These constituents are rather dazzling.
Straightforward, nevertheless Powerful.
Aid wished. Translation, rap lyrics, all wished. No doubt feel free to invent a converse.
Pinferencia tries to be the sterling AI model inference server ever!
Serving a model with REST API has by no formula been so straightforward.
In say so that you just can
- get a straightforward nevertheless sturdy formula to assist your model
- write minimal codes while defend controls over you provider
- defend faraway from any heavy-weight alternatives
- with out problems to integrate alongside with your CICD
- salvage your model and provider portable and runnable all over machines
You’re on the very finest space.
Pinferencia aspects consist of:
- Fleet to code, mercurial to cross alive. Minimal codes wished, minimal transformation wished. Authorized in defending with what you bear.
- 100% Test Coverage: Both observation and branch coverages, no kidding.
- Easy to utter, straightforward to love.
- Computerized API documentation online page. All API explained in info with online are trying-out characteristic.
- Aid any model, even a single feature is also served.
pip install "pinferencia[uvicorn]"
Like a flash Originate
Aid Any Model
from pinferencia import Server class MyModel: def predict(self, recordsdata): return sum(recordsdata) model = MyModel() provider = Server() provider.register( model_name="mymodel", model=model, entrypoint="predict", )
uvicorn app:provider --reload
Hooray, your provider is alive. Wander to http://127.0.0.1: 8000/ and bear enjoyable.
Any Deep Finding out Objects? Authorized as straightforward. Straightforward utter or load your model, and register it with the provider. Wander alive without prolong.
import torch from pinferencia import Server # utter your models model = "..." # or load your models (1) # from state_dict model = TheModelClass(*args, kwargs) model.load_state_dict(torch.load(PATH)) # complete model model = torch.load(PATH) # torchscript model = torch.jit.load('model_scripted.pt') model.eval() provider = Server() provider.register( model_name="mymodel", model=model, )
import tensorflow as tf from pinferencia import Server # utter 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.purchase in thoughts(test_images, test_labels, verbose=2) provider = Server() provider.register( model_name="mymodel", model=model, entrypoint="predict", )
Any model of any framework will correct work the the same formula. Now toddle
uvicorn app:provider --reload and trip!
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