Quickstart

Installation

Install `infernet-ml from your terminal:

uvpip

uv pip install infernet-ml

pip install infernet-ml

Example Usage

In this example we'll use the HuggingfaceInferenceClientWorkflow to perform inference on a Huggingface model.

Step 1: Import and Instantiate a Workflow

Import the HFInferenceClientWorkflow class as well as its input class from infernet_ml.

from infernet_ml.utils.hf_types import HFClassificationInferenceInput
from infernet_ml.workflows.inference.hf_inference_client_workflow import (
  HFInferenceClientWorkflow,
)

In this instance, we're going to use the text_classification task type, and use the Kaludi/Reviews-Sentiment-Analysis model. You can use any other model tagged as Text Classification from the Huggingface model hub.

workflow = HFInferenceClientWorkflow(
  model="Kaludi/Reviews-Sentiment-Analysis",
)

Step 2: Setup the Workflow

We're going to setup our model. Depending on the workflow, this does various tasks to make the model ready for inference:

• For workflows that execute the model themselves, this might do something like downloading the model weights.
• For workflows that use a remote inference service, this might setup the connection to the service, and ensure the model is available on the service.

workflow.setup()

Step 3: Perform Inference

Now we can perform inference on our model. All of the workflows in infernet-ml have a inference() method that takes in the input data and returns the output.

input = HFClassificationInferenceInput(
  text="Decentralizing AI using crypto is awesome!"
)
output_data = workflow.inference(input)

Step 4: Putting it All Together

Finally, we can display the results of our inference. In the case of Kaludi/Reviews-Sentiment-Analysis we expect the output to have different classes and their probabilities.

from infernet_ml.utils.hf_types import HFClassificationInferenceInput
from infernet_ml.workflows.inference.hf_inference_client_workflow import (
    HFInferenceClientWorkflow,
)

if __name__ == "__main__":
    workflow = HFInferenceClientWorkflow(
        model="Kaludi/Reviews-Sentiment-Analysis",
    )
    workflow.setup()
    input = HFClassificationInferenceInput(
        text="Decentralizing AI using crypto is awesome!"
    )
    output_data = workflow.inference(input)
    print(output_data)

Running this code, we'll get an output similar to the following:

{'output': [TextClassificationOutputElement(label='POSITIVE', score=0.9997395873069763), TextClassificationOutputElement(label='NEGATIVE', score=0.00026040704688057303)]}

And just like that, we've performed inference on a Huggingface model using infernet-ml!

Where to next?

This example shows one of our many workflows. Check out our architecture documentation, as well as Inference Workflows to see what other workflows are available and how to use them.