RagRetriever fails to find faiss-gpu installed with pip not conda
- `transformers` version: 4.5.1
- Platform: Linux-4.14.231-173.361.amzn2.x86_64-x86_64-with-glibc2.9
- Python version: 3.6.10
- PyTorch version (GPU?): 1.8.1+cu102 (True)
- Tensorflow version (GPU?): 2.4.1 (False)
- Using GPU in script?: yes nVidia V100 16Gb
- Using distributed or parallel set-up in script?: just single task in //
- rag: @patrickvonplaten, @lhoestq


## Information
Model I am using (RAG Retriever ...):

The problem arises when using:
[*  ] the official example scripts: (worked!)
[* ] my own modified scripts: (give details below)

The tasks I am working on is:
[*  ] my own task or dataset: (give details below)

## To reproduce 
Steps to reproduce the behavior:

1. Run transformers/examples/research_projects/rag/use_own_knowledge_dataset.py
This step worked fine yesterday prior to reboot.
2. Try to inspect output dataset directly using RagRetriever model in python... 3.6 > 
```python
from transformers import RagRetriever, RagSequenceForGeneration, RagTokenizer
retriever = RagRetriever.from_pretrained('facebook/dpr-ctx_encoder-single-nq-base', cache_dir=cache_dir, index_name="custom", indexed_dataset='./rag/out')
ImportError: 
RagRetriever requires the faiss library but it was not found in your environment. Checkout the instructions on the
installation page of its repo: https://github.com/facebookresearch/faiss/blob/master/INSTALL.md and follow the ones
that match your environment.
```

Also, if you import faiss, then faiss.__version__ does not exist.


Note for our environment we have to pip install faiss-gpu rather than conda since conda repos are blocked at proxy.
qds/NLP/aws_nlp/rag/out

A sample script to query the /path/to/my_knowledge_dataset/ would be handy.
Got it to work with rebuild... and pip install faiss and faiss-gpu
git clone https://...github.../rag
export TOKENIZERS_PARALLELISM=false
pip install torch torchvision  ray[default] datasets faiss faiss-gpu matplotlib seaborn pandas transformers awscli s3fs scikit-plot
python use_own_knowledge_dataset.py --csv_path ./text.csv --output_dir ./out/text
