The following tutorial is available on the Wallaroo Github Repository.

Wallaroo Inference Server: Hugging Face Summarizer

This notebook is used in conjunction with the Wallaroo Inference Server Free Edition for Hugging Face Summarizer. This provides a free license for performing inferences through the Hugging Face Summarizer model. For full demonstrations of this model, see Wallaroo Edge Hugging Face LLM Summarization Deployment Demonstration.

Prerequisites

• A deployed Wallaroo Inference Server Free Edition with one of the following options:
  • Wallaroo.AI HF Summarizer Standard - x64
  • Wallaroo.AI HF Summarizer Onnx - x64
  • Wallaroo.AI HF Summarizer Standard - GPU x64
• Access via port 8080 to the Wallaroo Inference Server Free Edition.

Note that GPU inference server require a VM with Nvidia GPU CUDA support.

Hugging Face LLM Summarizer Model Schemas

Inputs

The Hugging Face LLM Summarizer Model takes the following inputs.

Outputs

Wallaroo Inference Server API Endpoints

The following HTTPS API endpoints are available for Wallaroo Inference Server.

Pipelines Endpoint

• Endpoint: HTTPS GET /pipelines
• Returns:
  • List of pipelines with the following fields.
    • id (String): The name of the pipeline.
    • status (String): The pipeline status. Running indicates the pipeline is available for inferences.

Pipeline Endpoint Example

The following demonstrates using curl to retrieve the Pipelines endpoint. Replace the HOSTNAME with the address of your Wallaroo Inference Server.

!curl HOSTNAME:8080/pipelines

{"pipelines":[{"id":"hf-summarizer-standard","status":"Running"}]}

Models Endpoint

• Endpoint: GET /models
• Returns:
  • List of models with the following fields.
    • name (String): The name of the model.
    • sha (String): The sha hash of the model.
    • status (String): The model status. Running indicates the models is available for inferences.
    • version (String): The model version in UUID format.

Models Endpoint Example

The following demonstrates using curl to retrieve the Models endpoint. Replace the HOSTNAME with the address of your Wallaroo Inference Server.

!curl doc-example-hf-summarizer.westus2.cloudapp.azure.com:8080/models

{"models":[{"name":"hf-summarizer-standard","sha":"ee71d066a83708e7ca4a3c07caf33fdc528bb000039b6ca2ef77fa2428dc6268","status":"Running","version":"7dbae7b4-20d0-40f7-a3f5-eeabdd77f418"}]}

Inference Endpoint

• Endpoint: HTTPS POST /pipelines/hf-summarizer-standard
• Headers:
  • Content-Type: application/vnd.apache.arrow.file: For Apache Arrow tables.
  • Content-Type: application/json; format=pandas-records: For pandas DataFrame in record format.
• Input Parameters: DataFrame in /pipelines/hf-summarizer-standard OR Apache Arrow table in application/vnd.apache.arrow.file with the following inputs:
  • inputs (String Required): One or several articles to summarize.
  • return_text (Bool Optional): Whether or not to include the decoded texts in the outputs.
  • return_tensor (Bool Optional): Whether or not to include the tensors of predictions (as token indices) in the outputs.
  • clean_up_tokenization_spaces(Bool Optional): Whether or not to clean up the potential extra spaces in the text output.
• Returns:
  • Headers
    • Content-Type: application/json; format=pandas-records: pandas DataFrame in record format.
  • Data
    • check_failures (List[Integer]): Whether any validation checks were triggered. For more information, see Wallaroo SDK Essentials Guide: Pipeline Management: Anomaly Testing.
    • elapsed (List[Integer]): A list of time in nanoseconds for:
    • [0] The time to serialize the input.
    • [1…n] How long each step took.
    • model_name (String): The name of the model used.
    • model_version (String): The version of the model in UUID format.
    • original_data: The original input data. Returns null if the input may be too long for a proper return.
    • outputs (List): The outputs of the inference result separated by data type.
    • String: The string outputs for the inference.
      • data (List[String]): The summarized text string.
        • dim (List[Integer]): The dimension shape returned, always returned as [1,1] for this model deployment.
        • v (Integer): The vector shape of the data, always returned as 1 for this mnodel deployment.
    • pipeline_name (String): The name of the pipeline.
    • shadow_data: Any shadow deployed data inferences in the same format as outputs.
    • time (Integer): The time since UNIX epoch.

Inference Endpoint Example

The following example performs an inference using the pandas record input ./data/test_summarization.df.json with a text string to summarize.

!curl -X POST HOSTNAME:8080/pipelines/hf-summarizer-standard \
    -H "Content-Type: application/json; format=pandas-records" \
    -d @./data/test_summarization.df.json

[{"check_failures":[],"elapsed":[37000,3245048360],"model_name":"hf-summarizer-standard","model_version":"7dbae7b4-20d0-40f7-a3f5-eeabdd77f418","original_data":null,"outputs":[{"String":{"data":["LinkedIn is a business and employment-focused social media platform that works through websites and mobile apps. It launched on May 5, 2003. LinkedIn allows members (both workers and employers) to create profiles and connect with each other in an online social network which may represent real-world professional relationships."],"dim":[1,1],"v":1}}],"pipeline_name":"hf-summarizer-standard","shadow_data":{},"time":1696454765559}]