Raku-H2O-Client

Raku REST client for the open-source, distributed in-memory machine-learning platform H2O-3.

Installation

From Zef ecosystem:

zef install H20::Client

From GitHub:

zef install https://github.com/antononcube/Raku-H2O-Client.git

Setup

1. Download the latest H20 version.

2. Start in a OS-terminal application the H2O cluster with the shell command:

java -jar h2o.jar

3. In case that command give the message:

Only Java versions 8-17 are supported, system version is 22.0.2

3.1. Check the available Java distributions with:

/usr/libexec/java_home -V

3.2. Pick one with a version between 8-17 or download a one with one of these versions:

3.3. Setup JAVA_HOME and run the command java -jar h2o.jar again. For example, on macOS:

export JAVA_HOME=/Library/Java/JavaVirtualMachines/zulu-11.jdk/Contents/Home 
java -jar h2o.jar 

Usage examples

use H2O::Client;

my $h2o = H2O::Client.new('http://localhost:54321');

my $frame = $h2o.upload(
    [%(x => 1, group => 'a'), %(x => 2, group => 'b')],
    destination-frame => 'example.hex'
).wait.result;

say $frame.shape;       # (2 2)
say $frame.names;
say $frame<group>.type; # column proxy
say $frame.head(2);     # explicitly download a small preview

# (2 2)
# [group x]
# string
# [{group => a, x => 1} {group => b, x => 2}]

H2O::Client::Frame is a lightweight handle to a server-side frame. Its identity is the H2O frame key; metadata is fetched lazily and cached. Use refresh after external changes. H2O::Client::Column exposes column type, domain, and summary statistics. Long-running parse, model-build, and export operations return H2O::Client::Job; call wait before consuming result.

Rapids transformations

Frame transformations are explicit, composable Rapids expressions. They do not run until materialize is called with a destination key:

use Data::ExampleDatasets;

my @data = example-dataset(/'dplyr::starwars'/);
$h2o.upload(@data, destination-frame => 'starwars');

my $frame = $h2o.frame('starwars');
my $heavy = $frame
        .where($frame<mass>.expression.greater-than(60))
        .select(<name mass homeworld>)
        .materialize('starwars-heavy3.hex');

# H2O::Frame<starwars-heavy3.hex>[42 × 3]

The initial layer supports column selection, row filtering, arithmetic, comparisons, boolean operations, cbind, rbind, sum, and mean. It opens an H2O Rapids session lazily; call $h2o.close when retaining a connection for a long-running process is no longer necessary.

To start a local cluster:

my $h2o = H2O::Client.new;
$h2o.init(jar-path => '/path/to/h2o.jar', jvm-opts => <-Xmx4g>);
$h2o.shutdown;

shutdown stops only a process started by this client. Shutting down a cluster connected to externally requires the explicit :cluster option.

Workflows

For fully programmed workflows see the example Raku scripts:

• classification.raku

• clustering.raku

Or the Jupyter notebooks:

• Classification-demo.ipynb

• Data-manipulation-demo.ipynb

References

Documentation, downloads

[H2O1] H20.ai, H2O-3 Documentation.

[H2O2] H20.ai, H20 Latest Stable Release.

Packages

[AAp1] Anton Antonov, Data::ExampleDatasets, Raku package, (2021-2025), GitHub/antononcube.

[AAp2] Anton Antonov, Data::Importers, Raku package, (2024-2026), GitHub/antononcube.