https://github.com/codait/r4ml
Scalable R for Machine Learning
https://github.com/codait/r4ml
bigdata distributed machine-learning r scalable
Last synced: 12 months ago
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Scalable R for Machine Learning
- Host: GitHub
- URL: https://github.com/codait/r4ml
- Owner: CODAIT
- License: apache-2.0
- Created: 2017-03-20T23:25:29.000Z (over 9 years ago)
- Default Branch: master
- Last Pushed: 2018-09-11T17:31:12.000Z (almost 8 years ago)
- Last Synced: 2025-05-06T23:17:04.852Z (about 1 year ago)
- Topics: bigdata, distributed, machine-learning, r, scalable
- Language: R
- Homepage:
- Size: 35.1 MB
- Stars: 43
- Watchers: 37
- Forks: 13
- Open Issues: 2
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# 
# __**What is R4ML?**__
R4ML is a scalable, hybrid approach to ML/Stats using R, Apache SystemML, and Apache Spark
## __**R4ML Key Features**__
- R4ML is a git downloadable open source R package from IBM
- Created on top of SparkR and Apache SystemML (so it supports features from both)
- Acts as a R bridge between SparkR and Apache SystemML
- Provides a collection of canned algorithms
- Provides the ability to create custom ML algorithms
- Provides both SparkR and Apache SystemML functionality
- APIs are friendlier to the R user
## __**R4ML Architecture**__
## __**How to install**__
Quick install (run from R console):
# Download Apache Spark 2.1.0 (Note: Java must be installed)
download.file("http://archive.apache.org/dist/spark/spark-2.1.0/spark-2.1.0-bin-hadoop2.7.tgz", "~/spark-2.1.0-bin-hadoop2.7.tgz")
system("tar -xvf ~/spark-2.1.0-bin-hadoop2.7.tgz")
Sys.setenv("SPARK_HOME" = file.path(getwd(), "spark-2.1.0-bin-hadoop2.7"))
# Add the library path for SparkR
.libPaths(c(.libPaths(), "~/spark-2.1.0-bin-hadoop2.7/R/lib/"))
# Install R4ML dependencies
install.packages(c("uuid", "R6"), repos = "http://cloud.r-project.org")
# Download and install R4ML
download.file("http://codait-r4ml.s3-api.us-geo.objectstorage.softlayer.net/R4ML_0.8.0.tar.gz", "~/R4ML_0.8.0.tar.gz")
install.packages("~/R4ML_0.8.0.tar.gz", repos = NULL, type = "source")
# Load dependencies and use R4ML
library("SparkR", lib.loc = "~/spark-2.1.0-bin-hadoop2.7/R/lib/")
library("R4ML")
r4ml.session(sparkHome = file.path(getwd(), "spark-2.1.0-bin-hadoop2.7"))
More detailed instructions can be found [here](./docs/r4ml-install.md).
## __**How to Use R4ML**__
Once you have installed R4ML it is time to use it for scalable machine learning and
data analysis. Look at the section on [R4ML Examples](./docs/r4ml-examples.md).
## __**R4ML Documentation**__
After you follow the instruction at 'How to install', you can point your browser to
```
$R4ML_INSTALLED_LOCATION/R4ML/html/00Index.html
```
For example, if you have installed in the `/home/data-scientist/codait` then open a
web browser and type in the following in the url
```
file:///home/data-scientist/codait/R4ML/html/00Index.html
```