Pre- and post-processing

An efficiently serialized model is just the model and its direct dependencies. No custom logic or extra frameworks. This makes deserialization fast, but may make invoking the model inconvenient.
Truss bundles your serialized model with pre- and post-processing functions. Use them to:
  • Format model inputs and outputs, especially when the model needs something like an xgboost.DMatrix that isn't JSON-serializable
  • Add custom logic like saving model outputs to a data store
  • Factor out unnecessary logic from predict()
There is a GPU lock during predict() but not preprocess() and postprocess(). That means:
  • If you're interacting with the GPU, do it during predict() to avoid putting the GPU in a bad state.
  • If you're doing any long-running calculation not on a GPU (e.g. making a HTTP request), do it in a processing function so the GPU can be freed up for the next request.

model/ starting state

When you create a Truss, pre- and post-processing functions are added to your my-truss/model/ file as pass-through identity functions.
def preprocess(self, model_input: Any) -> Any:
Incorporate pre-processing required by the model if desired here.
These might be feature transformations that are tightly coupled to the model.
return model_input
def postprocess(self, model_output: Any) -> Any:
Incorporate post-processing required by the model if desired here.
return model_output
def predict(self, model_input: Any) -> Any:
model_output = {}
inputs = np.array(model_input)
result = self._model.predict(inputs).tolist()
model_output["predictions"] = result
return model_output
Open my-truss/model/ to modify the functions.


ML models are picky eaters. One of the difficulties of using a model in a production system is that it may only take a certain type of input, specifically formatted. This means anyone and anything interacting with the model needs to be aware of the nuances of its input requirements.
Truss allows you to instead bundle pre-processing Python code with the model itself. This code is run on every call to the model before the model itself is run, giving you the chance to define your own input format.
Example: ResNet pre-processing
Here is a pre-processing function from the TensorFlow example that downloads an image from a URL and sizes it for the ResNet model.
import requests
import tempfile
import numpy as np
def preprocess(self, model_input: Any) -> Any:
request = requests.get(model_input)
with tempfile.NamedTemporaryFile() as f:
input_image = tf.image.decode_png(
preprocessed_image = tf.keras.applications.resnet_v2.preprocess_input(
tf.image.resize([input_image], (224, 224))
return np.array(preprocessed_image)


Similarly, the output of a ML model can be messy or cryptic. Demystify the model results for your end user or format a web-friendly response in the post-processing function.
Example: ResNet post-processing
Here is a post-processing function from the same TensorFlow example that returns clean labels.
from scipy.special import softmax
def postprocess(self, model_output: Any) -> Any:
class_predictions = model_output["predictions"][0]
LABELS = requests.get(
class_probabilities = softmax(class_predictions)
top_probability_indices = class_probabilities.argsort()[::-1][:5].tolist()
return {
LABELS[index]: 100 * class_probabilities[index].round(3)
for index in top_probability_indices

Using processing functions

Once you've modified your processing functions, you can test them by reloading the Truss and invoking the model.
tr = truss.load("my-truss")