OVH Guides

Exporting an HuggingFace pipeline

Learn how to export an HuggingFace pipeline

Last updated 12th August, 2020.


HuggingFace is a popular machine learning library supported by OVHcloud ML Serving. This tutorial will cover how to export an HuggingFace pipeline.


  • A python environment with HuggingFace (transformers) installed

Save HuggingFace pipeline

Let\'s take an example of an HuggingFace pipeline to illustrate:

import transformers
import json

# Sentiment analysis pipeline
pipeline = transformers.pipeline('sentiment-analysis')

# OR: Question answering pipeline, specifying the checkpoint identifier
pipeline = transformers.pipeline('question-answering', model='distilbert-base-cased-distilled-squad', tokenizer='bert-base-cased')

# OR: Named entity recognition pipeline, passing in a specific model and tokenizer
model = transformers.AutoModelForTokenClassification.from_pretrained('dbmdz/bert-large-cased-finetuned-conll03-english')
tokenizer = transformers.AutoTokenizer.from_pretrained('bert-base-cased')
pipeline = transformers.pipeline('ner', model=model, tokenizer=tokenizer)

# Save pipeline
path = 'my_model_dir'
# Save manifest (needed by OVHcloud ML Serving to load your pipeline)
with open(path + '/manifest.json', 'w') as file:
        'type': 'huggingface_pipeline',
        'pipeline_class': type(pipeline).__name__,
        'tokenizer_class': type(pipeline.tokenizer).__name__,
        'model_class': type(pipeline.model).__name__,
    }, file, indent=2)

Your model is now serialized on your local file system in the my_model_dir directory.

The manifest.json should look like:

  "type": "huggingface_pipeline",
  "pipeline_class": "FeatureExtractionPipeline",
  "tokenizer_class": "DistilBertTokenizer",
  "model_class": "DistilBertModel"

Going further

These guides might also interest you...