In this tutorial, we are using an example task to demonstrate cxflow’s basic principles.


cxflow is a lightweight framework for machine learning which focuses on:

  • modularization and re-usability of ML components (datasets, models etc.)
  • rapid experimenting with different configurations
  • providing convenient instruments to manage and run your experiments

cxflow does not implement any building blocks, NN layers etc. Instead, you can use your favorite machine learning framework, such as TensorFlow, CNTK, or Caffe2. In other words, cxflow is back-end agnostic. Therefore, you don’t have to learn a new framework, if you already know one. In addition, you can easily convert the models you already have by making only minimal changes.

cxflow allows (and encourages) you to build modular projects, where the dataset, the model, and the configuration are separated and reusable. In the following sections, we will describe how those reusable modules should look like on a simple example.


The tutorial will be demonstrated on a simple task called majority. Given a vector of N bits, which bit is in majority?

Example of few 5-bit vectors:

input vector number of zeros number of ones bit in majority (target)
00101 3 2 0
00000 5 0 0
10101 2 3 1
11101 1 4 1


Full example may be found in our cxflow examples repository @GitHub.


The very first step in any machine learning task is to load and process the data. Every cxflow dataset is expected to implement the interface defined by cxflow.datasets.AbstractDataset. At the moment, the interfaces defines only the constructor API which accepts a string-encoded YAML. For regular projects, we recommend extending cxflow.datasets.BaseDataset which decodes the YAML configuration string for you.

The dataset is meant to wrap all data-related operations. It is responsible for correct data loading, verification and other useful operations. The main purpose of the dataset is providing various data streams that will be consequently used for training, validation and prediction in the production environment.

A typical cxflow dataset will implement the following:

  1. Training stream: an iteration of training data batches (train_stream method)
  2. Eval streams: iterations of additional streams not used for training. To provide a stream named <name>, method <name>_stream needs to return its iterator. In our example, we will use test stream provided by test_stream method.
  3. The constructor: accepts a YAML configuration in the form of a string (more on this later). We avoid the need to implement a constructor by extending cxflow.datasets.BaseDataset.
  4. Additional methods: such as fetch, split, or anything else you may need. cxflow is able to call arbitrary dataset methods by invoking cxflow dataset <method-name> command.

To generate the majority data and provide the data streams we will implement a MajorityDataset:

import cxflow as cx
import numpy.random as npr

class MajorityDataset(cx.BaseDataset):

    def _configure_dataset(self, n_examples: int, dim: int, batch_size: int, **kwargs) -> None:
        self.batch_size = batch_size
        self.dim = dim

        x = npr.random_integers(0, 1, n_examples * dim).reshape(n_examples, dim)
        y = x.sum(axis=1) > int(dim/2)

        self._train_x, self._train_y = x[:int(.8 * n_examples)], y[:int(.8 * n_examples)]
        self._test_x, self._test_y = x[int(.8 * n_examples):], y[int(.8 * n_examples):]

    def train_stream(self) -> cx.Stream:
        for i in range(0, len(self._train_x), self.batch_size):
            yield {'x': self._train_x[i: i + self.batch_size],
                   'y': self._train_y[i: i + self.batch_size]}

    def test_stream(self) -> cx.Stream:
        for i in range(0, len(self._test_x), self.batch_size):
            yield {'x': self._test_x[i: i + self.batch_size],
                   'y': self._test_y[i: i + self.batch_size]}

Let us describe the functionality of our MajorityDataset step by step. We shall begin with the _configure_dataset method. This method is called automatically by the dataset constructor, which provides it with the parameters from the configuration file (configuration will be explained later). In our case, we need n_examples (the number of examples in total), dim (the dimension of the generated data) and batch_size (how big our batches will be).

The method randomly generates a dataset of n_examples vectors of ones and zeros (variable x). For each of those vectors, it calculates the correct answer (variable y). Finally, it splits the dataset into training and testing data in the ratio of 8:2.

To sum up, once the dataset is constructed, it features four attributes (_train_x, _train_y, _test_x and _test_y) that represent the loaded data. Note that you have the option to rename them as desired.


In real-world cases, we usually don’t want to generate our data randomly. Instead, we can simply load them from a file (e.g. .csv) or from a database.

The train_stream function iterates over the training data. This function returns an iterator over batches. Each batch is a dictionary with keys x and y, where the value of x is a list of training vectors and the value of y is the list of the correct answers. The lists have the length of batch_size.

A batch (with batch_size=4) representing the example above looks like this:

    'x': [
    'y': [

Similarly, there is a test_stream function that iterates over the testing data.

A single iteration over the whole dataset is called an epoch. We train our machine learning models by iterating through the training stream for one or more epochs. The test stream is used only to estimate the performance of the model.


In this example, the training and testing streams are generated randomly and thus, they may slightly overlap and bias the performace estimation.

A detailed description of cxflow datasets might be found in the advanced section.


With the dataset ready, we now must define the model that is to be trained. A simple TensorFlow graph can solve our task. We will use the official cxflow-tensorflow package that provides convenient TensorFlow integration with cxflow. Please install this package before you proceed with this tutorial.

In cxflow_tensorflow, every model is a python class that is expected to extend the cxflow_tensorflow.BaseModel.

Let us define a class called MajorityNet.

import logging

import cxflow_tensorflow as cxtf
import tensorflow as tf
import tensorflow.contrib.keras as K

class MajorityNet(cxtf.BaseModel):
    """Simple 2-layered MLP for majority task."""

    def _create_model(self, hidden):
        logging.debug('Constructing placeholders matching the model.inputs')
        x = tf.placeholder(dtype=tf.float32, shape=[None, self._dataset.dim], name='x')
        y = tf.placeholder(dtype=tf.float32, shape=[None], name='y')

        logging.debug('Constructing MLP model')
        net = K.layers.Dense(hidden)(x)
        y_hat = K.layers.Dense(1)(net)[:, 0]

        logging.debug('Constructing loss and outputs matching the model.outputs')
        tf.pow(y - y_hat, 2, name='loss')
        predictions = tf.greater_equal(y_hat, 0.5, name='predictions')
        tf.equal(predictions, tf.cast(y, tf.bool), name='accuracy')

The only method that is necessary to implement is cxflow_tensorflow.BaseModel._create_model(). In our case, the _create_model method creates a simple MLP. If you know the fundamentals of TensorFlow, it should be easy to understand what is going on.

To be precise, the model registered the following computational graph nodes:

  1. Placeholders x and y corresponding to a single batch from the stream (only the batch sources x and y will be mapped to these placeholders).
  2. Variable loss denoting the mean square error of the model.
  3. Variable predictions denoting the output of the network, i.e., the bit predicted to be in majority.
  4. Variable accuracy denoting the fraction of correct predictions in the current batch.


For each of input/output variables listed in the configuration, there has to exist a computational graph node with the corresponding name. cxflow-tensorflow is not able to find the nodes if they are not properly named.

The _create_model method can accept arbitrary arguments - in our case, we allow to configure the number of hidden units. We will describe the configuration file from which the parameters are taken in the next section.

You can find detailed descriptions of cxflow models in the advanced section.


The configuration of the training is the final, most important part of our tutorial. The configuration or config defines which dataset will be used as the data source and which model will be employed for training.

The configuration file is in the form of a YAML document. Feel free to use JSON instead, however, YAML makes a lot of things easier.

The YAML document consists of four fundamental sections. A detailed description of cxflow configuration can be found in the advanced section.

  1. dataset
  2. model
  3. main_loop
  4. hooks

Let us describe the sections one by one.


In our case, we only need to tell cxflow which dataset to use. This is done by specifying the class of the dataset. In addition, we will specify the parameters of the dataset (those are passed to the _configure_dataset method of the dataset).

  class: majority.MajorityDataset
  n_examples: 500
  dim: 11
  batch_size: 4

We can pass arbitrary constants to the dataset that will be hidden in the **kwargs parameter of the _configure_dataset method of the dataset.


The whole dataset section will be passed as a string-encoded YAML to the dataset constructor. In the case of using cxflow.datasets.BaseDataset, the YAML is automatically decoded and the individual variables are passed to the _configure_dataset method.


Similarly to the dataset, the model is defined in the model section. In our case, we want to specify the class of the model along with optimizer and hidden as required by the _create_model method of the model. In addition, we will specify the name of the network which will be used for naming the logging directory.

In addition, we have to specify which TensorFlow variable names are the network inputs and which variable names are on the output. This can be done by listing their names in the inputs and outputs config items.

  name: MajorityExample
  class: majority.MajorityNet

    class: AdamOptimizer
    learning_rate: 0.001

  hidden: 100

  inputs: [x, y]
  outputs: [accuracy, predictions, loss]

Main Loop

As the model training is executed in epochs, it is naturally implemented as a loop. This loop (cxflow.MainLoop) can be extended, for example by adding more streams to the train stream. In our case, we also want to evaluate the test stream, so we will add it to the main_loop.extra_streams section of the config. cxflow will then invoke the <name>_stream method of the dataset to create the stream. In our case, the test_stream method will be invoked.

evaluate additional streams
  extra_streams: [test]


Hooks can observe, modify and control the training process. In particular, hook actions are triggered after certain events, such as after a batch or an epoch is completed (more info in advanced section).

The hooks to be used are specified in cxflow configuration similar to the following one:

hook configuration section
  - ComputeStats:
      variables: [loss, accuracy]
  - LogVariables
  - StopAfter:
      epochs: 10

This section can be read quite naturally. cxflow will now compute loss and accuracy means for each epoch and log the respective values. The training will be stopped after 10 epochs.


See API reference for full list of cxflow hooks.

Using cxflow

Once the classes and config are implemented, the training can begin. Let’s try it with

cxflow train majority/config.yaml

The command produces a lot of output. The first section describes the creation of the components. The second part presents the output of the hooks. Finally, our logging hook is the one that produces the information after each epoch. Now we can easily watch the progress of the training.

After the training is finished, note that there is a new directory log/MajorityExample_*. This is the logging directory where everything cxflow produces is stored, including saved models, the configuration file and various other artifacts.

Let’s register one more hook which saves the best model according to the test stream:

- SaveBest:
    stream: test

When we run the training again, we see that the newly created output directory contains the saved model as well.

Let’s resume the training from this model.

cxflow resume log/MajorityExample_<some-suffix>

It’s simple as that.

In case the model is good enough to be used in the production, it is extremely easy to use cxflow for this purpose. See the configuration advanced section for more details. Then, you can just run the following command:

cxflow eval predict log/MajorityExample_<some-suffix>