Distributed Grid Descent: an algorithm for hyperparameter tuning guided by Bayesian inference, designed to run on multiple processes and potentially many machines with no central point of control

Last update: Jan 01, 2022

Related tags

Algorithms DistributedGridDescent

Overview

Distributed Grid Descent

An implementation of Distributed Grid Descent: an algorithm for hyperparameter tuning guided by Bayesian inference, designed to run on multiple processes and potentially many machines with no central point of control as described in Appendix B of Working Memory Graphs [Loynd et al., 2019].

Note: This project is a work in progress. Please contact me if you like to contribute and help to develop a fully fledged python library out of it.

Usage

import numpy as np
from dgd import DistributedGridDescent

model = ... # model wrapper
data = {
    "train_data": ...
}

param_grid = {
    "learning_rate":[3e-3, 1e-3, 3e-4, 1e-4, 3e-5, 1e-5],
    "optimizer":["adam", "rmsprop"],
    "lr_annealing":[False, 0.95, 0.99],
    "batch_size":[32, 64, 128, 256, 1024],
    "num_linear_layers":[1, 2, 4, 8, 16],
    "num_neurons":[512, 256, 128, 64, 32, 16],
    "dropout":[0.0, 0.1, 0.3, 0.5],
    "l2":[0.0, 0.01, 0.1]
}

dgd = DistributedGridDescent(model, param_grid, metric=np.mean, n_jobs=-1)
dgd.run(data)

print(dgd.best_params_)
df = pd.DataFrame(dgd.results_).set_index("ID").sort_values(by=["metric"],ascending=False)

Examples and Tutorials

See sklearn_example.py, pytorch_example.py, rosenbrock_example.py and tensorflow_example.py in the examples folder for examples of basic usage of dgd.
See rosenbrock_server_example.py for an example of distributed usage.

Strong and weak scaling analysis

Algorithm

Input: Set of hyperparameters H, each having a discrete, ordered set of possible values.  
Input: Maximum number of training steps N per run.  
repeat  
    Download any new results.  
    if no results so far then
        Choose a random configuration C from the grid defined by H.
    else
        Identify the run set S with the highest metric.
        Initialize neighborhood B to contain only S.
        Expand B by adding all possible sets whose configurations differ from that of S by one step in exactly one hyperparameter setting.
        Calculate a ceiling M = Count(B) + 1.
        Weight each run set x in B M - Count(x).
        Sample a random run set S' from B according to run set weights.
        Choose configuration C from S'.
    end if
    Perform one training run of N steps using C.
    Calculate the runs Metric.
    Log the result on shared storage.
until terminated by user.

See Appendix B of Loynd et al., 2019 for details.

Distributed Grid Descent: an algorithm for hyperparameter tuning guided by Bayesian inference, designed to run on multiple processes and potentially many machines with no central point of control

Related tags

Overview

Distributed Grid Descent

Usage

Examples and Tutorials

Strong and weak scaling analysis

Algorithm

Owner

Martin

Infomap is a network clustering algorithm based on the Map equation.

A simple python implementation of A* and bfs algorithm solving Eight-Puzzle

sudoku solver using CSP forward-tracking algorithms.

Robotic Path Planner for a 2D Sphere World

A simple python application to visualize sorting algorithms.

Exact algorithm for computing two-sided statistical tolerance intervals under a normal distribution assumption using Python.

Algorithms and utilities for SAR sensors

Python-Strongest-Encrypter - Transform your text into encrypted symbols using their dictionary

8-puzzle-solver with UCS, ILS, IDA* algorithm

This is the code repository for 40 Algorithms Every Programmer Should Know , published by Packt.

This repository explores an implementation of Grover's Algorithm for knights on a chessboard.

Python Sorted Container Types: Sorted List, Sorted Dict, and Sorted Set

Leveraging Unique CPS Properties to Design Better Privacy-Enhancing Algorithms

Python based framework providing a simple and intuitive framework for algorithmic trading

BCI datasets and algorithms

A lightweight, object-oriented finite state machine implementation in Python with many extensions

QDax is a tool to accelerate Quality-Diveristy (QD) algorithms through hardware accelerators and massive parallelism

Greedy Algorithm-Problem Solving

8 Puzzle with A* , Greedy & BFS Search in Python

A Python project for optimizing the 8 Queens Puzzle using the Genetic Algorithm implemented in PyGAD.