hyperparametertuning

Parallel Hyperparameter Tuning for Neural Networks

This repository contains an example of how to perform parallel hyperparameter tuning for a neural network using Docker containers and the wfl library in Go. The example trains a simple feed-forward neural network on the classic MNIST handwritten digit recognition task and explores different combinations of hyperparameters to find the best-performing model.

Prerequisites

• Docker
• Go (>= 1.20)

Setup

1. Clone the repository.

git clone https://github.com/dgruber/wfl.git
cd wfl/examples/hyperparametertuning

1. Build the custom Docker container image for training the MNIST model.

make build

This command creates a Docker container image named parallel-training that includes the necessary dependencies for training the neural network and the train_mnist.py script.

Run

Execute the parallel hyperparameter tuning workflow using:

make run

This command starts the wfl workflow, which runs multiple training jobs concurrently with different combinations of hyperparameters. The workflow leverages the custom parallel-training Docker container image to ensure reproducibility and isolation between training jobs.

Once the workflow completes, it will print the best hyperparameters and the corresponding accuracy achieved for the MNIST model.