Getting Started

This document will show you how to get up and running with Emmental. We’ll show you how to get everything installed on your machine so that you can walk through real examples by checking out our Tutorials.

Installing the Emmental Package

Install Emmental by running:

$ pip install emmental

Note

Emmental only supports Python 3. Python 2 is not supported.

Tip

For the Python dependencies, we recommend using a virtualenv, which will allow you to install Emmental and its python dependencies in an isolated Python environment. Once you have virtualenv installed, you can create a Python 3 virtual environment as follows.:

$ virtualenv -p python3.6 .venv

Once the virtual environment is created, activate it by running:

$ source .venv/bin/activate

Any Python libraries installed will now be contained within this virtual environment. To deactivate the environment, simply run:

$ deactivate

The Emmental Framework

The Emmental framework can be broken into four components.

1. Dataset and Dataloader

   In this first component, the users’ input is parsed into Emmental’s dataset and then feed into Emmental’s dataloader.

2. Task

   In this component, we let user to use declarative language-like way to defeine the taksk, which includes task name (name), module used in the task (module_pool), task flow (task_flow), loss function used in the task (loss_func), output function (output_func), and the score functions (scorer).

3. Model

   Here, we initialize the Emmental model with the Emmental tasks. Users can define different types of models, such as single-task model, multi-task model, multi-modality task.

4. Learning

   Finally, Emmental provides learning component which is used to train the Emmental model. Optionally, users can use different training schedulers during learning process.

To demonstrate how to set up and use Emmental in your applications, we walk through each of these phases in real-world examples in our Tutorials.