Skip to content

欢迎使用Pinferencia

Pinferencia

Pinferencia?

Language grade: Python codecov License PyPI version

没听说过Pinferencia，这不是你的错。主要我的宣传经费，实在是不够多。

你是不是训练了一堆模型，然而别人谁用都不行。不是环境搞不定，就是bug命太硬。

你想:

要是我能有个API，谁能不陷入我的爱。不用安装不用等待，发个请求结果自己到来。

可是世上API千百万，却没有哪个我能玩得转。用来用去，看来还是我心太软，有些产品真的不能惯。

我多想这个世界变得简单，我的模型1分钟就能上线。然而现实这么残酷，一天两天过去，我的眼泪哗哗止不住。

到底谁能给予我这个恩赐啊，看来只有Pinferencia。

Pinferencia-GUI

$ pip install "pinferencia[streamlit]"
---> 100%

High起来！

欢欢乐乐，搞定App

app.py
import joblib
import uvicorn

from pinferencia import Server


# train your model
model = "..."

# or load your model
model = joblib.load("/path/to/model.joblib") # (1)

service = Server()
service.register(
    model_name="mymodel",
    model=model,
    entrypoint="predict", # (2)
)

  1. For more details, please visit https://scikit-learn.org/stable/modules/model_persistence.html

  2. entrypoint is the function name of the model to perform predictions.

    Here the data will be sent to the predict function: model.predict(data).

app.py
import torch
import uvicorn

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,
)
  1. For more details, please visit https://pytorch.org/tutorials/beginner/saving_loading_models.html
app.py
import tensorflow as tf
import uvicorn

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",
)
  1. For more details, please visit https://www.tensorflow.org/tutorials/keras/save_and_load
app.py
 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
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)
app.py
import uvicorn

from pinferencia import Server


# train your models
class MyModel:
    def predict(self, data):
        return sum(data)


model = MyModel()

service = Server()
service.register(
    model_name="mymodel",
    model=model,
    entrypoint="predict",
)
app.py
import uvicorn

from pinferencia import Server

# train your models
def model(data):
    return sum(data)

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

走一个～

$ uvicorn app:service --reload
INFO:     Started server process [xxxxx]
INFO:     Waiting for application startup.
INFO:     Application startup complete.
INFO:     Uvicorn running on http://127.0.0.1:8000 (Press CTRL+C to quit)