Huggingface

Hugging Face is an organization that builds and maintains several popular open-source software packages widely used in Artificial Intelligence research. In this article, you will find information and tutorials on how to use packages from the Hugging Face ecosystem on our clusters.

Transformers

Transformers is a python package that provides APIs and tools to easily download and train state-of-the-art pre-trained models on various tasks in multiple domains.

Installing Transformers

Our recommendation is to install it using our provided Python wheel as follows:

1. Load a Python module, thus module load python

2. Create and start a virtual environment.

3. Install PyTorch in the virtual environment with pip install.

(venv) [name@server ~] pip install --no-index transformers

Downloading pre-trained models

To download a pre-trained model from the Hugging Face model hub, choose one of the options below and follow the instructions on the login node of the cluster you are working on.

Using git lfs

Pre-trained models are usually made up of fairly large binary files. The Hugging Face makes these files available for download via Git Large File Storage. To download a model, load the git-lfs module and clone your chosen model repository from the model hub:

module load git-lfs/3.3.0
git clone https://huggingface.co/bert-base-uncased

Now that you have a copy of the pre-trained model saved locally in the cluster's filesystem, you can load it with a python script inside a job with the local_files_only option to avoid attempts to download it from the web:

from transformers import AutoModel AutoTokenizer
model = AutoModel.from_pretrained("/path/to/where/you/cloned/the/model", local_files_only=True)
tokenizer = AutoTokenizer.from_pretrained("/path/to/where/you/cloned/the/model", local_files_only=True)

Using python

It is also possible to download pre-trained models using Python instead of Git. The following must be executed on a login node as an internet connection is required to download the model files:

from transformers import AutoModel AutoTokenizer
model = AutoModel.from_pretrained("bert-base-uncased")
tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")

This will store the pre-trained model files in a cache directory, which defaults to $HOME/.cache/huggingface. You can change the cache directory by setting the environment variable TRANSFORMERS_CACHE before you import anything from the transformers package in your Python script. For example, the following will store model files in the current working directory:

import os
os.environ['TRANSFORMERS_CACHE']="./"
from transformers import AutoModel AutoTokenizer
model = AutoModel.from_pretrained("bert-base-uncased")
tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")

Whether you change the default cache directory location or not, you can load the pre-trained model from disk in a job by using the local_files_only option:

from transformers import AutoModel AutoTokenizer
model = AutoModel.from_pretrained("/path/to/where/model/is/saved", local_files_only=True)
tokenizer = AutoTokenizer.from_pretrained("/path/to/where/model/is/saved", local_files_only=True)