adjutant-discord 0.1.0

A package for managing ML experiments over Discord in conjunction with WandB.

Adjutant

Adjutant is a package for managing ML experiments over Discord in conjunction with WandB.

Adjutant allows users to run a Discord bot that provides updates on training jobs that have synced with WandB, and allows them to initiate new runs with different hyperparameters by posting in the Discord chat. Once the adjutant client is connected to Discord, users can start and get updates on training runs over Discord, from anywhere; no need to VPN and ssh into your office's servers, no need to open up your computer and try to remember where and in which files you need to set your hyperparameters. Just open up Discord and tell adjutant to start an experiment (or two, or three).

Installation

pip install adjutant-discord

Discord bot creation

To allow adjutant to post to Discord as a bot, first follow these instructions for creating a Discord bot and adding it to your Server. You then create an Adjutant object with your bot token.

Note: Be careful not to share your bot's token. Consider storing it in an environment variable or file that is not checked in to version control.

WandB setup

Adjutant is designed to work with WandB for ML experiment tracking. Create a WandB account at wandb.ai.

Wherever you plan to run Adjutant, make sure you are either logged in to your WandB account, or have an API key populated in the WANDB_API_KEY environment variable. More information is available in the WandB docs.

Adjutant commands

Once Adjutant is running and has connected to Discord (see the Basic Adjutant example below to get started), you can send it the following commands by posting in the chat.

Command	Effect	Example
$hello	Get a response from the bot	$hello
$experiment {hyperparams}	Launch a new experiment with the given hyperparameters (must provide run_experiment_script in constructor)	$experiment {"epochs": 10, "batch_size": 32}

Quickstart

For more advanced examples, please see examples, starting with the MNIST example.

Basic Adjutant

The most basic formulation of Adjutant provides updates on WandB experiments under the given project name. Your WandB entity name is your account name, and the project title is the name of the project you have created (or will create) to store experiments.

from adjutant import Adjutant
client = Adjutant('my-wandb-entity', 'my-wandb-project-title')
client.run('my-discord-token')

When you run the script, you will see your bot post to your Discord chat with information on the WandB runs it found for the project.

Adjutant with experiment launching

By providing a run_experiment_script constructor argument, Adjutant will be able to respond to user requests on Discord to run a new experiment. Adjutant will execute run_experiment_script in a subprocess so that it can still respond to new requests. run_experiment_script may also request another entity, e.g. Kubernetes, to initiate the experiment on its behalf rather than actually running the experiment itself.

First, here are the contents of run_experiment.sh, which takes a JSON-formatted string as its command line argument. Adjutant will pass this script the hyperparameters with which to run the experiment. In this script, train_model.py trains a new model with the supplied hyperparameters. For an example of what the training script might look like, see the MNIST example.

#!/bin/bash
python train_model.py "$1"

Now we can create a client that references run_experiment.sh.

from adjutant import Adjutant
client = Adjutant('my-wandb-entity',
                  'my-wandb-project-title',
                  run_experiment_script='./run_experiment.sh')
client.run('my-discord-token')

And we can run an experiment by posting in Discord with the $experiment command. Adjutant will start the experiment using run_experiment.sh and post back on Discord when the run finishes.