meghabagri/ablator

Model Ablation Tool-Kit for Deep Learning Model

ABLATOR

A distributed experiment execution framework for ablation studies. ABLATOR provides a wrapper for your model and a Trainer class for you to prototype on your method and scale to thousands of experimental trials with 1 code change.

Ablation studies are experiments used to identify the causal effects on a method performance. Method is a meta-model from a Bayesian point of view, where the model which we evaluate include the model parameters as well as meta-training arguments, such as Optimizer. In plain english, the causal effects we study encompass both the PyTorch model and the training configuration.

What are Ablators?

Ablators are materials that are depleted during operation (NASA). An experimental ABLATOR should not interfere with the experimental result.

Why ABLATOR?

1. Strictly typed configuration system prevents errors.
2. Seamless prototyping to production
3. Stateful experiment design. Stop, Resume, Share your experiments
4. Automated analysis artifacts
5. Template Training

What is the difference with using xxx

Comparison table with existing framework:

Framework	HPO	Configuration	Training	Tuning	Analysis
Ray	✅	❌	❌	✅	❌
Lighting	❌	❌	✅	❌	❌
Optuna	✅	❌	❌	❌	✅
Hydra	❌	✅	❌	❌	❌
ABLATOR	✔️	✔️	✔️	✔️	✔️

Features compared, hyperparameter selection (HPO), removing boilerplate code for configuring experiments (Configuration), removing boiler plate code for running experiments at scale (Tuning) and performing analysis on the hyperparameter selection (Analysis).

In summary, you will need to integrate different tools, for distributed execution, fault tollerance, training, checkpointing and analysis. Poor compatibility between tools, verisioning errors will lead to errors in your analysis.

You can use ABLATOR with any other library i.e. PyTorch Lighting. Just wrap a Lighting model with ModelWrapper

Spend more time in the creative process of ML research and less time on dev-ops.

Pre-Release - Phase

The library is under active development and a lot of the API endpoints will be removed / renamed or their functionality changed without notice.

Install

Use a python virtual enviroment to avoid version conflicts.

1. git clone [email protected]:fostiropoulos/ablator.git
2. cd ablator
3. pip install .

For Development pip install .[dev]