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. For example, does your novel layer really improve performance?

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	:white_check_mark:	:x:	:x:	:white_check_mark:	:x:
Lighting	:x:	:x:	:white_check_mark:	:x:	:x:
Optuna	:white_check_mark:	:x:	:x:	:x:	:white_check_mark:
Hydra	:x:	:white_check_mark:	:x:	:x:	:x:
ABLATOR	:heavy_check_mark:	:heavy_check_mark:	:heavy_check_mark:	:heavy_check_mark:	:heavy_check_mark:

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).

Using:

1. Ray: You will need to write boiler-plate code for integrating with a configuration system (i.e. Hydra), saving experiments artifacts or logging (i.e. integrate with Wandb).

2. Lighting: You will need to write boiler-plate code for HPO (i.e. using Optuna), Configuring experiments (i.e. Hydra) and horizontal distributed execution (i.e. integrate with Ray)

3. Hydra: The configuration system is not strongly typed (ABLATOR), and does not provide support for common ML use-cases where configuration attributes are Derived (inferred during run-time) or Stateless (change between trials). Additionally, ABLATOR provides support for custom objects that are dynamically inferred and initialized during execution.

4. ABLATOR: Combines Ray back-end, with Optuna for HPO and removes boiler-plate code for fault tollerant strategies, training, and analyzing the results.

Integrating different tools, for distributed execution, fault tollerance, training, checkpointing and analysis is error prone! 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. For examples please look examples

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.

pip install git+https://github.com/fostiropoulos/ablator.git

For Development

1. git clone git@github.com:fostiropoulos/ablator.git
2. cd ablator
3. pip install -e .[dev]