#132: Fixed outdated documentation

[MarleneKress79789]

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Fixes #132

[ahsimb]

We need to say what certificate is this. There are two and they look similar. One is basically a list of trusted CA. It is needed for the server's certificate validation by the client (that's when you use the --use-ssl-cert-validation). Another one is the client's own certificate. It may or may not include the private key. In the latter case the key may be provided as a separate file.

[MarleneKress79789]

i will add it to #133.

[ahsimb]

We need to elaborate on how to upload a model by downloading it from the Huggingface Hub to a local drive.

Firstly, a sample code like the one below would be useful. This code will create a model cache. This cache is actually what we need to upload to the BucketFS.

from transformers import AutoTokenizer, AutoModel

AutoTokenizer.from_pretrained(model_name, cache_dir=model_dir, token=user_token)
AutoModel.from_pretrained(model_name, cache_dir=model_dir, token=user_token)

For example, if model_name == 'me/my-awesome-model', and model_dir == 'my_model_dir' then the above code will create some files in the directory '.../my_model_dir/models--me--my-awesome-model'.

The '.../my_model_dir' is what we need to provide to the exasol_transformers_extension.upload_model in the local-model-path parameter. However, and this is important, it will grab EVERYTHING that it finds in this directory. If you downloaded several models there it will wrap them all in a single tar.gz file and start uploading. Therefore it is important to cache every model in its individual sub-directory. For example, if the root cache directory is 'my-models-cache' then I shall set the model_dir to something like '.../my-models-cache/my-awesome-model-cache'.

[MarleneKress79789]

Yes, you already mentioned that in #133. i don´t think it is relevant to this ticket, so i would suggest working on adding missing information to the documentation in #133 since that is the focus of that ticket.