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DISCO developer guide

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Welcome to the DISCO developer guide. Here you will have a first overview of the project, how to install and run an instance of DISCO and links to further documentation. All DISCO code is in JavaScript/TypeScript.

Structure

The DISCO project is composed of multiple parts. At the root level, there are six main folders: discojs, discojs-node, discojs-web, server, webapp and cli.

  • discojs, or Disco.js, is the TypeScript library that contains federated and decentralized learning logic which is independent of any platform. The library allows training and using machine learning models in a distributed and collaborative fashion. Additionally, discojs-node and discojs-web extend the platform-agnostic code in discojs. In other words, discojs contains most of the implementation but can't be used by itself, while discojs-node and discojs-web allow using discojs via Node.js or a browser. To some extents, you can think of discojs as an abstract class extended by discojs-nodeand discojs-web.
  • discojs-node lets you use Disco.js with Node.js. For example, the cli relies on discojs-node to train federated models from the command line. A user can also directly import the discojs-node package in their Node.js programs.
  • discojs-web allows using Disco.js through a browser. The webapp, discussed below, relies on discojs-web to implement a browser UI. The main difference between the two is how they handle storage: a browser doesn't have access to the file system (for security reasons) while a Node.js application does.
  • server contains the server implementation necessary to use Disco.js. Indeed, while the federated and decentralized learning logic is implemented by Disco.js, we still need a server to orchestrate users in both paradigms. In decentralized learning, the server exposes an API for users to query the necessary information to train models in a decentralized fashion, such as the list of other peers. Thus, the server never receives training data or model parameters. In federated learning, the server receives model updates but never training data. It keeps track of participants and updates the model weights. A server instance is always necessary to use DISCO, whether one is using a browser UI, the CLI or directly programming with discojs-node.
  • webapp implements a browser User Interface. In other words, it implements a website allowing users to use DISCO without coding. Via the browser, a user can create and participate in federated and decentralized training sessions, evaluate models, etc.
  • cli contains the Command Line Interface for Disco.js. For example, the CLI allows a user to create and join training sessions from the command line, benchmark performance by emulating multiple clients, etc.

Here is a summary diagram:

flowchart LR
  subgraph "discojs library"
    discojs-node-->|extends|discojs;
    discojs-web-->|extends|discojs;
  end
  subgraph User Interface
    webapp-->|uses|discojs-web;
    custom_browser["custom browser implementation"]-->|uses|discojs-web;
    server-->| uses |discojs-node;
    cli-->|uses|discojs-node;
    custom_node["custom Node.js scripts"]-->|uses|discojs-node;
  end
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Installation guide

The following instructions will install the required dependencies, build Disco.js and launch a DISCO server and a web client. If you run into any sort of trouble check our FAQ; otherwise please create a new issue.

1. Clone the repository

git clone [email protected]:epfml/disco.git
cd disco

2. Install Node.js

We recommend using nvm (Node Version Manager) to handle multiple Node.js versions. Start by installing nvm by following their installation instructions. After installation, you should be able to run

nvm -v
0.39.7 # at the time of writing

Use it to install the version we use in DISCO.

nvm install # it reads `.nvmrc` to select the correct version

nvm manages your different Node.js versions while npm handles your different Node.js project packages within one version.

3. Install the dependencies

npm ci

4. Build the projects

Then we need to build the packages, which means to compile TypeScript into JavaScript.

Disco is split in multiple packages, called workspaces in NPM, which are described in the Structure Section. You can add --workspaces (or shorter as -ws) to many npm commands to act on all packages. Or you can select a specific workspace via --workspace=$name-or-path-to-package (-w $name-or-path-to-package).

npm -ws run build

5. Download and extract the sample training datasets. These datasets are used in the automated tests.

./datasets/populate

6. Launch DISCO

As you may have seen, there are many ways to use DISCO. Here we will run a server and a web client. From there, a user can use DISCO from their browser.

  • First launch a server instance, which is used for federated and decentralized learning tasks, e.g. to list peers participating in a decentralized task.
npm -w server start

The server should be listening on http://localhost:8080/.

  • Secondly, start a web client, which will allow you to use DISCO from your browser. You will have to do so from another terminal since the previous one is now used by the server.
npm -w webapp start # from another terminal

The web client should be running on http://localhost:8081, if not first restart the server and then the web client.

You can now access DISCO at http://localhost:8081/

How to use DISCO

There are multiple ways to use and interact with DISCO, depending on your objective:

  • A non-technical user that wants to train models in a distributed manner without coding would want to use DISCO through the webapp. However, someone with expertise may still be needed if you want your own server (e.g., by cloning the repository).
  • A technical user may find it more flexible to use DISCO from a Node.js script, which gives users a finer control over the process. The discojs-node module is tailored to be used in Node.js scripts and allows to load data, helps starting a server and run distributed machine learning training tasks.
  • Finally, the cli (command line interface) can also be used to quickly start distributed model trainings. The CLI is more restricting than using discojs-node but allows to start training with multiple users in a single command. It is useful for benchmarking for example.

Training on your own datasets: DISCO provides pre-defined training tasks, such as CIFAR10, Titanic, etc. The Tasks document guide describes how to add custom tasks from the webapp UI, a discojs-node script or how to add support for a new pre-defined task.

webapp and server

The last step of the installation instructions describe how to start a web interface along with a helper server. The server is used to provide some predefined machine learning tasks and orchestrate distributed training.

From the root level, launch a server instance:

npm -w server start

The server should be listening on http://localhost:8080/.

Start the webapp:

npm -w webapp start # from another terminal

The web client should be running on http://localhost:8081. Running the last command should also output a Network address at which devices on the same network can access the UI. You can find more information in the Contributing to the webapp Section as well as the server README.

Importing discojs-node with Node.js

Using discojs-node is illustrated in the examples folder. Using discojs-node implies starting a server (or having access to one), loading local training data and configuring the model training.

cli

Training a model with the cli on pre-defined tasks is straightforward:

# From the root folder
npm -w cli start -- --task cifar10 --numberOfUsers 4 --epochs 15 --roundDuration 5
npm -w cli start -- --help # for all options

Adding CLI support for another task is described in the CLI README.

Further documentation

  • To contribute or modify the codebase have a look at the contributing guide which lists the following steps to onboard DISCO.
  • If you are only planning to use DISCO in your own scripts, you can find a standalone example relying on discojs-node in the examples folder. The example runs with Node.js outside any browser, with discojs-node and a server instance. A DISCO server is launched by the script which then loads data and emulates multiple users training a model in a federated manner.

Table of contents

As there are many guides in the project, here is a table of contents referencing them all: