Simple, but Powerful.

---

English Doc | 中文文档 | 中文Readme

Help wanted. Translation, rap lyrics, all wanted. Feel free to create an issue.

---

Pinferencia tries to be the simplest machine learning inference server ever!

Three extra lines and your model goes online.

Serving a model with GUI and REST API has never been so easy.

If you want to

• give your model a GUI and REST API
• find a simple but robust way to serve your model
• write minimal codes while maintain controls over you service
• avoid any heavy-weight solutions
• compatible with other tools/platforms

You're at the right place.

Features

Pinferencia features include:

• Fast to code, fast to go alive. Minimal codes needed, minimal transformation needed. Just based on what you have.
• 100% Test Coverage: Both statement and branch coverages, no kidding. Have you ever known any model serving tool so seriously tested?
• Easy to use, easy to understand.
• A pretty and clean GUI out of box.
• Automatic API documentation page. All API explained in details with online try-out feature.
• Serve any model, even a single function can be served.
• Support Kserve API, compatible with Kubeflow, TF Serving, Triton and TorchServe. There is no pain switching to or from them, and Pinferencia is much faster for prototyping!

Install

Recommend

pip install "pinferencia[streamlit]"

Backend Only

pip install "pinferencia"

Quick Start

Serve Any Model

from pinferencia import Server


class MyModel:
    def predict(self, data):
        return sum(data)


model = MyModel()

service = Server()
service.register(model_name="mymodel", model=model, entrypoint="predict")

Just run:

pinfer app:service

Hooray, your service is alive. Go to http://127.0.0.1:8501/ and have fun.

Any Deep Learning Models? Just as easy. Simple train or load your model, and register it with the service. Go alive immediately.

Hugging Face

Details: HuggingFace Pipeline - Vision

from transformers import pipeline

from pinferencia import Server

vision_classifier = pipeline(task="image-classification")


def predict(data):
    return vision_classifier(images=data)


service = Server()
service.register(model_name="vision", model=predict)

Pytorch

import torch

from pinferencia import Server


# train your models
model = "..."

# or load your models (1)
# from state_dict
model = TheModelClass(*args, **kwargs)
model.load_state_dict(torch.load(PATH))

# entire model
model = torch.load(PATH)

# torchscript
model = torch.jit.load('model_scripted.pt')

model.eval()

service = Server()
service.register(model_name="mymodel", model=model)

Tensorflow

import tensorflow as tf

from pinferencia import Server


# train your models
model = "..."

# or load your models (1)
# saved_model
model = tf.keras.models.load_model('saved_model/model')

# HDF5
model = tf.keras.models.load_model('model.h5')

# from weights
model = create_model()
model.load_weights('./checkpoints/my_checkpoint')
loss, acc = model.evaluate(test_images, test_labels, verbose=2)

service = Server()
service.register(model_name="mymodel", model=model, entrypoint="predict")

Any model of any framework will just work the same way. Now run uvicorn app:service --reload and enjoy!

Contributing

If you'd like to contribute, details are here