3051?src=pr&el=lastupdated). Read the [comment docs](https://docs.codecov.io/docs/pull-request-comments).
<|||||>On the Docker files, do we really need conda? (I try to stay away from conda as much as possible)<|||||>Well, conda brings Intel MKL with just `conda install mkl, mkl-devel` which improves PyTorch and TF by a significant factor. 

Depending on what level of performances we want to provide: 

- I totally remove MKL and install some Open BLAS/LAPACK libraries
- I'll build MKL in the images and include into PATH<|||||>MKL is also available on PyPi. Have a looks-y [here](https://software.intel.com/en-us/articles/installing-the-intel-distribution-for-python-and-intel-performance-libraries-with-pip-and) to check whether everything you need is there. This might also be of interest: https://software.intel.com/en-us/distribution-for-python/choose-download/linux
