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Acumos client library for building and pushing Python models

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Acumos Python Client User Guide

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acumos is a client library that allows modelers to push their Python models to the Acumos platform.


You will need a Python 3.4+ environment in order to install acumos. You can use Anaconda (preferred) or pyenv to install and manage Python environments.

If you’re new to Python and need an IDE to start developing, we recommend using Spyder which can easily be installed with Anaconda.

The acumos package can be installed with pip:

pip install acumos

Protocol Buffers

The acumos package uses protocol buffers and assumes you have the protobuf compiler protoc installed. Please visit the protobuf repository and install the appropriate protoc for your operating system. Installation is as easy as downloading a binary release and adding it to your system $PATH. This is a temporary requirement that will be removed in a future version of acumos.

Anaconda Users: You can easily install protoc from an Anaconda package via:

conda install -c anaconda libprotobuf

Acumos Python Client Tutorial

This tutorial provides a brief overview of acumos for creating Acumos models. The tutorial is meant to be followed linearly, and some code snippets depend on earlier imports and objects. Full examples are available in the examples/ directory of the Acumos Python client repository.

  1. Importing Acumos
  2. Creating A Session
  3. A Simple Model
  4. Exporting Models
  5. Defining Types
  6. Using DataFrames with scikit-learn
  7. Declaring Requirements
  8. Keras and TensorFlow
  9. Testing Models
  10. More Examples

Importing Acumos

First import the modeling and session packages:

from acumos.modeling import Model, List, Dict, create_namedtuple, create_dataframe
from acumos.session import AcumosSession

Creating A Session

An AcumosSession allows you to export your models to Acumos. You can either dump a model to disk locally, so that you can upload it via the Acumos website, or push the model to Acumos directly.

If you’d like to push directly to Acumos, create a session with the push_api argument:

session = AcumosSession(push_api="")

See the onboarding page of your Acumos instance website to find the correct push_api URL to use.

If you’re only interested in dumping a model to disk, arguments aren’t needed:

session = AcumosSession()

A Simple Model

Any Python function can be used to define an Acumos model using Python type hints.

Let’s first create a simple model that adds two integers together. Acumos needs to know what the inputs and outputs of your functions are. We can use the Python type annotation syntax to specify the function signature.

Below we define a function add_numbers with int type parameters x and y, and an int return type. We then build an Acumos model with an add method.

Note: Function docstrings are included with your model and used for documentation, so be sure to include one!

def add_numbers(x: int, y: int) -> int:
    '''Returns the sum of x and y'''
    return x + y

model = Model(add=add_numbers)

Exporting Models

We can now export our model using the AcumosSession object created earlier. The push and dump APIs are shown below. The dump method will save the model to disk so that it can be onboarded via the Acumos website. The push method pushes the model directly to Acumos.

session.push(model, 'my-model')
session.dump(model, 'my-model', '~/')  # creates ~/my-model

For more information on how to onboard a dumped model via the Acumos website, see the web onboarding guide.

Note: Pushing a model to Acumos will prompt you for an onboarding token if you have not previously provided one. The interactive prompt can be avoided by exporting the ACUMOS_TOKEN environment variable, which corresponds to an authentication token that can be found in your account settings on the Acumos website.

Defining Types

In this example, we make a model that can read binary images and output some metadata about them. This model makes use of a custom type ImageShape.

We first create a NamedTuple type called ImageShape, which is like an ordinary tuple but with field accessors. We can then use ImageShape as the return type of get_shape. Note how ImageShape can be instantiated as a new object.

import io
import PIL

ImageShape = create_namedtuple('ImageShape', [('width', int), ('height', int)])

def get_format(data: bytes) -> str:
    '''Returns the format of an image'''
    buffer = io.BytesIO(data)
    img =
    return img.format

def get_shape(data: bytes) -> ImageShape:
    '''Returns the width and height of an image'''
    buffer = io.BytesIO(data)
    img =
    shape = ImageShape(width=img.width, height=img.height)
    return shape

model = Model(get_format=get_format, get_shape=get_shape)

Note: Starting in Python 3.6, you can alternatively use this simpler syntax:

from acumos.modeling import NamedTuple

class ImageShape(NamedTuple):
    '''Type representing the shape of an image'''
    width: int
    height: int

Using DataFrames with scikit-learn

In this example, we train a RandomForestClassifier using scikit-learn and use it to create an Acumos model.

When making machine learning models, it’s common to use a dataframe data structure to represent data. To make things easier, acumos can create NamedTuple types directly from pandas.DataFrame objects.

NamedTuple types created from pandas.DataFrame objects store columns as named attributes and preserve column order. Because NamedTuple types are like ordinary tuple types, the resulting object can be iterated over. Thus, iterating over a NamedTuple dataframe object is the same as iterating over the columns of a pandas.DataFrame. As a consequence, note how np.column_stack can be used to create a numpy.ndarray from the input df.

Finally, the model returns a numpy.ndarray of int corresponding to predicted iris classes. The classify_iris function represents this as List[int] in the signature return.

import numpy as np
import pandas as pd
from sklearn.datasets import load_iris
from sklearn.ensemble import RandomForestClassifier

iris = load_iris()
X =
y =

clf = RandomForestClassifier(random_state=0), y)

# here, an appropriate NamedTuple type is inferred from a pandas DataFrame
X_df = pd.DataFrame(X, columns=['sepal_length', 'sepal_width', 'petal_length', 'petal_width'])
IrisDataFrame = create_dataframe('IrisDataFrame', X_df)

# ==================================================================================
# # or equivalently:
# IrisDataFrame = create_namedtuple('IrisDataFrame', [('sepal_length', List[float]),
#                                                     ('sepal_width', List[float]),
#                                                     ('petal_length', List[float]),
#                                                     ('petal_width', List[float])])
# ==================================================================================

def classify_iris(df: IrisDataFrame) -> List[int]:
    '''Returns an array of iris classifications'''
    X = np.column_stack(df)
    return clf.predict(X)

model = Model(classify=classify_iris)

Check out the sklearn examples in the examples directory for full runnable scripts.

Declaring Requirements

If your model depends on another Python script or package that you wrote, you can declare the dependency via the acumos.metadata.Requirements class:

from acumos.metadata import Requirements

Note that only pure Python is supported at this time.

Custom Scripts

Custom scripts can be included by giving Requirements a sequence of paths to Python scripts, or directories containing Python scripts. For example, if the model defined in depended on


this dependency could be declared like so:

from helper1 import do_thing

def transform(x: int) -> int:
    '''Does the thing'''
    return do_thing(x)

model = Model(transform=transform)

reqs = Requirements(scripts=['./'])

# using the AcumosSession created earlier:
session.push(model, 'my-model', reqs)
session.dump(model, 'my-model', '~/', reqs)  # creates ~/my-model

Alternatively, all Python scripts within model_workspace/ could be included using:

reqs = Requirements(scripts=['.'])

Custom Packages

Custom packages can be included by giving Requirements a sequence of paths to Python packages, i.e. directories with an file. Assuming that the package ~/repos/my_pkg contains:


then you can bundle my_pkg with your model like so:

from import do_thing

def transform(x: int) -> int:
    '''Does the thing'''
    return do_thing(x)

model = Model(transform=transform)

reqs = Requirements(packages=['~/repos/my_pkg'])

# using the AcumosSession created earlier:
session.push(model, 'my-model', reqs)
session.dump(model, 'my-model', '~/', reqs)  # creates ~/my-model

Requirement Mapping

Python packaging and PyPI aren’t perfect, and sometimes the name of the Python package you import in your code is different than the package name used to install it. One example of this is the PIL package, which is commonly installed using a fork called pillow (i.e. pip install pillow will provide the PIL package).

To address this inconsistency, the Requirements class allows you to map Python package names to PyPI package names. When your model is analyzed for dependencies by acumos, this mapping is used to ensure the correct PyPI packages will be used.

In the example below, the req_map parameter is used to declare a requirements mapping from the PIL Python package to the pillow PyPI package:

reqs = Requirements(req_map={'PIL': 'pillow'})

Keras and TensorFlow

Check out the Keras and TensorFlow examples in the examples/ directory of the Acumos Python client repository.

Testing Models

The acumos.modeling.Model class wraps your custom functions and produces corresponding input and output types. This section shows how to access those types for the purpose of testing. For simplicity, we’ll create a model using the add_numbers function again:

def add_numbers(x: int, y: int) -> int:
    '''Returns the sum of x and y'''
    return x + y

model = Model(add=add_numbers)

The model object now has an add attribute, which acts as a wrapper around add_numbers. The add_numbers function can be invoked like so:

result = model.add.inner(1, 2)
print(result)  # 3

The model.add object also has a corresponding wrapped function that is generated by acumos.modeling.Model. The wrapped function is the primary way your model will be used within Acumos.

We can access the input_type and output_type attributes to test that the function works as expected:

AddIn = model.add.input_type
AddOut = model.add.output_type

add_in = AddIn(1, 2)
print(add_in)  # AddIn(x=1, y=2)

add_out = AddOut(3)
print(add_out)  # AddOut(value=3)

model.add.wrapped(add_in) == add_out  # True

More Examples

Below are some additional function examples. Note how numpy types can even be used in type hints, as shown in the numpy_sum function.

from collections import Counter
import numpy as np

def list_sum(x: List[int]) -> int:
    '''Computes the sum of a sequence of integers'''
    return sum(x)

def numpy_sum(x: List[np.int32]) -> np.int32:
    '''Uses numpy to compute a vectorized sum over x'''
    return np.sum(x)

def count_strings(x: List[str]) -> Dict[str, int]:
    '''Returns a count mapping from a sequence of strings'''
    return Counter(x)

Acumos Python Client Release Notes


  • Bug fixes
    • The deprecated authentication API is now considered optional
    • A more portable path solution is now used when saving models, to avoid issues with models developed in Windows


  • Authentication
    • Username and password authentication has been deprecated
    • Users are now interactively prompted for an onboarding token, as opposed to a username and password


  • Requirements
    • Python script dependencies can now be specified using a Requirements object
    • Python script dependencies found during the introspection stage are now included with the model


  • Bug fixes
    • Don’t attempt to use an empty auth token (avoids blank strings to be set in environment)


  • Bug fixes
    • The normalized path of the system base prefix is now used for identifying stdlib packages


  • Bug fixes
    • Improved dependency inspection when using a virtualenv
    • Removed custom packages from model metadata, as it caused image build failures
    • Fixed Python 3.5.2 ordering bug in wrapped model usage


  • TensorFlow
    • Fixed a serialization issue that occurred when using a frozen graph


  • Model upload
    • The JWT is now cleared immediately after a failed upload
    • Additional HTTP information is now included in the error message


  • Authentication token
    • A new environment variable ACUMOS_TOKEN can be used to short-circuit the authentication process
  • Extra headers
    • AcumosSession.push now accepts an optional extra_headers argument, which will allow users and systems to include additional information when pushing models to the onboarding server


  • Modeling
    • Python 3.6 NamedTuple syntax support now tested
    • User documentation includes example of new NamedTuple syntax
  • Model wrapper
    • Model wrapper now has APIs for consuming and producing Python dicts and JSON strings
  • Protobuf and protoc
    • An explicit check for protoc is now made, which raises a more informative error message
    • User documentation is more clear about dependence on protoc, and provides an easier way to install protoc via Anaconda
  • Keras
    • The active keras backend is now included as a tracked module
    • keras_contrib layers are now supported


  • Replaced library-specific onboarding functions with “new-style” models
    • Support for arbitrary Python functions using type hints
    • Support for custom user-defined types
    • Support for TensorFlow models
    • Improved dependency introspection
    • Improved object serialization mechanisms

Acumos Python Client Developer Guide


We use a combination of tox, pytest, and flake8 to test acumos. Code which is not PEP8 compliant (aside from E501) will be considered a failing test. You can use tools like autopep8 to “clean” your code as follows:

$ pip install autopep8
$ cd acumos-python-client
$ autopep8 -r --in-place --ignore E501 acumos/ testing/ examples/

Run tox directly:

$ cd acumos-python-client
$ export WORKSPACE=$(pwd)  # env var normally provided by Jenkins
$ tox

You can also specify certain tox environments to test:

$ tox -e py34  # only test against Python 3.4
$ tox -e flake8  # only lint code

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