Wallaroo SDK Essentials Guide: Model Uploads and Registrations: Hugging Face

How to upload and use Hugging Face ML Models with Wallaroo

Table of Contents

Model Naming Requirements

Model names map onto Kubernetes objects, and must be DNS compliant. The strings for model names must be ASCII alpha-numeric characters or dash (-) only. . and _ are not allowed.

Wallaroo supports Hugging Face models by containerizing the model and running as an image.

ParameterDescription
Web Sitehttps://huggingface.co/models
Supported Libraries
  • transformers==4.27.0
  • diffusers==0.14.0
  • accelerate==0.18.0
  • torchvision==0.14.1
  • torch==1.13.1
FrameworksThe following Hugging Face pipelines are supported by Wallaroo.
  • Framework.HUGGING_FACE_FEATURE_EXTRACTION aka hugging-face-feature-extraction
  • Framework.HUGGING_FACE_IMAGE_CLASSIFICATION aka hugging-face-image-classification
  • Framework.HUGGING_FACE_IMAGE_SEGMENTATION aka hugging-face-image-segmentation
  • Framework.HUGGING_FACE_IMAGE_TO_TEXT aka hugging-face-image-to-text
  • Framework.HUGGING_FACE_OBJECT_DETECTION aka hugging-face-object-detection
  • Framework.HUGGING_FACE_QUESTION_ANSWERING aka hugging-face-question-answering
  • Framework.HUGGING_FACE_STABLE_DIFFUSION_TEXT_2_IMG aka hugging-face-stable-diffusion-text-2-img
  • Framework.HUGGING_FACE_SUMMARIZATION aka hugging-face-summarization
  • Framework.HUGGING_FACE_TEXT_CLASSIFICATION aka hugging-face-text-classification
  • Framework.HUGGING_FACE_TRANSLATION aka hugging-face-translation
  • Framework.HUGGING_FACE_ZERO_SHOT_CLASSIFICATION aka hugging-face-zero-shot-classification
  • Framework.HUGGING_FACE_ZERO_SHOT_IMAGE_CLASSIFICATION aka hugging-face-zero-shot-image-classification
  • Framework.HUGGING_FACE_ZERO_SHOT_OBJECT_DETECTION aka hugging-face-zero-shot-object-detection
  • Framework.HUGGING_FACE_SENTIMENT_ANALYSIS aka hugging-face-sentiment-analysis
  • Framework.HUGGING_FACE_TEXT_GENERATION aka hugging-face-text-generation
RuntimeContainerized aka tensorflow / mlflow

Hugging Face Schemas

Input and output schemas for each Hugging Face pipeline are defined below. Note that adding additional inputs not specified below will raise errors, except for the following:

  • Framework.HUGGING-FACE-IMAGE-TO-TEXT
  • Framework.HUGGING-FACE-TEXT-CLASSIFICATION
  • Framework.HUGGING-FACE-SUMMARIZATION
  • Framework.HUGGING-FACE-TRANSLATION

Additional inputs added to these Hugging Face pipelines will be added as key/pair value arguments to the model’s generate method. If the argument is not required, then the model will default to the values coded in the original Hugging Face model’s source code.

See the Hugging Face Pipeline documentation for more details on each pipeline and framework.

Wallaroo FrameworkReference
Framework.HUGGING-FACE-FEATURE-EXTRACTION

Schemas:

input_schema = pa.schema([
    pa.field('inputs', pa.string())
])
output_schema = pa.schema([
    pa.field('output', pa.list_(
        pa.list_(
            pa.float64(),
            list_size=128
        ),
    ))
])
Wallaroo FrameworkReference
Framework.HUGGING-FACE-IMAGE-CLASSIFICATION

Schemas:

input_schema = pa.schema([
    pa.field('inputs', pa.list_(
        pa.list_(
            pa.list_(
                pa.int64(),
                list_size=3
            ),
            list_size=100
        ),
        list_size=100
    )),
    pa.field('top_k', pa.int64()),
])

output_schema = pa.schema([
    pa.field('score', pa.list_(pa.float64(), list_size=2)),
    pa.field('label', pa.list_(pa.string(), list_size=2)),
])
Wallaroo FrameworkReference
Framework.HUGGING-FACE-IMAGE-SEGMENTATION

Schemas:

input_schema = pa.schema([
    pa.field('inputs', 
        pa.list_(
            pa.list_(
                pa.list_(
                    pa.int64(),
                    list_size=3
                ),
                list_size=100
            ),
        list_size=100
    )),
    pa.field('threshold', pa.float64()),
    pa.field('mask_threshold', pa.float64()),
    pa.field('overlap_mask_area_threshold', pa.float64()),
])

output_schema = pa.schema([
    pa.field('score', pa.list_(pa.float64())),
    pa.field('label', pa.list_(pa.string())),
    pa.field('mask', 
        pa.list_(
            pa.list_(
                pa.list_(
                    pa.int64(),
                    list_size=100
                ),
                list_size=100
            ),
    )),
])
Wallaroo FrameworkReference
Framework.HUGGING-FACE-IMAGE-TO-TEXT

Any parameter that is not part of the required inputs list will be forwarded to the model as a key/pair value to the underlying models generate method. If the additional input is not supported by the model, an error will be returned.

Schemas:

input_schema = pa.schema([
    pa.field('inputs', pa.list_( #required
        pa.list_(
            pa.list_(
                pa.int64(),
                list_size=3
            ),
            list_size=100
        ),
        list_size=100
    )),
    # pa.field('max_new_tokens', pa.int64()),  # optional
])

output_schema = pa.schema([
    pa.field('generated_text', pa.list_(pa.string())),
])
Wallaroo FrameworkReference
Framework.HUGGING-FACE-OBJECT-DETECTION

Schemas:

input_schema = pa.schema([
    pa.field('inputs', 
        pa.list_(
            pa.list_(
                pa.list_(
                    pa.int64(),
                    list_size=3
                ),
                list_size=100
            ),
        list_size=100
    )),
    pa.field('threshold', pa.float64()),
])

output_schema = pa.schema([
    pa.field('score', pa.list_(pa.float64())),
    pa.field('label', pa.list_(pa.string())),
    pa.field('box', 
        pa.list_( # dynamic output, i.e. dynamic number of boxes per input image, each sublist contains the 4 box coordinates 
            pa.list_(
                    pa.int64(),
                    list_size=4
                ),
            ),
    ),
])
Wallaroo FrameworkReference
Framework.HUGGING-FACE-QUESTION-ANSWERING

Schemas:

input_schema = pa.schema([
    pa.field('question', pa.string()),
    pa.field('context', pa.string()),
    pa.field('top_k', pa.int64()),
    pa.field('doc_stride', pa.int64()),
    pa.field('max_answer_len', pa.int64()),
    pa.field('max_seq_len', pa.int64()),
    pa.field('max_question_len', pa.int64()),
    pa.field('handle_impossible_answer', pa.bool_()),
    pa.field('align_to_words', pa.bool_()),
])

output_schema = pa.schema([
    pa.field('score', pa.float64()),
    pa.field('start', pa.int64()),
    pa.field('end', pa.int64()),
    pa.field('answer', pa.string()),
])
Wallaroo FrameworkReference
Framework.HUGGING-FACE-STABLE-DIFFUSION-TEXT-2-IMG

Schemas:

input_schema = pa.schema([
    pa.field('prompt', pa.string()),
    pa.field('height', pa.int64()),
    pa.field('width', pa.int64()),
    pa.field('num_inference_steps', pa.int64()), # optional
    pa.field('guidance_scale', pa.float64()), # optional
    pa.field('negative_prompt', pa.string()), # optional
    pa.field('num_images_per_prompt', pa.string()), # optional
    pa.field('eta', pa.float64()) # optional
])

output_schema = pa.schema([
    pa.field('images', pa.list_(
        pa.list_(
            pa.list_(
                pa.int64(),
                list_size=3
            ),
            list_size=128
        ),
        list_size=128
    )),
])
Wallaroo FrameworkReference
Framework.HUGGING-FACE-SUMMARIZATION

Any parameter that is not part of the required inputs list will be forwarded to the model as a key/pair value to the underlying models generate method. If the additional input is not supported by the model, an error will be returned.

Schemas:

input_schema = pa.schema([
    pa.field('inputs', pa.string()),
    pa.field('return_text', pa.bool_()),
    pa.field('return_tensors', pa.bool_()),
    pa.field('clean_up_tokenization_spaces', pa.bool_()),
    # pa.field('extra_field', pa.int64()), # every extra field you specify will be forwarded as a key/value pair
])

output_schema = pa.schema([
    pa.field('summary_text', pa.string()),
])
Wallaroo FrameworkReference
Framework.HUGGING-FACE-TEXT-CLASSIFICATION

Schemas

input_schema = pa.schema([
    pa.field('inputs', pa.string()), # required
    pa.field('top_k', pa.int64()), # optional
    pa.field('function_to_apply', pa.string()), # optional
])

output_schema = pa.schema([
    pa.field('label', pa.list_(pa.string(), list_size=2)), # list with a number of items same as top_k, list_size can be skipped but may lead in worse performance
    pa.field('score', pa.list_(pa.float64(), list_size=2)), # list with a number of items same as top_k, list_size can be skipped but may lead in worse performance
])
Wallaroo FrameworkReference
Framework.HUGGING-FACE-TRANSLATION

Any parameter that is not part of the required inputs list will be forwarded to the model as a key/pair value to the underlying models generate method. If the additional input is not supported by the model, an error will be returned.

Schemas:

input_schema = pa.schema([
    pa.field('inputs', pa.string()), # required
    pa.field('return_tensors', pa.bool_()), # optional
    pa.field('return_text', pa.bool_()), # optional
    pa.field('clean_up_tokenization_spaces', pa.bool_()), # optional
    pa.field('src_lang', pa.string()), # optional
    pa.field('tgt_lang', pa.string()), # optional
    # pa.field('extra_field', pa.int64()), # every extra field you specify will be forwarded as a key/value pair
])

output_schema = pa.schema([
    pa.field('translation_text', pa.string()),
])
Wallaroo FrameworkReference
Framework.HUGGING-FACE-ZERO-SHOT-CLASSIFICATION

Schemas:

input_schema = pa.schema([
    pa.field('inputs', pa.string()), # required
    pa.field('candidate_labels', pa.list_(pa.string(), list_size=2)), # required
    pa.field('hypothesis_template', pa.string()), # optional
    pa.field('multi_label', pa.bool_()), # optional
])

output_schema = pa.schema([
    pa.field('sequence', pa.string()),
    pa.field('scores', pa.list_(pa.float64(), list_size=2)), # same as number of candidate labels, list_size can be skipped by may result in slightly worse performance
    pa.field('labels', pa.list_(pa.string(), list_size=2)), # same as number of candidate labels, list_size can be skipped by may result in slightly worse performance
])
Wallaroo FrameworkReference
Framework.HUGGING-FACE-ZERO-SHOT-IMAGE-CLASSIFICATION

Schemas:

input_schema = pa.schema([
    pa.field('inputs', # required
        pa.list_(
            pa.list_(
                pa.list_(
                    pa.int64(),
                    list_size=3
                ),
                list_size=100
            ),
        list_size=100
    )),
    pa.field('candidate_labels', pa.list_(pa.string(), list_size=2)), # required
    pa.field('hypothesis_template', pa.string()), # optional
]) 

output_schema = pa.schema([
    pa.field('score', pa.list_(pa.float64(), list_size=2)),