4.3. Classic Tutorial#

In this section the basic steps for creating an algorithm for horizontally partitioned data are explained.

Note

The final code of this tutorial is published on Github. The algorithm is also published in our Docker registry: harbor2.vantage6.ai/demo/average

It is assumed that it is mathematically possible to create a federated version of the algorithm you want to use. In the following sections we create a federated algorithm to compute the average of a distributed dataset. An overview of the steps that we are going through:

Mathematically decompose the model
Federated implementation and local testing
Vantage6 algorithm wrapper
Dockerize and push to a registry

This tutorial shows you how to create a federated mean algorithm.

4.3.1. Mathematical decomposition#

The mean of \(Q = [q_1, q_2 ... q_n]\) is computed as:

\[Q_{mean} = \frac{1}{n} \sum \limits_{i=1}^{n} {q_i} = \frac{q_1 + q_2 + ... + q_n}{n}\]

When dataset \(Q\) is horizontally partitioned in dataset \(A\) and \(B\):

\[ \begin{align}\begin{aligned}A = [a_1, a_2 ... a_j] = [q_1, q_2 ... q_j]\\B = [b_{1}, b_{2} ... b_k] = [q_{j+1}, q_{j+2}...q_{n}]\end{aligned}\end{align} \]

We would like to compute \(Q_{mean}\) from dataset A and B. This could be computed as:

\[Q_{mean} = \frac{(a_1+a_2+...+a_j) + (b_1+b_2+...+b_k)}{j+k} = \frac{\sum A + \sum B }{j+k}\]

Both the number of samples in each dataset and the total sum of each dataset is needed. Then we can compute the global average of dataset \(A\) and \(B\).

Note

We cannot simply compute the average on each node and combine them, as this would be mathematically incorrect. This would only work if dataset A and B contain the exact same number of samples.

4.3.2. Federated implementation#

Warning

In this example we use python, however you are free to use any language. The only requirements are: 1) It has to be able to create HTTP-requests, and 2) has to be able to read and write to files.

However, if you use a different language you are not able to use our wrapper. Reach out to us on Discord to discuss how this works.

A federated algorithm consist of two parts:

A federated part of the algorithm which is responsible for creating the partial results. In our case this would be computing (1) the sum of the observations, and (2) the number of observations.
A central part of the algorithm which is responsible for combining the partial results from the nodes. In the case of the federated mean that would be dividing the total sum of the observations by the total number of observations.

Note

The central part of the algorithm can either be run on the machine of the researcher himself or in a master container which runs on a node. The latter is the preferred method.

In case the researcher runs this part, he/she needs to have a proper setup to do so (i.e. a Python environment with the necessary dependencies). This can be useful when developing new algorithms.

Federated part#

The node that runs this part contains a CSV-file with one column (specified by the argument column_name) which we want to use to compute the global mean. We assume that this column has no NaN values.

import pandas

def federated_part(path, column_name="numbers"):
    """Compute the sum and number of observations of a column"""

    # extract the column numbers from the CSV
    numbers = pandas.read_csv(path)[column_name]

    # compute the sum, and count number of rows
    local_sum = numbers.sum()
    local_count = len(numbers)

    # return the values as a dict
    return {
        "sum": local_sum,
        "count": local_count
    }

Central part#

The central algorithm receives the sums and counts from all sites and combines these to a global mean. This could be from one or more sites.

def central_part(node_outputs):
    """Combine the partial results to a global average"""
    global_sum = 0
    global_count = 0
    for output in node_outputs:
        global_sum += output["sum"]
        global_count += output["count"]

    return {"average": global_sum / global_count}

Local testing#

To test, simply create two datasets A and B, both having a numerical column numbers. Then run the following:

outputs = [
    federated_part("path/to/dataset/A"),
    federated_part("path/to/dataset/B")
]
Q_average = central_part(outputs)["average"]
print(f"global average = {Q_average}.")

4.3.3. Vantage6 integration#

Note

A good starting point would be to use the boilerplate code from our Github. This section outlines the steps needed to get to this boilerplate but also provides some background information.

Note

In this example we use a csv-file. It is also possible to use other types of data sources. This tutorial makes use of our algorithm wrapper which is currently only available for csv, SPARQL and Parquet files.

Other wrappers like SQL, OMOP, etc. are under consideration. Let us now if you want to use one of these or other datasources.

Now that we have a federated implementation of our algorithm we need to make it compatible with the vantage6 infrastructure. The infrastructure handles the communication with the server and provides data access to the algorithm.

The algorithm consumes a file containing the input. This contains both the method name to be triggered as well as the arguments provided to the method. The algorithm also has access to a CSV file (in the future this could also be a database) on which the algorithm can run. When the algorithm is finished, it writes back the output to a different file.

The central part of the algorithm has to be able to create (sub)tasks. These subtasks are responsible for executing the federated part of the algorithm. The central part of the algorithm can either be executed on one of the nodes in the vantage6 network or on the machine of a researcher. In this example we only show the case in which one of the nodes executes the central part of the algorithm. The node provides the algorithm with a JWT token so that the central part of the algorithm has access to the server to post these subtasks.

📂Algorithm Structure#

The algorithm needs to be structured as a Python package. This way the algorithm can be installed within the Docker image. The minimal file-structure would be:

project_folder
├── Dockerfile
├── setup.py
└── algorithm_pkg
    └── __init__.py

We also recommend adding a README.md, LICENSE and requirements.txt to the project_folder.

setup.py#

Contains the setup method to create a package from your algorithm code. Here you specify some details about your package and the dependencies it requires.

from os import path
from codecs import open
from setuptools import setup, find_packages

# we're using a README.md, if you do not have this in your folder, simply
# replace this with a string.
here = path.abspath(path.dirname(__file__))
with open(path.join(here, 'README.md'), encoding='utf-8') as f:
    long_description = f.read()

# Here you specify the meta-data of your package. The `name` argument is
# needed in some other steps.
setup(
    name='v6-average-py',
    version="1.0.0",
    description='vantage6 average',
    long_description=long_description,
    long_description_content_type='text/markdown',
    url='https://github.com/IKNL/v6-average-py',
    packages=find_packages(),
    python_requires='>=3.10',
    install_requires=[
        'vantage6-client',
        # list your dependencies here:
        # pandas, ...
    ]
)

Note

The setup.py above is sufficient in most cases. However if you want to do more advanced stuff (like adding static data, or a CLI) you can use the extra arguments from setup.

Dockerfile#

The Dockerfile contains the recipe for building the Docker image. Typically you only have to change the argument PKG_NAME to the name of you package. This name should be the same as as the name you specified in the setup.py. In our case that would be v6-average-py.

# This specifies our base image. This base image contains some commonly used
# dependancies and an install from all vantage6 packages. You can specify a
# different image here (e.g. python:3). In that case it is important that
# `vantage6-client` is a dependancy of you project as this contains the wrapper
# we are using in this example.
FROM harbor2.vantage6.ai/algorithms/algorithm-base

# Change this to the package name of your project. This needs to be the same
# as what you specified for the name in the `setup.py`.
ARG PKG_NAME="v6-average-py"

# This will install your algorithm into this image.
COPY . /app
RUN pip install /app

# This will run your algorithm when the Docker container is started. The
# wrapper takes care of the IO handling (communication between node and
# algorithm). You dont need to change anything here.
ENV PKG_NAME=${PKG_NAME}
CMD python -c "from vantage6.tools.docker_wrapper import docker_wrapper; docker_wrapper('${PKG_NAME}')"

`init.py`#

This contains the code for your algorithm. It is possible to split this into multiple files, however the methods that should be available to the researcher should be in this file. You can do that by simply importing them into this file (e.g. from .average import my_nested_method)

We can distinguish two types of methods that a user can trigger:

name	description	prefix	arguments
master	Central part of the algorithm. Receives a `client` as argument which provides an interface to the central server. This way the master can create tasks and collect their results.		`(client, data, args, *kwargs)`
Remote procedure call	Consumes the data at the node to compute the partial.	RPC_	`(data, args, *kwargs)`

Warning

Everything that is returned by thereturn statement is sent back to the central vantage6-server. This should never contain any privacy-sensitive information.

Warning

The client the master method receives is an AlgorithmClient (or a ContainerClient if you are using an older version), which is different than the client you use as a user.

For our average algorithm the implementation will look as follows:

import time

from vantage6.tools.util import info

def master(client, data, column_name):
    """Combine partials to global model

    First we collect the parties that participate in the collaboration.
    Then we send a task to all the parties to compute their partial (the
    row count and the column sum). Then we wait for the results to be
    ready. Finally when the results are ready, we combine them to a
    global average.

    Note that the master method also receives the (local) data of the
    node. In most usecases this data argument is not used.

    The client, provided in the first argument, gives an interface to
    the central server. This is needed to create tasks (for the partial
    results) and collect their results later on. Note that this client
    is a different client than the client you use as a user.
    """

    # Info messages can help you when an algorithm crashes. These info
    # messages are stored in a log file which is send to the server when
    # either a task finished or crashes.
    info('Collecting participating organizations')

    # Collect all organization that participate in this collaboration.
    # These organizations will receive the task to compute the partial.
    organizations = client.get_organizations_in_my_collaboration()
    ids = [organization.get("id") for organization in organizations]

    # Request all participating parties to compute their partial. This
    # will create a new task at the central server for them to pick up.
    # We've used a kwarg but is is also possible to use `args`. Although
    # we prefer kwargs as it is clearer.
    info('Requesting partial computation')
    task = client.create_new_task(
        input_={
            'method': 'average_partial',
            'kwargs': {
                'column_name': column_name
            }
        },
        organization_ids=ids
    )

    # Now we need to wait untill all organizations(/nodes) finished
    # their partial. We do this by polling the server for results. It is
    # also possible to subscribe to a websocket channel to get status
    # updates.
    info("Waiting for results")
    results = client.wait_for_results(task_id=task.get("id"))

    # Now we can combine the partials to a global average.
    global_sum = 0
    global_count = 0
    for result in results:
        global_sum += result["sum"]
        global_count += result["count"]

    return {"average": global_sum / global_count}

def RPC_average_partial(data, column_name):
    """Compute the average partial

    The data argument contains a pandas-dataframe containing the local
    data from the node.
    """

    # extract the column_name from the dataframe.
    info(f'Extracting column {column_name}')
    numbers = data[column_name]

    # compute the sum, and count number of rows
    info('Computing partials')
    local_sum = numbers.sum()
    local_count = len(numbers)

    # return the values as a dict
    return {
        "sum": local_sum,
        "count": local_count
    }

Local testing#

Now that we have a vantage6 implementation of the algorithm it is time to test it. Before we run it in a vantage6 setup we can test it locally by using the ClientMockProtocol which simulates the communication with the central server.

Before we can locally test it we need to (editable) install the algorithm package so that the Mock client can use it. Simply go to the root directory of your algorithm package (with the setup.py file) and run the following:

pip install -e .

Then create a script to test the algorithm:

from vantage6.tools.mock_client import ClientMockProtocol

# Initialize the mock server. The datasets simulate the local datasets from
# the node. In this case we have two parties having two different datasets:
# a.csv and b.csv. The module name needs to be the name of your algorithm
# package. This is the name you specified in `setup.py`, in our case that
# would be v6-average-py.
client = ClientMockProtocol(
    datasets=["local/a.csv", "local/b.csv"],
    module="v6-average-py"
)

# to inspect which organization are in your mock client, you can run the
# following
organizations = client.get_organizations_in_my_collaboration()
org_ids = ids = [organization["id"] for organization in organizations]

# we can either test a RPC method or the master method (which will trigger the
# RPC methods also). Lets start by triggering an RPC method and see if that
# works. Note that we do *not* specify the RPC_ prefix for the method! In this
# example we assume that both a.csv and b.csv contain a numerical column `age`.
average_partial_task = client.create_new_task(
    input_={
        'method':'average_partial',
        'kwargs': {
            'column_name': 'age'
        }
    },
    organization_ids=org_ids
)

# You can directly obtain the result (we dont have to wait for nodes to
# complete the tasks)
results = client.result.from_task(average_partial_task.get("id"))
print(results)

# To trigger the master method you also need to supply the `master`-flag
# to the input. Also note that we only supply the task to a single organization
# as we only want to execute the central part of the algorithm once. The master
# task takes care of the distribution to the other parties.
average_task = client.create_new_task(
    input_={
        'master': 1,
        'method':'master',
        'kwargs': {
            'column_name': 'age'
        }
    },
    organization_ids=[org_ids[0]]
)
results = client.result.from_task(average_task.get("id"))
print(results)

Building and Distributing#

Now that we have a fully tested algorithm for the vantage6 infrastructure. We need to package it so that it can be distributed to the data-stations/nodes. Algorithms are delivered in Docker images. So that’s where we need the Dockerfile for. To build an image from our algorithm (make sure you have docker installed and it’s running) you can run the following command from the root directory of your algorithm project.

docker build -t harbor2.vantage6.ai/demo/average .

The option -t specifies the (unique) identifier used by the researcher to use this algorithm. Usually this includes the registry address (harbor2.vantage6.ai) and the project name (demo).

Note

In case you are using docker hub as registry, you do not have to specify the registry or project as these are set by default to the Docker hub and your docker hub username.

docker push harbor2.vantage6.ai/demo/average

Note

Reach out to us on Discord if you want to use our registries (harbor2.vantage6.ai and harbor2.vantage6.ai).

4.3. Classic Tutorial#

4.3.1. Mathematical decomposition#

4.3.2. Federated implementation#

Federated part#

Central part#

Local testing#

4.3.3. Vantage6 integration#

📂Algorithm Structure#

setup.py#

Dockerfile#

__init__.py#

Local testing#

Building and Distributing#

`init.py`#