Distributed deep learning on Hadoop and Spark clusters.

Overview

Note: we're lovingly marking this project as Archived since we're no longer supporting it. You are welcome to read the code and fork your own version of it and continue to use this code under the terms of the project license.

CaffeOnSpark

What's CaffeOnSpark?

CaffeOnSpark brings deep learning to Hadoop and Spark clusters. By combining salient features from deep learning framework Caffe and big-data frameworks Apache Spark and Apache Hadoop, CaffeOnSpark enables distributed deep learning on a cluster of GPU and CPU servers.

As a distributed extension of Caffe, CaffeOnSpark supports neural network model training, testing, and feature extraction. Caffe users can now perform distributed learning using their existing LMDB data files and minorly adjusted network configuration (as illustrated).

CaffeOnSpark is a Spark package for deep learning. It is complementary to non-deep learning libraries MLlib and Spark SQL. CaffeOnSpark's Scala API provides Spark applications with an easy mechanism to invoke deep learning (see sample) over distributed datasets.

CaffeOnSpark was developed by Yahoo for large-scale distributed deep learning on our Hadoop clusters in Yahoo's private cloud. It's been in use by Yahoo for image search, content classification and several other use cases.

Why CaffeOnSpark?

CaffeOnSpark provides some important benefits (see our blog) over alternative deep learning solutions.

  • It enables model training, test and feature extraction directly on Hadoop datasets stored in HDFS on Hadoop clusters.
  • It turns your Hadoop or Spark cluster(s) into a powerful platform for deep learning, without the need to set up a new dedicated cluster for deep learning separately.
  • Server-to-server direct communication (Ethernet or InfiniBand) achieves faster learning and eliminates scalability bottleneck.
  • Caffe users' existing datasets (e.g. LMDB) and configurations could be applied for distributed learning without any conversion needed.
  • High-level API empowers Spark applications to easily conduct deep learning.
  • Incremental learning is supported to leverage previously trained models or snapshots.
  • Additional data formats and network interfaces could be easily added.
  • It can be easily deployed on public cloud (ex. AWS EC2) or a private cloud.

Using CaffeOnSpark

Please check CaffeOnSpark wiki site for detailed documentations such as building instruction, API reference and getting started guides for standalone cluster and AWS EC2 cluster.

  • Batch sizes specified in prototxt files are per device.
  • Memory layers should not be shared among GPUs, and thus "share_in_parallel: false" is required for layer configuration.

Building for Spark 2.X

CaffeOnSpark supports both Spark 1.x and 2.x. For Spark 2.0, our default settings are:

  • spark-2.0.0
  • hadoop-2.7.1
  • scala-2.11.7 You may want to adjust them in caffe-grid/pom.xml.

Mailing List

Please join CaffeOnSpark user group for discussions and questions.

License

The use and distribution terms for this software are covered by the Apache 2.0 license. See LICENSE file for terms.

Owner
Yahoo
This organization is the home to many of the active open source projects published by engineers at Yahoo Inc.
Yahoo
The easy way to combine mlflow, hydra and optuna into one machine learning pipeline.

mlflow_hydra_optuna_the_easy_way The easy way to combine mlflow, hydra and optuna into one machine learning pipeline. Objective TODO Usage 1. build do

shibuiwilliam 9 Sep 09, 2022
ParaMonte is a serial/parallel library of Monte Carlo routines for sampling mathematical objective functions of arbitrary-dimensions

ParaMonte is a serial/parallel library of Monte Carlo routines for sampling mathematical objective functions of arbitrary-dimensions, in particular, the posterior distributions of Bayesian models in

Computational Data Science Lab 182 Dec 31, 2022
An easier way to build neural search on the cloud

Jina is geared towards building search systems for any kind of data, including text, images, audio, video and many more. With the modular design & multi-layer abstraction, you can leverage the effici

Jina AI 17k Jan 01, 2023
It is a forest of random projection trees

rpforest rpforest is a Python library for approximate nearest neighbours search: finding points in a high-dimensional space that are close to a given

Lyst 211 Dec 29, 2022
Magenta: Music and Art Generation with Machine Intelligence

Magenta is a research project exploring the role of machine learning in the process of creating art and music. Primarily this involves developing new

Magenta 18.1k Dec 30, 2022
Decision tree is the most powerful and popular tool for classification and prediction

Diabetes Prediction Using Decision Tree Introduction Decision tree is the most powerful and popular tool for classification and prediction. A Decision

Arjun U 1 Jan 23, 2022
Dive into Machine Learning

Dive into Machine Learning Hi there! You might find this guide helpful if: You know Python or you're learning it 🐍 You're new to Machine Learning You

Michael Floering 11.1k Jan 03, 2023
A machine learning project that predicts the price of used cars in the UK

Car Price Prediction Image Credit: AA Cars Project Overview Scraped 3000 used cars data from AA Cars website using Python and BeautifulSoup. Cleaned t

Victor Umunna 7 Oct 13, 2022
Python 3.6+ toolbox for submitting jobs to Slurm

Submit it! What is submitit? Submitit is a lightweight tool for submitting Python functions for computation within a Slurm cluster. It basically wraps

Facebook Incubator 768 Jan 03, 2023
slim-python is a package to learn customized scoring systems for decision-making problems.

slim-python is a package to learn customized scoring systems for decision-making problems. These are simple decision aids that let users make yes-no p

Berk Ustun 37 Nov 02, 2022
BentoML is a flexible, high-performance framework for serving, managing, and deploying machine learning models.

Model Serving Made Easy BentoML is a flexible, high-performance framework for serving, managing, and deploying machine learning models. Supports multi

BentoML 4.4k Jan 04, 2023
Python ML pipeline that showcases mltrace functionality.

mltrace tutorial Date: October 2021 This tutorial builds a training and testing pipeline for a toy ML prediction problem: to predict whether a passeng

Log Labs 28 Nov 09, 2022
Responsible Machine Learning with Python

Examples of techniques for training interpretable ML models, explaining ML models, and debugging ML models for accuracy, discrimination, and security.

ph_ 624 Jan 06, 2023
AutoOED: Automated Optimal Experiment Design Platform

AutoOED is an optimal experiment design platform powered with automated machine learning to accelerate the discovery of optimal solutions. Our platform solves multi-objective optimization problems an

Yunsheng Tian 107 Jan 03, 2023
monolish: MONOlithic Liner equation Solvers for Highly-parallel architecture

monolish is a linear equation solver library that monolithically fuses variable data type, matrix structures, matrix data format, vendor specific data transfer APIs, and vendor specific numerical alg

RICOS Co. Ltd. 179 Dec 21, 2022
Price Prediction model is used to develop an LSTM model to predict the future market price of Bitcoin and Ethereum.

Price Prediction model is used to develop an LSTM model to predict the future market price of Bitcoin and Ethereum.

2 Jun 14, 2022
Generate music from midi files using BPE and markov model

Generate music from midi files using BPE and markov model

Aditya Khadilkar 37 Oct 24, 2022
ML Kaggle Titanic Problem using LogisticRegrission

-ML-Kaggle-Titanic-Problem-using-LogisticRegrission here you will find the solution for the titanic problem on kaggle with comments and step by step c

Mahmoud Nasser Abdulhamed 3 Oct 23, 2022
This is the material used in my free Persian course: Machine Learning with Python

This is the material used in my free Persian course: Machine Learning with Python

Yara Mohamadi 4 Aug 07, 2022
AtsPy: Automated Time Series Models in Python (by @firmai)

Automated Time Series Models in Python (AtsPy) SSRN Report Easily develop state of the art time series models to forecast univariate data series. Simp

Derek Snow 465 Jan 02, 2023