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h2oai
h2o-2
Java2013–2013
h2oai / h2o-2 · Archived

h2o-2

Please visit https://github.com/h2oai/h2o-3 for latest H2O

4Distinct people
4Mentions
0Explicit recommenders
2.2KGitHub stars
REPOSITORY AT A GLANCE

What to know before exploring

What it is

Please visit https://github.com/h2oai/h2o-3 for latest H2O

GitHub

Where it is used

No documented product use has been verified yet.

What people are saying

No recommendation phrase was found in the indexed comments.

No classified warning is attached to this page. Read the discussions before treating silence as approval.

Also in these discussions

No other repository is repeatedly linked in the same discussions.

This week on Hacker News

Nov 7–Nov 14 UTC · Direct repository links and separately labeled related project stories. Indexed through 11/14/2026, 5:04:57 PM UTC.

No direct repository links were found in the indexed HN discussions over the past seven days. Older discussions are available below.

Mentions over time

HN mentions by week4 dated mentions

Observed mentions: 2013-04-20 22:22:56 UTC to 2013-07-07 06:52:59 UTC. Weeks start Monday, UTC.

01232013-04-15 00:00:00 UTC to 2013-04-22 00:00:00 UTC (exclusive): 3 mentions; 1 submissions, 2 commentsApr 152013-04-22 00:00:00 UTC to 2013-04-29 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2013-04-29 00:00:00 UTC to 2013-05-06 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2013-05-06 00:00:00 UTC to 2013-05-13 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2013-05-13 00:00:00 UTC to 2013-05-20 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2013-05-20 00:00:00 UTC to 2013-05-27 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 commentsMay 202013-05-27 00:00:00 UTC to 2013-06-03 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2013-06-03 00:00:00 UTC to 2013-06-10 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2013-06-10 00:00:00 UTC to 2013-06-17 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2013-06-17 00:00:00 UTC to 2013-06-24 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2013-06-24 00:00:00 UTC to 2013-07-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2013-07-01 00:00:00 UTC to 2013-07-08 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 commentsJul 1

Counts come from the repository’s dated source evidence. Index cutoff: 2026-11-02 08:16:59 UTC.

View mention counts and date ranges
UTC ranges include their start and exclude their end. Select a period to filter the source evidence.
PeriodStart (UTC)End (exclusive, UTC)MentionsSubmissionsComments
Apr 152013-04-15 00:00:00 UTC2013-04-22 00:00:00 UTC312
Apr 222013-04-22 00:00:00 UTC2013-04-29 00:00:00 UTC000
Apr 292013-04-29 00:00:00 UTC2013-05-06 00:00:00 UTC000
May 62013-05-06 00:00:00 UTC2013-05-13 00:00:00 UTC000
May 132013-05-13 00:00:00 UTC2013-05-20 00:00:00 UTC000
May 202013-05-20 00:00:00 UTC2013-05-27 00:00:00 UTC000
May 272013-05-27 00:00:00 UTC2013-06-03 00:00:00 UTC000
Jun 32013-06-03 00:00:00 UTC2013-06-10 00:00:00 UTC000
Jun 102013-06-10 00:00:00 UTC2013-06-17 00:00:00 UTC000
Jun 172013-06-17 00:00:00 UTC2013-06-24 00:00:00 UTC000
Jun 242013-06-24 00:00:00 UTC2013-07-01 00:00:00 UTC000
Jul 12013-07-01 00:00:00 UTC2013-07-08 00:00:00 UTC101

What Hacker News says

4 sources
you should check out http://0xdata.com/ ; it's built from the ground up on a custom dkv to do in-memory ML. Reasons to check it out: 1 - it's open source https://github.com/0xdata/h2o 2 - ingest data from hdfs, s3, csv 3 - I've built systems like what you're discussing twice; the ML algorithms are often easier to write than expected while data management (moving data, sending updates, etc) which initially seems easier is much harder. 0xdata handles this for you. 4 - under active development 5 - it cleanly runs on your dev box with 1 or many nodes for development; deploying is a simple as uploading a jar to a cluster and putting a single file on each naming peers in the cluster 5a - see scripts to walk you through doing this disclosure: I work on it as of very recently =P
In Ask HN: Fast, In-Memory, Distributed data analysis and machine learning?
As far as I can tell, this is a different vision than that of Wakari. This effort seems to be about building a souped up parellel ML-oriented calculator around Hadoop. There is an R binding, there is a nascent Python binding (that "doesn't include the Python scientific frameworks because [they] are not that familiar with them"[1]. Wakari is an infrastructure for doing repeatable, collaborative analytics with Python. Our existing tools include a simple browser-based environment for code editing, IPython notebook, and sharing of plots and workflows. Our goal is to support multiple applications on that infrastructure, and not necessarily all written by Continuum. [1] https://github.com/0xdata/h2o/blob/master/py/h2o/h2o/cloud.p...
In H2O: fast statistical, machine learning and math runtime for big data
Innaresting. There was some blogging last year about consistency vs. availability issues, similar to what akka, riak, cloud Haskell etc are confronting. It occurs to me you need to do Bayesian inference to order packets on partition http://www.cliffc.org/blog/2012/05/ http://www.cliffc.org/blog/2012/06/21/progress-vacation/ ___________ very readable code, also, no generics in the few files i looke at. And, like twitter, scalaz, akka etc they write their own Futures https://github.com/0xdata/h2o/blob/8f6d81abeea8ef3221969d8aa...
In H2O: fast statistical, machine learning and math runtime for big data

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