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机器学习框架

下面是针对不同机器学习框架的常见模型载入方法：

app.py
import joblib

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. 更多详情，请访问 https://scikit-learn.org/stable/modules/model_persistence.html

  2. entrypoint 是model 执行预测的函数名。

    这里数据将被发送到predict函数：model.predict(data)。

app.py
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,
)
  1. 更多详情，请访问 https://pytorch.org/tutorials/beginner/saving_loading_models.html
app.py
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",
)
  1. 更多详情，请访问 https://www.tensorflow.org/tutorials/keras/save_and_load
app.py
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",
)
app.py
from pinferencia import Server

def model(data):
    return sum(data)

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