bentoml.tensorflow support Keras
bentoml.keras utilizes the native model format and
will give a better development experience to users who are more
familiar with Keras models. However, the native model format of Keras is
not optimized for production inference. There are known reports
of memory leaks during serving time at the time of BentoML 1.0
bentoml.tensorflow is recommended in production
environments. You can read bentoml.tensorflow documentation for more information.
BentoML requires TensorFlow version 2.7.3 or higher to be installed.
Saving a Keras Model#
The following example loads a pre-trained ResNet50 model.
import tensorflow as tf from tensorflow.keras.applications.resnet50 import ResNet50 # Use pre-trained ResNet50 weights model = ResNet50(weights='imagenet') # try a sample input with created model from tensorflow.keras.preprocessing import image from tensorflow.keras.applications.resnet50 import preprocess_input, decode_predictions img_path = 'ade20k.jpg' img = image.load_img(img_path, target_size=(224, 224)) x = image.img_to_array(img) x = np.expand_dims(x, axis=0) x = preprocess_input(x) preds = model.predict(x) print('Keras Predicted:', decode_predictions(preds, top=3)) # output: # Keras Predicted: [('n04285008', 'sports_car', 0.3447785)]
After the Keras model is ready, use
to save the model instance to BentoML model store.
Keras model can be loaded with
verify that the saved model can be loaded properly.
model = bentoml.keras.load_model("keras_resnet50:latest") print(decode_predictions(model.predict(x)))
Building a Service using Keras#
See Building a Service for more information on creating a prediction service with BentoML.
The following service example creates a
predict API endpoint that accepts an image as input
and return JSON data as output. Within the API function, Keras model runner created from the
previously saved ResNet50 model is used for inference.
import bentoml import numpy as np from bentoml.io import Image from bentoml.io import JSON runner = bentoml.keras.get("keras_resnet50:latest").to_runner() svc = bentoml.Service("keras_resnet50", runners=[runner]) @svc.api(input=Image(), output=JSON()) def predict(img): from tensorflow.keras.applications.resnet50 import preprocess_input, decode_predictions img = img.resize((224, 224)) arr = np.array(img) arr = np.expand_dims(arr, axis=0) arr = preprocess_input(arr) preds = runner.run(arr) return decode_predictions(preds, top=1)
When constructing a bentofile.yaml, there are two ways to include Keras as a dependency, via
python (if using pip) or
python: packages: - tensorflow
conda: channels: - conda-forge dependencies: - tensorflow
See Using Runners doc for a general introduction to the Runner concept and its usage.
runner.predict.run is generally a drop-in replacement for
model.predict for executing the prediction in the model
predict is the only prediction method exposed by
runner model, you can just use
runner.run instead of