Using ModelBuilder class for deploying PyMC models#


Many users face difficulty in deploying their PyMC models to production because deploying/saving/loading a user-created model is not well standardized. One of the reasons behind this is there is no direct way to save or load a model in PyMC like scikit-learn or TensorFlow. The new ModelBuilder class is aimed to improve this workflow by providing a scikit-learn inspired API to wrap your PyMC models.

The new ModelBuilder class allows users to use methods to fit(), predict(), save(), load(). Users can create any model they want, inherit the ModelBuilder class, and use predefined methods.

Let’s go through the full workflow, starting with a simple linear regression PyMC model as it’s usually written. Of course, this model is just a place-holder for your own model.

import arviz as az
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import pymc as pm

from numpy.random import RandomState

%config InlineBackend.figure_format = 'retina'

rng = np.random.default_rng(RANDOM_SEED)"arviz-darkgrid")
# Generate data
x = np.linspace(start=0, stop=1, num=100)
y = 0.3 * x + 0.5 + rng.normal(0, 1, len(x))

Standard syntax#

Usually a PyMC model will have this form:

with pm.Model() as model:
    # priors
    a = pm.Normal("a", mu=0, sigma=1)
    b = pm.Normal("b", mu=0, sigma=1)
    eps = pm.HalfNormal("eps", 1.0)

    # observed data
    y_model = pm.Normal("y_model", mu=a + b * x, sigma=eps, observed=y)

    # Fitting
    idata = pm.sample()

    # posterior predict
Auto-assigning NUTS sampler...
Initializing NUTS using jitter+adapt_diag...
Multiprocess sampling (4 chains in 4 jobs)
NUTS: [a, b, eps]
100.00% [8000/8000 00:01<00:00 Sampling 4 chains, 0 divergences]
Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 8 seconds.
Sampling: [a, b, eps, y_model]
Sampling: [y_model]
100.00% [4000/4000 00:00<00:00]

How would we deploy this model? Save the fitted model, load it on an instance, and predict? Not so simple.

ModelBuilder is built for this purpose. It is currently part of the pymc-experimental package which we can pip install with pip install pymc-experimental. As the name implies, this feature is still experimental and subject to change.

Model builder class#

Let’s import the ModelBuilder class.

from pymc_experimental.model_builder import ModelBuilder

To define our desired model we inherit from the ModelBuilder class. There are a couple of methods we need to define.

class LinearModel(ModelBuilder):
    # Give the model a name
    _model_type = "LinearModel"
    # And a version
    version = "0.1"

    def build_model(self, model_config, data=None):
        build_model creates the PyMC model

        model_config: dictionary
            it is a dictionary with all the parameters that we need in our model example:  a_loc, a_scale, b_loc
        data: Dict[str, Union[np.ndarray, pd.DataFrame, pd.Series]]
            Data we want our model fit on.
        # Note that we do not have to define a with-context

        # Create mutable data containers
        x_data = pm.MutableData("x_data", data["input"].values)
        y_data = pm.MutableData("y_data", data["output"].values)

        # prior parameters
        a_mu_prior = model_config.get("a_mu_prior", 0.0)
        a_sigma_prior = model_config.get("a_sigma_prior", 1.0)
        b_mu_prior = model_config.get("b_mu_prior", 0.0)
        b_sigma_prior = model_config.get("b_sigma_prior", 1.0)
        eps_prior = model_config.get("eps_prior", 1.0)

        # priors
        a = pm.Normal("a", mu=a_mu_prior, sigma=a_sigma_prior)
        b = pm.Normal("b", mu=b_mu_prior, sigma=b_sigma_prior)
        eps = pm.HalfNormal("eps", eps_prior)

        obs = pm.Normal("y", mu=a + b * x_data, sigma=eps, shape=x_data.shape, observed=y_data)

    def _data_setter(self, data: pd.DataFrame):
        _data_setter works as a set_data for the model and updates the data whenever we need to.
        data: Dict[str, Union[np.ndarray, pd.DataFrame, pd.Series]]
            It is the data we need to update for the model.

        with self.model:
            pm.set_data({"x_data": data["input"].values})
            if "output" in data.columns:
                pm.set_data({"y_data": data["output"].values})

    def create_sample_input(cls):
        Creates example input and parameters to test the model on.
        This is optional but useful.

        x = np.linspace(start=0, stop=1, num=100)
        y = 0.3 * x + 0.5
        y = y + np.random.normal(0, 1, len(x))
        data = pd.DataFrame({"input": x, "output": y})

        model_config = {
            "a_mu_prior": 0.0,
            "a_sigma_prior": 1.0,
            "b_mu_prior": 0.0,
            "b_sigma_prior": 1.0,
            "eps_prior": 1.0,

        sampler_config = {
            "draws": 1_000,
            "tune": 1_000,
            "chains": 3,
            "target_accept": 0.95,

        return data, model_config, sampler_config

Now we can create the LinearModel object.

But we need some example data. This is where defining a create_sample_input() method as done above is useful. It gives users of your model an easy way to generate data (and configurations) to test your model on.

data, model_config, sampler_config = LinearModel.create_sample_input()
model = LinearModel(model_config, sampler_config, data)

After making the object of class LinearModel we can fit the model using the .fit() method.

Fitting to data#

The fit() method takes one argument data on which we need to fit the model. The meta-data is saved in the InferenceData object where also the trace is stored. These are the fields that are stored:

  • id : This is a unique id given to a model based on model_config, sample_conifg, version, and model_type. Users can use it to check if the model matches to another model they have defined.

  • model_type : Model type tells us what kind of model it is. This in this case it outputs Linear Model

  • version : In case you want to improve on models, you can keep track of model by their version. As the version changes the unique hash in the id also changes.

  • sample_conifg : It stores values of the sampler configuration set by user for this particular model.

  • model_config : It stores values of the model configuration set by user for this particular model.

idata =
Auto-assigning NUTS sampler...
Initializing NUTS using jitter+adapt_diag...
Multiprocess sampling (3 chains in 4 jobs)
NUTS: [a, b, eps]
100.00% [6000/6000 00:01<00:00 Sampling 3 chains, 0 divergences]
Sampling 3 chains for 1_000 tune and 1_000 draw iterations (3_000 + 3_000 draws total) took 7 seconds.
Sampling: [a, b, eps, y]
Sampling: [y]
100.00% [3000/3000 00:00<00:00]
/Users/twiecki/miniforge3/envs/pymc5/lib/python3.10/site-packages/arviz/data/ UserWarning: The group fit_data is not defined in the InferenceData scheme

Saving model to file#

After fitting the model, we can probably save it to share the model as a file so one can use it again. To save() or load(), we can quickly call methods for respective tasks with the following syntax.

fname = ""

This saves a file at the given path, and the name
A NetCDF .nc file that stores the inference data of the model.

Loading a model#

Now if we wanted to deploy this model, or just have other people use it to predict data, they need two things:

  1. the LinearModel class (probably in a .py file)

  2. the file

With these, you can easily load a fitted model in a different environment (e.g. production):

model_2 = LinearModel.load(fname)
/Users/twiecki/miniforge3/envs/pymc5/lib/python3.10/site-packages/arviz/data/ UserWarning: fit_data group is not defined in the InferenceData scheme

Note that load() is a class-method, we do not need to instantiate the LinearModel object.



Next we might want to predict on new data. The predict() method allows users to do posterior prediction with the fitted model on new data.

Our first task is to create data on which we need to predict.

x_pred = np.random.uniform(low=1, high=2, size=10)
prediction_data = pd.DataFrame({"input": x_pred})

ModelBuilder provides two methods for prediction:

  1. point estimates (the mean) with predict()

  2. full posterior prediction (samples) with predict_posterior()

pred_mean = model_2.predict(prediction_data)
# samples
pred_samples = model_2.predict_posterior(prediction_data)
Sampling: [y]
100.00% [3000/3000 00:00<00:00]
Sampling: [y]
100.00% [3000/3000 00:00<00:00]

After using the predict(), we can plot our data and see graphically how satisfactory our LinearModel is.

fig, ax = plt.subplots(figsize=(7, 7))
ax.set(title="Posterior predictive regression lines", xlabel="x", ylabel="y")
%load_ext watermark
%watermark -n -u -v -iv -w -p pymc_experimental
Last updated: Wed Feb 22 2023

Python implementation: CPython
Python version       : 3.10.8
IPython version      : 8.7.0

pymc_experimental: 0.0.2

matplotlib: 3.6.2
numpy     : 1.23.5
pandas    : 1.5.2
arviz     : 0.14.0
pymc      : 5.0.1

Watermark: 2.3.1


  • Authored by Shashank Kirtania and Thomas Wiecki in 2023.

License notice#

All the notebooks in this example gallery are provided under the MIT License which allows modification, and redistribution for any use provided the copyright and license notices are preserved.

Citing PyMC examples#

To cite this notebook, use the DOI provided by Zenodo for the pymc-examples repository.


Many notebooks are adapted from other sources: blogs, books… In such cases you should cite the original source as well.

Also remember to cite the relevant libraries used by your code.

Here is an citation template in bibtex:

  author    = "<notebook authors, see above>",
  title     = "<notebook title>",
  editor    = "PyMC Team",
  booktitle = "PyMC examples",
  doi       = "10.5281/zenodo.5654871"

which once rendered could look like:

  • Thomas Wiecki , Shashank Kirtania . "Using ModelBuilder class for deploying PyMC models". In: PyMC Examples. Ed. by PyMC Team. DOI: 10.5281/zenodo.5654871