Milano is a tool for automating hyper-parameters search for your models on a backend of your choice.

Overview

License Documentation

Milano

(This is a research project, not an official NVIDIA product.)

Milano

Documentation

https://nvidia.github.io/Milano

Milano (Machine learning autotuner and network optimizer) is a tool for enabling machine learning researchers and practitioners to perform massive hyperparameters and architecture searches.

You can use it to:

Your script can use any framework of your choice, for example, TensorFlow, PyTorch, Microsoft Cognitive Toolkit etc. or no framework at all. Milano only requires minimal changes to what your script accepts via command line and what it returns to stdout.

Currently supported backends:

  • Azkaban - on a single multi-GPU machine or server with Azkaban installed
  • AWS - Amazon cloud using GPU instances
  • SLURM - any cluster which is running SLURM

Prerequisites

  • Linux
  • Python 3
  • Ensure you have Python version 3.5 or later with packages listed in the requirements.txt file.
  • Backend with NVIDIA GPU

How to Get Started

  1. Install all dependencies with the following command pip install -r requirements.txt.
  2. Follow this mini-tutorial for local machine or this mini-tutorial for AWS

Visualize

We provide a script to convert the csv file output into two kinds of graphs:

  • Graphs of each hyperparameter with the benchmark (e.g. valid perplexity)
  • Color graphs that show the relationship between any two hyperparameters and the benchmark

To run the script, use:

python3 visualize.py --file [the name of the results csv file] 
                     --n [the number of samples to visualize]
                     --subplots [the number of subplots to show in a plot]
                     --max [the max value of benchmark you care about]
Owner
NVIDIA Corporation
NVIDIA Corporation
Audio Visual Emotion Recognition using TDA

Audio Visual Emotion Recognition using TDA RAVDESS database with two datasets analyzed: Video and Audio dataset: Audio-Dataset: https://www.kaggle.com

Combinatorial Image Analysis research group 3 May 11, 2022
[AAAI-2021] Visual Boundary Knowledge Translation for Foreground Segmentation

Trans-Net Code for (Visual Boundary Knowledge Translation for Foreground Segmentation, AAAI2021). [https://ojs.aaai.org/index.php/AAAI/article/view/16

ZJU-VIPA 2 Mar 04, 2022
Implementation for "Seamless Manga Inpainting with Semantics Awareness" (SIGGRAPH 2021 issue)

Seamless Manga Inpainting with Semantics Awareness [SIGGRAPH 2021](To appear) | Project Website | BibTex Introduction: Manga inpainting fills up the d

101 Jan 01, 2023
Python and Julia in harmony.

PythonCall & JuliaCall Bringing Python® and Julia together in seamless harmony: Call Python code from Julia and Julia code from Python via a symmetric

Christopher Rowley 414 Jan 07, 2023
MediaPipe Kullanarak İleri Seviye Bilgisayarla Görü

MediaPipe Kullanarak İleri Seviye Bilgisayarla Görü

Burak Bagatarhan 12 Mar 29, 2022
Kaggle G2Net Gravitational Wave Detection : 2nd place solution

Kaggle G2Net Gravitational Wave Detection : 2nd place solution

Hiroshechka Y 33 Dec 26, 2022
An Unsupervised Graph-based Toolbox for Fraud Detection

An Unsupervised Graph-based Toolbox for Fraud Detection Introduction: UGFraud is an unsupervised graph-based fraud detection toolbox that integrates s

SafeGraph 99 Dec 11, 2022
A comprehensive list of published machine learning applications to cosmology

ml-in-cosmology This github attempts to maintain a comprehensive list of published machine learning applications to cosmology, organized by subject ma

George Stein 290 Dec 29, 2022
LightHuBERT: Lightweight and Configurable Speech Representation Learning with Once-for-All Hidden-Unit BERT

LightHuBERT LightHuBERT: Lightweight and Configurable Speech Representation Learning with Once-for-All Hidden-Unit BERT | Github | Huggingface | SUPER

WangRui 46 Dec 29, 2022
This was initially the repo for the project of [email protected] of Asaf Mazar, Millad Kassaie and Georgios Chochlakis named "Powered by the Will? Exploring Lay Theories of Behavior Change through Social Media"

Subreddit Analysis This repo includes tools for Subreddit analysis, originally developed for our class project of PSYC 626 in USC, titled "Powered by

Georgios Chochlakis 1 Dec 17, 2021
Deep Unsupervised 3D SfM Face Reconstruction Based on Massive Landmark Bundle Adjustment.

(ACMMM 2021 Oral) SfM Face Reconstruction Based on Massive Landmark Bundle Adjustment This repository shows two tasks: Face landmark detection and Fac

BoomStar 51 Dec 13, 2022
2D&3D human pose estimation

Human Pose Estimation Papers [CVPR 2016] - 201511 [IJCAI 2016] - 201602 Other Action Recognition with Joints-Pooled 3D Deep Convolutional Descriptors

133 Jan 02, 2023
PaddleBoBo是基于PaddlePaddle和PaddleSpeech、PaddleGAN等开发套件的虚拟主播快速生成项目

PaddleBoBo - 元宇宙时代,你也可以动手做一个虚拟主播。 PaddleBoBo是基于飞桨PaddlePaddle深度学习框架和PaddleSpeech、PaddleGAN等开发套件的虚拟主播快速生成项目。PaddleBoBo致力于简单高效、可复用性强,只需要一张带人像的图片和一段文字,就能

502 Jan 08, 2023
Repository containing detailed experiments related to the paper "Memotion Analysis through the Lens of Joint Embedding".

Memotion Analysis Through The Lens Of Joint Embedding This repository contains the experiments conducted as described in the paper 'Memotion Analysis

Nethra Gunti 1 Mar 16, 2022
Listing arxiv - Personalized list of today's articles from ArXiv

Personalized list of today's articles from ArXiv Print and/or send to your gmail

Lilianne Nakazono 5 Jun 17, 2022
Unsupervised Foreground Extraction via Deep Region Competition

Unsupervised Foreground Extraction via Deep Region Competition [Paper] [Code] The official code repository for NeurIPS 2021 paper "Unsupervised Foregr

28 Nov 06, 2022
PenguinSpeciesPredictionML - Basic model to predict Penguin species based on beak size and sex.

Penguin Species Prediction (ML) 🐧 👨🏽‍💻 What? 💻 This project is a basic model using sklearn methods to predict Penguin species based on beak size

Tucker Paron 0 Jan 08, 2022
A BaSiC Tool for Background and Shading Correction of Optical Microscopy Images

BaSiC Matlab code accompanying A BaSiC Tool for Background and Shading Correction of Optical Microscopy Images by Tingying Peng, Kurt Thorn, Timm Schr

Marr Lab 34 Dec 18, 2022
Koç University deep learning framework.

Knet Knet (pronounced "kay-net") is the Koç University deep learning framework implemented in Julia by Deniz Yuret and collaborators. It supports GPU

1.4k Dec 31, 2022
Machine Translation Implement By Bi-GRU And Transformer

Seq2Seq Translation Implement By Bidirectional GRU And Transformer In Pytorch Before You Run The Code You should download the data through the link be

He Wang 2 Oct 27, 2021